Field plots characteristics
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"7850",leadTitle:null,fullTitle:"Mitochondria and Brain Disorders",title:"Mitochondria and Brain Disorders",subtitle:null,reviewType:"peer-reviewed",abstract:"The mitochondrion is a unique and ubiquitous organelle that contains its own genome, encoding essential proteins that are major components of the respiratory chain and energy production system. 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He works as an anesthesiologist at Lotus Med Group and belongs to the Institutos Nacionales de Salud as an associated researcher.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"169249",title:"Prof.",name:"Víctor M.",middleName:null,surname:"Whizar-Lugo",slug:"victor-m.-whizar-lugo",fullName:"Víctor M. Whizar-Lugo",profilePictureURL:"https://mts.intechopen.com/storage/users/169249/images/system/169249.jpg",biography:"Víctor M. Whizar-Lugo graduated from Universidad Nacional Autónoma de México and completed residencies in Internal Medicine at Hospital General de México and Anaesthesiology and Critical Care Medicine at Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán in México City. He also completed a fellowship at the Anesthesia Department, Pain Clinic at University of California, Los Angeles, USA. Currently, Dr. Whizar-Lugo works as anesthesiologist at Lotus Med Group, and belongs to the Institutos Nacionales de Salud as associated researcher. He has published many works on anesthesia, pain, internal medicine, and critical care, edited four books, and given countless conferences in congresses and meetings around the world. He has been a member of various editorial committees for anesthesiology journals, is past chief editor of the journal Anestesia en México, and is currently editor-in-chief of the Journal of Anesthesia and Critical Care. Dr. Whizar-Lugo is the founding director and current president of Anestesiología y Medicina del Dolor (www.anestesiologia-dolor.org), a free online medical education program.",institutionString:"Institutos Nacionales de Salud",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"3",institution:null}],coeditorOne:{id:"345887",title:"Dr.",name:"José Ramón",middleName:null,surname:"Saucillo-Osuna",slug:"jose-ramon-saucillo-osuna",fullName:"José Ramón Saucillo-Osuna",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000033rFXmQAM/Profile_Picture_1611740683590",biography:"Graduated from the Facultad de Medicina de la Universidad Autónoma de Guadalajara, he specialized in anesthesiology at the Centro Médico Nacional de Occidente in Guadalajara, México. He is one of the most important pioneers in Mexico in ultrasound-guided regional anesthesia. Dr. Saucillo-Osuna has lectured at multiple national and international congresses and is an adjunct professor at the Federación Mexicana de Colegios de Anestesiología, AC, former president of the Asociación Mexicana de Anestesia Regional, and active member of the Asociación Latinoamericana de Anestesia Regional.",institutionString:"Centro Médico Nacional de Occidente",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"347258",firstName:"Marica",lastName:"Novakovic",middleName:null,title:"Dr.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"marica@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. 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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"}}]},chapter:{item:{type:"chapter",id:"19067",title:"SAR and Optical Images for Forest Biomass Estimation",doi:"10.5772/18930",slug:"sar-and-optical-images-for-forest-biomass-estimation",body:'Biomass, in general, includes the above-ground andbelow-ground living mass, such as trees, shrubs, vines, roots, and the dead mass of fine and coarse litter associated with the soil. Due to the difficulty in collecting field data of below-ground biomass, most previous researches on biomass estimation have been focused on the above-ground biomass (AGB).Different approaches have been applied for above ground biomass (AGB) estimation, where traditional techniques based on field measurement are the most accurate ways for collecting biomass data.A sufficient number of field measurements are a prerequisite for developing AGB estimation models and for evaluating its results. However, these approaches are often time consuming, labour intensive, and difficult to implement, especially in remote areas; also, they cannot provide the spatial distribution of biomass in large areas.
The advantages of remotely sensed data, such as in repetitively of data collection, a synoptic view, a digital format that allows fast processing of large quantities of data, and the high correlations between spectral bands and vegetation parameters, make it the primary source for large area AGB estimation, especially in areas of difficult access. Therefore, remote sensing-based AGB estimation has increasingly attracted scientific interest (Nelson et al., 1988; Sader et al., 1989; Franklin& Hiernaux, 1991; Steininger, 2000; Foody et al., 2003; Zheng et al., 2004; Lu, 2005). There are also other papers including (Dobsonet al., 1992; Rignotet al., 1995; Rignot et al., 1994; Quinones& Hoekman, 2004) with SAR-based methods in above ground biomass estimation.
One strategy that can be used for AGB estimation is to combine synthetic aperture radar (SAR) image texture with optical images based on the classification analysis. Limitation on the used only optical data is the insensitivity of reflectance to the change in biomass and different stands. The use of the SAR data has the potential to overcome this limitation. But presence of the speckle in SAR data is also a barrier to the exploitation of image texture. Reducing the speckle would improve the discrimination among different land use types, and would make the textual classifiers more efficient in radar images. Ideally, the filters will reduce speckle without loss of information.
Many adaptive filters that preserve the radiometric and texture information have been developed for speckle reduction.Adaptive filters based upon the spatial domain are more widely used than frequency domain filters. The most frequently used adaptive filters include Lee, Frost, Lee-Sigma and Gamma-Map. The Lee filter is based on the multiplicative speckle model, and it can use local statistics to effectively preserve edges and features (Lee, 1980). The Frost filter is also based on the multiplicative speckle model and the local statistics, and it has similar performance to the Lee filter (Frost, 1982). The Lee-Sigma filter is a conceptually simple but effective alternative to the Lee filter, and Lee-Sigma is based on the sigma probability of the Gaussian distribution of image noise (Lee, 1980). Lopes (Lopes et al., 1990) developed the Gamma-Map filter, which is adapted from the Maximum a Posterior (MAP) filter (Kuan, 1987). Lee, Frost and Lee-Sigma filters assume a Gaussian distribution for the speckle noise, whereas Gamma-Map filter assumes a Gamma distribution of speckle (Lopeset al., 1990a; Lopeset al, 1990b). Modified versions of Gamma-Map have also been proposed (Nezry et al., 1991; Baraldi&Parmiggiani, 1995). Nezry (Nezry et al., 1991) combined the ratio edge detector and the Gamma-Map filter into the refined Gamma-Map algorithm. Baraldi and Parmiggiani (1995) proposed a refined Gamma-Map filter with improved geometrical adaptively. Walessa and Datcu combined the edge detection and region growing to segment the SAR image and then applied speckle filtering within each segment under stationary conditions. Dong et al. (2001) proposed an algorithm for synthetic aperture radar speckle reduction and edge sharpening. The proposed algorithm was functions of an adaptive-mean filter. Achim et al. (2006)proposed a novel adaptive de-speckling filter using the introduced heavy-tailed Rayleigh density function and derived a maximum a posterior (MAP) estimator for the radar cross section (RCS). The authors (Sumantyo & Amini, 2008) proposed a filter based on the least square method for speckle reduction in SAR images.
In this chapter, we develop a method for the forest biomass estimation based on (Amini &Sumantyo, 2009). Both SAR and optical images are used in a multilayer perceptron neural network (MLPNN) that relates them to the forest measurements on the ground. We use a speckle noise model that proposed by the authors in 2008 (Sumantyo & Amini, 2008) for reducing the speckle noise in the SAR image. Reducing the speckle would improve the discrimination among different land use types, and would make the textual classifiers more efficient in SAR images. We investigate both quantitative and qualitative criteria in speckle reduction and texture preservation to evaluate the performance of the proposed filter on the forest biomass estimation.
