Forest Cover Monitoring and Prediction in A Lesser Himalayan Elephant Landscape

A multi-layer perceptron-Markov chain based LULC change analysis and prediction using remote sensing data in Prayagraj district, India

Land use land cover (LULC) mapping and temporal observations are indispensable drivers for sustainable development. This research showed the growth trends and land use transition for the Prayagraj district in the last three decades. Supervised classification of Landsat images was performed on 5-year temporal intervals using a maximum likelihood classifier. All satellite images were organized into six major LULC feature classes viz agriculture/open land, barren land, built-up, forest, sand, and water. The overall accuracy of LULC classification was achieved by more than 89% in all seven temporal points. Furthermore, the accuracy of the classified maps was estimated through area-based error matrix. The Land Change Modeler tool of TerrSet 2020 software was used to analyze the transition of classes and to incorporate the multi-layer perceptron-Markov chain (MLP-MC) technique. The transition potentials were included in MLP-MC with the help of sensitive explanatory variables and significant transitions of classes. Furthermore, these transition potentials and the Markov chain transition matrix were used to predict the future LULC dynamics and vulnerability. The change analysis revealed that a significant portion of the agriculture/open land gradually decreased and got converted to built-up land. The results depicted that agriculture/open land was reduced by 8.03% in the last three decades while the built-up region was grown by 199.61%. Forest area was continuously decreasing while the sand area increased due to river meandering. Overall, more than 75% of accuracy was achieved in MLP. The prediction model was first validated with observed data, and then the LULC scenario of 2035 and 2050 was simulated. LULC of 2050 showed that the built-up area would likely reach 13.90% of district area whereas the forest area would remain only 0.79%. The prediction model has given the output in the form of future LULC map along with projected potential transition maps. This would be useful for sustainable urban planning to deal with the alarming rate of built-up growth and agriculture/open land shrinkage.

Monitoring and predicting regional land use and land cover changes in an estuarine landscape of India

Deciphering land use and land cover (LULC) change patterns, identifying the variables that act as the major driving forces of change, and predicting possible changes are necessary tools of decision support for policymakers. Estuarine landscapes world over are under extreme pressure of developmental activities because of their resources. The developmental activities lead to unforeseen changes in the traditional land use practices, making it necessary for investigation of the possible outcomes. The present study aims to study the changing pattern of LULC in the East Godavari River Estuarine Ecosystem (EGREE) landscape during 1977-2015 using temporal satellite data and to predict the possible LULC changes by 2029. Cellular Automata-Markov model (CAMM) with and without the multi-criteria evaluator (MCE) and the multi-layer perceptron (MLP) models were used for future LULC prediction. Between 1977 and 2015, mangroves were converted to aquaculture (5.81 km²) on the landward side and were also lost to submergence at the seaward side (15 km²). All of the coastal scrub (69 km²) was lost to beach clearing. Over this period, the aquaculture area rose to 177 km². The CAMM with MCE was found to yield better predictions. A further rise was predicted in aquaculture (16%), built-up (30%), and Casuarina plantations (28%) by 2029. The study highlighted the LULC change patterns in EGREE, an important estuarine landscape of India. The information generated in this study can act as baseline information for the stakeholders and policy makers in decision-making of developmental projects, land acquisition, and diversion of agricultural land to non-agricultural purposes.

Assessment of historical forest cover loss and fragmentation in Asian elephant ranges in India

India is home of the largest remaining population of the Asian elephant (Elephas maximus L.) in the South and Southeast Asia. The forest loss and fragmentation is the main threat to the long-term survival of Asian elephants. In the present study, we assessed forest loss and fragmentation in the major elephant ranging provinces in India, viz., north-eastern, north-western, central, and southern since the 1930s. We quantified forest cover changes by generating and analyzing forest cover maps of 1930, 1975, and 2013, whereas fragmentation of contiguous forest areas was quantified by applying landscape metrics on the temporal forest cover maps. A total of 21.49% of the original forest cover was lost from 1930 to 1975, while another 3.19% forest cover was lost from 1975 to 2013 in the elephant ranges in India. The maximum forest loss occurred in the southern range (13,084 km²) followed by north-eastern (10,188 km²), central (5614 km²), and north-western (4030 km²) elephant ranges in the past eight decades. The forests in the central range were the most fragmented followed by southern, north-eastern, and north-western elephant ranges. The forest fragmentation in the southern range occurred at the fastest rate than central, north-eastern, and north-western ranges. The core forest areas shrunk by 39.6% from 1930 to 2013. The causative factors of forest change and situation of elephant-human conflict have been discussed. Study outcomes would be helpful in planning effective conservation strategies for Asian elephants in India.