Spectral indices used.
\r\n\tDiagnostic tools are advancing: micro-and nano-diagnostics, advanced molecular genetics, and diagnosis of the aberrant clotting factor synthesis development and the options for the staging of the genetic abnormality - severe, moderate, and mild expression.
\r\n\tTreatment developments and advances start with prevention, intra-uterine approaches, genetic manipulation, genetic engineering, the high specificity of replacement factors, and recombinant technology.
\r\n\tIn addition to the above, the book will provide an update on the prevention of transmission of pathogens and potentially toxic substances used to stabilize and preserve treatment commodities. The role of big data and artificial intelligence through both machine learning and the application of deep learning and digital footprinting will also be addressed.
\r\n\tIn the developing world, there is an urgent need to collect, preserve and process plasma for the manufacturing of high yield, safe, and stabilized cryoprecipitate, or pharmaceutical fractionation of purified and specific clotting factors, as well as improvement on diagnostic and sociomedical approaches with an emphasis on patient and family care, and management of bleeding episodes.
The global structure and productivity of ecosystems are deeply impacted by joined climate conditions and human drivers, causing general vegetation degradation [1]. The phenomenon has kept increasing in the last four decades and eventually affects whole ecosystem, soil productivity, biological systems, biotic diversity, and other environmental systems’ ability to support human needs in concerned areas. Main indicators are the decline in parameters, such as low biomass, less ecological production, fragmentation, or lower canopy cover [2, 3].
Inside the tropics, vegetation is globally sensitive to seasonal and inter-annual variation in precipitations and temperatures. Extremes seasonally, i.e., longer rainy season and shorter dry season in lowest latitudes, versus the reverse phenomenon towards medium latitudes, influence the vegetation distribution with several phenological and physiological adaptations, including cover and status changes [4, 5, 6]. Typically, forest colonizes wetter areas, while savannas cover drier areas, with a gradual species distribution such as dense forest, tree savannas, grassy/herbaceous savannas, and isolated desert shrubs or clumps of dry grasses known as steppes. However, transitions are not rigidly determined by climate [7]. There is an extensive overlap between forest and savanna creating a mosaic of landscapes, and most studies on the subject remain widely hypothesized and modeled with controversial results, supported by questionable evidences. Biases include the high species turnover around 1000 mm to 2500 mm rainfall, the (un)stable states of forest and savanna maintained by feedbacks between tree cover and disturbances, and for the satellite-based approaches, the structural (in)difference between trees or grasses layer [8].
These specificities are challenging to spatialize at a point that sub-Saharan African ecosystems have played a key role in the development of remote sensing of vegetation for decades [9, 10, 11]. Nowadays, several satellite-based models provide scalable spectral information relevant to vegetation distribution and changes, physiology, and phenology, in broad terms, to monitor and combat land degradation, especially in African drylands [12, 13, 14, 15]. As such, numerous spectral indices measure the vegetation parameters [16]. The Normalized Difference Vegetation Index, NDVI, especially, has purposely been widely used [17]. However, some limitations like sensitivity to soil background effects and atmospheric influence as well as values saturation under dense and multilayered canopy, usually alter the NDVI capacity to simultaneously predict senesced vegetation and efficiently discriminate individual anomalies, i.e., growth, vigor, leaf area index, biochemical components (anthocyanin, carotenoids, cellulose, etc.), water content or pigmentation [18, 19, 20] with accuracy. Then some previous studies focused on identifying or modeling the direction of change as well as underlying drivers of drylands vegetation [21]. Those models applied to two or more spatially close and interwoven vegetation species, require to implementation of specific processings. To the best of our knowledge, the recent progress in modeling sub-Saharan vegetation transition introduced the term of “bistability” around lower and upper transition boundaries between forest and savanna [8]. This model is based on paleo-ecological evidences (soil, topography) and climatic parameters change and oscillation (rainfall and temperature), that influence (for the firsts) and predispose (for the seconds) these two species to coexistence. With the support of a floristic survey, the ambiguity of mischaracterizing savanna as a degraded forest was clarified at some point, by identifying, the dense forest, the “bistable” forest, the “bistable” savanna, and the proper savanna.