In summary, the objectives of this chapter are:
The efficiency of the de-speckling filter on forest biomass estimation and,
Improved the accuracy of forest biomass estimation when using both SAR images texture and optical images in a non-linear classifier method (MLPNN).
In the rest of the chapter, we will have a survey on de-speckling filters and thenwe will describe a method for the forest biomass estimation and we finally give the experimental results for the study area.
Both the radiometric and texture aspects are less efficient for area discrimination in the presence of speckle. Reducing the speckle would improve the discrimination among different land use types, and would make the usual per-pixel or textual classifiers more efficient in radar images. Ideally, this supports that the filters reduce speckle without loss of information.
In the case of homogeneous areas (e.g. agricultural areas), the filters should preserve the backscattering coefficient values (the radiometric information) and edges between the different areas. In addition for texture areas (e.g. forest), the filter should preserve the spatial variability (textual information).
Many adaptive filters that preserve the radiometric and texture information have been developed for speckle reduction. Filtering techniques generally can be grouped into multi-look processing and posterior speckle filtering techniques. Multi-look processing is applied during image formation, and this procedure averages several statistically independent looks of the same scene to reduce speckle (Porcello et al. 1976). A major disadvantage of this technique is that the resulting images suffer from a reduction of the ground resolution that is proportional to the number of looks N (Martin and Turner 1993). To overcome this disadvantage, or to further reduce speckle, many posterior speckle-filtering techniques have been developed. These techniques are based on either the spatial or the frequency domain.
The Wiener filter (Walkup and Choens, 1974) and other filters with criteria of minimum mean-square error (MMSE) are examples of filtering algorithms that are based upon the frequency domain (Li 1988). The Wavelet approaches have been used to reduce speckle in SAR images, following Mallat’s (1989a, b) theoretical basis for multi-resolution analysis. Gagnon and Jouan (1997), Fukuda and Hirosawa (1998), and Simard et al. (1998) have successfully applied wavelet transformation to reduce speckle in SAR images. Gagnon and Jouan (1997) presented a Wavelet Coefficient Shrinkage (WCS) filter, which performs as well as the standard filters for low-level noise and slightly outperforms them for higher-level noise. The wavelet filter proposed by Fukuda and Hirosawa (1998) has satisfactory performance in both smoothing and edge preservation.
There are also other filters less frequently used, such as the mean filter, the median filter, the Kalman filter (Woods and Radewan 1977), the Geometric filter (Crimmins 1985), the adaptive vector linear minimum mean-squared error (LMMSE) filter (Lin and Allebach 1990), the Weighting filter (Martin and Turner 1993), the EPOS filter (Hagg and Sties 1994), the Modified K-average filter (Rao et al. 1995) and a texture-preserving filter (Aiazzi et al. 1997).
An electromagnetic wave scatters from two dimensional position (x, y) on the earth surface, the physical properties of the terrain cause changes in both the phase,
From the complex image, a variety of other products can be formed. For example, images of the real part
The real and imaginary images show some structure but appear extremely noisy, the phase image is noise-like and shows no structure, while the amplitude, intensity, and log images, though noisy, are clearly easier to interpret. The noise-like quantity characteristic of these types of images is known as speckle. It must be stressed that speckle is noise-like, but it is not noise; it is a real electromagnetic measurement, which is exploited, for example, in SAR interferometry (Oliver, and Quegan, 2004). Given that the SAR in making true measurements of the earth\'s scattering properties, why do such effect arise?
As the wave interacts with the target, each scatterer contributes a backscattered wave with a phase and amplitude change, so the total returned modulation or the observed value at each pixel of the incident wave is
This summation is over the number of scatters illustrated by the beam. The individual scattering amplitudes
The observed intensity or power
with mean value and standard deviation both equal to
We need to distinguish the measured value at a pixel and the parameter value
A SAR image comprise of some variable, corresponding to local RCS, that is combined with speckle to yield the observed intensity at each pixel. The intensity is given by
All the reconstruction methods for
Where
The ideal filter should eliminate the speckle so that the original signal
First, two classes can be considered: 1) the homogeneous class corresponding to the area where
Within the Homogeneous class: The filter should restore
Within the Heterogeneous class: the filter should smooth the speckle and, at the same time, preserve edges and texture information (
In practice, these two conditions are not always satisfied. A third class is then pointed out where the filter is no longer reliable, and original pixel values are then preserved. In the case of an isolated point target, the filter should conserve the observed value I. This is also the case when there are a few scatterers within the resolution cells.
According to above consideration, the following classes are pointed out as a function of the coefficient of variation value.
Class to be averaged: if
Class to be filters: if
Class to be preserved: If
Where
The threshold determination is given by the following consideration (Lopes, et al., 1990a).
For an L-look image
The approach of this chapter for reconstruction of backscattering coefficient (
Where
for L-look SAR.
The simplest approach to de-speckling is to average the intensity over several pixels within a window centred on a specific pixel. This is tantamount to assuming that the RCS is constant over the filter window. If this assumption is incorrect, the method is fundamentally flawed. The joint probability that all N pixels have this mean value is given by
for L-look SAR, where pixels are assumed independent, The MLE for
Multi-look de-speckling fails where the assumption of constant RCS within the window breaks down. The filter should then adapt to model theexcess fluctuations compared with speckle within the window.
In this chapter, the approach that we developed for de-speckling is based on the least square method. If the original intensity of the centre pixel in a window is I, then its corrected value can be obtained by performing a first-order expansion in Taylor saris about the local mean
Where
e: is the error that must be optimized; k: is selected to minimized e;
But a better estimate for
Where
The PDF of
Hence, the log likelihood is given by
and the corresponding Gamma MAP solution for RCS (Kuan, at al., 1987; Oliver, 1991) is given by the quadratic:
In regions of pure speckle, we would expect
In this chapter, we improve the Gamma-MAP filter by introducing an algorithm that detects and adapts to structural features, such as edges, lines, and points using lease square method.
The Gamma-MAP filter appears to give limited de-specking performance. Large windows yield good speckle reduction over homogeneous regions but lead to artifacts over a distance equal to the filter dimension in the presence of strong features. This means that background clutter has excess variations in the precisely those areas where one would like to accurately defined. Small windows are largely free of these artifacts but give inadequate speckle reduction. In our algorithm, iteration leads to a considerable reduction in the speckle.
In principal, it should be possible to base the iteration process on updating the current pixel value, denoted by x, rather than the original intensity I. However, this demands knowledge of the conditional probability P(x|
The equation (11) is nonlinear with respect to
Where x is the current pixel value,
Thus, we can write N observation equations for pixels with intensity
Fig 1 shows the process of the de-speckling model.
