摘 要

生态系统评估是人类掌握生态系统状况、实施生态系统科学管理的有效手段。随着人类对生态系统的扰动逐渐增强，生态系统的支撑作用日益显著，亟需开展生态系统综合评估研究，为区域生态保护和可持续发展提供科学支撑。黄河流域是我国重要的生态屏障和煤炭富集区，准确了解黄河流域及其煤炭区生态系统综合状况的演变规律及驱动因素，可为黄河流域生态保护和高质量发展提供决策支持。本研究以黄河流域及其煤炭区为研究对象，综合考虑生态系统格局、质量及服务三个方面，分别计算生态系统综合景观格局评估指数（Comprehensive Landscape Index，CLI）、生态系统质量评估指数（Remote Sensing Ecology Index，RSEI）、生态系统服务功能评估指数（Comprehensive Ecosystem Service，CES），分析其1989~2020年生态系统格局、质量及服务的变化特征；并构建生态系统综合状况评估模型（Comprehensive Ecosystem Assessment model，CEA），探究其1989~2020年生态系统综合状况的演变规律；然后从自然和社会因素探究其驱动因素；最后提出黄河流域和煤炭区的差异化生态优化策略。主要研究内容和结论如下：

（1）基于生态系统格局、质量和服务三个方面计算黄河流域及煤炭区CLI、RSEI和CES，结果表明：①黄河流域CLI由1989年的0.509下降至2020年的0.485，空间上呈现为东、西部高，中部黄土高原地区低；各煤炭区CLI下降，均值排序为中下游豫鲁区>上游宁东区>中上游蒙陕区>中游黄陇区>中游山西区；②黄河流域RSEI由1989年（0.389）持续上升至2020年（0.531），空间上表现为南高北低、由东南向西北递减（除宁夏平原、河套平原及黄河源保护区外）；各煤炭区RSEI增加，均值排序为中游黄陇区>中下游豫鲁区>中游山西区>中上游蒙陕区>上游宁东区；③1989~2020年，黄河流域CES以0.002/a的速率增强，上游毛乌素沙漠、内蒙古、宁夏等最低，秦岭、玛曲生态保护区、鲁南、豫南等最高；各煤炭区CES小幅提升，均值排序为中游黄陇区>中下游豫鲁区>中游山西区>上游宁东区>中上游蒙陕区；④空间变化趋势上，黄河流域和煤炭区生态系统格局以退化为主；生态系统质量以显著改善为主；生态系统服务功能以不显著改善为主。

（2）构建CEA模型评价黄河流域及煤炭区的生态系统综合状况，结果表明：①1989~2020年，黄河流域CEA以0.004/a的速率上升，由0.444提升至0.575；空间上，下游（包含豫、鲁二省）和流域源头区CEA最高，上游次之，中游西北部最低，研究区70.27%的区域得到显著改善；空间聚类上，全流域以不显著聚类为主，高高聚类次之，分布于源头区和下游（包含豫、鲁省区），低低聚类分布于中上游，高低和低高聚类占比最低；②1989~2020年各煤炭区CEA增加，空间上以显著改善为主，均值排序依次为中下游豫鲁区、中游黄陇区、中游山西区、上游宁东区、中上游蒙陕区。

（3）从自然和社会因素两方面探究黄河流域及煤炭区CEA变化的驱动因素，结果表明：①黄河流域CEA以NDVI为主导因子（q=0.499），其次为年均降雨量、年均气温、年均潜在蒸散量（0.395、0.350、0.265），人口密度、坡度、GDP的q值小于0.200，即植被和气象因素的影响大于社会和地形因素；煤炭区CEA均以NDVI为主导因子（除中上游蒙陕区以人口密度为主导因子外）；黄河流域和各煤炭区CEA均以NDVI与其余因子的交互作用最大；②降雨量、潜在蒸散量、NDVI、退耕还林/还草面积对1989~2020年黄河流域及煤炭区CEA的提升均以正向驱动作用为主，其中潜在蒸散量对中游山西区的负向驱动多于正向，退耕还林/还草面积由于上游宁东区植被成活率和保存率较低呈现微弱负向驱动；③煤炭区煤炭开发强度与CEA的耦合协调度上升，中游山西区和中上游蒙陕区由濒临失调（0.47、0.44）提升至勉强协调（0.55、0.51），上游宁东区由勉强协调（0.58）提升至初级协调（0.61），中游黄陇区、中下游豫鲁区处于中级协调、良好协调；下游煤炭区耦合协调度高于中上游，中游山西区、中上游蒙陕区最低。

