Modeling LULC using Multi-Layer Perceptron Markov change (MLP-MC) and identifying local drivers of LULC in hilly district of Manipur, India.

Identification and prediction of future land use and land cover (LULC) changes and their drivers are required for land resources management, formulation of better policy, practicing sustainable management strategies, and modeling future LULC. The present study has focused on the prediction of future LULC and assessment of local drivers of LULC change in the hill agroecosystem Senapati district of Manipur, Northeast India. The prediction of future LULC for 2029 was achieved based on LULC data of 1999, 2009, and 2019 satellite data using multi-layer perceptron Markov change (MLP-MC) of Land Change Modeler (LCM).

Spatio-temporal assessment on land use and land cover (LULC) and forest fragmentation in shifting agroecosystem landscape in Ukhrul district of Manipur, Northeast India.

The information on land use and land cover (LULC) plays a critical role in understanding the interactions between human activities and the natural environment. The changes in LULC have a significant impact on the ecological integrity of forests, biodiversity, and natural resources, which in turn trigger global environmental change. Forest fragmentation is an important conservation challenge that includes interdependent forest loss components and spatial shift patterns.