According to the diagram of Fig 1, a moving window, W, is placed in the top left centre of the SAR image to be filtered (Fig 2) and the mean and the standard deviation values of the pixels within the moving window centred on a specific pixel are computed. Based on the pixels in the window, a linear observation equation system is performed for all pixels in the window using the observation equation (13). The system is solved by using the least square method (LSM) to determine the correction
The proposed algorithm in Fig 1 proceeds as following steps:
Step 1: Initialization stage
Set the parameters and consider the lth pixel with intensity
Step 2: Perform intensity update (Filtered image)
Compute the mean and the standard deviation values of the moving window W centred in the lth pixel
Perform the linear observation equation system based on the equation (13) for all elements in the window W
Using the least square method to determine the correction
Compute the new value
Increment l and go to step 2 until l =
Step 3: Acceptance/ Rejection stage
1. Evaluation of the ratio of the original intensity image, I, to the derived RCS image,
2. Estimate the mean,
Where
3. IF {
ELSE {continue and go to step 1}.
The flowchart of the de-speckling model
Operation of the moving window with size of3×3
The methodology used for the forest inventory is distinct according to the vegetation type. In forest areas, different parameters are measured namely: diameter at breast height (DBH), total and commercial height, crown cover percent, and location of each plots. Total height is the height from the upper branches of a tree to the ground and the commercial height is the height of the main trunk of a tree. The crown cover percent is also percent of the number of trees in a hectare. We measured the total height during the field survey and used it in the allometric equation. In addition, the identification of botanical species is also conducted.
The field work consists of collecting some bio-physical and dendrometric parameters which allowed the biomass estimation of the plots and the physiognomic–structural characterization of the different vegetation types considered. The precise geographic coordinates of each plot are obtained using a high-precision Global Positioning System (GPS), which allows the localization of each plots, in the previously geo-referenced images.
The study area is located in the northern forests of Iran around the Rezvanshahr city (Fig. 3(a) ). The dominant trees of these forests are: Maple, Alder, Conifer, Beech, Hornbeam, Azedarach and Acorn. Remote sensing data also consist of: AVNIR-2 and PRISM images from ALOS and a JERS-1 image. The JERS-1 image has a spatial resolution of approximately 13m and, AVNIR-2 and PRISM images have the spatial resolutions of 10m and 2.5m respectively. According to Fig. 3(b), the ground data is collected at five plots in the study area. Each plot
a) Study area of the north of Iran, (b) Plots in the study area indicated with circles.
was a square with size of 50m×50m with 25 subplots with size of 10m×10m approximately. The minimum DBH considered was of 37cm. The plots were mostly covered by two classes: Acorn and Azedarach. The distribution of the classes with numbers of stands where measured in each subplots are shown in Table 1. Table 1 summarizes some of the ground measurements and resulting calculations.
The biomass in Table 1 is modelled based on the direct DBH and the total height measurements performed during the field survey and included afterwards in the general allometric equation (15) (Brown et al., 1989).
Where: DBH is in cm, height is in m, and biomass is in kg/tree.
For speckle reduction in the SAR image, the de-speckling model apply on the JERS-1 image of the study area and then its result is compared with several of the most widely used adaptive filters including the Kuan, Gamma, Lee and Frost filters.
In order to investigate the performance of the model, we use some quantitative criteria including speckle smoothing measures and texture preservation to evaluate the performance of the model.
Plot | # of subplots for Acorn Azedarach | Mean height (m) | Mean DBH (cm) | Mean Biomass (ton/tree) | # of stands for Acorn Azedar | Total mean biomass (ton) for Acorn Azedarach |
1 2 3 4 5 | 20 5 07 18 19 06 15 10 04 21 | 28.5 34 26.5 29 27.5 | 40 55 35 45 38 | 1.484 3.275 1.066 1.897 2.373 | 15 05 08 13 24 10 14 09 06 24 | 26.712 07.420 25.960 42.575 25.584 10.660 26.558 17.073 14.238 56.952 |
Field plots characteristics
The ratio of the original intensity image to the filtered image enable us to determine the extent to which the reconstruction filter introduces radiometric distortion so that the reconstruction departs from the expected speckle statistics. The mean and standard deviation (SD) can then be estimated over the ratio images. When the mean value differs significantly from one, it is an indication of radiometric distortion. If the reconstruction follows the original image too closely, the standard deviation would be expected to have a lower value than predicted. It would be larger than predicted if the reconstruction fails to follow genuine RCS variations. This provides a simple test that can be applied to any form of RCS reconstruction filters. Table 2, columns 2 and 3, shows the mean and standard deviation values of the ratio images for comparison of the filters.
Algorithm | Ratio image | Filtered image | ||
Mean | S. D | ENL | VTO | |
The model | 0.991 | 0.037 | 26.78 | 643.12 |
Kuan | 0.968 | 0.195 | 4.96 | 90.12 |
Gamma | 0.968 | 0.195 | 4.96 | 335.12 |
Enhanced Lee | 0.968 | 0.195 | 4.96 | 234.26 |
Enhanced Frost | 0.968 | 0.195 | 16.15 | 401.32 |
Comparison of the mean and SD in the ratio images, ENL and variance texture operator of the filtered images
According to Gagnon and Jouan (1997), Equivalent number of Looks (ENL) is often used to estimate the speckle noise level in a SAR image and is equivalent to the number of independent intensity values that are used per pixel.
It is the mean-to-standard deviation ratio, which is a measure of the signal-to-noise ratio and is defined over a uniform area as follows:
ENL is used to measure the degree of speckle reduction in this study. The higher the ENL value concludes the stronger the speckle reduction.
Texture preservation is another measure that is important in a SAR image for interpretation and classification. Therefore, the texture preserving capability should play an important role in measuring the performance of a speckle filter. A second-order texture, variance (Iron&Petersen, 1981), is used to measure the retention of texture information in the original and the filtered images.
The ENL and the second-order texture values of the filtered images are shown in Table 2 columns 4 and 5 respectively. Of the four commonly used filters, Enhanced Frost filter has higher speckle-smoothing capabilities than Kuan, Gamma and Enhanced Lee filters. The ENL value of the model is 26.78 that it is comparable to Enhanced Frost filter. According column 5,Variance Texture Operator (VTO), in Table 2, the texture preservation of the proposed filter is better than, or comparable to, those of the commonly used speckle filters. We concluded the model is slightly better than the commonly used filters in terms of preserving details in forestry areas. Furthermore, the model also affects in smoothing speckles. This improvement in the accuracy of the speckle reduction can be played an important role in the forest biomass estimation.
After reduction the speckle noise, the texture of SAR image must be measured. Of the many describing texture methods, the grey-level co-occurrence matrix (GLCM) is the most common (Marceau et al.,1990; Smith et al.,2002; Zhang et al.,2003) in remote sensing.
Nine texture measures are calculated from the GLCM for a moving window with size of 5×5 pixels that centred in pixel i, j of the de-speckled JERS-1image. After the Gram-Schmidt process, just four texture measures: contrast, correlation, maximum probability and standard-deviation are selected as the optimum measures in this area.
The PRISM image is transformed in the universal transverse Mercator (UTM) projection with a WGS84 datum based on the GPS measurements and is used as the base map. Two GPSs measured the coordinates of points along the roads of the study area. To place all data sets in a unified coordinate system, the AVNIR and JERS-1 image are registered to this map. The co-registered and geo-referenced data sets contain PRISM, AVNIR and SAR images are used to extract intensity values and texture measures respectively.
