文献类型： 外文期刊

作者： Liao, Guitang ¹ ; He, Peng ² ; Gao, Xuesong ³ ; Lin, Zhengyu ² ; Huang, Chengyi ⁴ ; Zhou, Wei ³ ; Deng, Ouping ³ ; Xu, Chenghua ¹ ; Deng, Liangji ³ ;

作者机构： 1.Chengdu Univ Informat Technol, Coll Resources & Environm, Chengdu 610225, Peoples R China

2.Sichuan Acad Agr Sci, Agr Informat & Rural Econ Res Inst, Chengdu 610066, Peoples R China

3.Sichuan Agr Univ, Coll Resource, Chengdu 611130, Peoples R China

4.Sichuan Agr Univ, Coll Environm Sci, Chengdu 611130, Peoples R China

关键词： Land use optimization; Production-living-ecological space; BP-ANN; CLUE-S; Different scales

期刊名称：ECOLOGICAL INDICATORS （ 影响因子：6.263； 五年影响因子：6.643 ）

ISSN： 1470-160X

年卷期： 2022 年 137 卷

页码：

收录情况： SCI

摘要： Rural production-living-ecological (PLE) space is the essential carrier of China's rural land resource planning and management. However, the pattern identification, optimal prediction, and multi-scale integration of rural PLE space lack sufficient scientific evidence and reliable quantitative analysis. To fill this gap, this paper proposes a multi-scale land use optimization method based on benefit coupling evaluation, BP-ANN and CLUE-S models. The typical hilly area of the upper reaches of the Yangtze River in southwest China were used as a case for empirical study. The results showed that there was a high correlation between land use patterns and benefits. The key to land use optimization in hilly areas is to increase the area of ecological land and improve the capacity of regional ecosystem services. Through the evaluation of the degree of comprehensive benefit coupling, 44 sample towns with an optimal land use pattern were effectively identified to construct the optimization model. At the regional scale, the BP-ANN model predicted the optimal ratio of land use structure for each township unit based on natural, social, and economic influencing factors. The optimization results reduced production land by 8.94% on average, increased ecological land by 9.2% on average, and kept living land relatively stable. The proportions of production, living, and ecological land were adjusted to 59.85%, 8.34%, and 31.81%, respectively, which can better meet the land space demand for food security and ecological protection in the future. Regarding the smaller scale, the CLUE-S model took the regional-scale optimization results as the goal to simulate the spatial distribution of land use. The proportion of production, living, and ecological land in the town of Taiping was optimized from the previous 88.17%, 6.25%, and 5.58% to 62.92%, 7.83%, and 29.25%, respectively. The optimization results not only ensure the stability of high-quality cultivated land, but also effectively improve ecological functions such as soil conservation and water purification. This novel method was proven to be effective for quantitative optimization of land use and multi-scale functional space cohesion and integration, providing scientific support for the sustainable use of rural land resources in China.