Data used in the Great Lakes Hybrid SAR-Optical/IR wetland and landscape indicator mapping.
\r\n\tThe applications are those related to intelligent monitoring activities such as the quality assessment of the environmental matrices through the use of innovative approaches, case studies, best practices with bottom-up approaches, machine learning techniques, systems development (for example algorithms, sensors, etc.) to predict alterations of environmental matrices. The goal is also to be able to protect natural resources by making their use increasingly sustainable.
\r\n\r\n\tContributions related to the development of prototypes and software with an open-source component are very welcome.
\r\n\r\n\tThis book is intended to provide the reader with a comprehensive overview of the current state of the art in the field of Ambient Intelligence. A format rich in figures, tables, diagrams, and graphical abstracts is strongly encouraged.
",isbn:"978-1-83969-069-3",printIsbn:"978-1-83969-068-6",pdfIsbn:"978-1-83969-070-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"3fbf8f0bcc5cdff72aaf0949d7cbc12e",bookSignature:"Dr. Carmine Massarelli",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10391.jpg",keywords:"Embedded Systems, Technologies, Sensors, Remote Sensing, Smart Homes, Smart Cities, Integrated Monitoring Techniques, Agroecosystem, Smart Public Spaces, Computer Vision, Image Processing, Open-Source",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 12th 2020",dateEndSecondStepPublish:"November 9th 2020",dateEndThirdStepPublish:"January 8th 2021",dateEndFourthStepPublish:"March 29th 2021",dateEndFifthStepPublish:"May 28th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Environmental technologist expert in the development of Smart Technologies for water management and environmental monitoring, characterization, and monitoring of contaminated and degraded sites, integration of spatial data such as standard methodologies, interoperability, spectral data infrastructures.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"315689",title:"Dr.",name:"Carmine",middleName:null,surname:"Massarelli",slug:"carmine-massarelli",fullName:"Carmine Massarelli",profilePictureURL:"https://mts.intechopen.com/storage/users/315689/images/system/315689.jpg",biography:"Main activities:\n-development of Smart Technologies for water management and environmental monitoring;\n-characterization and monitoring of contaminated and degraded sites;\n-implementation of early warning systems and impact assessment systems also from multitemporal monitoring;\n-integration of spatial data: methodologies, standards, interoperability, spatial data infrastructures;\n-use of open source IT systems for the processing, analysis, and integration of remote sensing data with airborne and satellite sensors for thematic purposes such as characterization, control, and analysis of the territory in support of environmental policies relating to contaminated sites;\n-evaluation of the contamination of environmental matrices with specific tests and chemical analyses;\n-installation of airborne sensors and definition of flight parameters for Earth observation, CASI-1500 hyperspectral and TABI-320 thermal sensors;\n-acquisition of spectral signatures of objects through Fieldspec portable spectroradiometer and creation of databases in SQL language;\n-use of tools such as Ground Penetrating Radar for the advanced investigation of the subsoil with law enforcement agencies.",institutionString:"National Research Council",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Research Council",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"9",title:"Computer and Information Science",slug:"computer-and-information-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"297737",firstName:"Mateo",lastName:"Pulko",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/297737/images/8492_n.png",email:"mateo.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"9542",title:"Improving Wetland Characterization with Multi-Sensor, Multi-Temporal SAR and Optical/Infrared Data Fusion",doi:"10.5772/8327",slug:"improving-wetland-characterization-with-multi-sensor-multi-temporal-sar-and-optical-infrared-data-fu",body:'\n\t\tWetlands provide habitat and food sources for wildlife, protect waterways, act as natural filtration systems, and serve major ecological roles in the overall health of our local, regional and global ecosystems. While wetlands serve such vital ecological functions, due to their limited capacity for adaptation wetland ecosystems are highly vulnerable to change, from both climatic (IPCC 2008) and anthropogenic sources. In the past, wetlands have been drained and converted to uplands at an alarming rate and the remaining wetlands are subject to a number of threats including eutrophication, climate change, invasive plant species, and the interaction between these stressors. Due to these vulnerabilities, there exists a need for policy and resource managers to have an operational strategy for monitoring the extent, composition, and vigor of wetlands at a synoptic scale. For regional areas, such as the coastal Great Lakes, Boreal Canada, or vast wetland complexes such as the Pantanal, Mesopotamian marshlands, or the Greater Everglades, cost-effective implementable methods are necessary. For fine scale studies, cost is generally less of an issue and the highest resolution data with the highest cost may be justified. In this chapter, we focus on methods for regional scale mapping and monitoring where the minimum mapping unit of interest is 5 acres, and thus use moderate resolution satellite imagery (20-30 m). The focus is on multi-sensor data fusion between SAR single channel and/or multi-channel data and Optical/IR sensor data. Case studies in the Great Lakes and Boreal peatlands demonstrate the advantages and widespread utilty of a hybrid SAR-Optical/IR approach.
\n\t\t\tWhile many definitions of wetlands exist, both scientific and legal, in essence wetlands are defined by: (1) the presence of water at, above, or near the ground surface; (2) hydric soils; and (3) vegetation species adapted to or tolerant of wet soil conditions. Remote sensing can be used to map and monitor two of the defining wetland features; vegetation type and surface water/wet soils.
\n\t\t\tWetlands have historically been one of the most difficult ecosystems to classify using remotely sensed data. This difficulty is partially due to the high variability in wetland morphology. Wetlands can exist in many shapes and sizes, from open wet areas with sparse vegetative cover to densely forested areas with seasonal flooding. Vegetative cover ranges from low herbaceous, to shrubby, to forest.
\n\t\t\tTraditionally, optical data have been used to map wetlands along with other land cover and land use. However, due to the complexity of wetland ecosystems it would be beneficial to include a fusion of sensors, operating in different frequencies (thermal, optical, lidar, radar, infrared) that measure various aspects of wetlands for improved mapping accuracy. Optical and infrared data are well suited to mapping vegetation ecosystem types and condition. Complementary to Optical/IR, Synthetic Aperture Radar (SAR) data are capable of detecting flooding beneath a vegetation canopy, monitoring water levels and soil moisture, and also for distinguishing other biophysical vegetation characteristics such as level of biomass.
\n\t\tMultispectral data that includes near infrared and shortwave infrared bands allow improved wetland detection and mapping over visible sensors alone. The near-infrared portion of the electromagnetic spectrum has been used to identify plant and hydrologic wetland conditions using both color infrared (CIR) aerial photography and satellite remote sensing systems (Ozesmi and Bauer, 2002). The most broadly used wetlands map in the United States, the National Wetlands Inventory (NWI), uses aerial CIR photography and photo interpretation techniques to provide fine scale maps of wetland distribution (Peters, 1994). However, this labor-intensive methodology requires a 10-year repeat interval for new map production (Wilen and Frayer, 1990).
\n\t\t\tTo effectively monitor changes to wetlands, data collection must be timely (1-5 year minimum) and cost effective. The National Oceanic and Atmospheric Administration’s Coastal Change and Analysis Program (C-CAP) uses the Landsat Thematic Mapper (TM) sensor to provide a more timely and cost effective national system of coastal wetland maps (Klemas et al., 1993) on a 5 year repeat interval. However, both NWI and NOAA C-CAP maps offer only broad classes of wetland, such as estuarine emergent or palustrine forested. Finer classes of actual species or ecosystem types are not mapped.
\n\t\t\tSince various targets reflect and absorb solar radiation differently, they can often be distinguished by their spectral reflectance signatures (Jensen, 2007). Spectral reflectance studies have been useful for determining regions of the electromagnetic spectrum which provide greatest discrimination between two or more wetland species (Schmidt & Skidmore 2003, Becker et al 2005). However, many studies have concluded that it is difficult to accurately classify wetland species types based solely on Optical/IR spectral characteristics (Ozesmi and Bauer, 2002).
\n\t\tSAR data have unique capabilities because the long microwave wavelengths penetrate vegetation cover and are sensitive to wet soil and flooded conditions that may exist beneath a canopy. An enhanced signature is often received from a canopy underlain by water due to a double-bounce effect of the incoming radiation from the smooth water surface and vertical stems of the canopy. The microwave scattering received by a SAR sensor from a wetland is dependent upon the wavelength, polarization, and incidence angle at which the energy was transmitted, the surface roughness, vegetative biomass, dielectric properties of the vegetation and soils (moisture in the plant canopy and on the ground), and the presence or absence of a flooded surface. Therefore, the SAR wavelength, polarization and incidence angle need to be carefully chosen to maximize the scattering to distinguish wetlands from uplands and to distinguish between wetland ecosystem types. A combination of wavelengths, polarizations, and/or incidence angles provides the most information about the various wetlands and thus the greatest capability to effectively map wetland ecosystem types with SAR.
\n\t\t\tCurrent and recently orbiting SAR satellites available commercially are of three different wavelengths; L-band (~23 cm wavelength); C-band (~5.7 cm wavelength), and X-band (~3.5 cm wavelength). Of these SAR satellites, many are of a fixed incidence angle, but some have varying incidence angles. To detect flooding beneath a vegetation canopy, steep incidence angles (<35 degrees) are generally best (Hess et al. 1990). For temperate, sub-tropical and boreal regions, longer wavelengths such as L-band are more useful for mapping forested and high biomass herbaceous wetlands than C-band or X-band. C-band or X-band data have limited ability to map flooding beneath forest canopies. C-band data are most useful in forests during leaf-off condition and for sparse canopies, and have been used to map extent of inundation in floodplain swamps of Roanoke (Townsend 2001, 2002, Lang et al. 2008).
\n\t\t\tIn Figure 1, theoretical scattering of a C-band sensor from forested versus herbaceous landscapes in various dry to flooded conditions is shown. Here it is demonstrated how the degree of inundation affects the scattering from the herbaceous canopy. In the case of wet soils and low inundation in an herbaceous canopy, enhanced backscatter is often observed at C-band, with some double-bounce effects. However, as the water levels increase the backscatter can first get stronger and then lessen until it reaches a low specular reflection case (where scattering is away from the sensor) from the water surface in the highly inundated situation (Kasischke and Bourgeau-Chavez 1997, Kasischke et al. 2003, Bourgeau-Chavez et al. 2005). Figure 1 also shows the typical scattering from a closed versus open canopy forest at C-band. With most scattering from the branches and leaves at C-band in the closed canopy case, and little to no penetration to the ground surface. However, the longer wavelength L-band generally penetrates a closed canopy forest and has been found to be best for discriminating flooded from non-flooded forests (Hess et al. 1990, Ramsey 1998, Bourgeau-Chavez et al. 2001). C-band is best for discriminating emergent wetlands from agriculture and herbaceous uplands (Bourgeau-Chavez et al. 2001).
\n\t\t\tSAR polarization is also important, and horizontal send and receive (HH) polarization has been found to be most useful for detecting wetlands. While, the cross-polarizations (Horizontal send and Vertical receive HV) are necessary for discrimination of woody versus herbaceous vegetation types due to their sensitivity to biomass (Ramsey 1998). VV polarization is also sensitive to soil moisture and flood condition (Bourgeau-Chavez et al. 2001).
\n\t\t\tDiagram showing theoretical scattering of C-band energy from forested and herbaceous ecosystems in dry, wet and flooded conditions, with open and closed forest canopies.
One primary advantage of using SAR over visible data is the detection of forested wetlands and the ability for SAR data to be collected irrespective of cloud cover or solar illumination because it is an active sensor. It is very difficult to detect flooding beneath a forested canopy with Optical/IR, unless there are large gaps in the canopy. Many researchers have found significant improvement in distinguishing swamp from other wetland classes and uplands with SAR (Lang et al. 2008, Grenier et al. 2007, Baghdadi et al. 2001, Hess et al. 1990, Bourgeau-Chavez et al. 2004).
\n\t\t\tSeveral researchers have evaluated the utility of SAR for wetland mapping using single and multi-date single channel SAR data (Costa et al. 1998, Whitcomb et al. 2009, Arzandeh and Wang 2002, Rao et al. 1999) and others have evaluated polarimetric SAR (Hess et al. 1990, 1995, Bourgeau-Chavez et al. 2001, Pope et al. 1994, 1997, Crawford et al. 1999, Wang and Davis 1997, Touzi et al. 2007). See Henderson and Lewis (2008) and Ramsey (1998) for a more thorough review of past research on wetland ecosystem analysis with SAR. Many early studies conducted to determine the utility of multi-polarization/multi-frequency data, focused on NASA’s Shuttle Imaging Radar - C (SIR-C) which was fully polarimetric at L- and C-bands (L-HH, L-HV, L-VH, L-VV, C-HH, C-HV, C-VH, C-VV and X-VV) and fully polarimetric AirSAR (P-band (72 cm), L-band, C-band) for wetland classification in tropical and temperate landscapes (Hess et al. 1995, Pope et al. 1994, 1997, Bourgeau-Chavez et al. 2001). These studies demonstrated the utility of multi-band data and early polarimetric analyses (e.g. HH-VV phase difference) for mapping forested and herbaceous wetlands (Pope et al. 1997).
\n\t\t\tWhile several researchers have evaluated the use of SAR alone for mapping wetlands, until more recently, few have evaluated SAR and Optical/IR fusion for wetland mapping (Lozano-Garcia and Hoffer 1993, Augustein and Warrender, 1998, Toyra et al. 2001, Rio and Lozano-Garcia 2000, Bourgeau-Chavez et al. 2004, 2008, 2009, Li and Chen 2005, Grenier et al. 2007). Since the SAR and Optical/IR data measure different features of wetlands, it is logical that a synergistic approach between the two sensor types would increase wetland mapping accuracy. Further since the presence of standing water causes the SAR energy to interact differently depending on the dominant vegetation type, it would be advantageous to combine SAR with optical and infrared data for mapping purposes.
\n\t\tOf the few broad-scale SAR wetland mapping efforts that have been undertaken,, Canada is incorporating Radarsat-1 and Radarsat-2 data with Landsat mosaics (100m resolution) and SPOT data in the development of the Canadian Wetland Inventory (Grenier et al. 2007, Fournier et al. 2005, pers. comm. Grenier 2009) which will cover the entire country. There is also an effort underway to use the JERS mosaics (100 m resolution summer and winter products) of Boreal North America alone to map wetlands across Canada, as has already been done for Alaska (Whitcomb et al. 2009).
\n\t\t\tIn this chapter, we review case study of multi-sensor, multi-date, SAR-optical/IR fusion methods and results for mapping wetlands in two main study areas, the Great Lakes and a Boreal peatland region in Alberta, Canada. The techniques are developed with broad scale mapping in mind, but are demonstrated on local to regional wetland areas. In all but one of these case studies, satellite SAR data are used in conjunction with Landsat TM, and in most cases SAR data from multiple sensors are fused. The exception, is the case studies on mapping the invasive plant species Phragmites australis on Lake St. Clair, where PALSAR data are used alone and compared to hyperspectral AVIRIS.