（4）基于黄河流域及煤炭区生态系统综合状况的演变规律和驱动因素，并考虑生态本底、煤炭资源赋存及开采现状，提出差异化生态优化策略。对于黄河流域，从植被建设与恢复、水土流失治理、水资源利用与保护、工矿/建设用地布局优化4个方面提出生态优化策略；对于煤炭区，应优先提升植被覆盖，水资源约束明显的煤炭区（中游山西区、中上游蒙陕区、上游宁东区）坚持水资源“节流为先—合理配置—清洁处理”，采用保水采煤技术；水土流失严重的煤炭区（中游山西区、中上游蒙陕区、中游黄陇区）应促进防沙治沙、水土流失防治与煤炭生产活动协调发展；煤炭开采与土地资源矛盾的煤炭区（中游山西区、中下游豫鲁区）应加强土地集约化，科学治理采煤破坏土地。

ABSTRACT

Ecosystem assessment is an effective tool for humans to grasp the state of ecosystems and implement scientific management of ecosystems. With the gradual increase of human disturbance to the ecosystem, the supporting role of the ecosystem is becoming more and more significant. It is urgent to carry out comprehensive assessment studies of the ecosystem, provide scientific support for regional ecological protection and sustainable development. The Yellow River Basin is an important ecological barrier and coal-rich area in China. An accurate understanding of the evolutionary patterns and drivers of the integrated state of the Yellow River Basin and its coal region ecosystems can provide decision support for its ecological protection and high-quality development. This study takes the Yellow River Basin and its coal area as the research object, considers three aspects of ecosystem pattern, quality and services, calculates the Comprehensive Landscape Index (CLI), Remote Sensing Ecology Index (RSEI) and Comprehensive Ecosystem Service Index (CES) respectively, and analyzes the changes in ecosystem pattern, quality and services from 1989 to 2020. A Comprehensive Ecosystem Assessment model (CEA) was constructed to explore the evolution of the comprehensive ecosystem condition in the study area from 1989 to 2020; then the driving factors were explored in terms of natural and social factors; finally, differentiated ecological optimisation strategies were proposed for the Yellow River Basin and the coal area. The main research elements and conclusions are as follows:

（1）Based on three aspects of ecosystem pattern, quality and services, CLI, RSEI and CES were calculated, and the results showed that: ①The Yellow River Basin CLI decreases from 0.509 in 1989 to 0.485 in 2020. Spatially, the basin CLI is high in the east and west and low in the central Loess Plateau region. The CLI of each coal zone is on a downward trend, with the mean values ranked as Henan and Shandong in the middle and lower reaches > the Eastern Ningxia in the upper reaches > the Mongolia and Shaanxi in the middle and upper reaches > the Huanglong in the middle reaches > the Shanxi in the middle reaches. ②A sustained increase in the RSEI of the Yellow River Basin from 0.389 in 1989 to 0.531 in 2020. Spatially, high in the south and low in the north, decreasing from south-east to north-west (except for the Ningxia and Hetao Plain and the Source Protection Area). RSEI increases across coal zones, with mean values ranked as the Huanglong in the middle reaches > the Henan and Shandong in the middle and lower reaches > the Shanxi in the middle reaches > the Mongolia and Shaanxi in the middle and upper reaches > the Eastern Ningxia in the upper reaches. ③From 1989 to 2020, the CES of the Yellow River basin enhanced at a rate of 0.002/a, with the lowest in the upper Mawusu Desert, Inner Mongolia and Ningxia and the highest in the Qinling Mountains, Maqu Ecological Reserve, Southern Shandong and Henan Province.The CES of coal zone has improved slightly, with mean values ranked as the Huanglong in the middle reaches > the Henan and Shandong in the middle and lower reaches > the Shanxi in the middle reaches > the Eastern Ningxia in the upper reaches > the Mongolia and Shaanxi in the middle and upper reaches. ④Spatial trends in ecosystem patterns, quality, and service functions in the Yellow River basin and coal region are dominated by degradation, significant improvement, and insignificant improvement.

