An Advanced Satellite Image Based Land Cover Interpretation Using Airborne Information

Abstract

Since 1990, a set of five ground cover maps known as COS (Carta de Uso e Ocupaço do Solo) have been used to cover mainland in India. Prior to 2015, both maps were generated through photointerpretation of orthophotos. Comparing old and current orthophotos revealed improvements in ground cover and land usage, which were then used to refine the survey, resulting in a new map. The other places that did not adjust were kept consistent in the maps for consistency. Although the value of the maps generated, the process is time-consuming and restricted to a single-date guide for the orthophotos. A modern method to map design was adopted starting in 2015. Mapping continues to depend on photointerpretation of orthophoto charts; however it is now assisted by satellite-derived items. The aims are threefold: I want to cut down on processing time, boost map quality, and extend nomenclature. In the most recent map, shift identification and classification analyses of Landsat data were used to direct photointerpretation in trees and shrublands, as well as plotting annual agriculture (COS 2015). In comparison to previous maps, product performance and map error have also been reduced. This technique is further explored in the most recent 2018 table, which is actually in development. NDVI differencing, thresholding, and clustering were used to classify changes in vegetation using Landsat 8 time series from 2015 to 2018. NDVI temporal models and classification criteria were used to identify Autumn/Winter crops and Spring/Summer crops using Sentinel-2 time series from 2017-2018. The advantages and disadvantages of the new mapping approach are discussed and debated.