\n\t\t\tThe Great Lakes Coastal Wetlands Consortium (GLCWC) was mandated to develop a monitoring plan for assessing the health of the coastal wetlands which are vital to the overall health and maintenance of the Great Lakes ecosystem (Bourgeau-Chavez et al. 2004, 8). The only way to monitor an ecosystem the scale of the Great Lakes basin is through integrated remote sensing and field observations in a GIS. Landscape indicators in need of monitoring through remote sensing include wetland type and extent, adjacent land cover, adjacent land use, intensity of land use, and invasive plant species. The GLCWC sought implementable, cost-effective yet robust methods with a minimum mapping unit of 5 acres. To meet these needs a few pilot studies were conducted to demonstrate various monitoring methods. A hybrid SAR Optical/IR methodology that used satellite sensor data of 30 m resolution and would allow for detection of areas as small as 1 hectare was found to be the most promising. This methodology would be cost-effective and data management for the entire Great Lakes Basin would be reasonable compared to higher resolution, smaller footprint imagery. And the use of two complementary data types in the mapping was expected to reduce omission and comission errors.
\n\t\t\t\tTwo study sites were selected for demonstration of this hybrid SAR-optical data fusion, coastal areas of Lakes St. Clair and Ontario (Figure 2). Coastal Lake St. Clair was chosen because it has a diversity of land cover and land use including a large amount of urban and suburban areas, rural farm fields, and a large amount of wetlands (various species) that occur at the delta as the river enters the lake. The Lake Ontario study area was chosen because it is mainly rural, with many agricultural fields, has isolated patches of herbaceous wetlands, and a relatively large amount of potentially forested wetlands. These two areas provide different sets of land cover and land use classes and thus an opportunity to test the hybrid-sensor methodology in diverse settings.
\n\t\t\t\tThe data used in this case study were archival SAR and Landsat data from multiple dates, and multiple sensors (Table 1). For the Landsat images, each containing 7 bands, the blue and thermal IR bands for each date were removed from the analysis. The blue channel is generally quite noisy and the thermal band is of coarser resolution than 30 m. The JERS (L-band) sensor used for this project had horizontal send and receive polarization (L-HH) and was operational from 1992-8. This sensor has a resolution of 30 m, incidence angle of 35º, and a footprint of 80 km x 80 km. To complement these data, we also acquired C-band (5.7 cm wavelength) SAR data from the European ERS-1 and Canadian Radarsat-1 (R-1) satellites. The ERS-1 sensor has vertical send and receive polarization (C-VV) and is collected at a central incidence angle of 23º. The Radarsat sensor has horizontal send and receive polarizations (C-HH). It also has pointing capabilities and can be collected in various modes and incidence angles. The R-1 data used were of standard beam 7 mode, which has an incidence angle of 47º. Both C-band sensors have 30 m resolution and 100 km x 100 km footprints.
\n\t\t\t\t\tGreat Lakes pilot study areas for hybrid SAR-Optical/IR mapping for the Great Lakes Coastal Wetlands Consortium project.
Site | \n\t\t\t\t\t\t\t\tSensor | \n\t\t\t\t\t\t\t\tSpring | \n\t\t\t\t\t\t\t\tSummer | \n\t\t\t\t\t\t\t\tFall | \n\t\t\t\t\t\t\t
Lake St. Clair | \n\t\t\t\t\t\t\t\tLandsat TM | \n\t\t\t\t\t\t\t\t23 March 2001 | \n\t\t\t\t\t\t\t\t30 August 2001 | \n\t\t\t\t\t\t\t\t30 October 2000 | \n\t\t\t\t\t\t\t
Lake St. Clair | \n\t\t\t\t\t\t\t\tJERS | \n\t\t\t\t\t\t\t\t28 March 1995 | \n\t\t\t\t\t\t\t\t10 August 1998 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t |
Lake St. Clair | \n\t\t\t\t\t\t\t\tRadarsat-1 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | 3 & 27 October 1998 | \n\t\t\t\t\t\t\t
Lake Ontario | \n\t\t\t\t\t\t\t\tLandsat TM | \n\t\t\t\t\t\t\t\t24 June 1993 | \n\t\t\t\t\t\t\t\t12 August 2002 | \n\t\t\t\t\t\t\t\t18 December 2002 | \n\t\t\t\t\t\t\t
Lake Ontario | \n\t\t\t\t\t\t\t\tJERS | \n\t\t\t\t\t\t\t\t11 April 1993 | \n\t\t\t\t\t\t\t\t8 July 1993 | \n\t\t\t\t\t\t\t\t17 October 1993 | \n\t\t\t\t\t\t\t
Lake Ontario | \n\t\t\t\t\t\t\t\tERS-1 | \n\t\t\t\t\t\t\t\t17 April 1993 | \n\t\t\t\t\t\t\t\t7 June 1993 | \n\t\t\t\t\t\t\t\t25 October 1993 | \n\t\t\t\t\t\t\t
Data used in the Great Lakes Hybrid SAR-Optical/IR wetland and landscape indicator mapping.
Since this was a demonstration project with minimal funding, data sets chosen were not optimal but were chosen based on cost and ease of availability (Table 1). At both sites three dates of Landsat data were used. There were a total of four radar scenes for Lake St. Clair and 6 radar scenes for Lake Ontario. There are as many as 6 years between the JERS and Landsat collections for Lake St. Clair and 10 years between data collections for Lake Ontario, but analysis of the imagery indicated that few major changes in land use have occurred in these areas during that time period. Also, it was not as much of an issue due to the methodology; a site would first be checked for vegetation cover in the more recent Landsat and then checked for flooding in the older SAR. However, it is realized that using data with such a wide time span will introduce errors. The optimal data set would be from the same year, with SAR and TM from the same months. However, the datasets met the needs of this investigation, which was simply to demonstrate a methodology.
\n\t\t\t\tFor the Lake St. Clair site, the imagery and products were evaluated by comparison to the NWI, land cover/land use maps, field checks (October 2003) and expert field knowledge (Field ecologist/botanist Dennis Albert of Michigan Natural Features Inventory).
\n\t\t\t\t\tAlthough the ancillary maps and field work showed many forested wetlands within our study area, the dates of JERS imagery show all of the forests the same, very bright in the spring (March 1995) and all are gray in the summer scene (August 1998). It is likely that all of the forests have a wet ground cover in the spring scene, there may even be wet, melting snow on the forest floor causing the enhanced signature from all of the forests, and in August all of the forests are dry with full foliage. However, the differences in backscatter in the herbaceous vegetation are apparent on these two dates, as well as in the October Radarsat scenes.
\n\t\t\t\t\t\n\t\t\t\t\t\tFigure 3 shows a red-green-blue false color composite of the 3 October 1998 R-1 scene, 10 August 1998 JERS scene, and 28 March 1995 JERS scene, respectively. In this composite, the orange areas were dominated by cattail (Typha spp.), and the green by Phragmites (Phragmites australis). Phragmites tends to be taller/denser and occurs in less wet locations than cattail. The red areas of the image are shorter and sparser vegetation, thus they do not
\n\t\t\t\t\tcause enhanced backscatter at L-band, only at C-band. The red areas along the fingers of the delta are cattail and bulrush (Scirpus spp.) beds and the red area within Dickinson Island is a flood channel with wild rice (Zizania aquatica), open submergent and emergent vegetation (Dennis Albert). The dark area in the center of Dickinson island to the west of the kidney shaped light blue forest area is a wet meadow and appears to be dry in our October 1998 C-band scene, it has strongest backscatter in the L-band spring scene (blue channel), but not enhanced backscatter. This combination of R-1 and JERS allows for a good interpretation of this scene, discerning tall dense herbaceous vegetation from short sparse herbaceous vegetation, and different hydrological and biophysical properties.
\n\t\t\t\t\tThree dates, two sensor false color composites from Radarsat-1 and JERS satellites over the Lake St. Clair Delta. This Figure clearly defines Typha (cattail), and the invasive species Phragmites australis from other upland and wetland ecosystems.
At the Lake Ontario test site, we had an ideal seasonal data set with three images each of JERS and ERS from spring, summer and fall of 1993. Figure 4 presents false color composites of the two sensor datasets. Both datasets highlight non-forested wetlands (based on NWI) as green and red shades. In the JERS data, these sites were dark from specular reflection in the April scene (blue), then some sites were bright in July (green in the composite) while other sites remained dark in July (red locations in the composite) and all sites were gray in October (Table 2). In November of 2003, we conducted a field check to determine any vegetation difference between the red and green areas. The red areas visited were dominated by mixed grasses. It is likely that in the spring imagery the vegetation is fallen over or decomposed, with a high water level leading to specular reflection. The water level must still be high enough to cover much of the grasses and cause specular reflection again in the July scene, but when the lake water level drops to 74.58 m in October (70 cm drop), this site has more vegetation exposed and stronger backscatter. In comparison, the green areas visited in the field contained a mixture of grasses, cattail and shrubs (mainly Cornus stolonifera). These sites were bright in the summer and gray in the fall in the JERS imagery. The water level in comparison to the vegetation was likely lower than at the other sites, causing the enhanced backscatter in July, but with lower soil moisture in the fall the site was gray. For the same two sites in the ERS, the grass site was dark in the spring but the mixed shrub/herbaceous site was gray. In the summer the mixed shrub site was bright while the grass site remained dark. In the fall all sites were gray. While similar patterns emerged for both sites, the contrast between the non-flooded adjacent forests and the wetlands is stronger with the JERS, making it easier to map the boundaries of the sites.
\n\t\t\t\t\tThree date ERS false color composite of 25 October, 7 June and 17 April 1993 ERS imagery over eastern Lake Ontario compared to a 17 October, 8 July and 11 April 1993 JERS composite.
Site | \n\t\t\t\t\t\t\t\tSensor | \n\t\t\t\t\t\t\t\tSpring Brightness | \n\t\t\t\t\t\t\t\tSummer Brightness | \n\t\t\t\t\t\t\t\tFall Brightness | \n\t\t\t\t\t\t\t
Grass | \n\t\t\t\t\t\t\t\tERS-1 C-VV | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tgray | \n\t\t\t\t\t\t\t
Shrub/herbaceous | \n\t\t\t\t\t\t\t\tERS-1 C-VV | \n\t\t\t\t\t\t\t\tgray | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t\tgray | \n\t\t\t\t\t\t\t
Grass | \n\t\t\t\t\t\t\t\tJERS L-HH | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tgray | \n\t\t\t\t\t\t\t
Shrub/herbaceous | \n\t\t\t\t\t\t\t\tJERS L-HH | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t\tgray | \n\t\t\t\t\t\t\t
Appearance of “grass” versus “shrub/herbaceous” sites in coastal Lake Ontario in spring, summer and fall of 1993. Grasses are red areas in JERS composite of Figure 3 and Shrub/herbaceous are green areas.
There are some forested wetlands within the Lake Ontario scene and they appear to be most notable in the April scene when the lake water level is the highest (note that coastal wetlands are hydrologically connected to the Great Lakes), and spring thaw has occurred and thus flooding is most likely. A comparison was made between assumed flooded forest and non-flooded forest for each JERS scene/date. The April scene had a 2.3 dB difference between flooded and non-flooded forest while the July date had only a 0.5 dB difference and the October date had a 1.7 dB difference. The April scene was then thresholded to values greater than that of the non-flooded forest. After median filtering the scene with a 5x5 window to remove speckle, the scene was overlaid on a 5, 4, 3 Landsat composite (Figure 5). The white areas of Figure 5 show the SAR-derived potentially flooded forests. The backscatter from urban areas is also enhanced and has not been filtered from this scene. There are also white areas that are likely row plantings of trees. The row structure produces an enhanced return. The urban areas can be removed by using either the Landsat scene to mask forest from non-forest or by using the ERS C-band data. The C-band data will have enhanced backscatter for the urban area but not for the flooded forests. The extent of some of the enhanced signatures appears to be slightly greater than what is seen in the NWI for some of the sites (Figure 5) and in other cases it is less.
\n\t\t\t\t\tLandsat 5, 4, 3 composite with SAR-derived potentially flooded forests (white areas) overlaid 9 (left). The NWI is presented for comparison (right) with forested wetlands colored green. Note that the NWI is not the exact area of the Landsat scene.
One technique that has been used somewhat operationally for detecting forested wetlands, is to map forest cover with Optical/IR and then create either a single-date thresholded image, as is presented here, or a multi-date SAR image to determine inundation and thus, forested wetland. Using remote sensing to detect flooding beneath a canopy is limited by the timing of the data acquisitions. Therefore, only the forests that were inundated on the date of image acquisition will be mapped as forested wetland. The multi-season data helps reduce this limitation, but relying on species type and other ancillary data such as hydric soils, as well as enhanced SAR backscatter to determine wetland status would be more reliable, hence hybrid approaches are recommended. One useful parameter the SAR can be used for is to monitor changes in extent of inundation from one date to the next (Bourgeau-Chavez et al. 2005, Lang et al. 2008, Townsend 2001, Wang et al. 2004).
\n\t\t\t\tSeveral techniques were considered and evaluated for merging the multi-sensor, multi-date Great Lakes datasets and producing a land cover land use map, but the best method appeared to be the simplest, which involved creating separate SAR and Landsat land cover/land use products and then fusing the products in a GIS. This method preserved the unique characteristics of each sensor, while taking advantage of their complementary nature.
\n\t\t\t\t\tThe two-step methodology was first applied to the Lake St. Clair study site where the 15-band Landsat layer was classified into 12 different land cover features using the maximum likelihood classification algorithm. The training sites for this supervised classification were collected from the reference field data and existing maps. The 12 classes created were: Water, High Density Urban, Low Density Urban, Forest, Emergent Wetland, Wetland Shrub, Wetland-permanent, Forage Crops, Cropland 1, Cropland 2, Cropland 3, Cropland 4. The second step of the process involved the classification of the 4-band radar image into 9 different classes. These classes were different than that of the Landsat imagery because the sensors are able to distinguish different phenomena. The classes for the radar classification were: Forest/Urban, Phragmites, Scirpus/ open submergent and emergent, Cattails, Wet Meadow, Forage Crops, Cropland1, Cropland2, and Cropland3
\n\t\t\t\t\t\tThe individual classification results were then fused into a single classification. This was performed by recoding the 12-class Landsat classification into values of 10s (10, 20, 30…120) and recoding the radar classification into values 1-9. Then the values were added together on a pixel-by-pixel basis, producing possible values between 11-129. These recombined classes were then interpreted, by comparison to the reference data, and assigned into a final 11-class file. This final class identification relied on only the Landsat for some classes (such as water, forest, low density urban), only the radar for others (Phragmites and Scirpus), and a combination of both for the majority of the classes. The final classes are described below in Table 3 and the final map is in Figure 6.
\n\t\t\t\t\t\tDue to limited funding and the archival nature of the dataset, an accuracy assessment was conducted based on existing maps. The NWI, which is the basemap for the GLCWC, was first used as a reference. Then IFMAP (Integrated Forest Monitoring, Assessment and Prescription), which relies on NWI to some extent, was used because it includes upland classes that NWI does not. IFMAP was produced by the Michigan Department of Natural Resources. It is mainly based on the analysis of seasonal Landsat imagery (collected between 1997-2001), but is supplemented with selected high resolution images, existing land cover maps, and large amounts of field work. IFMAP provides a very detailed description of land cover, but is only available for the Michigan portion of the study area and accuracy could therefore only be assessed on the U.S. side of the map. NWI and IFMAP have only broad wetland categories (e.g. palustrine emergent, scrub-shrub, etc).