（2）A CEA model was constructed to evaluate the integrated state of the ecosystem, and the results showed that : ①From 1989 to 2020, the CEA of the Yellow River Basin increases at a rate of 0.004/a, from 0.444 to 0.575. Spatially, the CEA is highest in the lower reaches (containing the provinces of Henan and Shandong) and in the headwaters of the basin, followed by the upper reaches and lowest in the northwestern middle reaches. 70.27% of the study area has been significantly improved. Spatial clustering is dominated by insignificant across the study area, followed by high-high clustering in the source area and downstream, low-low clustering in the middle and upper reaches, and the lowest proportion of high-low and low-high clustering. ②The increase in CEA for coal region from 1989 to 2020 is dominated by significant spatial improvements, with the mean values ranked in order of the Henan and Shandong in the middle and lower reaches, the Mongolia and Shaanxi in the middle and upper reaches, the Huanglong in the middle reaches, the Shanxi in the middle reaches, the Eastern Ningxia in the upper reaches.

（3）Exploring the drivers of CEA changes in terms of both natural and social factors, the results show that: ①The CEA of the Yellow River Basin has NDVI as the dominant factor (q=0.499), followed by average annual rainfall, temperature, and potential evapotranspiration (PET) (0.395, 0.350, and 0.265), and the q values of population density, slope, and GDP are less than 0.200, the influence of vegetation and meteorological factors is greater than that of social and topographic factors; the CEA of coal areas all have NDVI as the dominant factor (except for the Mongolia and Shaanxi in the middle and upper reaches where population density is the dominant factor); the interaction between NDVI and its remaining factors is the largest in the Yellow River basin and coal area. ②Rainfall, PET, NDVI, and reforestation/grass restoration area all have positive driving effects on the enhancement of CEA in the Yellow River basin and coal region from 1989 to 2020, with PET driving more negatively than positively in the Shanxi in the middle reaches, and reforestation/grass restoration area showing a weak negative driving effect due to the low vegetation survival and preservation rate in the Eastern Ningxia in the upper reaches. ③The coupling coordination between CREI and CEA increases. The Shanxi in the middle reaches and the Mongolia and Shaanxi in the middle and upper reaches improved from near dissonance (0.47, 0.44) to barely coordinated (0.55, 0.51), the Eastern Ningxia in the upper reaches from barely coordinated (0.58) to moderately coordinated (0.61), the Huanglong in the middle reaches and the Henan and Shandong in the middle and lower reaches as moderately coordinated and well coordinated. Coupling coordination is higher in the downstream coal zone than in the middle and upper reaches, and lowest in the Shanxi in the middle reaches and the Mongolia and Shaanxi in the middle and upper reaches.

（4）Based on the evolutionary patterns and drivers of the comprehensive condition of the ecosystems in the Yellow River Basin and coal areas, and considering the ecological background, coal resource endowment and mining status, a differentiated ecological optimisation strategy is proposed. For the Yellow River Basin, ecological optimization strategies are proposed in four areas: vegetation construction and restoration, erosion control, water resources utilization and protection, and optimization of industrial and mining/construction land layout. For coal areas, priority should be given to improving vegetation cover, and coal areas with obvious water resource constraints (the Shanxi in the middle reaches, the Mongolia and Shaanxi in the middle and upper reaches, the Eastern Ningxia in the upper reaches) should insist on water resources "flow saving first - reasonable allocation - clean treatment" and adopt water conservation coal mining technology. Coal areas with serious soil erosion (the Shanxi in the middle reaches, the Mongolia and Shaanxi in the middle and upper reaches, the Huanglong in the middle reaches) should promote the coordinated development of sand control and erosion prevention and control with coal production activities. Coal areas with contradictory coal mining and land resources (the Shanxi in the middle reaches and the Henan and Shandong in the middle and lower reaches) strengthen land intensification and scientific management of land damaged by coal mining.

[1] Wei F W, Cui S H, Liu N, et al. Ecological civilization: China's effort to build a shared future for all life on Earth[J]. National Science Review, 2021, 8(07): 8-12.

