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Book ChapterDOI

Revisiting hyperspectral remote sensing: origin, processing, applications and way forward

01 Jan 2020-pp 3-21

TL;DR: This chapter discusses the origin of hyperspectral remote sensing, its importance, preprocessing, inversion models suitable for hyperspectrals, as well as several possible applications, including but not limited to, vegetation analysis, agriculture, urban, water quality, and mineral identification.
Abstract: After several years of research and development in hyperspectral imaging systems that enriched our knowledge and enhanced our capacity to explore the Earth, these systems have been widely accepted by the remote sensing community. They have evolved as major techniques and have now entered the mainstream of the earth observation data users. This chapter discusses the origin of hyperspectral remote sensing, its importance, preprocessing, inversion models suitable for hyperspectral datasets, as well as several possible applications, including but not limited to, vegetation analysis, agriculture, urban, water quality, and mineral identification. The chapter concludes by looking at the way forward for hyperspectral remote sensing.
Citations
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Journal ArticleDOI
TL;DR: This study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.
Abstract: Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

9 citations


Journal ArticleDOI
TL;DR: Interband information overlapping enhances redundancy in hyperspectral data, which makes identification of application-specific optimal bands essential for obtaining accurate information about folia...
Abstract: Interband information overlapping enhances redundancy in hyperspectral data. This makes identification of application-specific optimal bands essential for obtaining accurate information about folia...

3 citations


Book ChapterDOI
Yanfei Wang1, Changchun Yang1, Xiaowen Li2Institutions (2)
04 Nov 2009

3 citations


Journal ArticleDOI
Abstract: Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast’s Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests.

2 citations


OtherDOI
22 Jan 2021

2 citations


References
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Journal ArticleDOI
Douglas M. Hawkins1Institutions (1)
TL;DR: The focus is on regression problems, which are those in which one of the measures, the dependent Variable, is of special interest, and the authors wish to explore its relationship with the other variables.
Abstract: Model fitting is an important part of all sciences that use quantitative measurements. Experimenters often explore the relationships between measures. Two subclasses of relationship problems are as follows: • Correlation problems: those in which we have a collection of measures, all of interest in their own right, and wish to see how and how strongly they are related. • Regression problems : those in which one of the measures, the dependent Variable, is of special interest, and we wish to explore its relationship with the other variables. These other variables may be called the independent Variables, the predictor Variables, or the coVariates. The dependent variable may be a continuous numeric measure such as a boiling point or a categorical measure such as a classification into mutagenic and nonmutagenic. We should emphasize that using the words ‘correlation problem’ and ‘regression problem’ is not meant to tie these problems to any particular statistical methodology. Having a ‘correlation problem’ does not limit us to conventional Pearson correlation coefficients. Log-linear models, for example, measure the relationship between categorical variables in multiway contingency tables. Similarly, multiple linear regression is a methodology useful for regression problems, but so also are nonlinear regression, neural nets, recursive partitioning and k-nearest neighbors, logistic regression, support vector machines and discriminant analysis, to mention a few. All of these methods aim to quantify the relationship between the predictors and the dependent variable. We will use the term ‘regression problem’ in this conceptual form and, when we want to specialize to multiple linear regression using ordinary least squares, will describe it as ‘OLS regression’. Our focus is on regression problems. We will use y as shorthand for the dependent variable and x for the collection of predictors available. There are two distinct primary settings in which we might want to do a regression study: • Prediction problems:We may want to make predictions of y for future cases where we know x but do not knowy. This for example is the problem faced with the Toxic Substances Control Act (TSCA) list. This list contains many tens of thousands of compounds, and there is a need to identify those on the list that are potentially harmful. Only a small fraction of the list however has any measured biological properties, but all of them can be characterized by chemical descriptors with relative ease. Using quantitative structure-activity relationships (QSARs) fitted to this small fraction to predict the toxicities of the much larger collection is a potentially cost-effective way to try to sort the TSCA compounds by their potential for harm. Later, we will use a data set for predicting the boiling point of a set of compounds on the TSCA list from some molecular descriptors. • Effect quantification:We may want to gain an understanding of how the predictors enter into the relationship that predicts y. We do not necessarily have candidate future unknowns that we want to predict, we simply want to know how each predictor drives the distribution of y. This is the setting seen in drug discovery, where the biological activity y of each in a collection of compounds is measured, along with molecular descriptors x. Finding out which descriptors x are associated with high and which with low biological activity leads to a recipe for new compounds which are high in the features associated positively with activity and low in those associated with inactivity or with adverse side effects. These two objectives are not always best served by the same approaches. ‘Feature selection’ skeeping those features associated withy and ignoring those not associated with y is very commonly a part of an analysis meant for effect quantification but is not necessarily helpful if the objective is prediction of future unknowns. For prediction, methods such as partial least squares (PLS) and ridge regression (RR) that retain all features but rein in their contributions are often found to be more effective than those relying on feature selection. What Is Overfitting? Occam’s Razor, or the principle of parsimony, calls for using models and procedures that contain all that is necessary for the modeling but nothing more. For example, if a regression model with 2 predictors is enough to explainy, then no more than these two predictors should be used. Going further, if the relationship can be captured by a linear function in these two predictors (which is described by 3 numbers sthe intercept and two slopes), then using a quadratic violates parsimony. Overfitting is the use of models or procedures that violate parsimonysthat is, that include more terms than are necessary or use more complicated approaches than are necessary. It is helpful to distinguish two types of overfitting: • Using a model that is more flexible than it needs to be. For example, a neural net is able to accommodate some curvilinear relationships and so is more flexible than a simple linear regression. But if it is used on a data set that conforms to the linear model, it will add a level of complexity without * Corresponding author e-mail: doug@stat.umn.edu. 1 J. Chem. Inf. Comput. Sci. 2004,44, 1-12