\n\t\t\t\t\t\tClass | \n\t\t\t\t\t\t\t\t\tDescription | \n\t\t\t\t\t\t\t\t
Water | \n\t\t\t\t\t\t\t\t\tIdentified through the Landsat, regardless of the radar results | \n\t\t\t\t\t\t\t\t
High Density Urban | \n\t\t\t\t\t\t\t\t\tIdentified by an urban class in the Landsat imagery and a urban_forest (bright) from the radar class | \n\t\t\t\t\t\t\t\t
Low Density Urban | \n\t\t\t\t\t\t\t\t\tIdentified through the Landsat, regardless of the radar results | \n\t\t\t\t\t\t\t\t
Scirpus | \n\t\t\t\t\t\t\t\t\tIdentified through the radar classification and reinforced by being classified as a wetland/vegetative class in the Landsat imagery | \n\t\t\t\t\t\t\t\t
Phragmites | \n\t\t\t\t\t\t\t\t\tIdentified through the radar classification and reinforced by being classified as a wetland/vegetative class in the Landsat imagery | \n\t\t\t\t\t\t\t\t
Cattail | \n\t\t\t\t\t\t\t\t\tIdentified through the radar classification and reinforced by being classified as a wetland/vegetative class in the Landsat imagery | \n\t\t\t\t\t\t\t\t
Wetlands_other | \n\t\t\t\t\t\t\t\t\tIdentified as wetlands in the landsat imagery but is different than Scirpus, Phragmites, and cattail | \n\t\t\t\t\t\t\t\t
Shrubland (shrub wetland) | \n\t\t\t\t\t\t\t\t\tIdentified through a Landsat class of shrubland and forest and has a radar classification of wet meadow or shrubland | \n\t\t\t\t\t\t\t\t
Cropland | \n\t\t\t\t\t\t\t\t\tIdentified by the four cropland classes from the Landsat imagery and the three cropland classes from the radar classification. | \n\t\t\t\t\t\t\t\t
Forage Crops/Low Herbaceous | \n\t\t\t\t\t\t\t\t\tIdentified by landsat imagery (forage, row crop) and radar imagery (forage) | \n\t\t\t\t\t\t\t\t
Forest | \n\t\t\t\t\t\t\t\t\tIdentified through a Landsat imagery forest class (Note: if the radar were from a time when forested wetlands could be identified, this landsat class would be combined with a radar class) | \n\t\t\t\t\t\t\t\t
Combined Landsat and radar landcover results for the Lake St. Clair study site.
Using over 3000 randomly selected validation points, comparison to the NWI as reference, resulted in 94% overall accuracy of our wetlands map. Comparison to IFMAP resulted in 72% overall accuracy when areas of wetland that IFMAP called “open water“were eliminated. Analysis of the imagery revealed that the timing of data collections and the lake levels can have a large effect on the boundaries mapped for emergents along the water’s edge. A SAR comparison of 2 image dates with a change of 19 cm in lake level showed a huge change in visibility of wetlands on the fingers of the St. Clair river delta (Figure 7). In this figure, lake water level is 19 cm higher on the first date, causing specular reflection (low return-dark). A decrease in inundation on the second date reveals the vegetation causing double bounce scattering (bright return-red). This exemplifies the need for multi-date data to “see” the wetlands that may be nearly completely inundated by water on a particular date in both SAR and optical/IR.
\n\t\t\t\t\t\tIn comparison to IFMAP, the hybrid SAR-Optical classification did well with low density urban, Typha, Phragmites, low herbaceous, Scirpus and cropland (all above 60% user’s accuracy, Typha above 86%). There were some issues with wet meadow (42.25% user’s accuracy) where there is confusion in IFMAP with tree species, row crop and herbaceous upland. Further investigation of this type is needed. The classes were quite different for IFMAP, but for lowland deciduous, the SAR-Optical map had 81% producer’s accuracy, 65% and 77% producer’s accuracy for emergent and non-forested wetland and 78% producer’s accuracy for low density urban.
\n\t\t\t\t\t\tA visual comparison was also made between our classification of the Canadian side of the study area using maps produced by Arzandeh and Wang (2002&2003) of Walpole Island, Ontario. In 2002 Arzandeh and Wang used a single Radarsat scene (1997) and Landsat data (1997), separately, to create two classification maps with eight categories including forest, urban, swamp, tall grass, water, agriculture, cattail and Phragmites. Their areas of emergent wetland (cattail and Phragmites) correspond well with the areas that we have mapped as emergent. However, their maps lack the detail that we gained by combining multiple dates and two bands of SAR imagery with the Landsat. Their goal was to use texture analysis of a single date of SAR imagery to improve classification accuracy with a single date of SAR. Generally, more than one date of imagery is available, but for those cases when only one date of imagery exists or can be acquired their techniques would be useful.
\n\t\t\t\t\t\tLandsat and SAR fused land cover classification results for the Lake St. Clair study area.
Two date false color composite of Radarsat imagery. Cyan is the 3 October 1998 image and red is the 27 October 1998 image. Water level dropped by 19 cm between the first and second date.
The same techniques used for Lake St. Clair were applied to the datasets for Lake Ontario, however the classes were slightly different. First, Landsat imagery was classified into 13 different landcover classes; Water, High Density Urban, Low Density Urban, Deciduous Forest,Coniferous Forest, Mixed Forest, Row Crops, Low Herbaceous, Bare Soil, Emergent Wetlands, Shrubland, Fields-Hay, Wetlands-other. The radar imagery was classified into 9 classes; Water, Urban/Flooded Forest, Flooded Shrubland/ Wet Meadow, Emergent Wetland, Forest1, Forest2, Row Crop 1, Row Crop 2, and Herbaceous Field. These individual Landsat and SAR classes were then fused into a single product, just as they were for Lake St. Clair. The 12 final combined classes are described in Table 4. The final map is presented in Figure 8.
\n\t\t\t\t\t\tA comparison was made between the SAR-Optical map and the NWI with 5000 random points Note that the NWI has more generalized classes so the comparison is not straight forward, and there are over 2 decades between the NWI creation and the SAR-Optical/IR map, so some differences may be due to changes in the wetland, either succession or loss. The results in comparison to the NWI showed 94% overall accuracy, with all classes greater than 89% user’s accuracy, except shrubby wetland for which we only had 13 points, none of which were correctly classed. The producer’s accuracy in the wetlands was 34% for woody wetland and 66% for emergent. For the NWI woody wetlands, we labeled 127 out of 208 as deciduous forest. The problem likely lies in what areas were in fact inundated during the radar satellite collections. A wet, normal or dry year will provide different wetland extents, 66% agreement for emergent wetlands is quite good considering the likely turnover of some areas to agriculture and the likely succession of some of the wetlands labeled as emergent in the 1970s NWI to wetland shrub, as indicated by the field visits and point source field data from a biocomplexity study conducted by Cornell University (Mark Bain).
\n\t\t\t\t\t\tClass | \n\t\t\t\t\t\t\t\t\tDescription | \n\t\t\t\t\t\t\t\t
Water | \n\t\t\t\t\t\t\t\t\tAreas identified as water in the Landsat imagery, regardless of the radar classification | \n\t\t\t\t\t\t\t\t
High Density Urban | \n\t\t\t\t\t\t\t\t\tAreas dominated by manmade materials, these areas were identified by high radar returns that were not forested areas in Landsat | \n\t\t\t\t\t\t\t\t
Low Density Urban | \n\t\t\t\t\t\t\t\t\tAreas with a mixture of manmade features and landscape vegetation, these areas were mainly identified by the Landsat classification | \n\t\t\t\t\t\t\t\t
Deciduous Forest | \n\t\t\t\t\t\t\t\t\tAreas of forest that lose their leaves throughout the season, identified through a combination of the Landsat and radar classifications | \n\t\t\t\t\t\t\t\t
Coniferous Forest | \n\t\t\t\t\t\t\t\t\tAreas of evergreen forest, identified through the combination of classifications | \n\t\t\t\t\t\t\t\t
Mixed Forest | \n\t\t\t\t\t\t\t\t\tAreas that are a mixture of coniferous forest and deciduous forest, identified through a combination of classifications | \n\t\t\t\t\t\t\t\t
Forested Wetland | \n\t\t\t\t\t\t\t\t\tAreas are forest but have standing water on the ground throughout much of the year, identified as forest in the Landsat imagery and as urban/flooded forest in the radar imagery | \n\t\t\t\t\t\t\t\t
Emergent Wetland | \n\t\t\t\t\t\t\t\t\tAreas of herbaceous vegetation that are wet at some times of the year, identified through the combination of radar classification and Landsat classification | \n\t\t\t\t\t\t\t\t
Wetland-shrub | \n\t\t\t\t\t\t\t\t\tAreas of short woody vegetation that are wet at some points throughout the year, these are identified through the combination of sensor classification results. | \n\t\t\t\t\t\t\t\t
Crop/pasture | \n\t\t\t\t\t\t\t\t\tAreas of herbaceous vegetation that are not plowed throughout the year, mainly identified through the Landsat classification | \n\t\t\t\t\t\t\t\t
Bare Soil | \n\t\t\t\t\t\t\t\t\tAreas of exposed soil, sand, and/or rock, these areas were mainly identified through the radar and were confirmed by the Landsat classification | \n\t\t\t\t\t\t\t\t
Row Crop | \n\t\t\t\t\t\t\t\t\tAreas that have herbaceous vegetation growing which is plowed at some point during the season. These areas were identified through a combination of the classification results. | \n\t\t\t\t\t\t\t\t
Classes for the combined Landsat and radar classification at the Lake Ontario study site.
The state of New York did not have a land cover/land use map comparable to IFMAP, and many errors were found in the National Land Cover Dataset (NLCD). However, field data collected in the largest coastal wetland complex in the image were available with GPS locations. The Biocomplexity study of Cornell University allowed for comparison of the SAR-Optical/IR hybrid map to 55 study points which represented cattail dominated, shrub, forested, and mixed emergent wetlands. The overall accuracy of this comparison was 89%, with 91% user’s accuracy for wetland shrub, 89% user’s for emergent wetland, and 67% for forested wetland. This assessment is quite good considering the likelihood of error in the geolocation of the study points due to the small plot size in reference to the 30 m pixels. Some of the points did fall on boundaries of open water/wetland or upland/wetland causing errors. The producer’s accuracy ranged from 25 to 75% in the reference categories of specific species types that we did not map. The cattail field points corresponded to the wetland shrub (dark pink) areas in the Landsat-SAR fused classification. Therefore, the pink areas of our Landsat-SAR map should be labelled as shrub/high biomass herbaceous wetlands.
\n\t\t\t\t\tLandsat and SAR fused land cover classification of the Lake Ontario study area.
These two study areas demonstrated the utility of a fusion of SAR and Optical/IR data for mapping landscape indicators of wetland health (wetland extent, adjacent land use intensity, etc.) in a region surrounded by high intensity urban versus a more rural area. In one case mapping of forested wetlands was possible and in the other, timing of data collections did not allow evaluation of forested wetland mapping. However, a simple approach to the mapping provided desirable results with the archival data, showing the complementary nature of the two types of sensors. Although the reference data and remote sensing data were not optimal [there are discrepancies in years of data collection (Landsat versus SAR (1990s-2001) and years of validation maps (1970s, 2000) and levels of vegetation classes between the SAR-Optical map and validation maps (fine scale species versus broad emergent classes)], these case studies demonstrated the added benefits of fusing Optical/IR data with another complementary sensor such as radar and resulted in a recommendation by the Great Lakes Coastal Wetlands Consortium for monitoring landscape indicators (Bourgeau-Chavez et al. 2008). In the next case study, current data are used and ongoing validation is being conducted for the specific species class levels. This case study is a continuing investigation and only preliminary results are shown here, however it provides a better validation of the results through field methods, further exemplifying the utility of SAR in wetland mapping.
\n\t\t\t\tOne of the main wetland stressors in the Great Lakes is invasion by the problematic species Phragmites (Phragmites australis). This species invades native habitat creating dense thickets and deep detritus that virtually eliminates ecological function. A predicted drop in Great Lakes water levels due to global climate change is anticipated to increase the spread of the invasive Phragmites in the Great Lakes coastal zone, and a method to map this species and its spread would be of great assistance to land managers for control.
\n\t\t\t\tSeveral studies have focused on detecting and mapping invasive species in small catchments of the Great Lakes with high resolution hyperspectral and/or lidar (e.g. Lopez et al. 2006, Wilcox et al. 2003). However, such high-resolution mapping of the entire Great Lakes coastline or comprehensive field studies would be very costly. Others have found 30 m satellite imagery including Landsat, SPOT and Hyperion to be useful for mapping invasives (Arzandeh and Wang 2003, Pengra et al. 2007), however Landsat has spectral limitations and Hyperion is no longer operational.
\n\t\t\t\tUsing a variation of the satellite SAR techniques described in the last section, which included a delineation of this invasive species using archival multi-date JERS and Radarsat- I, we are currently evaluating dual polarization PALSAR data, and have plans to incorporate Radarsat-2 data once it becomes available to distinguish a wider range of species.
\n\t\t\t\tALOS PALSAR is the follow-on to JERS which showed the greatest potential in previous studies for mapping Phragmites (Bourgeau-Chavez et al. 2004, 2008). PALSAR was launched in 2006, and is available in three modes with various imaging parameters. Here we evaluate the dual-polarization product which has 20 m resolution, two channels (L-HH, L-HV), 70 x 70 km footprint, and is collected at an incidence angle of 34 . Up until recently, most satellite SAR systems were of a single channel, however with the recent launch of ALOS PALSAR and Radarsat-2, the utility of multi-channel SAR and polarimetric data are beginning to be more broadly evaluated for a variety of applications, demonstrating further mapping capabilities beyond that of single and multi-channel data.
\n\t\t\t\t\tCoincident to this evaluation is an investigation of an airborne hyperspectral NASA AVIRIS collection from July 2008 over the St. Clair delta. The AVIRIS sensor has 224 bands (400-2500 nm) and was collected with 17 m resolution.
\n\t\t\t\tPlot of backscatter from PALSAR (L-HH to L-HV ratio in dB) in Phragmites, Cattail, wet meadow and Scirpus beds of the Lake St. Clair delta.
Initial observations of an October 2007 dual polarization PALSAR image over Lake St. Clair revealed that the ratio of the L-HV and L-HH bands shows a 4-5 dB difference between Phragmites dominated wetlands and other non-forested native wetland types (Figure 9). The reason for this great divergence is the large difference in vegetation height, density and biomass of invasive Phragmites versus any other native herbaceous vegetation in the Great Lakes (Figure 10). Typha generally ranges in height from 1 to 3 m, while Phragmites can reach heights of more than 3.5 meters. Further, Phragmites forms tall, dense rather impenetrable stands. It is the sensitivity of L-band SAR to these differences in biomass and hydrology that allows the distinction between stands dominated by these two species.
\n\t\t\t\t\tMulti-date composites of PALSAR imagery from 2006-8 show the dynamic changes in the various vegetation cover-types over the growing season (Figure 11). In the top composite of Figure 11, which is a false color multi-date L-HH representation, the Typha are yellow to orange, indicating there is a strong return signal in July and October, but low response in the spring (Table 5). In contrast, most of the Phragmites shows a high response in the spring and lower in the summer and fall with shades of blue, and purple. In comparison, in the lower image of the L-HV multi-date false color composite (L-HV is sensitive to biomass), the Phragmites is cyan, indicating a strong return in the spring, April and May images, while the Typha is dark in these months, and bright (red) in the fall image.