[2] 吴炳方, 曾源, 闫娜娜, 等. 生态系统遥感:内涵与挑战[J]. 遥感学报, 2020, 24(06): 609-617.

[3] Sepehr G, Shohreh N, Roya D, et al. “Bio to bits”: the Millennium Ecosystem Assessment (MA) as a metaphor for Big Data ecosystem assessment[J]. Information Technology & People, 2022, 35(2).

[4] FAO. State of the World's Forests 2016: Forests and agriculture- land use challenges and opportunities[M]. Rome, Italy: Food and Agriculture Organization of the United Nations, 2016.

[5] 欧阳志云, 王桥, 郑华, 等. 全国生态环境十年变化(2000—2010年)遥感调查评估[J]. 中国科学院院刊, 2014, 29(04): 462-466.

[6] Ma K, Wei F. Ecological civilization: a revived perspective on the relationship between humanity and nature[J]. National Science Review, 2021, 8(7): 112.

[7] Peña L V D L, Taelman S E, Préat N, et al. Towards a comprehensive sustainability methodology to assess anthropogenic impacts on ecosystems: Review of the integration of Life Cycle Assessment, Environmental Risk Assessment and Ecosystem Services Assessment[J]. The Science of the total environment, 2021, 808: 152125.

[8] Toosi N B, Soffianian A R, Fakheran S, et al. Mapping disturbance in mangrove ecosystems: Incorporating landscape metrics and PCA-based spatial analysis[J]. Ecological Indicators, 2022, 136: 108718.

[9] Huang H, Chen W, Zhang Y, et al. Analysis of ecological quality in Lhasa Metropolitan Area during 1990–2017 based on remote sensing and Google Earth Engine platform[J]. Journal of Geographical Sciences, 2021, 31(2): 265-280.

[10] Wang B X, Cheng W M. Effects of Land Use/Cover on Regional Habitat Quality under Different Geomorphic Types Based on InVEST Model[J]. Remote Sensing, 2022, 14(5): 1279.

[11] 戴尔阜, 王亚慧. 横断山区产水服务空间异质性及归因分析[J]. 地理学报, 2020, 75(03): 607-619.

[12] Pang G J, Wang X J, Chen D L, et al. Evaluation of a climate simulation over the Yellow River Basin based on a regional climate model (REMO) within the CORDEX[J]. Atmospheric Research, 2021, 254: 105522.

[13] Wang G, Yue D, Niu T, et al. Regulated Ecosystem Services Trade-Offs: Synergy Research and Driver Identification in the Vegetation Restoration Area of the Middle Stream of the Yellow River[J]. Remote Sensing, 2022, 14(3): 718-718.

[14] 彭苏萍, 毕银丽. 黄河流域煤矿区生态环境修复关键技术与战略思考[J]. 煤炭学报, 2020, 45(04): 1211-1221.

[15] 卞正富, 于昊辰, 雷少刚, 等. 黄河流域煤炭资源开发战略研判与生态修复策略思考[J]. 煤炭学报, 2021, 46(05): 1378-1391.

[16] 时光, 任慧君, 乔立瑾, 等. 黄河流域煤炭高质量发展研究[J]. 煤炭经济研究, 2020, 40(08): 36-44.

[17] 王国法, 张铁岗, 王成山, 等. 基于新一代信息技术的能源与矿业治理体系发展战略研究[J]. 中国工程科学, 2022, 24(01): 176-189.

[18] Qu W, Jin Z, Zhang Q, et al. Estimation of Evapotranspiration in the Yellow River Basin from 2002 to 2020 Based on GRACE and GRACE Follow-On Observations[J]. Remote Sensing, 2022, 14(3): 730.

[19] Yin L C, Feng X M, Fu B J, et al. A coupled human-natural system analysis of water yield in the Yellow River basin, China[J]. Science of the Total Environment, 2021, 762: 143141.

[20] Fang M Z, Si G X, Yu Q, et al. Study on the Relationship between Topological Characteristics of Vegetation Ecospatial Network and Carbon Sequestration Capacity in the Yellow River Basin, China[J]. Remote Sensing, 2021, 13(23): 4926-4926.

