Principal Component Analysis for Evaluating the Seasonal Vegetation Anomalies from MODIS NDVI Time-series Datasets

Abstract

Abstract: In this paper, the author presents a method for analyzing vegetative seasonal (SW) anomalies using principal component analysis (PCA). The analysis was done on the Seasonal Maximum Value Composite of MODIS/VEGETATION NDVI obtained for the Northern Karnataka region over a spatiotemporal period (2000-2019). With multi-temporal data sets, the PCA was applied as a data transform to highlight areas of localized change. The objective is to monitor vegetation and evaluate land degradation in the Northern Karnataka Region. Although they offer broad spatial coverage and internal consistency of data sets, satellite remotely sensed data can be effectively used to characterize land surface conditions and land surface changes.