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Book ChapterDOI

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

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: In this paper, two nonparametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions were employed for the prediction of above ground biomass using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA).

18 citations

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.

16 citations

Journal ArticleDOI
TL;DR: The current work explored the solution to handle class imbalance by resampling the datasets before the application of classification algorithms by proposing a new computationally efficient class wise resampled technique which is based on SMOTE and centroid-based clustering.
Abstract: In the era of climate change, monitoring and effective retrieval of soil, water bodies, vegetation parameters etc. are of utmost importance which is successfully being executed using remote sensing from last few decades. The advancement of technologies has enabled us to reach effective decision making through these sensors. The advantage of acquiring multitemporal spatially continuous data sometimes turns into a disadvantage due to class imbalance where minority class instances are often misclassified by most of the classifiers. The current work explored the solution to handle this problem by resampling the datasets before the application of classification algorithms by proposing a new computationally efficient class wise resampling technique which is based on SMOTE and centroid-based clustering. The experiment was conducted on two benchmarked publicly available hyperspectral datasets. The output of the current work shows the superiority of the current work over past studies based on the performance evaluation metrics, accuracy, precision, recall and kappa values.

10 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...

9 citations

Journal ArticleDOI
TL;DR: In this article, the authors provided modified hyperspectral indices through detection of optimum bands for estimating species diversity within Shoolpaneshwar Wildlife Sanctuary (SWS) in India.
Abstract: Species diversity quantification is a crucial step towards the biodiversity conservation and ecosystem health. The technological advancements and existing limitations of multispectral remote sensing has increased the popularity of hyperspectral remote sensing which found its use in the estimation of species diversity. The contiguous narrow bands available in hyperspectral data enables the improvised assessment of diversity index but the overlapping of the information could result in the redundancy that needs to be handled. Due to this, the idenfication of optimal bands is very important; hence, the current study provides modified hyperspectral indices through detection of optimum bands for estimating species diversity within Shoolpaneshwar Wildlife Sanctuary (SWS), India. Narrow hyperspectral bands of EO-1 Hyperion image were screened and the best optimum wavelength from visible and Near Infrared (NIR) regions were identified based on coefficient of determination (r2) between band reflectance and in situ measured species diversity. For in situ species diversity measurements, quadrat sampling was carried out in SWS and different Diversity Indices (DIs) namely the Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI were calculated. The identified optimum wavelengths were then employed for modifying 38 existing spectral indices which were then investigated for testing their relation with the in situ DIs. The obtained optimum bands in visible and NIR regions were found to be in correspondence with four DIs. Among several indices used in this study, during validation, modified Non-linear index, modified Red Edge Position Index, modified Structure Insensitive Pigment Index and modified Red Green Ratio Index were identified as the best hyperspectral indices for determining Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI, respectively.

8 citations

References
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Journal ArticleDOI
TL;DR: In this article, the spectral properties of vegetation both in the optical and thermal range of the electromagnetic spectrum as affected by its attributes have been discussed to reduce data dimension and minimize the information redundancy.
Abstract: Remote sensing is being increasingly used in different agricultural applications. Hyperspectral remote sensing in large continuous narrow wavebands provides significant advancement in understanding the subtle changes in biochemical and biophysical attributes of the crop plants and their different physiological processes, which otherwise are indistinct in multispectral remote sensing. This article describes spectral properties of vegetation both in the optical and thermal range of the electromagnetic spectrum as affected by its attributes. Different methods have been discussed to reduce data dimension and minimize the information redundancy. Potential applications of hyperspectral remote sensing in agriculture, i.e. spectral discrimination of crops and their genotypes, quantitative estimation of different biophysical and biochemical parameters through empirical and physical modelling, assessing abiotic and biotic stresses as developed by different researchers in India and abroad are described.

104 citations

Journal ArticleDOI
TL;DR: This study demonstrates the retrieval of leaf Ca+b with Sentinel-2A imagery by red-edge indices and by an inversion method based on a hybrid canopy reflectance model that accounts for tree density, background and shadow components common in sparse forest canopies.

95 citations

Journal ArticleDOI
TL;DR: An innovative technique, a "smoothness test" for water vapor amount retrieval and for automatic spectral calibration, is developed for HATCH, which includes an original fast radiative transfer equation solver and a correlated-k gaseous absorption model based on HITRAN 2000 database.
Abstract: The High-accuracy Atmospheric Correction for Hyperspectral Data (HATCH) model was developed for deriving high-quality surface reflectance spectra from remotely sensed hyperspectral imaging data. This paper presents the novel techniques applied in HATCH. An innovative technique, a "smoothness test" for water vapor amount retrieval and for automatic spectral calibration, is developed for HATCH. HATCH also includes an original fast radiative transfer equation solver and a correlated-k gaseous absorption model based on HITRAN 2000 database. Spectral regions with overlapping absorptions by different gases are handled by precomputing a correlated-k lookup table for various gas mixing ratios. The interaction between multiple scattering and absorption is explicitly handled through the use of the correlated-k method for gaseous absorption. Finally, some results are presented for HATCH applied to Airborne Visible Infrared Imaging Spectoradiometer data and together with comparison of the results between HATCH and the Atmosphere Removal program. The limitations in HATCH include that the HATCH assumes a Lambertian surface, and adjacent effect is not considered. HATCH assumes aerosols to be spatially homogeneous in a scene.

94 citations

Journal ArticleDOI
TL;DR: Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment as mentioned in this paper, and Earth observation (EO) technology provides an in...
Abstract: Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. Earth observation (EO) technology provides an in...

92 citations

Journal ArticleDOI
Ke Liu, Qingbo Zhou, Wen-bin Wu1, Tian Xia1, Huajun Tang 
TL;DR: This review provides a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques, and categorizes the approaches used for crop Los Angeles Index (LAI) estimation into three types: approaches based on statistical models, physical models, and hybrid inversions.

77 citations