Work

High-Resolution Remote Sensing and People-to-Pixel Integration for Mapping Farmland Abandonment in Central Himalayan Villages

Year: 2025

Type: article

Abstract: Farmland abandonment is increasingly prevalent, especially in the Central Himalaya. Precise mapping of abandoned areas is crucial for understanding their status and socioecological impacts. However, distinguishing abandoned farmland from transitional classes like fallow land and barren land is challenging without high-resolution satellite imagery and field verification. In this context, this work analyzes farmland abandonment in three ecological villages of the Nepal Himalaya using high-resolution satellite imagery and a people-to-pixel approach. First, the study villages were divided into grids based on their areas, and satellite imagery was printed for ground truthing. Second, ground truthing was conducted to identify active and abandoned farmland areas using the Field Area Measure App and satellite imagery. We measured the extent of abandoned farmland and assessed its current conditions. Third, the measured abandoned farmland shapefiles were exported for precise on-screen mapping using the Geographic Information System, alongside detailed land-cover mapping. Next, the accuracy assessment was performed using Google Earth satellite imagery, and the overall mapping accuracy was found to be 95.8%. Mapping results show that the highest areas of abandoned farmland were found in the Mountain region with 19.2% of total farmland, followed by the Hill region (12.7%) and the Tarai region (2.6%). Out of the total abandoned farmland, 49.2% is currently covered with bushes and shrubs, 42.9% with weeds and grasses, and the remaining 7.9% with woodlands. The findings emphasize the importance of integrating satellite technology with people engagement to address complex land-use challenges and offer critical insights for sustainable land management in the Nepal Himalaya and similar regions worldwide.

Source: Remote Sensing

DOI: 10.3390/rs17223726

Authors: Basanta Paudel, Yili Zhang, Binghua Zhang, Changjun Gu, Linshan Liu, Narendra Raj Khanal