Modelling Spatial Drivers for LU/LC Change Prediction Using Hybrid Machine Learning Methods in Javadi Hills, Tamil Nadu,
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RESEARCH ARTICLE
Modelling Spatial Drivers for LU/LC Change Prediction Using Hybrid Machine Learning Methods in Javadi Hills, Tamil Nadu, India Sam Navin MohanRajan1 • Agilandeeswari Loganathan2 Received: 13 April 2020 / Accepted: 2 November 2020 Ó Indian Society of Remote Sensing 2020
Abstract The land-use/land-cover (LU/LC) information can be extracted through continuous monitoring and observation of the global environment in the field of RS and GIS (remote sensing and geographic information system). With many inventions on satellite technologies, RS plays a crucial role throughout the world, and the researchers had shown their interest in finding the past, present, and future LU/LC information using the RS satellite data. In this research work, the non-forestand forest-covered changes of Javadi Hills located in India were simulated and predicted using the hybrid machine learning models. The Markov chain–artificial neural network with cellular automata (MC–ANN–CA) and Markov chain–logistic regression with cellular automata (MC–LR–CA) were used and compared using the actual LU/LC maps of 2009, 2012, and 2015 along with the spatial variables (slope, aspect, hill shade, and distance road map). The results of the comparative analysis between the predicted and actual map of 2015 had shown a higher percentage of correctness in the MC–ANN–CA model for the spatial variables like slope, aspect, and distance road map. The LU/LC for 2021 and 2027 was predicted using the MC–ANN–CA model. By 2021, the forest-covered area will decrease by nearly - 0.38%, and the non-forestcovered area will increase by 0.79%. By 2027, forest-covered areas will decrease by - 0.52%, and non-forest-covered areas will increase by 1.06%, respectively, indicating the impacts of human and urbanization on LU/LC in Javadi Hills. Keywords Remote sensing Geographic information system Random forest classification Artificial neural network Logistic regression Cellular automata LU/LC prediction
Introduction The surface of the earth consists of natural and artificial land cover. The natural surface includes grasslands, water bodies, barren lands, forest-covered areas, desert regions, hills, snow, and glaciers. The artificial surface includes build-up areas, agricultural farms, and artificial turf lands. Every living organism on earth depends on nature and its benefits. Human plays a crucial role in utilizing and protecting the LU/LC environment. On the other hand, humans also fail to protect the environment, and so, the & Agilandeeswari Loganathan [email protected] 1
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
2
Department of Digital Communications, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
LU/LC change happens across the globe (Heidarlou et al. 2019; Fonji and Taff 2014). Many environmentalists had shown their interest in mineral mapping, agricultural field analysis, and LU/LC classification using the spatia
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