\n\t\t\t\t\tPhotos of Phragmites: tall scale in winter (left), high density in summer (middle), and typical Typha (right).
While either L-HH or L-HV multi-date imagery appear to be useful for distinguishing Phragmites from Typha, there are some stands that would be confused with the L-HV alone (see arrows with Phragmites label on Figure 11); these are areas of Phragmites that are red in the lower image and green in the upper image. The different signatures in the PALSAR imagery from Phragmites-dominated stands is likely due to differences in water levels in the various seasons. “Phragmites 2“ stands are located in diked areas, and may be wetter and sparser than “Phragmites 1“ stands (Table 5).
\n\t\t\t\t\tPALSAR Band | \n\t\t\t\t\t\t\t\tImage Date | \n\t\t\t\t\t\t\t\tPhragmites 1 appearance | \n\t\t\t\t\t\t\t\tPhragmites 2 appearance | \n\t\t\t\t\t\t\t\tTypha appearance | \n\t\t\t\t\t\t\t
L-HH | \n\t\t\t\t\t\t\t\t28 July 2006 | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t
L-HH | \n\t\t\t\t\t\t\t\t09 October 2007 | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t
L-HH | \n\t\t\t\t\t\t\t\t17 April 2008 | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\td ark | \n\t\t\t\t\t\t\t
L-HV | \n\t\t\t\t\t\t\t\t09 October 2007 | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t
L-HV | \n\t\t\t\t\t\t\t\t26 May 2008 | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t
L-HV | \n\t\t\t\t\t\t\t\t17 April 2008 | \n\t\t\t\t\t\t\t\tbright | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t\tdark | \n\t\t\t\t\t\t\t
Appearance of two different Phragmites dominated stands versus Typha stands in the L-HH and L-HV imagery on the various dates.
A simple unsupervised maximum likelihood classification of the four dates of PALSAR data resulted in four classes of potential Phragmites and two potential classes of Typha spp. Note that the July 2006 image was from the single channel mode of ALOS PALSAR and thus had only the L-HH channel, analysis was therefore conducted on an input of 7 channels. Field observations using a GPS positioning system, in situ photos, and field notes were used to assess the preliminary “potential Phragmites“ map. Using these field data for validation (29 points), the PALSAR multi-date preliminary map had 92% overall accuracy, with 100%
\n\t\t\t\t\tFalse color composites of PALSAR imagery over Lake St. Clair delta. Top is L-HH from 28 July 2006 in red, 09 October 2007 in green and 17 April 2008 in blue. Bottom is L-HV composite with 09 October 2007 image in red, 26 May 2008 in green and 17 April 2008 in blue.
user’s and 80% producer’s accuracy for Typha, and 82% producer’s and 100% user’s accuracy for Phragmites. Note that the misclassified pixels for Phragmites were small areas of shrub or Typha within a larger Phragmites dominated area, thus the error is likely due to resolution (20 m in this case). The utility of the 10 m resolution PALSAR product (although only L-HH) may resolve this error and is being investigated.
\n\t\t\t\tA comparison of the Optical/IR spectral signatures of Typha latifolia and Phragmites australis are shown in Figure 12. These signatures were collected in the field using a spectroradiometer (FieldSpec 3 JR). The vast differences in these signatures indicate that separation using Optical/IR remote sensing should be fairly easy, however, using the spectral angle mapper technique the results were poorer than the SAR methods.
\n\t\t\t\t\tSpectral signature comparison of Typha latifolia (black) versus Phragmites australis (red).
Spectral angle mapping is a physically-based spectral classification that uses n-dimensional (n = number of bands) angles to match pixels to reference spectra. It was run in ENVI, and determines similarity between the two spectra by calculating the angle and treating them as vectors in n-dimensional space. Smaller angles represent closer matches to the reference spectrum. The endmember spectra for Phragmites australis and Typha spp. used by the classifier were derived from field truth data provided by Michigan Natural Features Inventory.
\n\t\t\t\t\tUsing methods similar to Lopez (2006), the spectral angle mapping of the 224 band (400-2500 nm), 17 m resolution AVIRIS data using field training data, resulted in 82 % overall accuracy with 83% user’s and 77% producer’s accuracy for Typha, but only 57% user’s and 67% producer’s accuracy for Phragmites. Note that the validation is based on only 17 points, because many of the points were used in the training
\n\t\t\t\t\tThese are preliminary results and the investigation is ongoing. Methods are being investigated to determine the best bands from AVIRIS to combine with the L-band SAR for a data fusion approach. Evaluations will reveal the utility of SAR alone and in combination with hyperspectral, but the goal is to also determine if any of the bands of existing satellite Optical/IR systems could be used in lieu of hyperspectral data.
\n\t\t\t\tThe last case study is based on the need to understand carbon storage (peat accumulation) and loss (mainly through fire) in boreal peatlands, which are widely recognized as being one of the largest terrestrial reservoirs for carbon (C) in the Northern Hemisphere. Estimating carbon storage and release requires an accurate mapping of peatland type. Peatlands are defined as wetlands with well developed peat (partially decayed plant matter) accumulation, generally more than 30 cm deep (Charman 2002). Peatlands actually represent diverse ecosystem types that differ in hydrology and vegetation, from forested rain-fed bogs to grass-dominated, saturated, or near-saturated stream-fed fens.
\n\t\t\t\tEarly research on the ability to map boreal peatlands at a regional scale demonstrated the utility of merging SAR and Optical/IR data. Early observations included JERS, R-1, and Landsat imagery. Figure 13 presents Landsat and JERS images in comparison to a detailed peatland map (based on air photo interpretation and intensive field truth circa 1970-80s) with open, forested, and wooded categories of bogs, fens and swamps delineated. This preliminary analysis showed that Landsat would be useful for finding many of the open fens (see linear features circled in yellow; Figure 13). Whereas wooded fens do not look different from bogs in the Landsat image, they can be distinguished in the JERS SAR multi-date imagery (see features circled in pink; Figure 13). Note that the majority of peatlands in this study region are wooded bogs (salmon color in the peatland map). The linear open fens are dark in the SAR similar to the open swamps and marshes. Here, C-band Radarsat should prove useful in distinguishing types of open peatlands. The complementary information obtained from the spectral reflectance properties of the vegetation combined with the structural and moisture information from SAR should allow the mapping of both peatland type (bog, fen, swamp) and level of biomass (open, sparse tree cover, forested).
\n\t\t\t\tTwo dates of PALSAR imagery were obtained over the local Alberta study area from July and August of 2007. The dual-polarization PALSAR data included two channels, L-HH and L-HV. To complement this, a spring and summer data set of Landsat TM imagery were also obtained from April and August of 2001. Lastly we obtained two R-I images from July 1997 and 2005. These areas are so remote and vegetation growth is slow enough, that a 6-10 year difference in data collection is not problematic. The only large changes between the years would be wildfires, but we obtained all data on location and extent of wildfire from the Canada Forest Service for the time period of study and no fires occurred within the study area during that time.
\n\t\t\t\t\n\t\t\t\t\t\tFigure 14 shows two false color composites, one from the two dates of L-HV data from PALSAR and the other from two dates of L-HH data, with the first date as red and the second as cyan. These composites show how the cyan colored areas help distinguish fen from bog in the L-HH composite (bottom figure), since fens are characterized by fluctuating water levels and flowing water, whereas bogs tend to have water levels that remain below the moss covered surface with small changes in the short time period of the two image dates (July and August 2007). The L-HV cross polarized data provide information on the level of biomass which is essential in discrimination of low herbaceous open fens versus fen woodlands and open, wooded and forested bogs. This channel also clearly distinguishes the high biomass upland areas from the lowlands (Figure 14). Further, by using multi-date data, the seasonal changes in moisture and water levels are useful for discrimination of wetlands that generally have large changes in moisture/flood conditions (stream-fed fens) versus those that have smaller changes (rain-fed bogs).
\n\t\t\t\t\tWhile most boreal peatlands in central Alberta are characterized by low canopy closure, allowing C-band R-1 to be useful, evaluation of the L-band JERS and PALSAR data demonstrate the additional definition of forests from ecosystems with low amounts of aboveground biomass and varying surface wetness conditions. While C-HH R-1 provides information on low aboveground biomass wetland types, JERS L-HH and PALSAR more clearly define the differences between forested wetland types, as well as distinguishing high and low aboveground biomass areas. The R-1 data were included in the mapping methods for distinguishing swamps since Grenier et al. (2007) found R-1 useful for such purposes.
\n\t\t\t\t\tComparison of how different wetland types (bottom left, wetland map based on air photo and field truth) appear in 3 date JERS L-band imagery (1995-7, top left) versus Landsat imagery (Sept 88, top right). The pink circled areas are wooded fens and the yellow circled features are open fens. Most of the area in these scenes is wooded bog.
Two date false color composites of PALSAR L-HV (top) and PALSAR L-HH (bottom) from July (red) and August (cyan) of 2007.
Unlike the methodology used in the Great Lakes case study, here all data (SAR and Optical/IR) were fused in an object-based GIS analysis, using Definiens Professional software. Note, however, that Definiens Professional allows the user to choose which bands are used for each category being mapped. Thus, only Landsat may be used for one category, while only SAR is used for a second, and all data are used for a third, etc.
\n\t\t\t\t\tObject-based classification methods involve two phases: 1) spatial objects are formed using a region-growing segmentation algorithm to merge homogeneous pixels; and then 2) image classification techniques are applied. The segmentation phase provides additional attributes describing the spatial context and morphology of features which can inform the classification beyond spectral values alone. Segmentation can also be reiterated at various scales to capture the range of features contained in the image. This allows heterogeneous cover types (i.e., wetlands containing some open water pixels for example mixed with denser canopy) to be grouped depending on the segmentation scale chosen by the operator, and can significantly improve map accuracy (Grenier et al. 2007).
\n\t\t\t\t\tWe first created segmentation regions defined by the Landsat and SAR. Then using data from April and August Landsat, two dates of PALSAR L-HV and L-HH and two dates of R-1 C-HH, we developed a top-down classification approach in Definiens. The methodology relied on a combination of thresholds and nearest neighbor classifiers in a decision tree.
\n\t\t\t\t\tUsing decision rules, we first distinguished land from open water using PALSAR L-HH and TM band 5 thresholds to map open water, with all non-water pixels being classified as land (Figure 15). Next the land was divided into burn and non-burn categories using a nearest neighbor classification of the Landsat data. Note that these burns occurred after the detailed Airphoto peatland map of Figure 13 was created, which we rely on as reference in our validation. Non-burned areas were then divided into upland versus wetland, with upland forest classified using PALSAR L-HV and TM band 3 from August 2001. Next the wetland classes were mapped. First open fen was mapped using L-HV and Landsat TM. Finally, a nearest neighbor classification was conducted on the remaining classes: Woodland Bog, Wooded Fen, and Swamp using April TM 3, 4, 5 and two dates of PALSAR HH, and two dates of R-1. Figure 15 shows the process, with the final map in Figure 16.
\n\t\t\t\t\tThe final peatland map had 80% (Table 5) overall accuracy compared to the air-photo based map (circa 1970-80s photos), which was created from pre-burn photos (Bourgeau-Chavez et al., in prep.). The bog had 77 % user’s and 91 % producer’s accuracy, fen had 60% user’s and producer’s accuracy, upland had 88% user’s and 76% producer’s accuracy. Note that in the air photo-based reference map, both upland forest and open water were mapped as a merged class, and this is likely causing some error. Also, the time difference between the reference 70-80’s air photo map and the SAR-optical map of 2000’s likely resulted in changes to the landscape, notably the fires that occurred in 1988 and 1998. Also, fens are very difficult to map on air photos and there may be errors in the reference maps. We did find errors in some areas mapped as Marsh in the air photo-based map.
\n\t\t\t\tThis initial research demonstrates the strong potential of a SAR-Optical/IR approach for application to large areas for a better understanding of the spatial variation in peatland types across the boreal landscape. Similar data fusion methods have been (Li and Chen 2005) or are being used (Fournier et al. 2007, Grenier et al. 2007) for mapping peatlands of Canada. The CWI methods were described earlier (Grenier et al. 2007), but are much coarser classes. Li and Chen (2005) mapped peatlands of eastern Canada into open versus treed bog, marsh, swamp and open fen with high accuracy. Their methods involved the use of several dates of R-1 data (45 incidence), Landsat and a DEM. While we found R-1 to be of limited use (as did Grenier et al. 2007) in western Canada, it should be noted that eastern Canadian peatlands are quite different than western peatlands, and the various methods will need to be assessed for transferability.
\n\t\t\t\t\tWe are currently processing imagery to increase the Alberta study area to include a three scene mosaic of PALSAR from the two dates, using current Landsat from spring, summer, and fall, and ERS data. ERS data are being used over Radarsat, because of coverage of the larger area. Field visits are planned to areas in disagreement between the air photo map and SAR-Optical/IR map for validation and improvement of the mapping approach. Additional peatland study sites will also be evaluated in eastern Canada, and Alaska, as well as the Upper Peninsula of Michigan.
\n\t\t\t\t\tProcess Tree images from the top down approach used for mapping Peatlands in the Central Alberta study area using Definiens.
Hybrid SAR-optical derived map of Peatland types in central Alberta.
SAR-Optical Map | \n\t\t\t\t\t\t\t\tReference AirPhoto Map | \n\t\t\t\t\t\t\t||||||||
\n\t\t\t\t\t\t\t\t | Bog | \n\t\t\t\t\t\t\t\tFen | \n\t\t\t\t\t\t\t\tSwamp | \n\t\t\t\t\t\t\t\tMarsh | \n\t\t\t\t\t\t\t\tUpland | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | totals | \n\t\t\t\t\t\t\t\tuser\'s accuracy | \n\t\t\t\t\t\t\t|
Bog | \n\t\t\t\t\t\t\t\t50 | \n\t\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t\t0 | \n\t\t\t\t\t\t\t\t13 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | 65 | \n\t\t\t\t\t\t\t\t0.77 | \n\t\t\t\t\t\t\t|
Fen | \n\t\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t\t6 | \n\t\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | 10 | \n\t\t\t\t\t\t\t\t0.60 | \n\t\t\t\t\t\t\t|
Swamp | \n\t\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | 11 | \n\t\t\t\t\t\t\t\t0.64 | \n\t\t\t\t\t\t\t|
Marsh | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | 0 | \n\t\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t\t0.00 | \n\t\t\t\t\t\t\t|
Water | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | 26 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | 26 | \n\t\t\t\t\t\t\t\t1.00 | \n\t\t\t\t\t\t\t|
Upland | \n\t\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | 1 | \n\t\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | 24 | \n\t\t\t\t\t\t\t\t0.88 | \n\t\t\t\t\t\t\t|
Totals | \n\t\t\t\t\t\t\t\t55 | \n\t\t\t\t\t\t\t\t10 | \n\t\t\t\t\t\t\t\t9 | \n\t\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t\t62 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | |
producer\'s | \n\t\t\t\t\t\t\t\t0.91 | \n\t\t\t\t\t\t\t\t0.60 | \n\t\t\t\t\t\t\t\t0.78 | \n\t\t\t\t\t\t\t\t0.00 | \n\t\t\t\t\t\t\t\t0.76 | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t |
Accuracy assessment of SAR-Optical Map on vertical axis and Airphoto map as reference (top). Note that Upland on the airphoto map was labeled “Z” and represented open water and upland forest.