[21] 卓静, 朱延年, 何慧娟, 等. 生态恢复工程对陕北地区生态系统格局的影响[J]. 生态学报, 2020, 40(23): 8627-8637.

[22] Turner M G, O'Neill R V, Gardner R H, et al. Effects of changing spatial scale on the analysis of landscape pattern [J]. Land-scape Ecologg, 1989 (3) :153-162.

[23] Ripple W J, Bradshaw G A, Spies T A. Measuring forest landscape patterns in the Cascade Range of Oregon, USA[J]. Biological Conservation, 1991, 57: 73-88.

[24] 黎晓亚, 马克明, 傅伯杰, 等. 区域生态安全格局:设计原则与方法[J]. 生态学报, 2004(05): 1055-1062.

[25] 王晓峰, 勒斯木初, 张明明. “两屏三带”生态系统格局变化及其影响因素[J]. 生态学杂志, 2019, 38(07): 2138-2148.

[26] 吴丹, 邹长新, 林乃峰, 等. 长江经济带生态系统格局演变特征分析[J]. 环境科学与技术, 2020, 43(04): 208-213.

[27] 苏凯, 孙小婷, 王茵然, 等. 基于GIS与RS的北方防沙屏障带生态系统格局演变[J]. 农业机械学报, 2020, 51(09): 226-236.

[28] Yue H, Liu Y, Li Y, et al. Eco-environmental quality assessment in China's 35 major cities based on remote sensing ecological index[J]. IEEE Access, 2019, 7: 51295-51311.

[29] 潘竟虎, 董磊磊. 2001-2010年疏勒河流域生态系统质量综合评价[J]. 应用生态学报, 2016, 27(9): 2907-2915.

[30] 徐涵秋. 区域生态环境变化的遥感评价指数[J]. 中国环境科学, 2013, 33(05): 889-897.

[31] 李婷婷, 马超, 郭增长. 基于RSEI模型的贺兰山长时序生态质量评价及影响因素分析[J]. 生态学杂志, 2021, 40(04): 1154-1165.

[32] 农兰萍, 王金亮. 基于RSEI模型的昆明市生态环境质量动态监测[J]. 生态学杂志, 2020, 39(06): 2042-2050.

[33] Jiang F, Zhang Y, Li J, et al. Research on remote sensing ecological environmental assessment method optimized by regional scale[J]. Environmental Science and Pollution Research, 2021, 28(48): 68174-68187.

[34] 王渊, 赵宇豪, 吴健生. 基于Google Earth Engine云计算的城市群生态质量长时序动态监测——以粤港澳大湾区为例[J]. 生态学报, 2020, 40(23): 8461-8473.

[35] 杨泽康, 田佳, 李万源, 等. 黄河流域生态环境质量的时空格局与演变趋势[J]. 生态学报, 2021, 41(19): 7627-7636.

[36] Huang H, Chen W, Zhang Y, et al. Analysis of ecological quality in Lhasa Metropolitan Area during 1990–2017 based on remote sensing and Google Earth Engine platform[J]. Journal of Geographical Sciences, 2021, 31(2): 265-280.

[37] MA (Millennium Ecosystem Assessment). Ecosystem and human well-being[M]. Washington DC: Island Press, 2005: 27-28.

[38] Costanza R, D’Arge R, de Groot R, et al. The value of the world’s ecosystem services and natural capital[J]. Nature: International weekly journal of science, 1997, 387(6630): 253-260.

[39] Villa F, Bagstad K J, Johnson G W, et al. Scientific instruments for climate change adaptation: estimating and optimizing the efficiency of ecosystem service provision[J]. Economia Agraria Y Recursos Naturales, 2011, 11(1).

[40] Sherrouse B C, Semmens D J. Social Values for Ecosystem Services, Version 3.0(SolVES 3.0): Documentation and User Manual[R]. Reston, Virginia：U.S. Geological Survey, 2015.

[41] Sharp R, Tallis H T, Ricketts T, et al. InVEST+ VERSION+ User's Guide[R]. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy，and World Wildlife Fund, 2016.

[42] 侯红艳, 戴尔阜, 张明庆. InVEST模型应用研究进展[J]. 首都师范大学学报(自然科学版), 2018, 4.