This study approaches the forest-savanna transition study, by investigating its different spectral behaviors and their statistical meaning, in a context lacking field data, and from an open-source/open-data perspective. The triple aim is to assess the dynamics, discriminate species disturbance based on their empirical spatial distribution, and predict their extent and boundaries. As such, assuming a blurred boundary and an overlapping spatial gradient between the two species, some phenological and physiological characteristics are considered as separately as possible in terms of anomalies, and further integrated beneath the same model, so as to locate the spots requiring permanent monitoring or sustainable actions, without mischaracterizing punctual changes, factual distribution, and most accurate delineation.
The study was conducted on the sub-Saharan mixed ecoregion, highly dependent on varied annual precipitations (AP) and yearly medium to high average temperatures (T0), which both influence relative humidity (RH) changes. The area belongs to the medium Cameroon (central Africa), between latitudes 500’-805’N and longitudes 1000′-1505′E, a climatic transition between the agroecological zones of western highlands (AP = 1800-2500 mm; T0 = 19.5°C; RH = 75%), bi-modal rainforest (AP = 1700-2000 mm; T0 = 24°C; RH = 80%), and then Guinean high savanna (AP = 1500–1800 mm; T0 = 30°C; RH = 60%) to Sudano-Sahelian savanna (AP = 400–1200 mm; T0 = 28°C; RH = 50%) for the core area. Specifically, the vegetation density broadly reflects the climate gradient of dense moist broadleaf forest and highland forest to sparse extensive savannas featuring the co-dominance of woody shrubs, grassland in plains, and herbaceous steppe at the edge of Sahel (Figure 1) [22].
Location of the study: (A) Cameroon in the context of African ecoregions based on Olson et al. (2001); (B) distribution of ecoregions in Cameroon; (C) preview of the subset ecoregions; (D) Sentinel2-a median image of the subset for mid-November 2020 to end march 2021.
The study was conducted using European Spatial Agency, ESA, Sentinel2-A multispectral instrument (MSI) data, which represents a very valuable opportunity for the fine characterization and monitoring of vegetation types on large scales, but is poorly investigated for the tropical biome study [11, 23]. This sensor provides 13 varied spectral bands from 0.443 to 2.190 micrometers, a 10-day repeat cycle, and a spatial resolution up to 10 meters (Additional material 1).
The phenological dry season, globally from November to March, was selected because of its high temperatures and less rainfalls, assuming they are ideal conditions to observe the vegetation adaptations to extremes, for years 2015 to 2021. In the GEE cloud coding environment and using the JavaScript opensource simplified coding, the median reducer function was appended as the pixel-wise computation of all bi-annual collection images, based on a band per band processing [24]. Then, applying a date filter from November 15 to end of March 31, a boundary filter for the study area, and a cloud cover acceptance filter below 10%, one image of 13 bands was outputted per bi-annual periods 2015–2016 to 2020–2021, displayed a,nd converted from 16 bits to 8 bits before further processings. Offline tools, i.e., desktop software, were used to extract statistics and for some complementary processings using less memory, including final layouts. Namely, Erdas Imagine 2020, ArcGIS Pro 2020, and Microsoft Excel with extension Xlstat were specifically used.
In arid and semi-arid regions, common changes in density, spatial distribution, chlorophyll, pigmentation, water stress, anthocyanin, nitrogen, carotenoids, leaf structure, and browning or senescence differently impact the biomass [25]. Previous spectral indices-based applications investigated that, the visible (0.4–0.7 μm) wavelengths respond to photosynthetic and non-photosynthetic pigments, the NIR (0.7–1.4 μm) wavelengths respond to the cellular structure and exhibit solar-induced fluorescence (SIF) and the SWIR (1.4–3 μm) wavelengths respond to senescent non-photosynthetic vegetation. As such, the anisotropic behavior of vegetation at visible-SWIR wavelengths has been parameterized to describe vegetation structure [26].