Many techniques focus on using multispectral data, such as Landsat or Aster, alone or in combination with ancillary data sets such as soils and topography for wetland mapping. However, research has shown how SAR and multispectral sensors complement each other in the classification and monitoring of wetland ecosystems and that SAR represents one of the most promising sensor types for improving wetland mapping capability (Bourgeau-Chavez 2004, 2008, Grenier 2007, etc). While multispectral data measure spectral reflectance and emittance characteristics of various cover types and wetness in open canopied ecosystems, SAR is sensitive to variations in biomass, structure and soil moisture and flood condition of landscapes including forests and other closed canopy ecosystems. Forested wetlands are the most difficult to identify remotely because of the inability of traditional multispectral sensors to detect moisture beneath the canopy. Radar can not only penetrate a closed canopy to detect flooding, but since radars are active systems, can acquire data independently of solar illumination and cloud cover conditions. Thus, data can be collected during specific conditions relevant to finding seasonally flooded wetlands or seiche-influenced wetlands. These SAR data can be used not only to detect and define wetlands, but also to monitor extent of inundation and in some cases level of inundation (Bourgeau-Chavez et al. 2005, Lang et al.\n\t\t\t\t2008).
\n\t\t\tThe case studies shown here demonstrate the improved mapping capabilities by including SAR in the traditional Optical/IR methods of mapping. It is important to note that the timing of the acquisitions can be very important for detection of flooding beneath a canopy, as was seen in the two JERS dates of imagery over Lake. St. Clair, where neither image date was able to be used from mapping the flood condition. Further, the importance of multi-temporal data was demonstrated in the image interpretation for all study sites, but particularly for the Canadian peatland study in which the subtle changes in backscatter due to changes in moisture levels allowed the distinction of wooded fens from bogs. Using knowledge of the phenological changes which occur in one wetland over another, as long as they are changes that can be detected by a particular sensor, can be key in distinguishing otherwise similar appearing ecosystems in the mapping process.
\n\t\t\tWhile the results shown here focus on amplitude data from SAR, the new era of SAR sensors have full polarimetric capability and as such, polarimetric decomposition can be used to understand the type of scattering occurring from a particular ecosystem. Decomposition variables can be used alone, or as additional bands in the more typical multi-band classifiers. Variables such as phase difference, which were used in the past have been further developed to include complex analysis of the full scattering matrix. Decomposition methods are being developed to use Radsarsat-2 for peatland discrimination using a single date of imagery (Touzi et al. 2007). These new techniques are in the early stages of development and have not been tested on a variety of sites yet. But there is great potential for these new polarimetric satellite sensors, which are just beginning to be explored.
\n\t\tThis research was funded by the Great Lakes Coastal Wetlands Consortium, NASA and the U.S. Fish and Wildlife Service. Special thanks to Brian Benscoter and Merritt Turetsky for their assistance with the “truth“ maps of Alberta peatlands.
\n\t\tRice is the staple food of half of human population globally and fulfills over 21% calorific requirement of world population. About 90% of the rice is produced and consumed in Asia. During 1960s to 1970 when the major rice producing countries relied on rice as a subsistence crop, the major emphasis was on high yield. As these countries attained food security and standard of living of the rice eating population improved, consumers became conscious about grain quality. Their potential as exporters of surplus rice produced, gave a further impetus to grain quality research. The world population is expected to reach 9.8 billion from the current 7.6 billion by 2050 (The World Population Prospects: The 2017 Revision, published by the UN Department of Economic and Social Affairs). The current challenge to rice improvement programs is to feed the ever-growing population with diminishing natural resources and environmental fluctuations on one-hand and varieties that have grain quality that the consumer demands, on the other. The economic value and the consumer acceptance/preference of a rice variety depend on rice grain quality [1, 2, 3]. Rice grain quality is a complex trait and is therefore difficult to define comprehensively. Rice quality comes from a polygenic group of traits that are affected by environmental factors, crop management and the resulting interactions among these. It involves the physical appearance, milling quality, cooking, sensory and nutritional value. The emphasis laid on each of these traits depends on regional consumer preference, market demand, and intended functional use. For instance, consumers in North Asia prefer short and bold rice grains with low amylose, whereas in several states of India, most parts of Pakistan and Iran prefer long, slender grains having intermediate amylose content [4]. One of the major challenges facing the rice improvement programs is to have simple, robust, high throughput methods for assessing various quality traits that can reflect consumer preference. We review here the key grain quality traits and the classical and modern methods used in rice improvement programs to evaluate them. A comprehensive list of quality evaluation methods for different parameters is given in Table 1.
S. No. | Quality parameter | Recent quality evaluation method(s) |
---|---|---|
Cooking and eating quality | ||
1. | Apparent amylose content | HPLC-SEC [5] DSC [6] NIRS [7, 8] |
2. | Cooking time | Measured indirectly by estimating gelatinization temperature using DSC [6] |
3. | Kernel elongation | None |
4. | Grain volume expansion | None |
5. | Gelatinization temperature | Measurement of starch gelatinization by DSC, photometric method, alkali photometry, or RVA pasting curve [9] |
6. | Pasting properties | Brabender visco-amylograph, micro Visco-analyzer [10, 11] |
Textural and sensory quality | ||
7. | Gel consistency | None |
8. | Texture profiling | Instron hardness testing. Parallel plate plastometer, consistometer, texturometer, hardness tester, viscoelastograph, tensipresser, surface tensiometer, Kramer shear or texture press, extrusion and back extrusion, puncture test |
9. | Sensory evaluation | None |
10. | Aroma profiling | Detection and quantification of 2-acetyl-1-pyrroline by GC-MS [3] Detection of total volatile metabolome by GC-MS |
11. | Rancidity test | Detection of free fatty acids by titration or colorimetry [12] |
Nutritional quality | ||
12. | Protein content | NIRS [13] |
13. | Lipid content | Metabolomics approach using LC-MS [14] or GC-MS |
14. | Resistant starch content | None |
15. | Nonstarch polysaccharide content and dietary fiber content | CE [15], HPLC coupled with mass spec detector |
16. | Micronutrients | AAS, ICP-OES, ICP-MS [16, 17], XRF |
17. | Digestibility | Time-resolved NMR [18] |
Summary of evaluation methods used for determining rice quality.
Major factors determining market value are immediately discernible by the consumers and include physical properties like, whiteness, translucence, uniform shape and yield of edible polished grain. Visual characters of rice grains like grain dimensions, chalk, color and whole grain recovery are important attributes that affect the choice of consumers’ and millers. Therefore, these are among some of the first selection criteria in varietal improvement programs [19, 20, 21]. Grain size depends on the length of the grain in its greatest dimension, while grain shape is based on length-to-breadth ratio [20]. The classification of rice samples based on size and shape is not standardized across different countries and different marketing areas [22, 23]. The routine classification system used by the International Rice Research Institute (IRRI) breeding programs for grain size is as follows: short (≤5.50 mm), medium/intermediate (5.51–6.60 mm), long (6.61–7.50 mm), and very long (>7.50 mm). Similarly, the grain shapes of rice can be described based on the length-to breadth ratio values, and the classification used in IRRI is: bold (≤2.0), medium (2.1–3.0), and slender (>3.0) [23]. Chalky areas in rice grains present on the dorsal (white belly), ventral side (white back) or in the center are opaque white parts of the endosperm and generally, associated with poor quality in many rice markets thus these grains have lower market acceptability [24]. Classification of the grains is based on the proportion of the grain that is chalky: none (0%), small (<10%), medium (10–20%), and large (>20%) [23, 25, 26]. The starch granules in the chalky areas of the grain have air spaces between them, are small and less compact compared to bigger and tightly packed granules in translucent areas and hence are more prone to breakage during milling [27, 28]. Chalk thus affects both the esthetic value and head rice yield decreasing the marketability of rice. Chalk is caused by both environment and genetic factors. Increase in nighttime air temperatures during grain filling stage can increase chalk and reduce head rice yields [29, 30]. Rice grain dimensions are conventionally measured using transparent rulers, vernier calipers and photographic enlargers [31], while the proportion of grain that is chalky is visually scored. Measuring of grain dimensions using manual methods is both labor intensive and time-consuming. Moreover, visual scoring of chalk involves subjectivity. Now-a-days, image analysis methods are being used in advanced laboratories that are very convenient and objective [31, 32, 33].
Yin et al. [34] divided the dimensions of grain shape into grain length, grain width, length-to-width ratio, grain area, grain circumference, grain diameter, and grain roundness. Several important genes have been characterized in previous studies that control grain shape traits, e.g., GS3 [35] affecting grain length, qSW5/GW5/GSE5 [14, 36, 37] affecting grain width, GL7/GW7 [38] shaping both grain length and grain width. In various studies across different environments and genetic backgrounds, a major effect quantitative trait loci (QTL) for grain length, GS3 was identified near the centromeric region of chromosome 3 [12, 35, 39, 40]. However, a functional marker in the second exon of GS3 was identified that explains 80–90% of the kernel length variation [41]. Bai et al. [42] identified four QTLs for grain length on chromosomes 3 and 7; and 10 QTLs for grain width and 9 QTLs for grain thickness on chromosomes 2, 3, 5, 7, 9 and 10, respectively. A total of 28 QTLs were detected, of which numerous were reported for the first time. Four major and six minor QTLs for grain shape were also identified in their study. Later on, qGL7 was narrowed down to an interval covering a 258 kb region in the Nipponbare genome between InDel marker RID711 and SSR marker RM6389, and co-segregated with InDel markers RID710 and RID76. The dimensions of grain shape were dissected by Yin et al. [34] into grain length, grain width, length-to-width ratio, grain area, grain circumference, grain diameter, and grain roundness. By contrast, a few QTLs for grain chalkiness have been finely mapped and characterized functionally. Chalk5 was the first cloned and functionally characterized gene that controls rice grain chalkiness which encodes a vacuolar H+-translocating pyrophosphatase [43]. Two methods are commonly applied for genetic dissection of these complex traits: QTL mapping in bi-parental recombinant populations and genome-wide association studies (GWAS) using diverse varieties. In general, genetic diversity and mapping resolution are limitations in the bi-parental linkage approach, while conventional GWAS is often mystified by complicated population structure and low power to map the low-frequency alleles [44, 45]. Genome-wide high-resolution mapping for the traits of grain shape and grain chalkiness was performed by Gong et al. [46] in hybrid rice using multiple collaborative populations for joint analyses.
Milling yield is an important quality character especially from the commercial standpoint [47]. It includes milled rice yield and head rice yield. Milling yield is the estimate of the quantity of total milled rice obtained from a unit of rough rice (paddy) and produced by removing the hulls, germ, and most of the bran. It includes intact and broken kernels and generally expressed as percentage [48]. Head rice is the intact or “whole” kernels and includes milled kernels having equal to or more than three-fourth length. The economic value of broken kernels is only 50–60% that of head rice, supporting the immense impact it has on marketability. Bran consists of several layers of outer covering of the endosperm. These layers include the pericarp, testa (seed coat), the nucellus and the aleurone, including the germ, are collectively called bran. Both, the degree of milling, which is an estimate of the degree to which the bran layers are removed from the endosperm, and fissuring of grains contribute to the percentage of broken kernels and hence, determine the overall milling quality [49]. Fissures or cracks in the grains weaken the strength of the grain and predispose them to break when exposed to mechanical forces during milling process [50]. Post-harvest drying of rice is one of the greatest factors that affect the percentage of broken kernels. Alternate wetting and drying of grains, drying at high temperatures and non-equilibrated grains before polishing lead to a decrease in head rice recovery [51, 52, 53, 54, 55]. Milling quality is determined with the help of laboratory-sized mills. They include dehuskers that remove husk, polishers or Test Rice Whitening Machine and graders, indent cylinders and shaker tables to segregate broken kernels from milled rice. Lam and Proctor [56] determined that linoleic and oleic acids were the main fatty acids released during milled rice surface lipids hydrolysis. Limited number of QTLs has been identified for milling quality. Two have been fine mapped but none has been cloned so far [57].
Rice is mainly consumed as polished grain in contrast to other staple cereals like wheat and maize that are consumed after the grain is ground to flour. Therefore, the quality characters of rice grain assume greater importance. The chief component of milled rice grain is starch which constitutes approximately 78% (14% moisture) or 90% (dry weight) of the endosperm [58]. Thus, the properties of starch mainly determine the cooking and eating quality of rice grains. Three important traits of starch that determine the cooking and organoleptic properties of rice grain are: apparent amylose content (AAC), gelatinization temperature (GT) and gel consistency.
The amylose fraction, essentially the linear polymer of glucose, forms only a small component of starch. The other major form of starch is the highly branched amylopectin molecule. Amylose is an important quality trait of rice and is considered as an indirect predictor of cooking and sensory quality [59, 60, 61]. Iodine-binding assay, generally used for measuring amylose content, also detects long-chain amylopectin in addition to ‘true’ amylose [62]. Hence, amylose is referred to as apparent amylose content (AAC). AAC of starch ranges from 0.8 to 1.3% in waxy rice, whereas it constitutes 8–37% [58] in non-waxy rice, the rest being amylopectin. AAC is directly proportional to water absorption, volume expansion, fluffiness, hardness and inversely proportional to cohesiveness, tenderness, stickiness and glossiness of cooked rice. Based on AAC, rice can be classified as: waxy (0–2%), very low (3–9%), low (10–19%), intermediate (20–25%), and high (>25%) [10]. Despite overestimating the actual amylose content and other limitations, iodine—binding assay that produces blue iodine—amylose complex when iodine binds to gelatinized rice flour which is quantified using a spectrophotometer, remains the method of choice for determining AAC. The two methods approved for the estimation of amylose content in milled rice are: the AACCI Method61-03.01 and ISO Method 6647-1:2015 [63, 64]. Auto-analyzers are also being used for routine amylose estimations in several rice improvement programs [65].
In general, the AAC is related to sensory quality of cooked rice however, there are varieties that have the same AAC but differ in their cooked rice hardness [66]. To account for such differences, a complementary test called gel consistency (GC) is routinely used [32]. It measures the length moved by rice flour gel, before it sets. Rice is classified into three GC groups based on gel length: hard and very flaky (≤40 mm), medium and flaky (41–60 mm), and soft (>61 mm). The differences in GC groups are explained on the basis of the proportion of hot water soluble amylose compared to that of insoluble amylose. The varieties with higher proportion of hot water insoluble amylose exhibit hard GC [67, 68]. Studies have indicated that long-chain amylopectin that remains in the gelatinized starch granule is probably the hot water insoluble amylose [69, 70]. According to Matsue et al. [71], amylose and protein content, amylographic characteristics, and even palatability showed significant difference depending on the position of spikelets in a panicle.