Intensity value and texture measures from the co-registered and geo-referenced data sets are used in the algorithm to estimate the forest biomass. The data sets are related to the forest biomass through a classification analysis. The correspondence between the data sets and ground plots is made using PCIGeomatica software, where the ground plot GPS locations are superimposed on the data set. For each selected pixel (or point) from data set, a window with size of 5×5 pixels around the point is used and the average intensity values for the PRISM and three channels of the AVNIR images with four texture values of the JERS-1 image are calculated. Thus each selected point contains a vector with eight attributes where the first four elements are the average intensity values and the second four elements are the texture measures values. These vectors of data set construct the feature space. The vectors belong to the pixels of the ground plots and subplots are used as training patterns in the classification process.
The classification analysis is done with a MLPNN. A multi layers neural network is made up of sets of neurons assembled in a logical way and constituting several layers. Three distinct types of layers are present in the MLPNN. The input layer is not itself a processing layer but is simply a set of neurons acting as source nodes which supply input feature vector components to the second layer. Typically, the number of neurons in the input layer is equal to the dimensionality of the input feature vector. Then there is one or more hidden layers, each of these layers comprising a given number of neurons called hidden neurons. Finally, the output layer provides the response of neural network to the pattern vector submitted in the input layer. The number of neurons in this layer corresponds to the number of classes that the neural network should differentiate (Haykin, 1999; Miller et al., 1995;.
The network that is used in this study arrange in layers as following. The number of neurons in the output layer is taken to be equal to the number of classes desired for the classification. Here, the output layer of the network used to categorize the image in five classes should contain five neurons. The input layer contains eight neurons corresponding to the number of attributes in the input vectors. The input vector to the network for pixel i of the data sets is the form
Thus, a three-layer neural network with the structure 8-10-5 (eight input neurons, ten hidden neurons and five output neurons) is used to classify the data sets into five classes.
Training the neural network involves tuning all the synaptic weights so that the network learns to recognize given patterns or classes of samples sharing similar properties. The learning stage is critical for effective classification and the success of an approach by neural networks depends mainly on this phase. The network is trained by using back-propagation rule (Paola& Schowengerdt, 1995). After training the network, the parameters are selected as: Momentum value 0.9, Learning rate 0.1, and the number of iteration 2000. The numbers of training data are 200 patterns of the subplots that are selected randomly from the classes, in which each class is represented with at least 40 patterns. The set of training patterns is presented repeatedly to the neural network until it has learnt to recognize them. A training pattern is said to have been learnt when the absolute difference between the output of each output neuron and its desired value is less than a given threshold. Indeed, it is pointless to train the network to reach the target outputs 0 or 1 since the sigmoid function never attains its minimum and maximum (Masters, 1993). For classification of data sets into five classes, the threshold is set to 0.4. The network is trained when all training patterns have been learnt. Once the network is trained, the weights of the network are applied on the data sets to classify into five classes: class1 Azedarach, class2 Acorn, class3 Beech, class4 Grassland and class5 None. The result of the classified image is shown in Fig. 4.
The classified image with MLPNN.
After classification, it is needed to determine the degree of classification accuracy. The most commonly used method of representing the degree of accuracy of a classification is to build confusion matrix.
The confusion matrix is usually constructed by a test sample of patterns for each of the five classes. A set of test sample with 105 patterns based on the ground truth collection were randomly selected in the classified image for accuracy assessment. The values 70% and 65% are achieved for overall accuracy and kappa coefficient respectively. One reason for misclassification can be due to poor selection of training areas, so that some training patterns don’t accurately reflect the characteristics of the classes used. Another reason can be due to poor selection of land cover categories, resulting in correct classification of areas from the point of view of the network, but not from that of the user. Thus the classification accuracy can be improved by redefining the training patterns and land cover categories.
In order to show the texture of SAR image and the neural network classifier improve the accuracy of the classification and then forest biomass estimation, we employ the Maximum Likelihood (ML) classifier method using only the intensity values of the PRISM and AVNIR images. The overall classification accuracy of 57% is achieved with ML classifier. The accuracy of 70% with the neural network is significantly better than the accuracy of 57% with ML.
In comparison between the MLPNN and ML classifiers, the advantages of MLPNN that is used in this study are:
It can accept all kind of numerical inputs whether or not these conform to statistical distribution or not.
It can recognize inputs that are similar to those which have been used to train them.
Because the network consists of a number of layers of neurons, it is tolerant to noise present in the training patterns.
Thus, we can estimate the forest biomass of the classes in the classified image which has been classified based on the SAR image texture and the MLPNN classifier. We also evaluate the biomass for two classes based on the allometric equation (15) for the classic method based on the ML classifier and the proposed method. The results are shown in Table 3, where the classic method and the proposed method have been applied in the classified image to estimate the biomass for two classes.
The classic method Acorn Azedarach | The proposed method Acorn Azedarach | |||
Area (ha) | 853.217 | 1129.552 | 937.312 | 1241.320 |
Mean height (m) | 34 | 28.5 | 34 | 28.5 |
Mean DBH (cm) | 55 | 45 | 55 | 45 |
# of tree (ha) | 34 | 23 | 34 | 23 |
Mean biomass (kg/tree) | 3272 | 1861.99 | 3272 | 1861.99 |
Total biomass (tons/ha) | 94918.85 | 48374.08 | 104274.085 | 53160.484 |
Estimated biomass for the classic method and the proposed method by both optical and sar data.
For the accuracy assessment of the proposed method, Table 4 shows how well the results agree with the ground measurements results from Table 1, when the classic method and the proposed method are used for biomass estimation. Table 4 shows the estimated biomass when both methods are used. The root mean square error (RMSE) of estimated biomass with both methods is indicated in the table. The RMSE values is decreased when the model is used (RMSE=2.175 ton) compared the classic method (RMSE=5.34 ton).
Plot | Measured biomass (ton) for Azedarach Acorn | The classic method Estimated biomass (ton) for Azedarach Acorn | The proposed method Estimated biomass (ton) for Azedarach Acorn |
1 26.712 07.42 29.13 10.40 27.43 09.12 2 25.960 42.575 30.40 46.39 27.13 41.43 3 25.584 10.660 18.13 06.43 23.32 08.86 4 26.558 17.073 22.13 24.32 23.16 21.36 5 14.238 56.952 17.43 66.13 15.29 58.56 RMSE 4.71 5.97 1.97 2.38 Mean RMSE 5.34 2.17 |
Accuracy assessment for the classic method and the proposed model using the ground measurements from Table 1.
From the above paragraphs, the accuracy of the proposed method is better than, or comparable to, the classic method used for biomass estimation. We conclude using both optical image and SAR image texture in a non-linear classifier method, neural network, significantly improve the accuracy of the forest biomass estimation.
It is often difficult to transfer one model developed in a specific study area to other study areas because of the limitation of the model itself and the nature of remotely sensed data. Foody (Foody et al., 2003) discussed the problems encountered in model transfer. Many factors, such as uncertainties in the remotely sensed data (image preprocessing and different stages of processing), AGB calculation based on the field measurements, the disparity between remote sensing acquisition date and field data collection, and the size of sample plot compared with the spatial resolution of remotely sensed data, could affect the success of model transferability. Each model has its limitation and optimal scale for implementation. Models developed in one study area may be transferred to (1) across-scene data, which have similar environmental conditions and landscape complexity, to estimate AGB in a large area; and (2) multi-temporal data of the same study area for AGB dynamical analysis if the atmospheric calibration is accurately implemented. The spectral signatures, vegetation indices, and textures are often dependent on the image scale and environmental conditions. Caution must be taken to ensure that there is consistency between the images used in scale, atmospheric and environmental conditions. Calibration and validation of the estimated results may be necessary using reference data when using transferred models.
