Multi-layer perceptron - markov chain based geospatial analysis of land use and land cover change: A case study of Stoney Creek Watershed, BC, Canada

This thesis study analyzed the land use and land cover (LULC) changes in Stoney Creek Watershed, BC, Canada using the combination of remote sensing, GIS and modeling approaches. The Object-Based Image Analysis (OBIA) tool in PCI Geomatica 2017 software was applied to generate unsupervised classification LULC maps using Landsat TM and OLI images of the years 1986, 1999 and 2016. Various band ratio were computed to improve different classification results. Esri ArcMap 10.5 was used to produce all the LULC maps for subsequent modeling. A modeling method using Multi-layer perceptron (MLP) neural network and Markov Chain (MC) was performed to predict LULC changes in 2026, using hard and soft prediction results. The outcomes of this study could provide valuable information of LULC patterns and dynamics for supporting both environmental and economic development in this area.