Conventional genetic studies have revealed that AAC is under the control of one major gene with several modifiers [56]. Among non-waxy parents, high amylose is completely dominant over low or intermediate amylose, and intermediate is dominant over low [72]. With the advent of molecular marker technology, it is now easy to apprehend complex quantitative traits [73]. Amylose content is reported to be mainly controlled by the waxy gene locus (Wx) present on chromosome 6, which encodes the granule-bound starch synthase (GBSS) [74].This enzyme is required for amylose synthesis, and several alleles are encoded by the Wx locus [75, 76]. Three alleles of the waxy gene—Wx, Wxa and Wxb are known, which exist in waxy (sticky) rice, indica and japonica sub-species, respectively. The activity of the encoded protein, GBSS differs in different genetic backgrounds [77]. A single nucleotide polymorphism (SNP) at the splice site of intron 1 differentiates low amylose varieties from intermediate and high varieties. This SNP defines the Wxa and Wxb alleles for high and low amylose, respectively [78]. In the Wxin allele [76] it was identified that an SNP in exon 6, results in an amino acid substitution from serine to tyrosine that distinguishes high and intermediate amylose varieties [75].
Gelatinization temperature (GT) is another important physicochemical parameter that ranges from 55 to 80°C and provides information regarding the cooking time of rice and its texture [79]. The temperature at which the semi-crystalline structure of starch begins to melt in hot water with loss of birefringence is termed GT [1]. GT is classified into three classes: low (55–69°C), intermediate (70–74°C) or high (75–79°C) [27]. GT is dependent on the amylopectin fine structure of starch with higher proportion of short chains (DP 6–12) decreasing the GT [80, 81]. Consumer preferences are varied throughout the world but varieties with intermediate GT are mostly preferred [82]. The two most commonly used methods for GT determination are: alkali spreading value (ASV) and Differential Scanning Calorimetry (DSC). ASV is based on the disintegration of starch granules present in milled rice grains in dilute KOH. The extent of disintegration is numerically scored on a scale of 1–7 [31, 68]. Though ASV is a high throughput method for the determination of GT, it is an indirect and subjective test. In contrast, DSC is an instrumental method based on measuring in real time, the first peak of the endotherm as the starch granules gelatinize [6, 83, 84]. DSC is a precise but an expensive method for measuring GT and cannot be routinely used to screen thousands of breeding lines in rice improvement programs. GT is also determined by an amylograph method [85] which tracks the viscosity changes that take place when rice flour-water slurry is heated with continuous stirring and was approved as the AACCI Method 61-01.01. The temperature at which the viscosity of 20% slurry begins to rise, determines the GT. The instrument used extensively in advanced rice quality labs is Rapid Viscoamylograph (RVA) [1]. It determines the viscosity changes during the heating and cooling of relatively small rice flour samples (6 g) AACCI Method 61-02.01.
A QTL corresponding to the alk locus was identified by Fan et al. [35], having a major effect on alkali spreading value. Alk/alk codes for starch synthase IIa (SSIIa) which is responsible for the vital differences in amylopectin chain length distribution [81]. Specifically, four haplotypes are able to distinguish between low and high GT. But a marker which is able to identify genotypes with the intermediate class of GT has yet to be discovered. GT is classified into two groups by allelic variation in SSlla [81, 86]. The SNPs in SSlla define four haplotypes [87, 88] and two haplotypes associate with high and two with low GT. Varieties having intermediate GT are found in all haplotype groups [89], thereby suggesting that another locus interacts with SSlla to produce the intermediate phenotype. SNP mutations in the rice alk gene have been shown to alter the amylose content in grains [88]. Although several alleles of Waxy/waxy and Alk/alk genes linked with different forms of starch have been identified [87], other starch biosynthesis genes in addition to Waxy/waxy and Alk/alk also affect rice cooking and eating quality. However, starch structure does not clarify all the variation in rice grain quality parameters present in all rice germplasm [90].
Aroma is a prized sensory trait of cooked rice that increases its market value. Among more than 100 identified volatile compounds, 2-acetyl-1-pyrroline (2-AP) is the major chemical compound contributing to the fragrance of Basmati rice, Jasmine rice and Pandanus leaves [91, 92, 93, 94]. Aroma is traditionally detected by smelling after reaction with 0.1 M KOH. However, this method is subjective and is also harmful to the nasal cavity of the analyst upon continuous and prolonged exposure. To solve this problem, gas chromatography coupled with flame ionization detector (GC-FID) or mass spectrometry (GC-MS) is being used in advanced rice breeding facilities. However, these methods are expensive and involve high running and maintenance costs. Therefore, molecular markers related to 2-AP are routinely used in rice breeding programs working on aroma.
Genetics studies of aroma have been an attractive research topic and many researchers studied it by employing various sensory tests. A few scientists like Reddy and Reddy [95] described two to three recessive or dominant genes that determine the fragrance, but most researchers believe that Basmati fragrance is under the control of a single recessive gene [96, 97]. Almost two decades of attempts to know the genetics of aroma at molecular level concluded in mapping of a single locus (fgr) on chromosome 8. QTL mapping [98, 99] followed by fine mapping [94], sequence analysis and complementation test [100] have helped to determine that Betaine Aldehyde Dehydrogenase (BADH2) gene possessing 15 exons and 14 introns is the fragrance causing gene (fgr). Several studies have suggested that a recessive allele of BADH2 carrying fragment deletions, badh2 includes 7 bp deletion in 2nd exon, an 8 bp deletion in 7th exon and an 803 bp deletion between exons 4 and 5 [101, 102]. This characterization of fragrant and non-aromatic rice varieties suggested that these events might have occurred after the divergence of aromatic and non-aromatic varieties from the common ancestor. On the other hand, the functional BADH2 converts AB-ald (presumed 2-AP precursor) into GABA (4-aminobutyraldehyde) in non-fragrant rice and the non-functional BADH2 causes accumulation of AB-ald and thereby enhances 2-AP biosynthesis in fragrant rice [100]. A study by Kovach et al [103] suggested that Basmati cultivars were nearly identical to the ancestral japonica haplotype across 5.3 Mb region flanking BADH2 thereby, demonstrating the close evolutionary relationship of Basmati cultivars with japonica varietal group. Due to instability in expression of Badh2 gene and complexity in fragrance determination, marker assisted selection (MAS) is considered to be a useful tool for screening this trait.
Detailed studies were done by Sood and Siddiq [104] on the geological distribution of kernel elongation gene(s) in rice and reported that varieties showing high kernel elongation on cooking were known to be traditionally cultivated in the northwest part of undivided India. Kernel elongation upon cooking is an endosperm character significantly influenced by factors like environment, aging, etc. Basmati rices are characterized by doubling of kernel length upon cooking. Despite being an important trait, not many reports are available on the inheritance of kernel elongation on cooking. Among the limited number of studies on this trait, one study had reported identification of a QTL between two RFLP markers viz., RZ323 and RZ562 and mapped it at a distance of 14.6 cM on chromosome 8 [105].
Rice is consumed as a staple for providing sustenance to its consumers’. With improving purchasing power of the rice consumers’ post green revolution, nutritional quality of rice gained importance. As starch is the main constituent of milled rice grain, it is the major source of energy and affects its nutritional quality. It has been reported that starch is digested at different rates in human gastro-intestinal tract [106]. The digestibility of starch is measured by estimating the rise in blood glucose level of humans upon consumption of a food containing 50 g available carbohydrates compared to a standard solution containing 50 g glucose [107, 108, 109]. This glycemic response is reported as glycemic index (GI). However, estimation of GI involves low-throughput and expensive clinical assays, therefore, it is not routinely used in screening for low GI rices [110]. In vitro estimation of nutritional fractions of starch can be carried out by estimating the content of total sugars, total starch, rapidly digestible starch, slowly digestible starch and resistant starch [111, 112]. Apart from starch, the other major macronutrients present in milled rice grain are: storage proteins (7%), storage lipids (<1%) and non-starch polysaccharides (NSPs, trace amounts). These macronutrients significantly affect the nutritional quality, textural and sensory traits, and functional properties [113] even though they constitute minor components of milled rice grain. Storage proteins are major source of proteins in developing countries, are hypoallergenic and possess superior amino acid composition [114]. The Kjeldahl method with modifications to accommodate smaller sample sizes (AACCI Method 46-13.01) [63] is widely used method for the estimation of total proteins. Individual amino acids can be quantified after acid hydrolysis using pre-column derivatization with a fluorescent derivatizing reagent followed by HPLC separation [115, 116]. Rice lipids serve nutritional and functional role. They provide protection against cardiovascular diseases and cancer [117] and also affect the pasting properties. Crude fat in rice grains is routinely analyzed using a standard method (AACCI Method 30-10.01). The fatty acid composition of the bran layer can also be analyzed using gas-liquid chromatography (GLC) [118]. NSPs are concentrated in the bran layer and only trace amounts are detected in the milled rice grains but have nutritional importance because of their unique composition compared to other cereals [109].
Nutritional components such as minerals, vitamins and phytochemicals are concentrated in the bran layer and are either absent or present at low levels in milled grains. The iron and zinc content are generally low and some of which is lost during milling. So a modest increase in these levels in rice would provide a significant nutritional boost to the hundreds of millions of people who depend on it. Hence there is an imperative need for a shift in emphasis toward development of nutritionally high quality rice. This is achieved by evaluating the available germplasm lines for micro nutrient content and by generation of knowledge regarding their inheritance pattern to use in future breeding programs. Micronutrients are being quantified by using atomic absorption spectroscopy (AAS), X-ray fluorescence spectrometry (XRF), inductively coupled plasma-mass spectrometry (ICP-MS), laser-induced breakdown spectroscopy (LIBS), and inductively coupled plasma-optical emission spectrometry (ICP-OES) [16, 17].
Integration of marker assisted breeding with conventional breeding creates a possibility to track the introgression of nutritional quality associated QTLs and genes into a popular/elite cultivar from various germplasm sources [119]. Two consistent QTLs for protein content in milled rice were reported by Zhong et al. [120] as qPr1 and qPr7 and located in the marker interval of RM493-RM562 and RM445-RM418 on chromosome 1 and 7, respectively. Gande et al. [121] identified 24 candidate genes namely OsNAC, OsZIP8a, OsZIP8c and OsZIP4b showed significant phenotypic variance of 4.5, 19.0, 5.1 and 10.2%, respectively. The QTL associated with increased grain protein content has been cloned and designated as Gpc-B1 [122].
Rice quantity and quality are directly or indirectly influenced by decrease in suitable arable land due to increase in urbanization, urban migration, soil deterioration and problems relating to climate fluctuations. Rice eating and cooking quality traits appear to be simple but the genetic machinery is too complex and needs to be deciphered. Rice appearance quality is a complex trait and involves interaction between quality and yield and also between quality and environment. Grain chalkiness is of primary concern since it affects milling, appearance, eating and cooking qualities [123]. To reduce chalkiness, genotypes with low chalk formation at high temperature after heading can be identified and utilized through MAS. Biochemical, physiological and molecular mechanisms have to be worked out by identifying and cloning chalkiness functional genes. The most challenging issue facing milling industry is to obtain high head rice recovery, since it is directly related to profitability to both the farmers and millers. Genetic understanding of milling quality is still limited [57]. Improvement of milling quality requires (i) search for QTLs with large effect (ii) robust and accurate analytical tools to measure the trait (iii) improvement in postharvest handling and storage techniques (iv) Breeding efforts through MAS. With the expeditious progress in functional genomics and development of high throughput genotyping technologies, more number of rice functional genes will be cloned in the future.
Increased awareness among the rice consuming population toward sensory and nutritional traits makes it necessary to develop evaluation techniques that can directly correlate with the consumer perception. To improve eating and sensory quality of rice it is important to integrate methods in textural analysis and rheology with taste and flavor metabolomics. Nutritional quality of rice is another trait that needs to be included in rice improvement programs. Rice has an important role to play to mitigate the impact of non-communicable diseases like diabetes. Since starch forms about 90% of milled rice grain weight, its structure (amylose content, branching pattern) and digestibility (resistant starch) affect its nutritional quality. Clinical evaluation of rice digestibility is difficult, therefore, methods for accurate in vitro estimations should be developed and validated in vivo. Available germplasm can be screened for resistant starch, amylose content, digestibility, and other health-promoting properties [110]. Cooking and processing methods have a major impact on digestibility and eating quality [33]. Further research is needed to assess how these cooking and processing techniques affect the structural, physical-chemical, and mechanical properties of rice. Robust and innovative modeling approaches that link the physical-chemical changes that occur during cooking (amylose leaching, gelatinization, water absorption) with rice grain digestibility and nutritional value and consumer demands could help in identifying the key determinants of rice grain cooking and sensory quality.
Supporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
",metaTitle:"IntechOpen Women in Science Program",metaDescription:"Supporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"At IntechOpen, we’re laying the foundations for the future by publishing the best research by women in STEM – Open Access and available to all. Our Women in Science program already includes six books in progress by award-winning women scientists on topics ranging from physics to robotics, medicine to environmental science. Our editors come from all over the globe and include L’Oreal–UNESCO For Women in Science award-winners and National Science Foundation and European Commission grant recipients.
\\n\\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\\n\\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\\n\\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
\\n\\nInterested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
\\n\\n“My scientific path has given me the opportunity to work with colleagues all over Europe, including Germany, France, and Norway. Editing the book Graph Theory: Advanced Algorithms and Applications with IntechOpen emphasized for me the importance of providing valuable, Open Access literature to our scientific colleagues around the world. So I am highly enthusiastic about the Women in Science book collection, which will highlight the outstanding accomplishments of women scientists and encourage others to walk the challenging path to becoming a recognized scientist." Beril Sirmacek, TU Delft, The Netherlands
\\n\\nAdvantages of Publishing with IntechOpen
\\n\\n\\n"}]'},components:[{type:"htmlEditorComponent",content:'At IntechOpen, we’re laying the foundations for the future by publishing the best research by women in STEM – Open Access and available to all. Our Women in Science program already includes six books in progress by award-winning women scientists on topics ranging from physics to robotics, medicine to environmental science. Our editors come from all over the globe and include L’Oreal–UNESCO For Women in Science award-winners and National Science Foundation and European Commission grant recipients.