The data sources used for AGB estimation may include field-measured sample data, remotely sensed data, and ancillary data. A high-quality sample dataset is a prerequisite for developing AGB estimation models as well as for validation or assessment of the estimated results. Direct measurement of AGB in the field is very difficult. In general, AGB is calculated using the allometric equations based on measured DBH and/or height, or from the conversion of forest stocking volume. These methods generate many uncertainties and calibration or validation of the calculated AGB is necessary. Previous research has discussed the uncertainties of using the allometric equations (Brown& Gaston, 1995; Keller et al., 2001; Ketterings, 2001; Fearnside, 1992) and of conversion from stocking volume (Masters, 1993). It is important to ensure that the remote sensing data, ancillary data, and sample plots are accurately registered when ancillary data are used for AGB estimation. Understanding and identifying the sources of uncertainties and then devoting efforts to improving them are keys to a successful AGB estimation. More research is needed in the future for reducing the uncertainties from different sources in the AGB estimation procedure. Many remote sensing variables, including spectral signatures, vegetation indices, transformed images, and textures, may become potential variables for AGB estimation. However, not all variables are required because some are weakly related to AGB or they have high correlation with each other. Hence, selection of the most suitable variables is a critical step for developing an AGB estimation model. In general, vegetation indices can partially reduce the impacts on reflectance caused by environmental conditions and shadows, thus improving correlation between AGB and vegetation indices, especially in those sites with complex vegetation stand structures (LU, 2004). On the other hand, texture is an important variable for improving AGB estimation performance. One critical step is to identify suitable textures that are strongly related to AGB but are weakly related to each other. However, selection of suitable textures for AGB estimation is still a challenging task because textures vary with the characteristics of the landscape under investigation and images used. Identifying suitable textures involves the determination of appropriate texture measures, moving window sizes, image bands, and so on (Franklin&Hiernaux, 1991). Not all texture measures can effectively extract biomass information. Even for the same texture measure, selecting an appropriate window size and image band is crucial. A small window size, such as 3×3, often exaggerates the difference within the moving windows, increasing the noise content on the texture image. On the other hand, too large a window size, such as 11×11 or larger, cannot effectively extract texture information due to smoothing the textural variation too much. Also, a large window size implies more processing time. In practice, it is still difficult to identify which texture measures, window sizes, and image bands are best suited to a specific research topic and there is a lack of guidelines on how to select an appropriate texture. More research is needed to develop suitable techniques for identification of the most suitable textures for biomass estimation.
In addition to remotely sensed above ground biomass estimation in data, different soil conditions, terrain factors, and climatic conditions may influence AGB estimation because they affect AGB accumulation rates and development of forest stand structures. Incorporation of these ancillary data and remote sensing data may improve AGB estimation performance. Geographical Information System (GIS) techniques can be useful in developing advanced models through the combination of remote sensing and ancillary data.
In this chapter, we proposed a method for forest biomass estimation. One speckle noise model was used for reducing the speckle noise in SAR images. The speckle model was slightly better than the commonly used filters in terms of preserving details in forestry areas. A combination of spectral responses from optical images and textures from SAR images improved biomass estimation performance comparing pure spectral responses or textures. Intensity values of ALOS-AVNIR-2 and PRISM images and texture features of JERS-1 image were used in a multilayer perceptron neural network (MLPNN) that relates them to the forest variable measurements on the ground. We showed the biomass estimation accuracy was significantly improved when MLPNN was used in comparison to estimating the biomass by using classic method only. The RMSE values was decreased when the proposed method was used (RMSE=2.175 ton) compared the classic method (RMSE=5.34 ton).
The concept of frailty is frequently mentioned in studies related to the elderly population—health status, self-care dependence, healthcare resources or even the configuration of the wards where care is provided. Looking at the scientific knowledge and clinical practice, frailty in the elderly is considered a relevant dimension of quality of life. Moreover, there is a tendency to accept that individuals with severe frailty have to be considered vulnerable and should be protected.
Frailty has been viewed as a cornerstone of geriatric medicine and a platform of biological vulnerability to a host of other geriatric syndromes and adverse health outcomes [1], such as long-term nursing home stay, injurious falls and death, in community-dwelling older adults independent of medical comorbidities and age. The expression “frailty elderly” was used for the first time in 1970, by researchers from the Federal Council on Aging (FCA) of the United States, with the purpose of describing elderly people who lived in unfavourable socioeconomic conditions and presented physical weakness and cognitive deficit that, with advancing age, began to demand more care; in the 1980s, frailty in the elderly people was understood mainly as synonymous of disability or the presence of a disease, chronic or extreme condition linked with ageing [2]. In 1990, the expression “frailty elderly” was referred for the first time on the Journal of the American Geriatrics Society index [2].
The term “frailty” started to be used frequently in terms of diagnosis, clinical decisions and provision of care. Frailty and cognitive and functional decline are relatively common in older dependent people with health problems. One of the challenges for researchers today has been to study the physical characteristics and psychological symptoms of frailty and to relate them to adverse health outcomes. In this chapter, we intend to analyse the matters that have most attracted the attention of researchers and health professionals who deal with people in situations of frailty.
Understanding frailty has become crucial for caring for the elderly. In older people with dementia, the assessment of frailty is more important than determining the degree of dementia, since it is crucial to develop appropriate care people need; there are old people with moderate dementia but with a severe level of frailty.
In this chapter, we intend to review the concepts of frailty, operationalization strategies and assessment tools and clarify some ideas from the debate on what frailty is.
The concept of frailty has grown in importance because of a need to evaluate the health status of older persons and a need to prevent or at least delay the onset of late-life disability and its adverse consequences [3]. There is to date no clear consensus regarding the definition of frailty; some definitions have been proposed, each with their own strengths and weaknesses [3].
Frailty is a multidimensional concept and can be defined as a dynamic state that affects an individual with declines in one or more domains, such as physical, cognitive, social, attention or senses [4]. There is usually a dependence on self-care and need of support from others. Elderly does not mean frailty, but the ageing process led to frailty, which means that there are changes that reflect ageing-related alterations and involve intrinsic and extrinsic factors which are typical of ageing.
The occurrence of frailty is mainly a state of vulnerability resulting from comorbidities and the overall decline in organ functions. The progression to later stages of dementia often signals a loss of autonomy, dependence and reduction in physical and cognitive function. Frailty of people is positively related with their caregiver burden and associated with higher levels of depression on the caregiver. A lack of understanding about frailty has been identified as a barrier to providing optimal care to elderly people, for example, people with advanced dementia [4].
Frailty is an emerging concept used in the field of geriatrics and gerontology, to make reference to the clinical condition of the elderly. There is a deficit of information regarding the incidence and prevalence of frailty in the elderly, mainly due to the lack of consensus definition that can be used as reference in different populations. There is usually a “clinical sense” about what is frailty and what a frail elderly person is, but there is no agreement, a standard definition regarding this concept, that can assist in the diagnosis of frailty condition. As mentioned above, frailty is often considered an inherent condition of ageing, an attitude that can cause late interventions with minimal potential for prevention or reversing the consequences and adverse effects from the problem.
