Hierarchical multistep remote sensing classification enhances land use mapping accuracy in anthropogenically modified landscapes
Journal Article
Overview
abstract
Abstract: This study addresses the challenge of accurately classifying land use and land cover (LULC) changes in landscapes influenced by anthropogenic activities. By leveraging multi-temporal satellite imagery and a hierarchical multistep classification approach, we enhance the differentiation of LULC transitions, improving model accuracy and environmental monitoring. This study presents an enhanced LULC classification framework that uses multi-temporal satellite imagery and hierarchical, multistep analysis to improve the accuracy of class detection in landscapes. We compare three supervised, pixel-based classification approaches - Single-step, Sequential Binary, and Accuracy-based Binary Classification - across a case study in the Furnas Reservoir Watershed, Southeast Brazil. The Accuracy-based Binary Classification method achieved the highest overall accuracy (87.37%), outperforming the other approaches by prioritizing classes with higher classification accuracy. Seasonal composite imagery and feature-engineering, such as spectral indices and quality mosaics, improved classification precision, particularly in heterogeneous and seasonally variable landscapes. The findings underscore the importance of integrating temporal dynamics in LULC mapping to inform sustainable land management in regions undergoing rapid environmental change.