We computed twelve spectral indices, whose three were selected as reference data according to their ability to better highlight the land cover targeted, i.e., second modified soil adjusted vegetation index, MSAVI2 [27], to assess the vegetation cover, normalized difference soil drought index NDSoDI [14], to highlight the dry bare soils, and new water index, NWI [28], to map the surface water. After testing dozens of other indices, two assumptions were emitted for an efficient last casting, such as
Name | Equation | Primary goal | Reference |
---|---|---|---|
MSAVI2 | Map vegetation cover while reducing soil background | [27] | |
NDSoDI | Map dry soils while reducing surroundings source of moisture | [14] | |
NWI | Extract surface water | [28] | |
PVR | Photosynthetic vigor for crop monitoring | [29] | |
GVMI | Vegetation water content and evapotranspiration | [30] | |
PBI | General biochemical reflectance | [31] | |
REIP1* | Canopy chlorophyll, nitrogen content and polluted soil dynamics | [32] | |
mCRIG | Chlorophyll, carotenoids and anthocyanin | [33] | |
LWCI | Water content, change and stress | [34] | |
MARI | Chlorophyll, carotenoids and anthocyanin | [35] | |
SRPI** | Nitrogen content, water and chlorophyll | [36] | |
PSRI | Pigmentation and vigor changes dues to vegetation senescence | [37] |
Spectral indices used.
Was inverted after first preview, to better highlight vegetation patterns instead of soil dynamics.
Was adapted to SENTINEL2-A covered wavelengths by using band 6.
These indices were stacked and previewed with, MSAVI2, NDSoDI and NWI seeking the following:
Relationships between each analytical spectral index and both referential ones. The bright green curve represents the dependent variable, while the two others are explanatory variables (MSAVI2 = dark green; NDSoDI = light brown). N = 500 for all variables and regression parameters. The following is noticed from the trends of curves: PVR, PBI and GVMI have a neat positive correlation with MSAVI2, but a sharp negative one with NDSoDI; conversely, MARI and PSRI curves describe the exact opposite trends (positive with NDSoDI and negative with MSAVI2); mCRIG and LWCI curves evolve in another direction cutting the MSAVI2 and NDSoDI curves, while IREIP1 curve shows no real relationship them.
This process creates a difference image between two or more bands in a multi-temporal image analysis, so to detect changes in the type or the conditions of surface features. Depending on the study, change vector analysis (CVA) can use calculation principles of the Mahalanobis or the Euclidean distance as in this study, according to the following expression [38]:
Where
Four classes of magnitude are represented for either degradation or re-growing [39] (Additional material 4). For each binarized image, a CVA process was performed, the magnitudes of increase and decrease patterns were used, while the stability magnitudes were ignored. A total of twelve difference images per index resulted, i.e., six per selected magnitude. Considering the need of complementarity among indices, only one CVA magnitude, i.e., increase or decrease, was selected per index for further processing. Criteria used to optimize the selection were the original goal of each index, as well as its spatial and statistical relationship with MSAVI2, NDSoDI and NWI (Table 2).
Index | Targeted asset | Compared trends to MSAVI2 / NDSoDI / NWI | CVA |
---|---|---|---|
PVR | Age & Cover | Same with a plus / Inverted / Inverted | Decrease |
GVMI | Water content & Cover | Same with a plus / Inverted / Inverted | Decrease |
PBI | Inner composition & Cover | Same with a plus / Inverted / Inverted | Decrease |
IREIP1 | Height, Cover & Health | Different / Inverted / Inverted | Decrease |
mCRIG | Health & Composition | Different / Inverted / Inverted | Increase |
LWCI | Water stress | Different / Inverted / Close | Decrease |
MARI | Health & Inner composition | Inverted / Close / Inverted | Increase |
SRPI | Health & Inner composition | Inverted / Close / Inverted | Increase |
PSRI | Age & Cover | Inverted / Close / Inverted | Increase |
CVA magnitude selected per spectral index.