The concept of frailty, widely used in the recent years, focuses primarily on the physical dimensions. That is why it is understood that the criteria for assessing presence/absence are the physical signs and symptoms, sedentary behaviour, weight loss, exhaustion, slowed gait, decreased muscle strength, with three or more of these five criteria we are facing physical frailty and the presence of one or two criteria indicates pre-physical frailty [5].
The diagnosis of frailty relies currently on the assessment of a small subset of easily measurable clinical markers. Just as conceptual disagreements arise about what frailty means, there are also disagreements about how to evaluate it. While recognizing the multifactorial nature of frailty, it is important to develop an “operational definition” of frailty that is simple enough to be used clinically and to guide prevention and care [3].
Frailty among older persons appears in the investigation as a dynamic process, characterized by frequent changes over time. The evolution of frailty incorporates quantitative and qualitative data, which motivated researchers to invest in modelling. Recent studies have highlighted age, medical factors and higher socioeconomic status to be protective [6]. In the study carried out by the Canadian Study of Health and Aging (CSHA) [6], it was concluded that cognitive status and frailty are associated. Functional decline contributes to increase costs in caring for people with dementia. Despite all the research related to Alzheimer’s disease, very little has been indicated as effective therapies to deal with the disease, although it is known that cognitive decline is one of the first symptoms to appear and that interventions at this level can delay the evolution of the disease [6].
Andrade et al. [2] state that currently, two research groups have distinguished in the pursuit of consensus on the definition of frailty in the elderly: one of them in the United States, at the Johns Hopkins University, and the other one in Canada, the Canadian Initiative on Frailty and Aging (CIF-A). The group of researchers from the Johns Hopkins University produced an operational definition of frailty in the elderly and proposed measurable and objective criteria to the phenomenon. This operational definition starts from the hypothesis that the term is a geriatric syndrome and it can be identified by means of a phenotype that includes five measurable components: (a) unintentional weight loss, greater than 4.5 kg or more than 5% of body weight in the last year; (b) signs of fatigue; (c) reduction of handgrip strength, assessed with a specific instrument and adjusted to the person’s sex and body mass; (d) little physical activity assessed by calorie consumption (measured in kcal), adjusted by sex; and (e) reduction of march activity in seconds, distance of 4.5 m adjusted by gender and height [2].
A second definition was formulated by researchers from the CIF-A, indicated above. This is based on a multidimensional construct—frailty was defined using a more holistic approach, which emphasizes the complex aetiology of the phenomenon, understood as a not optimal condition in elderly, multifactorial and dynamic in nature, relating it to its history or trajectory of life [2]. The indicated trajectory can be shaped by biological, psychological and social, whose interactions result in resources and/or individual deficits in a given context. A tool was developed to measure frailty in the elderly—the Edmonton Frail Scale (EFS)—contemplating nine domains: (I) cognition, (II) general state of (III) functional independence, (IV) support, (V) medication use, (VI) nutrition, (VII) humour, (VIII) continence and (IX) functional performance. These authors consider this scale more comprehensive, especially considering aspects of cognition, humour and social support [2].
Some definitions of frailty promote a multidimensional approach based on an evaluation according to “frailty indexes”, which are calculated considering the accumulation of possible deficits, such as the presence of diseases, abnormal laboratory values, signs and symptoms or disabilities [7, 8].
It is difficult to establish a typology of frailty, given its multidimensional nature. On the one hand, frailty results from an articulation of factors of a physical and psychological nature. On the other hand, it is possible to assess frailty to highlight one or another aspect. Also, the investigation indicates that emotional management strategies can interfere with the signs and symptoms of frailty and with the ability to adjust to different disabilities.
Given the definitive trends in frailty, and although the creation of a typology is sometimes an academic task, we will try to describe four types of frailty in the elderly, on the assumption that they intersect and present common dimensions: physical, cognitive, social and emotional.
Frailty is a clinical situation known for the great vulnerability of the person in terms of the different physiological systems. In addition to the physical dimension, frailty is characterized by problems at the social, emotional and cognitive levels, despite the possibility of delaying its evolution in early stages [3, 9]. Fried et al. [10] proposed a clinical phenotype of frailty, defining it as a situation of increased vulnerability in the person for homeostatic resolution after pronounced distress. This growing vulnerability increases the risk of adverse outcomes, such as falls, fractures, hospitalization and ultimately mortality in elderly people living in organizations in the community or in their own homes.
Four main mechanisms can be identified in the progression of frailty: atherosclerosis, sarcopenia, cognitive deterioration and malnutrition [11]. It has been proven that malnutrition can be the cause of cognitive and functional decline and that the lack of some nutrients can cause cognitive frailty and vascular dementia [11].
There is an evident relationship between functionality and cognition, as evidenced by research evidence and some assessment tools (e.g., Clinical Dementia Rating). Many cross-sectional studies demonstrated the relationship between general cognitive function, emotions and physical frailty [12]. However, it is important to keep in mind that the decline in cognition and capacity of emotional management, given its functions and nature, evokes so many limitations to functionality that it becomes relevant to consider a cognitive frailty as a specific type.
Many studies have focused on the proposed entity of “cognitive frailty” to describe a clinical condition that is characterized by simultaneous occurrence of physical frailty and cognitive impairment in the absence of overt dementia [13]. Alzheimer’s disease is characterized by an association between physical and cognitive decline, but in the opposite direction, people with physical limitations are more predisposed to suffer emotional and cognitive problems. However, it should be noted that in recent years studies are more focused on physical frailty, with a relative paucity of data available for concomitant transitions in cognitive status [6].
An International Consensus Group studied the “cognitive frailty” condition. “Cognitive frailty”, although so defined, implies the presence of physical and cognitive decline. The key symptoms to characterize cognitive frailty are as follows: (1) presence of physical frailty and cognitive impairment and (2) exclusion from the concomitant presence of any type of dementia [14]. At the same time, the group indicated that “cognitive frailty” implies a rigorous diagnosis in terms of memory performance but also of other cognitive functions [14].
“Cognitive frailty” could represent a cognitive entity with specific neuropsychological patterns (executive and selective attention) [14]. The mechanisms in action and how deterioration occurs are not yet fully understood.
The loss of emotional management capacities and of establishing social interactions generates potential situations of frailty. It is also evident that any types of frailty (physical or psychological) also interfere with the emotional and social spheres. Usually, people with frailty (with cognitive impairment) experienced high levels of emotional discomfort and behavioural changes. Even without significant cognitive changes, symptoms usually emerge that emphasize the importance of emotions and social interactions: sadness, loneliness, nervousness, concern for oneself, self-concept, self-care and sense of hope.
The relationship between emotions, behaviour and frailty emerges in studies that explore this association. Emotion, which can be considered positive or negative, interferes with the perception of self-efficacy and the subjective sense of well-being. Furthermore, studies conducted in older adults found that positive emotions were associated with lower disability in the execution of daily living activities, higher levels of mobility, less physical dependence and major likelihood of survival, as well as higher level of adjustment to chronic health problems; on the other hand, negative emotions are correlated with stress sensations and poor coping abilities [15].
