Remote sensing strategies to monitoring land use maps with AVHRR and MODIS data over the South Asia regions

Abstract

In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space-time dynamics of the LULC map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Keywords: AVHRR GIMMS NDVI3g; Land use and land cover; Precision assessment; Random forest classification; South Asia.