The moving standard deviation index, MSDI, is a filter applied to satellite images multispectral or derivative channels using the moving standard deviation calculation, generally to assess degradation [40]. One common application is the vegetation and soil of semi-arid systems, where the variability of the MSDI is used to indicate levels of habitat degradation [41, 42, 43]. MSTDI has been proven efficient to operate well in complex regions [40, 42, 44].
Here, the five derivative images per selected CVA were used as entries. A standard deviation was computed for each entry. Difference between consecutive standard deviations were calculated, giving four new images. Then, the averaged MSDI (aMSDI) was conceived as follows:
For all
The global Moran’s I index was computed to assess the spatial autocorrelation of aMSDI outputs along the 1544 pixels transect. Its integration in spatial analysis is important to avoid incorrect statistical inference from inefficient or biased parameter estimates [45]. The formula is expressed such as follows:
Where
Values closer to
Ordinary Least Squares (OLS) regression was computed as the non-spatial measure of the spatial convergence/divergence of aMSDI. This method predicts or models a dependent variable relationship with a set of explanatory variables. The regression coefficients are usually estimated by using least-square techniques such as:
Where,
Here, each aMSDI of the nine indices was considered as dependent variable, whereas MSAVI2’s aMSDI of both CVA, were simultaneous explanators. Three OLS parameters were selected for interpretation, i.e., adjusted R-squared, corrected Akaike’s information criterion (AICc) which assesses the best-fit model between spatial and non-spatial Ordinary Least Squares (OLS) models, root mean squares error (RMSE), and three graphs, i.e., scatterplots of the relationship among variables, histograms of standard deviation probability and plots of residuals versus predicted.
At this point, the spatial correlation was assessed through the cross-correlation mapping process using only the finest
Further, the individual maps of anomalies were used to produce a single one. The first step consisted in stacking the aMSDI for each
Based on visual trends, binarized images were regrouped in three axes of three indices of vegetation each. A principal component analysis, PCA, was performed for each ax using the covariance reducer algorithm, that reduces some number of
Training:Testing | Total | ||||
---|---|---|---|---|---|
Vegetation1 | Vegetation2 | Vegetation3 | Soil/Built-up | Water | |
100:100 | 100:100 | 100:100 | 100:100 | 100:100 | 500:500 |
Samples distribution.
Three machine learning algorithms were performed, such as the Classification and Regression Trees (CART), Random Forest (RF) and Support vector machine, (SVM) [47, 48, 49]. The metrics used to assess general performance of each classifier were overall accuracy, OA, and the kappa coefficient, KC [50, 51]. Whereas, the thorough assessment of their efficiency on individual LULC classes was done using the error matrix to extract the producer accuracy (PA) and the user accuracy (UA). Eqs. (7) and (8) formulate the main quality measures of the learning:
with,
Where,
Overview of the study design.
Globally, each index showed a different interseason sensitivity to dynamics for the two main magnitudes of change in the vegetation distribution. The indices detecting vigor, biochemical composition, and water content, i.e., PVR, PBI and GVMI, that are highly correlated with MSAVI2, invaded the upper medium area for the increasing magnitude, while their increasing patterns were more concentrated on the south and lower medium area (Figure 4a and
a. Decrease trends of CVA per index. The background layers are from 2021 to 2022. b. Increase trends of CVA per index. The background layers are from 2021 to 2022.