[43] Terrado M, Acuna V, Ennaanay D, et al. Impact of climate extremes on hydrological ecosystem services in a heavily humanized Mediterranean basin[J]. Ecological Indicators, 2014, 37(1): 199-209.

[44] 杨芝歌, 周彬, 余新晓, 等. 北京山区生物多样性分析与碳储量评估[J]. 水土保持通报， 2012(3): 42-46.

[45] 周彬, 余新晓, 陈丽华, 等. 基于InVEST模型的北京山区土壤侵蚀模拟[J]. 水土保持研究， 2010(6): 9-13.

[46] Aneseyee A B, Noszczyk T, Elias T E. The InVEST habitat quality model associated with land use/cover changes: Aqualitative case study of the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia. Remote Sensing [J]. 2020, 12(7): 1103.

[47] da Cunha E R, Santos C A G, da Silva R M, et al. Assessment of current and future land use/cover changes in soil erosion in the Rio da Prata basin (Brazil)[J]. The Science of the total environment, 2021, 818: 151811.

[48] He B, Chang J X, Guo A J, et al. Assessment of river basin habitat quality and its relationship with disturbance factors: A case study of the Tarim River Basin in Northwest China[J]. Journal of Arid Land, 2022, 14(02): 167-185.

[49] Shi D, Shi Y, Wu Q, et al. Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing[J]. Remote Sensing, 2021, 13: 3540.

[50] Shi D, Shi Y, Wu Q, et al. Multidimensional Assessment of Food Provisioning Ecosystem Services Using Remote Sensing and Agricultural Statistics[J]. Remote Sensing, 2020, 12(23): 3955.

[51] Qiao W, Peng H, Feng Z, er al. Integrated Monitoring and Assessment Framework of Regional Ecosystem under the Global Climate Change Background[J]. 2014, 2014.

[52] 侯鹏, 翟俊, 曹巍, 等. 国家重点生态功能区生态状况变化与保护成效评估——以海南岛中部山区国家重点生态功能区为例[J]. 地理学报, 2018, 73(03): 429-441.

[53] 刘纪远, 邵全琴, 樊江文. 三江源区草地生态系统综合评估指标体系[J]. 2009, 28(3): 273-283.

[54] Wang H , Hou P , Jiang J , et al. Ecosystem Health Assessment of Shennongjia National Park, China[J]. Sustainability, 2020, 12(18): 7672.

[55] Li P, Agusdinata D B, Suditha P H, et al. Ecosystem services and trade-offs: implications for land dynamics and sustainable livelihoods in Northern Lombok, Indonesia[J]. Environment Development and Sustainability, 2021, 23(3): 4321-4341.

[56] Liang Y J, Hashimoto S, Liu L J. Integrated assessment of land-use/land-cover dynamics on carbon storage services in the Loess Plateau of China from 1995 to 2050[J]. Ecological Indicators, 2021, 120: 106939.

[57] Pan Z Z, He J H, Liu D F, et al. Ecosystem health assessment based on ecological integrity and ecosystem services demand in the Middle Reaches of the Yangtze River Economic Belt, China[J]. Science of the Total Environment, 2021, 774: 144837.

[58] 孙滨峰. 东北森林带生态系统格局、质量、服务功能和胁迫十年变化研究(2000-2010)[D]. 北京:中国科学院大学, 2015.

[59] 孙传谆, 甄霖, 王超, 等. 天然林资源保护一期工程生态成效评估——以甘肃小陇山地区为例[J]. 地理科学进展, 2017, 36(06): 732-740.

[60] 张蕾. 基于“格局—质量—服务”视角的生态用地时空变化测度及空间布局优化[D]. 武汉:武汉大学, 2017.

[61] 李营, 陈云浩, 陈辉, 等. 小尺度潮间带生态系统遥感综合评估方法构建[J]. 中国环境科学, 2018, 38(12): 4661-4668.

[62] 张桐华. 基于多源遥感数据的黄河流域干旱特征分析[D]. 郑州:华北水利水电大学, 2020.

[63] 张振东, 常军. 2001—2018年黄河流域植被NPP的时空分异及生态经济协调性分析[J]. 华中农业大学学报, 2021, 40(02): 166-177.

