土地利用
荒漠化
地理
土地利用、土地利用的变化和林业
人口
环境资源管理
中国
自然资源
比例(比率)
生态学
城市化
环境科学
地图学
生物
社会学
人口学
考古
作者
Song Jiang,Jijun Meng,Likai Zhu,Haoran Cheng
标识
DOI:10.1016/j.scitotenv.2021.149697
摘要
Land use conflict describes the incoordination of land use structure when meeting the diverse human demands under the deterioration of natural environment, which is a sensitive indicator of human-environmental interaction. The increased demand for different land use types due to rapid population growth and urbanization in China places tremendous pressure on limited land resources, which raises great concerns about land use conflict. To solve them, nation-scale assessment is essential, but such kind of research is still lacking due to the high data requirements. Here we drew on the conceptual framework of ecological risk assessment and the theories in landscape ecology, and developed a methodology to derive the spatio-temporal patterns of land use conflict in China from 2001 to 2017. We then used multilevel regression model to identify the driving factors of land use conflict at different levels. The results showed that the areas with strong land use conflict had a higher frequency of land use change, indicating that our model based on the framework of ecological risk assessment could effectively measure land use conflict. Land use conflict showed significant differences between two sides of the Hu Huanyong line, an important division line of population density and socio-economic background. The Main types of land use conflict in China included the strong competition between the use of cultivated land and grassland, the rapid expansion of construction land and the high risk of desertification. Among the driving forces, population density had a positive impact on land use conflict at the upper level, and altitude had a negative impact at the bottom level. Our research provides essential information to solve land use conflict through scientific land use planning and management and further to achieve the sustainable use of land resources.
科研通智能强力驱动
Strongly Powered by AbleSci AI