\n\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\n\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\n\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
\n\nInterested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
\n\n“My scientific path has given me the opportunity to work with colleagues all over Europe, including Germany, France, and Norway. Editing the book Graph Theory: Advanced Algorithms and Applications with IntechOpen emphasized for me the importance of providing valuable, Open Access literature to our scientific colleagues around the world. So I am highly enthusiastic about the Women in Science book collection, which will highlight the outstanding accomplishments of women scientists and encourage others to walk the challenging path to becoming a recognized scientist." Beril Sirmacek, TU Delft, The Netherlands
\n\n\n\n\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. From 2004 to 2011, he was a Research Assistant with the Communications Engineering Department at the University of Málaga. In 2011, he became an Assistant Professor in the same department. From 2012 to 2015, he was with Ericsson Spain, where he was working on geo-location\ntools for third generation mobile networks. Since 2015, he is a Marie-Curie fellow at the Denmark Technical University. His current research interests include the areas of mobile communication systems and channel modeling in addition to atmospheric optical communications, adaptive optics and statistics",institutionString:null,institution:{name:"University of Malaga",country:{name:"Spain"}}}],filtersByRegion:[{group:"region",caption:"North America",value:1,count:5698},{group:"region",caption:"Middle and South America",value:2,count:5172},{group:"region",caption:"Africa",value:3,count:1689},{group:"region",caption:"Asia",value:4,count:10244},{group:"region",caption:"Australia and Oceania",value:5,count:888},{group:"region",caption:"Europe",value:6,count:15650}],offset:12,limit:12,total:117315},chapterEmbeded:{data:{}},editorApplication:{success:null,errors:{}},ofsBooks:{filterParams:{topicId:"23"},books:[{type:"book",id:"9538",title:"Demographic Analysis - Selected Concepts, Tools, and Applications",subtitle:null,isOpenForSubmission:!0,hash:"f335c5d0a39e8631d8627546e14ce61f",slug:null,bookSignature:"Ph.D. Andrzej Klimczuk",coverURL:"https://cdn.intechopen.com/books/images_new/9538.jpg",editedByType:null,editors:[{id:"320017",title:"Ph.D.",name:"Andrzej",surname:"Klimczuk",slug:"andrzej-klimczuk",fullName:"Andrzej Klimczuk"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10207",title:"Sexual Abuse - an Interdisciplinary Approach",subtitle:null,isOpenForSubmission:!0,hash:"e1ec1d5a7093490df314d7887e0b3809",slug:null,bookSignature:"Dr. Ersi Abaci Kalfoglou and Dr. Sotirios Kalfoglou",coverURL:"https://cdn.intechopen.com/books/images_new/10207.jpg",editedByType:null,editors:[{id:"68678",title:"Dr.",name:"Ersi Abaci",surname:"Kalfoglou",slug:"ersi-abaci-kalfoglou",fullName:"Ersi Abaci Kalfoglou"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10660",title:"Heritage",subtitle:null,isOpenForSubmission:!0,hash:"14096773aa1e3635ec6ceec6dd5b47a4",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/10660.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10662",title:"Pedagogy",subtitle:null,isOpenForSubmission:!0,hash:"c858e1c6fb878d3b895acbacec624576",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/10662.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10811",title:"Urban Transition - Perspectives on Urban Systems and Environments",subtitle:null,isOpenForSubmission:!0,hash:"4885cfa30ba6184b0da9f575aee65998",slug:null,bookSignature:"Ph.D. Marita Wallhagen and Dr. Mathias Cehlin",coverURL:"https://cdn.intechopen.com/books/images_new/10811.jpg",editedByType:null,editors:[{id:"337569",title:"Ph.D.",name:"Marita",surname:"Wallhagen",slug:"marita-wallhagen",fullName:"Marita Wallhagen"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10911",title:"Higher Education",subtitle:null,isOpenForSubmission:!0,hash:"c76f86ebdc949d57e4a7bdbec100e66b",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/10911.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10913",title:"Indigenous Populations",subtitle:null,isOpenForSubmission:!0,hash:"c5e8cd4e3ec004d0479494ca190db4cb",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/10913.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10914",title:"Racism",subtitle:null,isOpenForSubmission:!0,hash:"0737383fcc202641f59e4a5df02eb509",slug:null,bookSignature:"",coverURL:"https://cdn.intechopen.com/books/images_new/10914.jpg",editedByType:null,editors:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],filtersByTopic:[{group:"topic",caption:"Agricultural and Biological Sciences",value:5,count:9},{group:"topic",caption:"Biochemistry, Genetics and Molecular Biology",value:6,count:18},{group:"topic",caption:"Business, Management and Economics",value:7,count:2},{group:"topic",caption:"Chemistry",value:8,count:7},{group:"topic",caption:"Computer and Information Science",value:9,count:11},{group:"topic",caption:"Earth and Planetary Sciences",value:10,count:5},{group:"topic",caption:"Engineering",value:11,count:15},{group:"topic",caption:"Environmental Sciences",value:12,count:2},{group:"topic",caption:"Immunology and Microbiology",value:13,count:5},{group:"topic",caption:"Materials Science",value:14,count:4},{group:"topic",caption:"Mathematics",value:15,count:1},{group:"topic",caption:"Medicine",value:16,count:62},{group:"topic",caption:"Nanotechnology and Nanomaterials",value:17,count:1},{group:"topic",caption:"Neuroscience",value:18,count:1},{group:"topic",caption:"Pharmacology, Toxicology and Pharmaceutical Science",value:19,count:6},{group:"topic",caption:"Physics",value:20,count:2},{group:"topic",caption:"Psychology",value:21,count:3},{group:"topic",caption:"Robotics",value:22,count:1},{group:"topic",caption:"Social Sciences",value:23,count:3},{group:"topic",caption:"Technology",value:24,count:1},{group:"topic",caption:"Veterinary Medicine and Science",value:25,count:2}],offset:12,limit:12,total:8},popularBooks:{featuredBooks:[{type:"book",id:"7802",title:"Modern Slavery and Human Trafficking",subtitle:null,isOpenForSubmission:!1,hash:"587a0b7fb765f31cc98de33c6c07c2e0",slug:"modern-slavery-and-human-trafficking",bookSignature:"Jane Reeves",coverURL:"https://cdn.intechopen.com/books/images_new/7802.jpg",editors:[{id:"211328",title:"Prof.",name:"Jane",middleName:null,surname:"Reeves",slug:"jane-reeves",fullName:"Jane Reeves"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8545",title:"Animal Reproduction in Veterinary Medicine",subtitle:null,isOpenForSubmission:!1,hash:"13aaddf5fdbbc78387e77a7da2388bf6",slug:"animal-reproduction-in-veterinary-medicine",bookSignature:"Faruk Aral, Rita Payan-Carreira and Miguel Quaresma",coverURL:"https://cdn.intechopen.com/books/images_new/8545.jpg",editors:[{id:"25600",title:"Prof.",name:"Faruk",middleName:null,surname:"Aral",slug:"faruk-aral",fullName:"Faruk Aral"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9961",title:"Data Mining",subtitle:"Methods, Applications and Systems",isOpenForSubmission:!1,hash:"ed79fb6364f2caf464079f94a0387146",slug:"data-mining-methods-applications-and-systems",bookSignature:"Derya Birant",coverURL:"https://cdn.intechopen.com/books/images_new/9961.jpg",editors:[{id:"15609",title:"Dr.",name:"Derya",middleName:null,surname:"Birant",slug:"derya-birant",fullName:"Derya Birant"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9157",title:"Neurodegenerative Diseases",subtitle:"Molecular Mechanisms and Current Therapeutic Approaches",isOpenForSubmission:!1,hash:"bc8be577966ef88735677d7e1e92ed28",slug:"neurodegenerative-diseases-molecular-mechanisms-and-current-therapeutic-approaches",bookSignature:"Nagehan Ersoy Tunalı",coverURL:"https://cdn.intechopen.com/books/images_new/9157.jpg",editors:[{id:"82778",title:"Ph.D.",name:"Nagehan",middleName:null,surname:"Ersoy Tunalı",slug:"nagehan-ersoy-tunali",fullName:"Nagehan Ersoy Tunalı"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8686",title:"Direct Torque Control Strategies of Electrical Machines",subtitle:null,isOpenForSubmission:!1,hash:"b6ad22b14db2b8450228545d3d4f6b1a",slug:"direct-torque-control-strategies-of-electrical-machines",bookSignature:"Fatma Ben Salem",coverURL:"https://cdn.intechopen.com/books/images_new/8686.jpg",editors:[{id:"295623",title:"Associate Prof.",name:"Fatma",middleName:null,surname:"Ben Salem",slug:"fatma-ben-salem",fullName:"Fatma Ben Salem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7434",title:"Molecular Biotechnology",subtitle:null,isOpenForSubmission:!1,hash:"eceede809920e1ec7ecadd4691ede2ec",slug:"molecular-biotechnology",bookSignature:"Sergey Sedykh",coverURL:"https://cdn.intechopen.com/books/images_new/7434.jpg",editors:[{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",slug:"sergey-sedykh",fullName:"Sergey Sedykh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9839",title:"Outdoor Recreation",subtitle:"Physiological and Psychological Effects on Health",isOpenForSubmission:!1,hash:"5f5a0d64267e32567daffa5b0c6a6972",slug:"outdoor-recreation-physiological-and-psychological-effects-on-health",bookSignature:"Hilde G. Nielsen",coverURL:"https://cdn.intechopen.com/books/images_new/9839.jpg",editors:[{id:"158692",title:"Ph.D.",name:"Hilde G.",middleName:null,surname:"Nielsen",slug:"hilde-g.-nielsen",fullName:"Hilde G. Nielsen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9208",title:"Welding",subtitle:"Modern Topics",isOpenForSubmission:!1,hash:"7d6be076ccf3a3f8bd2ca52d86d4506b",slug:"welding-modern-topics",bookSignature:"Sadek Crisóstomo Absi Alfaro, Wojciech Borek and Błażej Tomiczek",coverURL:"https://cdn.intechopen.com/books/images_new/9208.jpg",editors:[{id:"65292",title:"Prof.",name:"Sadek Crisostomo Absi",middleName:"C. Absi",surname:"Alfaro",slug:"sadek-crisostomo-absi-alfaro",fullName:"Sadek Crisostomo Absi Alfaro"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9139",title:"Topics in Primary Care Medicine",subtitle:null,isOpenForSubmission:!1,hash:"ea774a4d4c1179da92a782e0ae9cde92",slug:"topics-in-primary-care-medicine",bookSignature:"Thomas F. Heston",coverURL:"https://cdn.intechopen.com/books/images_new/9139.jpg",editors:[{id:"217926",title:"Dr.",name:"Thomas F.",middleName:null,surname:"Heston",slug:"thomas-f.-heston",fullName:"Thomas F. Heston"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9343",title:"Trace Metals in the Environment",subtitle:"New Approaches and Recent Advances",isOpenForSubmission:!1,hash:"ae07e345bc2ce1ebbda9f70c5cd12141",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",bookSignature:"Mario Alfonso Murillo-Tovar, Hugo Saldarriaga-Noreña and Agnieszka Saeid",coverURL:"https://cdn.intechopen.com/books/images_new/9343.jpg",editors:[{id:"255959",title:"Dr.",name:"Mario Alfonso",middleName:null,surname:"Murillo-Tovar",slug:"mario-alfonso-murillo-tovar",fullName:"Mario Alfonso Murillo-Tovar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8697",title:"Virtual Reality and Its Application in Education",subtitle:null,isOpenForSubmission:!1,hash:"ee01b5e387ba0062c6b0d1e9227bda05",slug:"virtual-reality-and-its-application-in-education",bookSignature:"Dragan Cvetković",coverURL:"https://cdn.intechopen.com/books/images_new/8697.jpg",editors:[{id:"101330",title:"Dr.",name:"Dragan",middleName:"Mladen",surname:"Cvetković",slug:"dragan-cvetkovic",fullName:"Dragan Cvetković"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7831",title:"Sustainability in Urban Planning and Design",subtitle:null,isOpenForSubmission:!1,hash:"c924420492c8c2c9751e178d025f4066",slug:"sustainability-in-urban-planning-and-design",bookSignature:"Amjad Almusaed, Asaad Almssad and Linh Truong - Hong",coverURL:"https://cdn.intechopen.com/books/images_new/7831.jpg",editors:[{id:"110471",title:"Dr.",name:"Amjad",middleName:"Zaki",surname:"Almusaed",slug:"amjad-almusaed",fullName:"Amjad Almusaed"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:12,limit:12,total:5141},hotBookTopics:{hotBooks:[],offset:0,limit:12,total:null},publish:{},publishingProposal:{success:null,errors:{}},books:{featuredBooks:[{type:"book",id:"9208",title:"Welding",subtitle:"Modern Topics",isOpenForSubmission:!1,hash:"7d6be076ccf3a3f8bd2ca52d86d4506b",slug:"welding-modern-topics",bookSignature:"Sadek Crisóstomo Absi Alfaro, Wojciech Borek and Błażej Tomiczek",coverURL:"https://cdn.intechopen.com/books/images_new/9208.jpg",editors:[{id:"65292",title:"Prof.",name:"Sadek Crisostomo Absi",middleName:"C. Absi",surname:"Alfaro",slug:"sadek-crisostomo-absi-alfaro",fullName:"Sadek Crisostomo Absi Alfaro"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9139",title:"Topics in Primary Care Medicine",subtitle:null,isOpenForSubmission:!1,hash:"ea774a4d4c1179da92a782e0ae9cde92",slug:"topics-in-primary-care-medicine",bookSignature:"Thomas F. Heston",coverURL:"https://cdn.intechopen.com/books/images_new/9139.jpg",editors:[{id:"217926",title:"Dr.",name:"Thomas F.",middleName:null,surname:"Heston",slug:"thomas-f.