Clark and Watson [16] emphasize the relationship between emotions and functionality, which is understood by the well-known association between emotions and behaviour. They concluded, in a study carried out with older adults, that positive emotions may be associated with lower disability in the execution of daily living activities, better mobility, good functional status and major likelihood of survival; on the contrary, negative emotions can be correlated with distress and poor coping abilities. Mulasso et al. [15] provide empirical evidence to the multidimensional theorization and definition of frailty, hypothesizing that a reduced level of positive emotions and high level of negative emotions may contribute to increases in the severity of frailty condition; on the other hand, they highlighted the role of emotion experience in interventions for the prevention of frailty, such as interventions of physical exercise or cognitive training associated with frequent experience of positive emotions.
Simultaneously, studies emphasize also the need to identify risks for frailty [4, 6, 9]. All dimensions that constitute limitations on functionality, carrying out activities of daily living, cognitive impairment and social isolation can and should be considered risks for frailty [4]. There are currently models, mathematical equations and Bayesian networks that allow identifying these risks and even predicting them, conjugating certain variables. Usually, these models take into account demographic, social and clinical variables. These models can have good performance, isolated or conjugated with other evaluation tools. Moreover, they can predict frailty evolution and enable dependent persons to be identified for further specific assessment or interventions.
There are many studies that explore frailty, types of frailty and predictors of frailty every year. The relationship between frailty and functionality and the psychological sphere and relationship between the frailty of the recipient of care and burden on the caregiver are increasingly studied.
Armstrong et al. [17] used of a large database (n = 23,952) with comprehensive health information on home care clients (aged 65+) of eight Community Care Access Centres (CCACs) in Ontario, Canada. In this large cohort of older home care clients, they found that greater evidence of frailty as defined by each of the three measures was associated with greater risk of adverse outcomes. This result additionally confirmed the potential utility of a frailty concept for identifying vulnerable individuals within the home healthcare sector. They concluded that mathematical models can utilize data collected during clinical assessments to provide a quantitative indicator of a client’s level of frailty.
Dudzińska-Griszek, Szuster and Szewieczek [18] developed a study whose aim was to assess conditions that influence grip strength in geriatric inpatients. A comprehensive geriatric assessment was complemented with assessment for the frailty phenotype. Functional assessment included Barthel Index of Activities of Daily Living (Barthel Index), Instrumental Activities of Daily Living Scale and Mini-Mental State Examination. The conclusion was that cognitive function, somatic comorbidity and medical treatment affect grip strength as a measure of physical frailty in geriatric inpatients.
A retrospective cohort study on 18,341 Medicare Advantage enrollees aged 65+ was conducted by Anzaldi et al. [19] in Massachusetts. When analysing the clinical information systems, they identified the presence of 10 syndromes commonly found in the elderly (falls, malnutrition, dementia, severe urinary incontinence, absence of faecal control, visual impairment, walking impairment, pressure ulcers, lack of social support and weight loss), as well as references to the presence of frailty identified in the natural language processing (NLP) algorithm. The main conclusion was that patients identified as “frail” by providers in clinical notes have higher rates of healthcare utilization and more geriatric syndromes than other patients. Certain geriatric syndromes were more highly correlated with descriptions of frailty than others.
Shimada et al. [20] studied the cognitive frailty in 4570 older adults. The aim of the study was to analyse the extent to which a new perspective of cognitive frailty could be considered as a predictor of dementia. There are 2326 women and the average age was 71.9 ± 5.5 years. Physical frailty was defined as the presence of more than one of these symptoms: slow walking speed and muscle weakness. Cognitive frailty was defined as comorbid physical frailty and cognitive impairment. They concluded that cognitive impairment and cognitive frailty could be considered risk factors for dementia. Findings showed clearly that individuals with comorbid physical frailty and cognitive impairment could have a higher risk of dementia than healthy older adults or older adults with either physical frailty or cognitive impairment alone.
The estimation of the prevalence of frailty in patients admitted to intensive care unit (ICU) and its impact on intra-ICU mortality, at 1 month and at 6 months, was developed by Cuenca et al. [21]. A prospective cohort study was conducted. Frailty was present in 35% of patients admitted to the ICU, associated with higher rates of mortality.
Ma et al. [22] carried out a study to determine social frailty status via developing a simple self-reported screening tool, termed the HALFT scale, and to examine the association between social frailty and physical functioning, cognition, depression and mortality among community-dwelling older adults. They state that social frailty is related to adverse health-related outcomes. Moreover, they added that research into the relationship between social frailty and physical functioning remains limited. A prospective cohort study was carried out, with 1697 community-dwelling adults aged ≥60 years from Beijing. The scale developed was based on five items: unhelpful to others, limited social participation, loneliness, financial difficulty and not having anyone to talk to.
The prevalence of social frailty in the participants was 7.7%. Social frailty was positively associated with physical frailty, low levels of physical activity and poor physical functioning. Researchers also found that social frailty was associated with dementia, memory decline, depression and cognitive impairment. Having experienced a negative or traumatic event was also associated with social frailty. Additionally, social frailty was associated with physical functioning, cognition and depression and predicts mortality; they emphasize that interventions aimed at preventing or delaying social frailty are warranted.
In a cross-sectional study carried out by Mulasso et al. [15] the association between frailty and emotional experience was studied in a sample of Italian community-dwelling older adults. Participants consisted of 104 older adults (age 76 ± 8 years; 59.6% women) living in Italy. Frailty and emotion perception were measured with appropriate and valid tools. The Mini-Mental State Examination was used as a screening tool for cognitive functions (people with a score ≤ 20 points were excluded). The researchers stated that frailty increases individual vulnerability to external stressors and involves high risk for adverse geriatric outcomes [15]; findings demonstrate that emotion perception may influence frailty, which is really relevant for the evaluation and prevention of frailty in older adults.
A theoretical study based on research studies that equate the role of nutrition and nutrients in cognitive and functional decline was developed by Gomez-Gomez and Sapico [23]. They state that one of the most important factors to consider in the development of cognitive deterioration is oxidative stress. Consequently, they added that increasing antioxidants in the diet may be one of the therapeutic strategies in the management of these patients.
Some studies were analysed, mainly those that showed the effectiveness of antioxidants in the adjustment of oxidative stress, given their function as free radical scavengers, or factors that potentiate the antioxidant effect. Anyway, the studies emphasized that the inappropriate use of antioxidants could have side effects and become toxic at high doses. Given the multiplicity and some divergence in the results, additional studies are required as well as clinical trials to increase the clinical effectiveness [23].
Several studies were analysed, namely, those that have shown the effectiveness of antioxidants in the adjustment of oxidative stress, either by their function as free radical scavengers or potentiating the antioxidant effect. Studies showed that the inappropriate use of antioxidants could have side effects and toxicity at high doses. However, it was indicated that additional studies are required as well as clinical trials to increase the clinical effectiveness [23].
Abreu et al. [4] examined the healthcare needs of community-dwelling older people, trying to understand the relationship between frailty, functional dependence and healthcare needs among community-dwelling people with moderate to severe dementia. A sample of 83 participants was recruited. The Edmonton Frail Scale was used to evaluate frailty, in addition to tools that were chosen to collect data on other variables. A set of 26 healthcare needs was defined to support the assessment. There was a significant association between “severe frailty” and “severe dementia” and “fully dependent” and “severely or fully dependent in the activities of daily living”. The most prevalent healthcare needs in the sample were food preparation, medication/taking pills, looking after their home, toilet use, sensory problems, communication/interaction, bladder, bowels, eating and drinking, memory, sleeping and fall prevention. In particular, the study shows a set of needs that are present simultaneously in both frailty and dementia stages, according to their severity. They found in the study that 16.7% of people with moderate dementia were also diagnosed with severe frailty. Concerning the needs assessment, the authors state that the concept of “severe dementia” is clearly a limiter in the matter of frailty. As an alternative, they suggest the expression of “advanced dementia”, encompassing people with severe dementia and people with moderate dementia but who also have severe frailty.
