Assessment of land-use dynamics of the Aravalli range (India) using integrated geospatial and CART approach

World's oldest Aravalli range provides various ecosystem services that are continually vulnerable due to human-induced intervention. Urgently, it needs to be addressed the study of ecologically sensitive areas change. Therefore, the current study assessed a long-time-series land-use dynamics pattern between 1975 and 2059 using remote sensing and machine learning-based approaches. The land-use trend was assessed using the CART (Classification and Regression Tree) supervised classification technique on the Google Earth Engine (GEE) platform for the last 44 years. The MLP-NN (Multilayer Perceptron-Neural Network) algorithm has simulated the 2019 land use map and the upcoming 40-years of decadal land use prediction using the CA (Cellular Automata) Markov model in the LCM (Land Change Modeler) TerrSet platform. We highlight the conversion between spatial land cover and land use patterns and their conversion between classes. The results show that 3676 km(2) and 776.8 km(2) (i.e., 4.86% and 1.02%) converted into barren land and settlement from 1975 to 2019, 5772.7 km(2) (7.63%) of forest land has decreased in Aravalli. In 2059, a total of 16360.8 km(2) (21.64%) of forest land will be converted to a settlement class. Like mining and settlement, these human interventions are induced tacitly, which provokes ecological imbalances by breaching environmental integrity and hampering the progress of Sustainable Development Goals. This study would help the planers of the cities, forest managers, and the government develop the conservation management plan and sustainable city expansion projects.

Automated summary

Using remote sensing and machine learning approaches, this study assessed the land-use dynamics in the Aravalli range from 1975 to 2059, revealing a significant decrease in forest land and a future trend of conversion to settlement areas. These human interventions threaten ecological balance and hinder the progress towards Sustainable Development Goals.