-heston",fullName:"Thomas F. Heston"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8697",title:"Virtual Reality and Its Application in Education",subtitle:null,isOpenForSubmission:!1,hash:"ee01b5e387ba0062c6b0d1e9227bda05",slug:"virtual-reality-and-its-application-in-education",bookSignature:"Dragan Cvetković",coverURL:"https://cdn.intechopen.com/books/images_new/8697.jpg",editors:[{id:"101330",title:"Dr.",name:"Dragan",middleName:"Mladen",surname:"Cvetković",slug:"dragan-cvetkovic",fullName:"Dragan Cvetković"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9343",title:"Trace Metals in the Environment",subtitle:"New Approaches and Recent Advances",isOpenForSubmission:!1,hash:"ae07e345bc2ce1ebbda9f70c5cd12141",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",bookSignature:"Mario Alfonso Murillo-Tovar, Hugo Saldarriaga-Noreña and Agnieszka Saeid",coverURL:"https://cdn.intechopen.com/books/images_new/9343.jpg",editors:[{id:"255959",title:"Dr.",name:"Mario Alfonso",middleName:null,surname:"Murillo-Tovar",slug:"mario-alfonso-murillo-tovar",fullName:"Mario Alfonso Murillo-Tovar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9785",title:"Endometriosis",subtitle:null,isOpenForSubmission:!1,hash:"f457ca61f29cf7e8bc191732c50bb0ce",slug:"endometriosis",bookSignature:"Courtney Marsh",coverURL:"https://cdn.intechopen.com/books/images_new/9785.jpg",editors:[{id:"255491",title:"Dr.",name:"Courtney",middleName:null,surname:"Marsh",slug:"courtney-marsh",fullName:"Courtney Marsh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7831",title:"Sustainability in Urban Planning and Design",subtitle:null,isOpenForSubmission:!1,hash:"c924420492c8c2c9751e178d025f4066",slug:"sustainability-in-urban-planning-and-design",bookSignature:"Amjad Almusaed, Asaad Almssad and Linh Truong - Hong",coverURL:"https://cdn.intechopen.com/books/images_new/7831.jpg",editors:[{id:"110471",title:"Dr.",name:"Amjad",middleName:"Zaki",surname:"Almusaed",slug:"amjad-almusaed",fullName:"Amjad Almusaed"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9376",title:"Contemporary Developments and Perspectives in International Health Security",subtitle:"Volume 1",isOpenForSubmission:!1,hash:"b9a00b84cd04aae458fb1d6c65795601",slug:"contemporary-developments-and-perspectives-in-international-health-security-volume-1",bookSignature:"Stanislaw P. Stawicki, Michael S. Firstenberg, Sagar C. Galwankar, Ricardo Izurieta and Thomas Papadimos",coverURL:"https://cdn.intechopen.com/books/images_new/9376.jpg",editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. Stawicki"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7769",title:"Medical Isotopes",subtitle:null,isOpenForSubmission:!1,hash:"f8d3c5a6c9a42398e56b4e82264753f7",slug:"medical-isotopes",bookSignature:"Syed Ali Raza Naqvi and Muhammad Babar Imrani",coverURL:"https://cdn.intechopen.com/books/images_new/7769.jpg",editors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9279",title:"Concepts, Applications and Emerging Opportunities in Industrial Engineering",subtitle:null,isOpenForSubmission:!1,hash:"9bfa87f9b627a5468b7c1e30b0eea07a",slug:"concepts-applications-and-emerging-opportunities-in-industrial-engineering",bookSignature:"Gary Moynihan",coverURL:"https://cdn.intechopen.com/books/images_new/9279.jpg",editors:[{id:"16974",title:"Dr.",name:"Gary",middleName:null,surname:"Moynihan",slug:"gary-moynihan",fullName:"Gary Moynihan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7807",title:"A Closer Look at Organizational Culture in Action",subtitle:null,isOpenForSubmission:!1,hash:"05c608b9271cc2bc711f4b28748b247b",slug:"a-closer-look-at-organizational-culture-in-action",bookSignature:"Süleyman Davut Göker",coverURL:"https://cdn.intechopen.com/books/images_new/7807.jpg",editors:[{id:"190035",title:"Associate Prof.",name:"Süleyman Davut",middleName:null,surname:"Göker",slug:"suleyman-davut-goker",fullName:"Süleyman Davut Göker"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],latestBooks:[{type:"book",id:"7434",title:"Molecular Biotechnology",subtitle:null,isOpenForSubmission:!1,hash:"eceede809920e1ec7ecadd4691ede2ec",slug:"molecular-biotechnology",bookSignature:"Sergey Sedykh",coverURL:"https://cdn.intechopen.com/books/images_new/7434.jpg",editedByType:"Edited by",editors:[{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",slug:"sergey-sedykh",fullName:"Sergey Sedykh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8545",title:"Animal Reproduction in Veterinary Medicine",subtitle:null,isOpenForSubmission:!1,hash:"13aaddf5fdbbc78387e77a7da2388bf6",slug:"animal-reproduction-in-veterinary-medicine",bookSignature:"Faruk Aral, Rita Payan-Carreira and Miguel Quaresma",coverURL:"https://cdn.intechopen.com/books/images_new/8545.jpg",editedByType:"Edited by",editors:[{id:"25600",title:"Prof.",name:"Faruk",middleName:null,surname:"Aral",slug:"faruk-aral",fullName:"Faruk Aral"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9569",title:"Methods in Molecular Medicine",subtitle:null,isOpenForSubmission:!1,hash:"691d3f3c4ac25a8093414e9b270d2843",slug:"methods-in-molecular-medicine",bookSignature:"Yusuf Tutar",coverURL:"https://cdn.intechopen.com/books/images_new/9569.jpg",editedByType:"Edited by",editors:[{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9839",title:"Outdoor Recreation",subtitle:"Physiological and Psychological Effects on Health",isOpenForSubmission:!1,hash:"5f5a0d64267e32567daffa5b0c6a6972",slug:"outdoor-recreation-physiological-and-psychological-effects-on-health",bookSignature:"Hilde G. Nielsen",coverURL:"https://cdn.intechopen.com/books/images_new/9839.jpg",editedByType:"Edited by",editors:[{id:"158692",title:"Ph.D.",name:"Hilde G.",middleName:null,surname:"Nielsen",slug:"hilde-g.-nielsen",fullName:"Hilde G. Nielsen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7802",title:"Modern Slavery and Human Trafficking",subtitle:null,isOpenForSubmission:!1,hash:"587a0b7fb765f31cc98de33c6c07c2e0",slug:"modern-slavery-and-human-trafficking",bookSignature:"Jane Reeves",coverURL:"https://cdn.intechopen.com/books/images_new/7802.jpg",editedByType:"Edited by",editors:[{id:"211328",title:"Prof.",name:"Jane",middleName:null,surname:"Reeves",slug:"jane-reeves",fullName:"Jane Reeves"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8063",title:"Food Security in Africa",subtitle:null,isOpenForSubmission:!1,hash:"8cbf3d662b104d19db2efc9d59249efc",slug:"food-security-in-africa",bookSignature:"Barakat Mahmoud",coverURL:"https://cdn.intechopen.com/books/images_new/8063.jpg",editedByType:"Edited by",editors:[{id:"92016",title:"Dr.",name:"Barakat",middleName:null,surname:"Mahmoud",slug:"barakat-mahmoud",fullName:"Barakat Mahmoud"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10118",title:"Plant Stress Physiology",subtitle:null,isOpenForSubmission:!1,hash:"c68b09d2d2634fc719ae3b9a64a27839",slug:"plant-stress-physiology",bookSignature:"Akbar Hossain",coverURL:"https://cdn.intechopen.com/books/images_new/10118.jpg",editedByType:"Edited by",editors:[{id:"280755",title:"Dr.",name:"Akbar",middleName:null,surname:"Hossain",slug:"akbar-hossain",fullName:"Akbar Hossain"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9157",title:"Neurodegenerative Diseases",subtitle:"Molecular Mechanisms and Current Therapeutic Approaches",isOpenForSubmission:!1,hash:"bc8be577966ef88735677d7e1e92ed28",slug:"neurodegenerative-diseases-molecular-mechanisms-and-current-therapeutic-approaches",bookSignature:"Nagehan Ersoy Tunalı",coverURL:"https://cdn.intechopen.com/books/images_new/9157.jpg",editedByType:"Edited by",editors:[{id:"82778",title:"Ph.D.",name:"Nagehan",middleName:null,surname:"Ersoy Tunalı",slug:"nagehan-ersoy-tunali",fullName:"Nagehan Ersoy Tunalı"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9961",title:"Data Mining",subtitle:"Methods, Applications and Systems",isOpenForSubmission:!1,hash:"ed79fb6364f2caf464079f94a0387146",slug:"data-mining-methods-applications-and-systems",bookSignature:"Derya Birant",coverURL:"https://cdn.intechopen.com/books/images_new/9961.jpg",editedByType:"Edited by",editors:[{id:"15609",title:"Dr.",name:"Derya",middleName:null,surname:"Birant",slug:"derya-birant",fullName:"Derya Birant"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8686",title:"Direct Torque Control Strategies of Electrical Machines",subtitle:null,isOpenForSubmission:!1,hash:"b6ad22b14db2b8450228545d3d4f6b1a",slug:"direct-torque-control-strategies-of-electrical-machines",bookSignature:"Fatma Ben Salem",coverURL:"https://cdn.intechopen.com/books/images_new/8686.jpg",editedByType:"Edited by",editors:[{id:"295623",title:"Associate Prof.",name:"Fatma",middleName:null,surname:"Ben Salem",slug:"fatma-ben-salem",fullName:"Fatma Ben Salem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"316",title:"Lepidopterology",slug:"lepidopterology",parent:{title:"Animal Biology",slug:"animal-biology"},numberOfBooks:1,numberOfAuthorsAndEditors:11,numberOfWosCitations:4,numberOfCrossrefCitations:14,numberOfDimensionsCitations:17,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicSlug:"lepidopterology",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"6156",title:"Lepidoptera",subtitle:null,isOpenForSubmission:!1,hash:"b5d586ee7920aa6388b521b833916453",slug:"lepidoptera",bookSignature:"Farzana Khan Perveen",coverURL:"https://cdn.intechopen.com/books/images_new/6156.jpg",editedByType:"Edited by",editors:[{id:"75563",title:"Dr.",name:"Farzana Khan",middleName:null,surname:"Perveen",slug:"farzana-khan-perveen",fullName:"Farzana Khan Perveen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:1,mostCitedChapters:[{id:"56325",doi:"10.5772/intechopen.70098",title:"Contact-Mediated Eyespot Color-Pattern Changes in the Peacock Pansy Butterfly: Contributions of Mechanical Force and Extracellular Matrix to Morphogenic Signal Propagation",slug:"contact-mediated-eyespot-color-pattern-changes-in-the-peacock-pansy-butterfly-contributions-of-mecha",totalDownloads:775,totalCrossrefCites:6,totalDimensionsCites:8,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Joji M. Otaki",authors:[{id:"208068",title:"Associate Prof.",name:"Joji",middleName:"M.",surname:"Otaki",slug:"joji-otaki",fullName:"Joji Otaki"}]},{id:"56320",doi:"10.5772/intechopen.70050",title:"Synergistic Damage Response of the Double-Focus Eyespot in the Hindwing of the Peacock Pansy Butterfly",slug:"synergistic-damage-response-of-the-double-focus-eyespot-in-the-hindwing-of-the-peacock-pansy-butterf",totalDownloads:579,totalCrossrefCites:6,totalDimensionsCites:7,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Mayo Iwasaki and Joji M. Otaki",authors:[{id:"208068",title:"Associate Prof.",name:"Joji",middleName:"M.",surname:"Otaki",slug:"joji-otaki",fullName:"Joji Otaki"},{id:"208071",title:"MSc.",name:"Mayo",middleName:null,surname:"Iwasaki",slug:"mayo-iwasaki",fullName:"Mayo Iwasaki"}]},{id:"56208",doi:"10.5772/intechopen.69958",title:"Molecular Phylogeny and Taxonomy of Lepidoptera with Special Reference to Influence of Wolbachia Infection in the Genus Polytremis",slug:"molecular-phylogeny-and-taxonomy-of-lepidoptera-with-special-reference-to-influence-of-wolbachia-inf",totalDownloads:784,totalCrossrefCites:1,totalDimensionsCites:1,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Weibin Jiang",authors:[{id:"207420",title:"Dr.",name:"Weibin",middleName:null,surname:"Jiang",slug:"weibin-jiang",fullName:"Weibin Jiang"}]}],mostDownloadedChaptersLast30Days:[{id:"57369",title:"Introductory Chapter: Lepidoptera",slug:"introductory-chapter-lepidoptera",totalDownloads:5859,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Farzana Khan Perveen and Anzela Khan",authors:[{id:"75563",title:"Dr.",name:"Farzana Khan",middleName:null,surname:"Perveen",slug:"farzana-khan-perveen",fullName:"Farzana Khan Perveen"}]},{id:"57731",title:"Taxocenotic and Biocenotic Study of Lepidoptera (Rhopalocera) in Rucamanque: A Forest Remnant in the Central Valley of the Araucanía Region, Chile",slug:"taxocenotic-and-biocenotic-study-of-lepidoptera-rhopalocera-in-rucamanque-a-forest-remnant-in-the-ce",totalDownloads:662,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Hernán Navarrete Parra and Ramón Rebolledo Ranz",authors:[{id:"193813",title:"Dr.",name:"Ramón Eduardo",middleName:null,surname:"Rebolledo Ranz",slug:"ramon-eduardo-rebolledo-ranz",fullName:"Ramón Eduardo Rebolledo Ranz"},{id:"217930",title:"Prof.",name:"Hernán",middleName:null,surname:"Navarrete",slug:"hernan-navarrete",fullName:"Hernán Navarrete"}]},{id:"56208",title:"Molecular Phylogeny and Taxonomy of Lepidoptera with Special Reference to Influence of Wolbachia Infection in the Genus Polytremis",slug:"molecular-phylogeny-and-taxonomy-of-lepidoptera-with-special-reference-to-influence-of-wolbachia-inf",totalDownloads:784,totalCrossrefCites:1,totalDimensionsCites:1,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Weibin Jiang",authors:[{id:"207420",title:"Dr.",name:"Weibin",middleName:null,surname:"Jiang",slug:"weibin-jiang",fullName:"Weibin Jiang"}]},{id:"57286",title:"Mitochondrial Genomes of Lepidopteran Insects Considered Crop Pests",slug:"mitochondrial-genomes-of-lepidopteran-insects-considered-crop-pests",totalDownloads:708,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Viviana Ramírez-Ríos, Javier Correa Alvarez and Diego Villanueva-\nMejia",authors:[{id:"206827",title:"Dr.",name:"Diego",middleName:"F.",surname:"Villanueva-Mejía",slug:"diego-villanueva-mejia",fullName:"Diego Villanueva-Mejía"},{id:"214479",title:"Dr.",name:"Javier",middleName:null,surname:"Correa Alvarez",slug:"javier-correa-alvarez",fullName:"Javier Correa Alvarez"},{id:"219660",title:"MSc.",name:"Viviana",middleName:null,surname:"Ramírez-Ríos",slug:"viviana-ramirez-rios",fullName:"Viviana Ramírez-Ríos"}]},{id:"57355",title:"Lepidoptera Collection Curation and Data Management",slug:"lepidoptera-collection-curation-and-data-management",totalDownloads:795,totalCrossrefCites:1,totalDimensionsCites:1,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Jurate De Prins",authors:[{id:"213731",title:"Dr.",name:"Jurate",middleName:null,surname:"De Prins",slug:"jurate-de-prins",fullName:"Jurate De Prins"}]},{id:"56325",title:"Contact-Mediated Eyespot Color-Pattern Changes in the Peacock Pansy Butterfly: Contributions of Mechanical Force and Extracellular Matrix to Morphogenic Signal Propagation",slug:"contact-mediated-eyespot-color-pattern-changes-in-the-peacock-pansy-butterfly-contributions-of-mecha",totalDownloads:775,totalCrossrefCites:6,totalDimensionsCites:8,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Joji M. Otaki",authors:[{id:"208068",title:"Associate Prof.",name:"Joji",middleName:"M.",surname:"Otaki",slug:"joji-otaki",fullName:"Joji Otaki"}]},{id:"56320",title:"Synergistic Damage Response of the Double-Focus Eyespot in the Hindwing of the Peacock Pansy Butterfly",slug:"synergistic-damage-response-of-the-double-focus-eyespot-in-the-hindwing-of-the-peacock-pansy-butterf",totalDownloads:579,totalCrossrefCites:6,totalDimensionsCites:7,book:{slug:"lepidoptera",title:"Lepidoptera",fullTitle:"Lepidoptera"},signatures:"Mayo Iwasaki and Joji M. Otaki",authors:[{id:"208068",title:"Associate Prof.",name:"Joji",middleName:"M.",surname:"Otaki",slug:"joji-otaki",fullName:"Joji Otaki"},{id:"208071",title:"MSc.",name:"Mayo",middleName:null,surname:"Iwasaki",slug:"mayo-iwasaki",fullName:"Mayo Iwasaki"}]}],onlineFirstChaptersFilter:{topicSlug:"lepidopterology",limit:3,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[{type:"book",id:"10176",title:"Microgrids and Local Energy Systems",subtitle:null,isOpenForSubmission:!0,hash:"c32b4a5351a88f263074b0d0ca813a9c",slug:null,bookSignature:"Prof. Nick Jenkins",coverURL:"https://cdn.intechopen.com/books/images_new/10176.jpg",editedByType:null,editors:[{id:"55219",title:"Prof.",name:"Nick",middleName:null,surname:"Jenkins",slug:"nick-jenkins",fullName:"Nick Jenkins"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:8,limit:8,total:1},route:{name:"profile.detail",path:"/profiles/251636/alexander-idoko",hash:"",query:{},params:{id:"251636",slug:"alexander-idoko"},fullPath:"/profiles/251636/alexander-idoko",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var t;(t=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(t)}()