Extreme drought is increasing in frequency and intensity in many regions globally. Understanding the changes in vegetation resistance and resilience under aggravated drought is essential for maintaining regional ecosystem stability. In this study, a drought event–vegetation response framework was developed to explore vegetation resistance and resilience changes. The normalized difference vegetation index (NDVI) was correlated with the standardized precipitation evapotranspiration index (SPEI) at multiple timescales to screen out the vegetation response time to drought. Then, the SPEI for the response time was detected using run theory to identify drought events during the period 2000–2017. Finally, drought-induced NDVI anomaly changes were identified using a sliding window to explore the changes in resistance and resilience to drought. This study focuses on Jilin province, China, which contains a famous environmentally vulnerable area. The results illustrate that the response time of vegetation to drought is 3 months. The northwest of Jilin province is considered to be drought-vulnerable because it has suffered the most drought events, i.e., 19–21 times, with severities in the range of 2.6–3.2 and durations in the range of 3.6–4.1 months. Grassland shows the weakest resistance and the strongest resilience, and tree cover shows the strongest resistance and the weakest resilience under severe drought disturbance among all vegetation. As the severity and duration of drought increase, the resistance decreases, and the resilience increases. During the growing season, the drought from May to July significantly impacts the vegetation resistance. Drought occurring from June to July has much less impact on resilience. Drought in August to September has less impact on resistance and a more significant impact on resilience. The results of this study may increase our knowledge regarding the response of vegetation to drought and guide ecosystem stability restoration.

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.

The scientific and reasonable division of geographical units of cultivated land reserve resource areas is an important foundation for comprehensive land consolidation (CLC). Saline–alkali land is an important cultivated land reserve resource, and is significant for ensuring food security. This paper constructs a two-level land consolidation index system for cultivated land reserve resource areas. The Kruskal graph theory clustering algorithm was used to divide the study area into four types of ecological zones. On the basis of ecological zones, the study area was further divided into three types of consolidation units. Finally, the spatial relationship between ecological zoning and land use types was clarified, and the classification distribution of land consolidation potential was determined through an analysis of the CLC of two-level geographical units. Da’an City, Jilin province, China, was selected as the study area. The results of this study show the following: (1) In Da’an City, the conservation priority areas are concentrated in the north, the development priority areas are concentrated in the central and southern areas, and the comprehensive upgrade areas and adjustment rebuilding areas are in the transition zones. (2) The CLC potential trend is low in the north but high in the south in Da’an City. This paper proposes a framework for a geographical unit delineation method for saline–alkali-enriched cultivated land reserve resource zones, and analyzes the spatial layout of ecological protection demands and land consolidation potential in Da’an City. The results and conclusions of this study will provide a reference for CLC in cultivated land reserve resource areas.