1,617 citations


Journal ArticleDOI
Abstract: Recent upgrades to the MODTRAN atmospheric radiation code improve the accuracy of its radiance predictions, especially in the presence of clouds and thick aerosols, and for multiple scattering in regions of strong molecular line absorption. The current public-released version of MODTRAN (MODTRAN3.7) features a generalized specification of cloud properties, while the current research version of MODTRAN (MODTRAN4) implements a correlated-k (CK) approach for more accurate calculation of multiply scattered radiance. Comparisons to cloud measurements demonstrate the viability of the CK approach. The impact of these upgrades on predictions for AVIRIS viewing scenarios is discussed for both clear and clouded skies; the CK approach provides refined predictions for AVIRIS nadir and near-nadir viewing.

676 citations


Journal ArticleDOI
Abstract: We investigated the utility of high spectral and spatial resolution imagery for the automated species-level classification of individual tree crowns (ITCs) in a tropical rain forest (TRF). Laboratory spectrometer and airborne reflectance spectra (161 bands, 437–2434 nm) were acquired from seven species of emergent trees. Analyses focused on leaf-, pixel- and crown-scale spectra. We first described the spectral regions and factors that most influence spectral separability among species. Next, spectral-based species classification was performed using linear discriminant analysis (LDA), maximum likelihood (ML) and spectral angle mapper (SAM) classifiers applied to combinations of bands from a stepwise-selection procedure. Optimal regions of the spectrum for species discrimination varied with scale. However, near-infrared (700–1327 nm) bands were consistently important regions across all scales. Bands in the visible region (437–700 nm) and shortwave infrared (1994–2435 nm) were more important at pixel and crown scales. Overall classification accuracy decreased from leaf scales measured in the laboratory to pixel and crown scales measured from the airborne sensor. Leaf-scale classification using LDA and 40 bands had 100% overall accuracy. Pixel-scale spectra from sunlit regions of crowns were classified with 88% overall accuracy using a ML classifier and 60 bands. The highest crown-scale (ITC) accuracy was 92% with LDA and 30 bands. Producer's accuracies ranged from 70% to 100% and User's accuracies ranged from 81% to 100%. The SAM classifier performed poorly at all scales and spectral regions of analysis. ITCs were also classified using an object-based approach in which crown species labels were assigned according to the majority class of classified pixels within a crown. An overall accuracy of 86% was achieved with an object-based LDA classifier applied to 30 bands of data. Object-based and crown-scale ITC classifications were significantly more accurate with 10 narrow-bands relative to accuracies achieved with simulated multispectral, broadband data. We concluded that high spectral and spatial resolution imagery acquired over TRF canopy has substantial potential for automated ITC species discrimination.

669 citations


Journal ArticleDOI
Abstract: A computer code (acronym 5S) has been developed that allows estimation of the solar radiation backscattered by the Earth-surface-atmosphere system, as it is observed by a satellite sensor. Given the Lambertian ground reflectance, the apparent reflectance of the observed pixel is estimated by taking into account the effects of gaseous absorption, scattering by molecules and aerosols and, to some extent, inhomogeneity in the ground reflectance. The input parameters (observation geometry, atmosphere model, ground reflectance and spectral band) can be either selected from some proposed standard conditions (e.g. spectral bands of a satellite sensor) or user-defined. Besides the pixel apparent reflectance, the code provides the gaseous transmittance, the irradiance at the surface and the different contributions to the satellite signal according to the origin of the measured radiance. Some complementary results are also available; among others, benchmark calculations permit assessment of the code accuracy.

607 citations


Journal ArticleDOI
TL;DR: Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as R/sub 750//R/sub 710/ perform better than when all single spectral reflectance channels from hyper-spectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized.
Abstract: Radiative transfer theory and modeling assumptions were applied at laboratory and field scales in order to study the link between leaf reflectance and transmittance and canopy hyper-spectral data for chlorophyll content estimation. This study was focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma Region, Canada, where field measurements, laboratory-simulation experiments, and hyper-spectral compact airborne spectrographic imager (CASI) imagery of 72 channels in the visible and near-infrared region and up to 1-m spatial resolution data were acquired in the 1997, 1998, and 1999 campaigns. A different set of 14 sites of the same species were used in 2000 for validation of methodologies. Infinite reflectance and canopy reflectance models were used to link leaf to canopy levels through radiative transfer simulation. The closed and dense (LAI>4) forest canopies of Acer saccharum M. used for this study, and the high spatial resolution reflectance data targeting crowns, allowed the use of optically thick simulation formulae and turbid-medium SAILH and MCRM canopy reflectance models for chlorophyll content estimation by scaling-up and by numerical model inversion approaches through coupling to the PROSPECT leaf radiative transfer model. Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as R/sub 750//R/sub 710/ perform better than when all single spectral reflectance channels from hyper-spectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized.

540 citations


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