Abstract
Abstract. Imaging Hyperspectral data are advent as potential solutions in modeling, discrimination and mapping of vegetation species. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, chlorophyll, and leaf nutrient concentration. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and combinations of suitable spectral band. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. In this study, a ground-based hyperspectral imaging data (AISA VNIR 400–960 nm, Spectral Resolution @ 2.5 nm) was used for spectral vegetation discrimination and characterization of natural desertic tree species. This study assessed the utility of hyperspectral imagery of 240 narrow bands in discrimination and classification of desert tree species in Jodhpur region using ENVI software. Vegetation indices derived from hyperspectral images used in the Analysis for tree species classification discrimination study. Prominent occurring two desertic tree species, viz., Neem and Babul in Jodhpur region could be effectively discriminated. Study demonstrated the potential utility of narrow spectral bands of Hyperspectral Imaging data in discriminating vegetation species in a desertic terrain.
Highlights
Remote sensing is the method of acquire data about the Earth’s surface without physical contact with the object
While almost all the sensors in space platform are of broad band type with spectral bandwidth of about 70 nanometre, it is worthwhile to check the suitability of such broad band data for the envisaged application of vegetation mapping
SR = NIR / RED Enhanced Vegetation Index (EVI) The EVI was developed to improve the Normalized Difference Vegetation Index (NDVI) by optimizing the vegetation signal in the study area by using the blue reflectance to correct for soil background signals and reduce atmospheric influences including aerosol scattering (Fig.15c)
Summary
Remote sensing is the method of acquire data about the Earth’s surface without physical contact with the object. The most significant advancement in the remote sensing has been the development of hyperspectral sensors and software to analyze the resulting image data. From last decade hyperspectral image analysis has developed in the most powerful and fastest growing technologies in the field of remote sensing. The study area is located in arid part of western Rajasthan with latitude of 26o 8' 30" to 26o 23' 28" North and longitude of 72o 52' 46" to 73o 10' 52" East (Fig.1) and total coverage area of 80 sq. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India height of 2 metre using a Tripod mounted Hyperspectral Imaging System. At 2 metre height, the system provides data with spatial resolution of 2mm
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