Modeling and Predicting Land Use Land Cover Spatiotemporal Changes: A Case Study in Chalus Watershed, Iran

Abstract

Land use and land cover (LULC) change is a main driver of global environmental change and has destructive effects on the structure and function of the ecosystem. This study attempts to detect temporal and spatial changes in LULC patterns of the Chalus watershed during the last two decades using multitemporal Landsat images and predict the future LULC changes and patterns of the Chalus watershed for the year 2040. A hybrid method between segment-based and pixel-based classification was applied for each Landsat image in 2001, 2014, and 2021 to produce LULC maps of the Chalus watershed. In this study, the transition potential maps and the transition probability matrices between LULC types were provided by the support vector machine algorithm and the Markov chain model, respectively, to project the 2021 and 2040 LULC maps. The achieved K-index values that compared the simulated LULC map with the actual LULC map of the year 2021 resulted in a Kstandard = 0.9160, Kno = 0.9379, Klocation = 0.9318, and KlocationStrata = 0.9320, showing good agreement between the actual and simulated LULC map. Analysis of the historical LULC changes depicted that during 2001–2021, the significant increase of agricultural land (14317 ha) and barren area (9063 ha), and the sharp decline of grassland (26215 ha), and forest cover (5989 ha) were the major LULC changes in the Chalus watershed. The model predicted that forest cover will continue to decrease from 29.46 % (50720.2667 ha) in 2021 to 25.67 % of area (44207.78694 ha) in 2040, as well as, unceasing expansion of barren area, agricultural land, and built-up area will be expected by 2040. Therefore, understanding the spatiotemporal dynamics of LULC change is extremely important to implement essential measures and minimize the destructive consequences of these changes.

Keywords

• Change prediction
• decision forest
• deforestation
• land use and land cover (LULC) change analysis
• Markov chain model
• segmentation classification