Moreover, all magnitudes agreed to the same total areas for all indices, when adding the unchanged magnitudes. For the extreme cases, LWCI indicates a brutal decrease in between 2015 and 2016 and 2016–2017 (Change1, Figure 5a), from 61,609 km2 to 22,030 km2, i.e., 58% of the total change for the study period (Figure 5b). While, GVMI indicates an important increase between 2019 and 2020 and 2020–2021 (Change 5, Figure 5a), from 1439 km2 to 41,215 km2, i.e., 69.4% of the total change for the study period (Figure 5b). Then all changes were complementary between the two magnitudes, except for IREIP1 in the last period, 2020–2021/2021–2022, which the decrease was unsignificant (4km2) compared to the increase (Figure 5a&
Areas(a), percentages(b) and hierarchy of changes (c) based on the CVA decrease (row1) and increase (row2) magnitudes.
The outputs of the aMSDI were all obtained in the same interval,
From the spatial autocorrelation model synthesized in Table 4, the expected Moran’s I index and p-values are identical for all, −
aMSDI | Moran’s I | Expected Moran’s I | Variance | Z-Scores > +2.58 | P-Values |
---|---|---|---|---|---|
MSAVI2_Decr | 0.9 | −0.000648 | 0.000457 | 42.2 | 0.00 |
MSAVI2_Incr | 0.81 | −0.000648 | 0.000457 | 37.9 | 0.00 |
PVR | 0.95 | −0.000648 | 0.000458 | 44.3 | 0.00 |
PBI | 0.85 | −0.000648 | 0.000458 | 39.6 | 0.00 |
GVMI | 0.84 | −0.000648 | 0.000457 | 39.5 | 0.00 |
IREIP1 | 0.76 | −0.000648 | 0.000457 | 35.4 | 0.00 |
mCRIG | 0.86 | −0.000648 | 0.000457 | 40.3 | 0.00 |
LWCI | 0.91 | −0.000648 | 0.000457 | 42.5 | 0.00 |
MARI | 0.91 | −0.000648 | 0.000457 | 42.5 | 0.00 |
SRPI | 0.86 | −0.000648 | 0.000457 | 40.4 | 0.00 |
PSRI | 0.84 | −0.000648 | 0.000458 | 39.3 | 0.00 |
Spatial autocorrelation model report. N = 1544 for all variables and parameters.
First outputs of the cross-correlation mapping algorithm informed on the spatial relationship between each MSAVI2’s aMSDI and individual aMSDI of analytical indices. About the MSAVI2 decrease CVA, the highest positive correlation of aMSDI was with PVR (
Cross-correlation maps between aMSDIs of MSAVI2 and the selected aMSDI for each index.
The multi-regression performed between each analytical aMSDI and simultaneously both MSAVI2’s aMSDI on
OLS regression results synthesis (
Spatial synthesis of the cross-correlation mapping algorithm confirms the distribution of patterns and global trends. The combined correlation among the nine indices’ aMSDI were found strong at different degrees, for both highest and lowest values, then indicating convergences of significant or non-significant anomalies at the South-west and North-East of the area (Figure 8). For the three windows of calculation, they were clearly separated from predicted vegetation statuses, i.e., highly and lowly affected, soil, built-up and water features (Figure 8). From the cross-correlation map synthesis, the simple linear combination (SLC) outputs for each window discriminated positive from negative spatial trends. Then, the largest spots of extremely severe anomalies are located in the south-west, south-east and north-east areas, well separated on
Anomaly trends and spots. The synthesized correlation maps on the first row shows patchwork of high correlation in the medium area for the
The repeated sampling of vegetation gave spectral curves with at least three pairs of same trends for vegetation (Figure 9a). As spatial evidence, the patterns of ML were identical for the three algorithms, with three distinguished classes of vegetation, well separated from soil/built-up and water (Figure 9b). The three classifiers performed with a high and identical OA of
Spectral curves (A), spatial patterns (B) and statistics (C) of the three axes of vegetation on LULC. In details: (A) CART; (B) RF; (C) SVM: (D) stacked axes of vegetation with soil (NDSoDI) and water (NWI) features for 2021–2022; E) Sentinel2-a image of 2021–2022.
