Land Use Change Detection and Prediction in Upper Siem Reap River, Cambodia

• Main Authors: Kosal Chim, Jon Tunnicliffe, Asaad Shamseldin, Tetsuji Ota
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Hydrology
• Subjects: land change modeler (LCM); multi-layer perceptron neural network (MLPNN); Markov chain (MC); Angkor temple complex; classification; forest cover
• Online Access: https://www.mdpi.com/2306-5338/6/3/64
• ISSN: 2306-5338

Siem Reap River has played a crucial role in maintaining the Angkor temple complex and livelihood of the people in the basin since the 12th century. Land use in this watershed has changed considerably over the last few decades, which is thought to have had an influence on river. This study was carried out as part of assessing the land use and climate change on hydrology of the upper Siem Reap River. The objective was to reconstruct patterns of annual deforestation from 1988 to 2018 and to explore scenarios of land use 40 and 80 years into the future. A supervised maximum likelihood classification was applied to investigate forest cover change in the last three decades. Multi-layer perceptron neural network-Markov chain (MLPNN-MC) was used to forecast land use and land cover (LULC) change for the years 2058 and 2098. The results show that there has been a significantly decreasing trend in forest cover at the rate 1.22% over the last three decades, and there would be a continuous upward trend of deforestation and downward trend of forest cover in the future. This study emphasizes the impacts of land use change on water supply for the Angkor temple complex (World Heritage Site) and the surrounding population.