Abstract
The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.
Highlights
The over-utilization of land resources with the increase of population has caused a series of ecological and environmental problems, such as forest reduction, soil erosion, climate change, etc., which have made the unmatched contradiction of the population, resources and global environmental change more prominent [1,2]
Many studies generally used the CA-Markov model based on the Analytic Hierarchy Process in operating of the MCE, which was been integrated in the IDRISI [22,41,46]
The CA-Markov model based on the Boosted Regression Trees (BRT) model achieved a high satisfactory accuracy, and the simulation process was objective in the generation of the suitable maps collection due to the BRT
Summary
The over-utilization of land resources with the increase of population has caused a series of ecological and environmental problems, such as forest reduction, soil erosion, climate change, etc., which have made the unmatched contradiction of the population, resources and global environmental change more prominent [1,2]. The simulation and prediction of land use changes are mainly carried out by establishing mathematical models [3], which mainly include the Markov model [4], Cellular Automata model [5,6,7], System Dynamics model [8,9], and Artificial Neural Networks [10,11]. The Cellular Automata-Markov (CA-Markov) combines the ability to simulate the spatial variation of complex systems of CA model and the long-term prediction of the Markov model. It has been used in land use simulation [12,13,14], species distribution [15], and ecological environment [16,17]. How to reduce the impact of subjectivity in the creation of suitability maps collection is very important to the use of the CA-Markov model
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