Usually, scales assess some domains of frailty in old people (cognition, general health status, functional independence, social support, medication usage, nutrition, mood, continence and functional performance). These tools are important on clinical point of view, for research and decision-making. Several tools that evaluate functionality and cognition also evaluate several dimensions that we are traditionally including in frailty.
Armstrong et al. [17] indicate, in the scope of their study, three conceptually different approaches to the measurement of frailty: (1) Changes in Health, End-Stage Disease and Signs and Symptoms (CHESS) scale, (2) Edmonton Frail Scale (EFS), (3) the frailty index (FI) and the Tilburg Frailty Indicator (TFI).
The CHESS scale is a tool that uses information from the person’s clinical assessment, which is used to calculate the person’s level of decline. The tool was developed using statistical methods, based on the items available in the inter-RAI instruments. It is not a tool for objectively assessing frailty, but it allows assessing the “instability” of health status, which is also a predictor of mortality [17]. The scores ranging from 0 (meaning no instability) to 5 (for the highest level of instability) have been demonstrated to be a strong predictor of mortality (P < 0.0001) in continuing care patients [24].
The EFS is a brief multidimensional clinical measure, widely used and designed to use in both inpatient and outpatient settings [25]. The scale assesses nine domains of frailty in old people (cognition, general health status, functional independence, social support, medication usage, nutrition, mood, continence and functional performance) [25]. Total score can vary from 0 to 17. The participants were classified into categories, and a higher score represents a higher degree of frailty. Severe frail and non-frail participants were defined according of the EFS score from not frail (0–5), vulnerable (6–7), mild frailty (8–9), moderate frailty (10–11) and severe frailty (12–17). The EFS is a measure of frailty compared to the clinical impression of specialists after their more comprehensive assessment. A larger part of the assessment tools is focused primarily on determining the person’s level of functioning in terms of managing activities of daily living and instrumental activities of daily living. In post-operative older adults, high scores on the EFS have been shown to be associated with increased complications and a lower chance of being discharged home after surgery [17].
The FI was developed by Rockwood and Mitnitski based on an idea of “accumulation of deficits” [17]. The FI is based on the view that frailty is a non-specific multifactorial state, best characterized by the quantity, rather than the quality, of the health deficits that the person accumulates during the course of life [26]. The FI is thus calculated as the proportion of potential deficits present in the person and can be calculated from the information present in most previous systems of clinical data (databases) [17].
The TFI is a tool widely used to assess 3 frailty domains and their 15 components. It is a user-friendly questionnaire and has good psychometric properties assessed in the initial validation process, constituting a good strategy for multidimensional assessment of frailty in community settings [27]. The instrument consists of two parts. Part A includes life-course determinants of frailty (sex, age and marital status), and part B assesses 15 components of frailty. The score on total frailty has a range of 0–15; people with a score ≥ 5 are considered frail; for physical, psychological and social frailty, the score ranges are 0–8, 0–4 and 0–3, respectively [28].
Studies carried out in different countries have demonstrated that these tools have in general good psychometric properties and are reliable and valid instruments for assessing frailty in community-dwelling older people [4, 17, 24, 25, 26, 27, 29, 30].
Frailty’s assessment is inseparable from an objective and competent evaluation of healthcare needs. Frailty is a multidimensional concept and can be defined as a dynamic state that affects an individual with declines in one or more domains, such as physical, cognitive, social, attention or senses. The assessment of frailty is of limited interest if healthcare professionals do not invest in assessing the needs of frailty people in healthcare. This assessment must be multidimensional, multifactorial, longitudinal and comprehensive, covering all activities of life.
There are many debates on what are health needs assessment and problem identification. What is important to note is that care needs assessment is a systematic and sequential process, conducted by a care professional, which begins with the assessment of dependency focus, accounts for the presence and efficacy of current help, recognizes perceived need and finally determines the type of intervention needed to meet those needs [31].
It has been recognized that needs in the elderly should be patient-centred; holistic; analysed on by dependent people, caregivers and professionals; communicated to other professionals; and met in order to achieve better coordination between leading disciplines; needs assessment enhances the patient and carers experience and leads to more accurate information, but the level of reassessment by other professionals and the incidence of service duplication should also be reduced [31].
Care needs assessment has to promote an objective, competent evaluation of the self-care deficits. A self-care deficit is an inability to perform certain daily activities dependent on health and well-being. Common activities of daily living are the following: eating, bathing, getting dressed, toileting, transferring and continence. Self-care deficits can arise from physical or mental impairments. In elderly people, some of these problems accumulate and comorbidities appear. Health professionals play an important role when it comes to addressing self-care deficits through assessment and intervention. For assessment, evaluation of needs and identification of focuses of attention are necessary. Intervention can include, but is not limited to, helping patients to manage signs and symptoms, adhere to the therapeutic regime, adjust to deficits and strive to preserve, as far as possible, their self-care capacity.
With the ageing of the population and increased longevity, the need to provide palliative care is emphasized. However, this increased need is not usually accompanied by the availability of beds, which requires the use of indicators to manage the availability of palliative care provision. When to begin palliative care is a troublesome question for patients, families and healthcare providers [32]. Severe frailty is a relevant marker, along with functional dependence, cognitive impairment, symptom distress and family support for beginning palliative care. Frailty, independent of specific diseases, can be associated with a limited life expectancy and therefore is an important indication for palliative care [32]. Frailty is an essential model for palliative care in older adults as optimal medical treatment for the frail patient typically includes preventive, life-prolonging, rehabilitative and palliative measures in varying proportion and intensity based on the individual patient’s needs and preferences [33].
Frailty elderly usually have dependence on self-care and need of support from others. Elderly does not mean frailty, but the ageing process led to frailty, which means that there are changes that reflect ageing-related alterations and involve intrinsic and extrinsic factors which are typical of ageing [4]. Usually, scales assess some domains of frailty in old people (cognition, general health status, functional independence, social support, medication usage, nutrition, mood, continence and functional performance). The occurrence of frailty is mainly a state of vulnerability resulting from comorbidities and the overall decline in organ functions. The progression to later stages of frailty often signals a loss of autonomy, dependence and reduction in physical and cognitive function.
Frailty is commonly positively related with caregiver burden and associated with higher levels of depression on the caregiver. A lack of understanding about frailty has been identified as a barrier to providing optimal care to elderly people. Self-care deficit theories suggest people are better able to recover when they maintain some independence over their own self-care. The evaluation of frailty is closely linked to the identification of dependencies in self-care. The use of frailty and self-care dependence assessment helps to determine the focus of attention, to respect vulnerability, to limit dependence as much as possible and to provide quality, safety and competent care.
The Edited Volume, also known as the IntechOpen Book, is an IntechOpen pioneered publishing product. Edited Volumes make up the core of our business - and as pioneers and developers of this Open Access book publishing format, we have helped change the way scholars and scientists publish their scientific papers - as scientific chapters.
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