Concerning the vegetation types, discrimination, and extent, RF and SVM gave the exact areas for the three classes, CART agreed with them for the second and third axes, whereas the stacked derivative only agreed with all the algorithms concerning the third ax, i.e.,
The efficiency of the whole proposed methodology was assessed and discussed on selected aspects and the comparison with existing methods was basically empirical. At first, depending on their goals, previous MSDI-based studies analyzed only the standard deviation of the red and near infrared wavelengths, while those integrating vegetation indices were limited to three of them [40, 43, 52]. Because the goal of proposing aMSDI in this study was to assess consecutives dry season anomalies and discriminate them from empirical statuses of the forest-savanna specificities, we integrated nine spectral indices, selected on the basis of targeted phenological or physiological weaknesses, and whose computations basically integrate several wavelengths. Interestingly, although only one CVA magnitude was chosen per index, all individual models showed the expected visual convergence of similarity or dissimilarity trends.
Moreover, previous applications stated that the common calculation of MSDI on raw spectral index, gives outputs with a minimum value of zero and a maximum value determined by those of the pixels evaluated [43]. Consequently, outputs value cannot be directly compared. Here, by applying the averaging process to binarized CVA,
Besides, common attempts of mapping distribution, typology, and delineation of forest and savanna, have always been supported by fieldwork, based on climate parameters, as well as including paleo-ecological evidences and detailed floristic survey to be efficient [8]. The methodology presented in this paper has predicted three different axes of vegetation, resulting from the PCA processing and thresholding (Table 5). For each of the six study periods, the first ax in the south part is composed by a dense and potentially healthier vegetation, highly correlated with the referential data MSAVI2. Whereas the other two axes, more and more sparse towards north, are divergent with the first one and somehow each with another (Figure 10a&
Vegetation trends | Regrouping | PCA threshold |
---|---|---|
1st axe | PVR, PBI, GVMI | |
2nd axe | IREIP1, MARI, PRSI | |
3rd axe | mCRIG, LWCI, SRPI |
Vegetation axes, proposed groups and PCA thresholds used to binarize.
a. the three main axes of vegetation’s spatial distribution. b. Pixels’ value along the transect of the vegetation’s axes.
To answer the interrogations behind these ambiguities, a simple multicollinearity test was run, showing how independent one ax is from another. When the correlation between two independent variables is considerably high, it is a problem in the modeling process. The VIF (variance inflation factor) and tolerance were used for diagnosis. VIF is the reciprocal of tolerance, knowing that, tolerance is
Axe | VIF | Tolerance | Percentile for value 1 |
---|---|---|---|
AXE1 | 1.33 | 0.75 | [49.9–99.97] |
AXE2 | 1.12 | 0.89 | [62.5–99.97] |
AXE3 | 1.35 | 0.74 | [78.3–99.97] |
Multicollinearity test results.
Although from this study, we cannot properly use the qualifier of “bistable” forest or savanna, because it highly depends upon climate and paleo-ecological parameters, it is important to notice how ambiguous is the distribution and blurry are the boundaries. Thoroughly, on any ML output, three zooms distributed on three different latitudes helped to notice different types of transitions (Figure 11). Between the lower latitudes 5030’-6030’North, the transition is from the first (moist broadleaf forest) to third ax of vegetation (shrubland savanna), although the second ax (grassland savanna) would have been “expected.” Between the middle latitudes 6030’-7030’North, the transition mixes in the below area, the “-‘unexpected” third ax (shrubland savanna) with the ‘expected’ first ax (moist broadleaf forest) of vegetation, before the wide expansion of the ‘expected’ second ax (grassland savanna). At this point, the only “expected” transition was inside the upper latitudes 7030’-8030’North, where the second ax (grassland savanna) gradually gave way to third ax (shrubland savanna) of vegetation. These elements of analysis support the qualifier of “bistable” area, while still questioning the anisotropic distribution with latitudes, and encouraging the finest scale of analysis, i.e., spatial and spectral resolution.
