Journal ArticleDOI
Synergetic use of in situ and hyperspectral data for mapping species diversity and above ground biomass in Shoolpaneshwar Wildlife Sanctuary, Gujarat
Ramandeep Kaur M. Malhi,Akash Anand,Ashwini N. Mudaliar,Prem Chandra Pandey,Prashant K. Srivastava,G. Sandhya Kiran +5 more
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TLDR
In this paper, the authors used quadrat sampling in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, which was used to compute Shannon-Weiner Diversity Index (H′), above ground biomass (AGB) was calculated measuring the Height and Diameter at Breast Height (DBH) of different trees in the sampling plots.Abstract:
Biodiversity loss in tropical forests is rapidly increasing, which directly influence the biomass and productivity of an ecosystem. In situ methods for species diversity assessment and biomass in synergy with hyperspectral data can adeptly serve this purpose and hence adopted in this study. Quadrat sampling was carried out in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, which was used to compute Shannon–Weiner Diversity Index (H′). Above ground biomass (AGB) was calculated measuring the Height and Diameter at Breast Height (DBH) of different trees in the sampling plots. Four spectral indices, namely Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Photochemical Reflectance Index (PRI), and Structure Insensitive Pigment Index (SIPI) were derived from the EO-1 Hyperion Data. Spearman and Pearson’s correlation analysis was performed to examine the relationship between H′, AGB and spectral indices. The best fit model was developed by establishing a relationship between H′ and AGB. Fifteen models were developed by performing multiple linear regression analysis using all possible combinations of spectral indices and H′ and their validation was performed by relating observed H′ with model predicted H′. Pearson’s correlation relation showed that SIPI has the best relationship with the H′. Model 15 with a combination of NDVI, PRI and SIPI was determined as the best model for retrieving H′ based on its statistics performance and hence was used for generating species diversity map of the study area. Power model showed the best relationship between AGB and H′, which was used for the development of AGB map.read more
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Journal ArticleDOI
Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data
Prashant K. Srivastava,Manika Gupta,Ujjwal Singh,Rajendra Prasad,Prem Chandra Pandey,Akhilesh Singh Raghubanshi,George P. Petropoulos +6 more
TL;DR: In this paper, the authors evaluated the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices.
Journal ArticleDOI
Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India
Sultanova Umida Rustamovna,Ramandeep Kaur M. Malhi,Akash Anand,Prashant K. Srivastava,Sumit Kumar Chaudhary,Manish Kumar Pandey,Mukund Dev Behera,Amit Kumar,Prachi Singh,G. Sandhya Kiran +9 more
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).
Journal ArticleDOI
An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas
Ramandeep Kaur M. Malhi,Akash Anand,Prashant Srivastava,G. Sandhya Kiran,George P. Petropoulos,Christos Chalkias +5 more
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.
Book ChapterDOI
Revisiting hyperspectral remote sensing: origin, processing, applications and way forward
Prashant K. Srivastava,Ramandeep Kaur M. Malhi,Prem Chandra Pandey,Akash Anand,Prachi Singh,Manish Kumar Pandey,Ayushi Gupta +6 more
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.
Book ChapterDOI
Future perspectives and challenges in hyperspectral remote sensing
Prem Chandra Pandey,Heiko Balzter,Prashant K. Srivastava,George P. Petropoulos,Bimal K. Bhattacharya +4 more
TL;DR: This chapter provides a perspective on the evolution of hyperspectral RS methods and applications along with challenges and barriers faced during research and innovation activities to current and prospective users of high spectral resolution data to extract meaningful information for their research and applications.
References
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