Identifying hotspots in land use land cover change and the drivers in a semi-arid region of India.
The study examines long-term land use/land cover change (LUCC) at a finer scale in a semi-arid region of India. The objectives were to study and quantify the spatiotemporal LUCC and uncover the major drivers causing the change in the Mula Pravara river basin, which is located in a semi-arid region of Maharashtra state, India. Advanced very high-resolution radiometer (AVHRR)-Normalized Difference Vegetation Index (NDVI) 3g data for the years 1982-2015 were used to identify the 'hotspot' with significant positive and negative LUCC. Multi-temporal Landsat imagery was used to produce finer scale land use maps. From 1991 to 2016, the agricultural land area increased by approximately 98% due to the conversion of uncultivable and fallow lands to agriculture. The built-up area increased by 195%, and in recent years, an urban expansion has occurred in agricultural lands close to the urban fringe areas. There has been a shift from food crops to commercial crops, as observed from the steep increase in the amount of land under horticultural plantations, by 1601% from 2001 to 2016. In addition, the area under forest canopy was reduced in the protected regions. Institutional factors that improved access to water resources were the major drivers of change in hotspots, especially in the context of agriculture. Technological and economic factors were the other supporting factors that contributed to the change. This study demonstrates the advantages of using satellite remote sensing techniques to monitor the LUCC, which is useful for predicting future land changes and aids in planning adaptation strategies.
['Duraisamy|Vijayasekaran|V|http://orcid.org/0000-0002-5250-1152', 'Bendapudi|Ramkumar|R|', 'Jadhav|Ajit|A|']
D000383:Agriculture D003247:Conservation of Natural Resources D004784:Environmental Monitoring D065928:Forests D007194:India D045483:Rivers D063809:Satellite Imagery
Land use land cover Semi-arid India Drivers of change Remote sensing GIS