陕西省作为我国重要的能源开发战略区，其煤炭资源的开发与工业化进程对区域生态环境具有深远的影响。陕西省彬州市地处黄土高原腹地，既是黄河流域中游生态屏障的关键节点，也是陕西省能源密集型产业发展的核心区域之一。近年来，伴随煤炭资源的大规模开采、工业园区扩张及城镇化加速，区域景观格局发生剧烈重组，生态脆弱性与风险性显著增强。在此背景下，探究能源开发主导的景观格局演变规律，评估生态风险的空间分异特征进而构建生态安全格局，对协调区域能源开发与生态保护、实现可持续发展具有重要的理论价值和实践意义。

本研究以彬州市为研究对象，基于2000年、2005年、2010年、2015年和2020年的Landsat遥感影像，利用GEE平台，结合随机森林算法完成土地利用的高精度监督分类（总体精度＞90%，Kappa系数＞0.88）。在此基础上，从景观类型动态和景观格局指数两个维度，综合运用土地利用动态度、转移矩阵及景观格局指数（斑块密度PD、最大斑块指数LPI、聚集度AI等），从时空维度分析了彬州市2000-2020年景观格局的动态演化规律。结合“干扰-脆弱性-损失度模型”评估景观生态风险。并基于形态学空间分析法与最小累积阻力模型，构建区域生态安全格局并提出优化策略。研究结果表明：

（1）彬州市景观格局整体呈现“耕地持续减少、建设用地快速扩张、林地修复成效显著”的演变特征。2000-2020年，耕地面积减少9842.40hm²（比例下降8.26%），建设用地扩张2232.63hm²（增幅达340%），林地面积增加6589.89hm²（比例突破10%），草地与水域破碎化程度加剧，能源开发对自然生态空间的占压效应明显增强。耕地的最大斑块指数（LPI）下降9.10%，优势度减弱；林地与建设用地的连通性增强，聚集度指数分别增加3.84%和13.22%，景观整体破碎化程度加深。

（2）景观生态风险总体以中风险等级为主，高风险区集中分布于泾河两岸及工矿聚集区，低风险区集中于韩家镇等自然基底稳定区域；20年间生态风险呈良性发展趋势，高风险区面积减少32256.09hm²，降幅为74.50%，低、较低风险区面积分别增加65.20%和79.50%，反映退耕还林等生态修复措施成效显著；全局空间自相关分析（Moran'sI＞0.3）表明风险分布具有显著集聚性，高-高聚集区集中于城关街道等人类活动密集带，景观损失度高且抗风险能力弱；低-低聚集区分布于西南部低干扰区域，土地利用类型稳定，生态韧性较强。揭示了彬州市生态风险呈现局部改善、空间异质性突出的特征。

（3）彬州市共识别出17个生态源地（总面积14703.05hm²，占比12.41%），集中分布于南部及中部林草区。基于8个阻力因子构建综合阻力面，发现阻力值呈现南低北高趋势，城镇化区域阻力较大，植被覆盖区阻力较低，并将研究区划分为五类阻力区，其中低阻力区占21.33%，高阻力区占8.29%，提取了55条生态廊道，并识别37个关键生态节点，最终提出“一核一带两屏三廊”的生态安全格局优化模式。

As a key strategic area for energy development in China, Shaanxi Province has experienced profound ecological impacts due to coal mining and industrialization. Located in the heart of the Loess Plateau, Binzhou City serves as both a critical ecological barrier in the middle reaches of the Yellow River and a core zone for Shaanxi’s energy-intensive industrial development. In recent years, large-scale coal exploitation, industrial park expansion, and accelerated urbanization have led to significant landscape reorganization, increasing ecological vulnerability and risk. Against this backdrop, exploring the landscape pattern evolution driven by energy development and assessing the spatial differentiation of ecological risk are of great theoretical and practical significance for balancing regional energy development with ecological conservation and achieving sustainable development.

This study takes Binzhou City as the research area and employs five periods of Landsat remote sensing imagery from 2000, 2005, 2010, 2015, and 2020. Land use classification was performed using the GEE platform and the Random Forest algorithm, achieving high classification accuracy (overall accuracy > 90%, Kappa coefficient > 0.88). Based on the dynamics of landscape types and landscape pattern indices, land use change rate, transition matrix, and indices such as patch density (PD), largest patch index (LPI), and aggregation index (AI) were integrated to analyze the spatiotemporal evolution of landscape patterns from 2000 to 2020. Based on the "disturbance-vulnerability-loss model", we assessed landscape ecological risks. Furthermore, an ecological security pattern was constructed using morphological spatial pattern analysis (MSPA) and the minimum cumulative resistance (MCR) model, with optimization strategies proposed. The findings reveal that:

(1) The overall landscape pattern of Binzhou City presents the evolutionary characteristics of "continuous reduction of cultivated land, rapid expansion of construction land, and significant results of forest restoration". From 2000 to 2020, the cultivated land area decreased by 9842.40hm² (a decrease of 8.26%), the construction land expanded by 2232.63hm² (an increase of 340%), and the forest area increased by 6589.89hm² (a proportion exceeding 10%). The degree of fragmentation of grassland and water areas has intensified, and the occupation effect of energy development on natural ecological space has significantly increased. The LPI of cultivated land dropped by 9.10%, indicating reduced dominance, while forest and construction land showed improved connectivity, with aggregation indices rising by 3.84% and 13.22%, respectively, leading to increased landscape fragmentation.

(2) The ecological risk pattern was dominated by moderate-risk areas. High-risk zones were mainly located along the Jing River and in mining-concentrated areas, while low-risk zones were primarily distributed in ecologically stable regions such as Hanjia Town. Over the 20-year period, ecological risk showed an overall improving trend: high-risk areas decreased by 32,256.09 ha (a reduction of 74.50%), while low and relatively low-risk areas increased by 65.20% and 79.50%, respectively, indicating the effectiveness of ecological restoration measures such as reforestation. Global spatial autocorrelation analysis (Moran's I > 0.3) revealed significant spatial clustering. High–high clusters appeared in densely populated areas like Chengguan Subdistrict with severe landscape loss and low resilience, while low–low clusters were in stable, low-interference southwestern regions, indicating localized ecological improvement and marked spatial heterogeneity.

(3) A total of 17 ecological source areas were identified in Binzhou City, covering an area of 14,703.05 hectares and accounting for 12.41% of the total study area. These sources were primarily concentrated in the forest-grassland regions in the southern and central parts of the city. Based on eight resistance factors, a comprehensive ecological resistance surface was constructed, revealing a spatial pattern of lower resistance in the south and higher resistance in the north. Urbanized areas exhibited higher resistance values, while areas with dense vegetation cover showed lower resistance. Using the natural breaks method, the study area was divided into five resistance zones, with low- and high-resistance zones comprising 21.33% and 8.29%, respectively. Based on this, 55 ecological corridors and 37 key nodes were identified, forming an optimized pattern of “one core, one belt, two barriers, and three corridors.”

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