Zooms on the transitions, a sign of anisotropic distribution with latitudes. Yellow square = lower latitudes (5030’-6030’N) transition; red square = middle latitudes (6030’-7030’N) transition; blue = upper latitudes (7030’-8030’N) transition.
Anomalies versus LULC classes. (A) SVM classification map. (B) Two extreme classes of anomalies at
Finally, the display of anomalies with the LULC classes disambiguated the confusion of savanna and degraded forest. The observation was made by overlaying the highest and the lowest values of anomalies in the most concentrate area, on the SVM output. On three spots covered by grassland, shrubland and bare soil, the modeled extremely severe anomalies concern just a part of each class. Whereas, on two spots of lower to no-anomalies, savanna as well as bare soils are partially concerned (Figure 12).
This study has conducted an experimentation on the forest-savanna vegetation, with the goal of assessing dynamics, assuming anomalies and predicting boundaries. On Google Earth Engine platform and using Sentinel2-A satellite images of seven consecutive dry seasons, from 2015 to 2016 to 2021–2022, twelve spectral indices were selected according to their different phenological and physiological assessment of the vegetation, and other natural features to be discriminate. Using the processing of change vector analysis, CVA, it was successfully observed that each index brings a substantial information, to better assess increase or decrease patterns of the vegetation cover. Further, proposing the averaged moving standard deviation index, aMSDI, to face potential issues of simple MSDI, the scale of spatial trends appraisal was found identical between the same interval
To USGS for the free availability of satellite data. To all experts-developers of Google Earth Engine platform APIs. To StatsN’Maps Consulting Firm, for the logistic support. To our laboratories.
The authors declare no conflict of interest.
Conception, A.H.N.M.; study design, A.H.N.M.; acquisition of data, A.H.N.M., I.C.N.P., F.C.L.T., L.M.B., M.T. and J.V.M.M.; execution, A.H.N.M.; analysis and interpretation, A.H.N.M.; writing-original draft preparation, A.H.N.M.; writing-review and editing A.H.N.M., I.C.N.P., F.C.L.T., L.M.B., M.T. and J.V.M.M.; All authors have read and agreed to the published version of the manuscript.
Links to the code are available upon request.
Additional material 1. Characteristics of Sentinel-2A bands.
Sentinel-2A MSI | ||
---|---|---|
Name | Range (μm) | Bands (Resolution/m) |
Coastal aerosol | 0.421 − 0.457 | B1(60) |
Blue | 0.439 − 0.535 | B2(10) |
Green | 0.537 − 0.582 | B3(10) |
Red | 0.646 − 0.685 | B4(10) |
Red_edge1 | 0.694 − 0.714 | B5(10) |
Red_edge2 | 0.731 − 0.749 | B6(10) |
Red_edge3 | 0.768 − 0.796 | B7(10) |
NIR wide | 0.767 − 0.908 | B8(10) |
NIR narrow | 0.848 − 0.881 | B8A(20) |
Water vapor | 0.931 − 0.958 | B9(60) |
Cirrus | 1.338 − 1.414 | B10(60) |
SWIR1 | 1.539 − 1.681 | B11(20) |
SWIR2 | 2.072 − 2.312 | B12(20) |
Additional material 2. The spectral indices used. The first row is the supporting or reference data.
Additional material 3. Binarized indices and thresholds defined.
Additional material 4. Direction and magnitude of change as proposed by Kuzera et al. (2005).
Additional material 5a. Averaged MSDI patterns of MSAVI2.
Additional material 5b. Averaged MSDI patterns for PVR, PBI and GVMI.
Additional material 5c. Averaged MSDI patterns for IREIP1, mCRIG and LWCI.
