Open AccessDamage assessment of chilli thrips using high resolution multispectral satellite data
Author(s) -
M. Prabhakar,
Merugu Thirupathi,
Golla Srasvan Kumar,
Uppu Sai Sravan,
M. Kalpana,
K.A. Gopinath,
Nikhil Kumar
Publication year - 2021
Publication title -
journal of agrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 11
eISSN - 2583-2980
pISSN - 0972-1665
DOI - 10.54386/jam.v21i4.284
Subject(s) - multispectral image , remote sensing , environmental science , normalized difference vegetation index , satellite , vegetation (pathology) , hyperspectral imaging , satellite imagery , canopy , radiometry , vegetation index , spectral bands , leaf area index , agronomy , geography , ecology , biology , medicine , engineering , pathology , aerospace engineering
Remote sensing technology offers an effective, rapid and reliable tool for assessing pest severity in vegetation. Ground based hyperspectral radiometry studies revealed significant difference in the reflectance spectra between healthy and thrip damaged vegetation. Space borne multispectral reflectance from Sentinel 2A satellite data of chilli thrip infested canopy has significant differences in red region (Band 4 – 664.6 nm), NIR region (Bands 5, 6, 7, 8 & 8A having central wavelengths at 704.1, 740.5, 782.8 & 832.8 nm, respectively) and SWIR region (Bands 11 & 12 having central wavelengths at 1613.7 and 2202.4 nm). In this study, an attempt was made to discriminate healthy and pest affected chilli crop in the multispectral satellite imagery using several multispectral vegetation indices. Of these, land surface water index, LSWI (p=0.018) and normalized difference water index, NDWI (p=0.001) were found significant. These indices were used to classify chilli fields in the satellite imagery into severe, moderate and healthy classes. Superior performance of LSWI over NDWI with overall accuracy of 93.80 and Kappa Coefficient of 0.89 was observed. Moran's Index was used to study the spatial distribution of chilli thrips and observed strong clustering (I= 0.9073, p=0.0001).