Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 10; p. 2757
• Main Authors: Li, Kongming, Feng, Mingming, Biswas, Asim, Su, Haohai, Niu, Yalin, Cao, Jianjun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 12.05.2020; MDPI AG
• Subjects: CA-Markov model; land use prediction; logistic regression; LUCC driving factors; remote sensing; remote sensing; land use prediction; logistic regression; CA-Markov model; LUCC driving factors
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20102757

Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, and sustainable development. Detecting and predicting LUCC, a dynamic process, and its driving factors will help in formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection. In this study, taking Gansu Province as a case study example, we explored the LUCC pattern and its driving mechanism from 1980 to 2018, and predicted land use and cover in 2030 using the integrated LCM (Logistic-Cellular Automata-Markov chain) model and data from satellite remote sensing. The results suggest that the LUCC pattern was more reasonable in the second stage (2005 to 2018) compared with that in the first stage (1980 to 2005). This was because a large area of green lands was protected by ecological engineering in the second stage. From 1980 to 2018, in general, natural factors were the main force influencing changes in land use and cover in Gansu, while the effects of socioeconomic factors were not significant because of the slow development of economy. Landscape indices analysis indicated that predicted land use and cover in 2030 under the ecological protection scenario would be more favorable than under the historical trend scenario. Besides, results from the present study suggested that LUCC in arid and semiarid area could be well detected by the LCM model. This study would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis in arid and semiarid regions.