Additional material 5d. Averaged MSDI patterns for MARI, SRPI and PSRI.
Additional material 6. Spatial autoregression sample plots of aMSDI for PVR (left) and MSAVI2 increase patterns (right). red square = spatial correlation targeted, for n = 1544 pixels.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. Vikhe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/323731/images/13613_n.jpg",biography:"Dr Deepak M.Vikhe .\n\n\t\n\tDr Deepak M.Vikhe , completed his Masters & PhD in Prosthodontics from Rural Dental College, Loni securing third rank in the Pravara Institute of Medical Sciences Deemed University. He was awarded Dr.G.C.DAS Memorial Award for Research on Implants at 39th IPS conference Dubai (U A E).He has two patents under his name. He has received Dr.Saraswati medal award for best research for implant study in 2017.He has received Fully funded scholarship to Spain ,university of Santiago de Compostela. He has completed fellowship in Implantlogy from Noble Biocare. \nHe has attended various conferences and CDE programmes and has national publications to his credit. His field of interest is in Implant supported prosthesis. Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Univeristy of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:null},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:null},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. Her knowledge of English is at an advanced level.",institutionString:null,institution:null},{id:"332914",title:"Dr.",name:"Muhammad Saad",middleName:null,surname:"Shaikh",slug:"muhammad-saad-shaikh",fullName:"Muhammad Saad Shaikh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jinnah Sindh Medical University",country:{name:"Pakistan"}}},{id:"315775",title:"Dr.",name:"Feng",middleName:null,surname:"Luo",slug:"feng-luo",fullName:"Feng Luo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sichuan University",country:{name:"China"}}},{id:"423519",title:"Dr.",name:"Sizakele",middleName:null,surname:"Ngwenya",slug:"sizakele-ngwenya",fullName:"Sizakele Ngwenya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"419270",title:"Dr.",name:"Ann",middleName:null,surname:"Chianchitlert",slug:"ann-chianchitlert",fullName:"Ann Chianchitlert",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419271",title:"Dr.",name:"Diane",middleName:null,surname:"Selvido",slug:"diane-selvido",fullName:"Diane Selvido",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"419272",title:"Dr.",name:"Irin",middleName:null,surname:"Sirisoontorn",slug:"irin-sirisoontorn",fullName:"Irin Sirisoontorn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Walailak University",country:{name:"Thailand"}}},{id:"355660",title:"Dr.",name:"Anitha",middleName:null,surname:"Mani",slug:"anitha-mani",fullName:"Anitha Mani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"355612",title:"Dr.",name:"Janani",middleName:null,surname:"Karthikeyan",slug:"janani-karthikeyan",fullName:"Janani Karthikeyan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334400",title:"Dr.",name:"Suvetha",middleName:null,surname:"Siva",slug:"suvetha-siva",fullName:"Suvetha Siva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"334239",title:"Prof.",name:"Leung",middleName:null,surname:"Wai Keung",slug:"leung-wai-keung",fullName:"Leung Wai Keung",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Hong Kong",country:{name:"China"}}}]}},subseries:{item:{id:"20",type:"subseries",title:"Animal Nutrition",keywords:"Sustainable Animal Diets, Carbon Footprint, Meta Analyses",scope:"An essential part of animal production is nutrition. Animals need to receive a properly balanced diet. One of the new challenges we are now faced with is sustainable animal diets (STAND) that involve the 3 P’s (People, Planet, and Profitability). We must develop animal feed that does not compete with human food, use antibiotics, and explore new growth promoters options, such as plant extracts or compounds that promote feed efficiency (e.g., monensin, oils, enzymes, probiotics). These new feed options must also be environmentally friendly, reducing the Carbon footprint, CH4, N, and P emissions to the environment, with an adequate formulation of nutrients.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11416,editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/186575",hash:"",query:{},params:{id:"186575"},fullPath:"/profiles/186575",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()