Multi-Layer Perceptron Neural Network and Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change

Abstract

We combined multi-layer perceptron (MLP) neural network and Markov Chain (MC) modeling with object-based image analysis (OBIA) to map and predict land use and land cover (LULC) changes in Stoney Creek Watershed (SCW), British Columbia, Canada. Unsupervised classification was performed using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to produce LULC maps of years 1986, 1999 and 2016. The classification resulted in an overall accuracy of 91.50%. The results show that coniferous forest in SCW experienced a sharp loss while agriculture area increased (4.77% land gain) from 1986 to 2016. LULC scenarios were predicted through MLP neural network and MC modeling based on LULC change analysis data and transition potential. The results indicated that ‘Coniferous Forest’ LULC type had the highest (3.38% land loss) transition potential and ‘Water’ and ‘Urban Area’ LULC types had the lowest transition potential. Application of the proposed method provided valuable information of LULC patterns and dynamics for planners and researchers. The method also has the potential for improved management in other watersheds with similar LULC types.

Keywords: geospatial analysis, land use and land cover (LULC) change, landsat imagery, markov Chain (MC) model, multi-layer perceptron (MLP) neural network, object-based image analysis (OBIA)