Optimization Simulation and Comprehensive Evaluation Coupled with CNN-LSTM and PLUS for Multi-Scenario Land Use in Cultivated Land Reserve Resource Area

The scientific development and utilization of cultivated land reserve resource areas is an important basis for realizing national food security and regional ecological protection. This paper focuses on land use optimization simulations to explore the paths of sustainable land use in cultivated land reserve resources areas. Deep learning technology was introduced to calculate the growth probability of each land use type. A land use change simulation method coupling CNN-LSTM and PLUS was constructed to dynamically simulate the land use pattern, and the spatial accuracy of the simulation was improved. Markov chains and multi-objective planning (MOP) model were used to set historical development (HD) scenarios, ecological conservation (EP) scenarios, land consolidation (LC) scenarios, and sustainable development (SD) scenarios. The comprehensive impact of land use change on ecosystem service value (ESV), agricultural production benefits (APBs), and carbon balance (CB) was evaluated by systematically analyzing the quantitative and spatial distribution characteristics of land use change in different scenarios from 2020 to 2030. Da’an City, Jilin province, China was selected as the study area. The results of this study show the following: (1) The CNN-LSTM coupled with the PLUS model was designed to capture the dynamic change characteristics of land use, which achieves high accuracy (Kappa of 0.8119). (2) In the EP scenario, the increase in ESV was 4.36%, but the increase in APB was only 7.33%. In the LC scenario, APB increased by 22.11%, while ESV decreased by 3.44%. In the SD scenario, a dynamic balance was achieved between ESV and APB, and it was the optimal path for sustainable development. (3) The SD scenario performed best, with a CB of 5,532,100 tons, while the EP scenario was the lowest, at only 1,493,500 tons. The SD scenario shows the optimal potential of combining carbon reduction and agricultural development. In this paper, deep learning and spatial modeling for multi-scenario simulation were integrated, and a scientific basis for the planning and management of cultivated land reserve resource areas was provided.