Land use optimization of rural production–living–ecological space at different scales based on the BP–ANN and CLUE–S models. (April 2022)

• Record Type: Journal Article
• Authors: Liao, Guitang; He, Peng; Gao, Xuesong; Lin, Zhengyu; Huang, Chengyi; Zhou, Wei; Deng, Ouping; Xu, Chenghua; Deng, Liangji
• Is Part Of: Ecological indicators. Volume 137(2022)
• Publication Date: 2022-04
• Subjects: Land use optimization -- Production–living–ecological space -- BP-ANN -- CLUE-S -- Different scales; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.108710
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: A multi–scale land use optimization method based on BP–ANN and CLUE–S models is proposed. Effectively identify the optimal land use pattern of rural PLE space. Quantitatively optimize the land use spatial distribution of rural PLE space. Realize the cohesion and integration of multi–scale rural land use function space. Abstract: 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.