[64] 刘玉斌, 李宝泉, 王玉珏, 等. 基于生态系统服务价值的莱州湾-黄河三角洲海岸带区域生态连通性评价[J]. 生态学报, 2019, 39(20): 7514-7524.

[65] 刘耕源, 杨青, 黄俊勇. 黄河流域近十五年生态系统服务价值变化特征及影响因素研究[J]. 中国环境管理, 2020, 12(03): 90-97.

[66] 王尧, 陈睿山, 夏子龙, 等. 黄河流域生态系统服务价值变化评估及生态地质调查建议[J]. 地质通报, 2020, 39(10): 1650-1662.

[67] 杨洁, 谢保鹏, 张德罡. 基于InVEST模型的黄河流域产水量时空变化及其对降水和土地利用变化的响应[J]. 应用生态学报, 2020, 31(08): 2731-2739.

[68] 杨洁, 谢保鹏, 张德罡. 基于InVEST和CA-Markov模型的黄河流域碳储量时空变化研究[J]. 中国生态农业学报(中英文), 2021, 29(06): 1018-1029.

[69] Liu C L, Li W L, Xu J, et al. Global trends and characteristics of ecological security research in the early 21st century: A literature review and bibliometric analysis[J]. Ecological Indicators, 2022, 137: 108734.

[70] 傅伯杰, 刘世梁, 马克明. 生态系统综合评价的内容与方法[J]. 生态学报, 2001(11): 1885-1892.

[71] 樊杰, 王亚飞, 王怡轩. 基于地理单元的区域高质量发展研究——兼论黄河流域同长江流域发展的条件差异及重点[J]. 经济地理, 2020, 40(1): 1-11.

[72] 王金南. 黄河流域生态保护和高质量发展战略思考[J]. 环境保护, 2020(S1): 18-21

[73] 卞正富, 于昊辰, 侯竟, 等. 西部重点煤矿区土地退化的影响因素及其评估[J]. 煤炭学报, 2020, 45(1): 338-350.

[74] 侯竟, 于昊辰, 牟守国, 等. 中国西部煤矿区土地退化时空特征及其影响因素研究[J]. 煤炭科学技术, 2020, 48(11): 206-216.

[75] 刘纪远, 宁佳, 匡文慧, 等. 2010-2015年中国土地利用变化的时空格局与新特征[J]. 地理学报, 2018, 73(05): 789-802.

[76] Ning J, Liu J Y, Kuang W H, et al. patiotemporalpattens and characteristics of land-use change in China during 2010-2015.Journal of Geographical Sciences, 2018, 28(5): 547-562.

[77] 梁加乐, 陈万旭, 李江风, 等. 黄河流域景观破碎化时空特征及其成因探测[J]. 生态学报, 2022, 42(05): 1993-2009.

[78] Duan Z, Bastiaanssen W. First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure[J]. Remote Sensing of Environment, 2013, 131: 1-13.

[79] Allen R G, Pereira L S, Raes D, et al. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56[J]. Fao, Rome, 1998, 300(9): D05109.

[80] Wang L, Diao C Y, Xian G, Yin D M, Lu Y, Zou S Y, Erickson T A. A summary of the special issue on remote sensing of land change science with Google earth engine[J]. Remote Sensing of Environment, 2020, 248: 112002．

[81] Mateo G G, Gómez C L, Amorós L J, et al. Multitemporal cloud masking in the Google Earth Engine [J]. Remote Sensing, 2018, 10(7): 1079.

[82] 李营, 陈云浩, 陈辉, 等. 小尺度潮间带生态系统遥感综合评估方法构建[J]. 中国环境科学, 2018, 38(12): 4661-4668.

[83] 刘世梁, 武雪, 朱家蓠, 等. 耦合景观格局与生态系统服务的区域生态承载力评价[J]. 中国生态农业学报(中英文), 2019, 27(05): 694-704.

[84] 李慧杰, 牛香, 王兵, 等. 生态系统服务功能与景观格局耦合协调度研究——以武陵山区退耕还林工程为例[J]. 生态学报, 2020, 40(13): 4316-4326.

[85] 奥勇, 蒋嶺峰, 白召弟, 等. 基于格网GIS的黄河流域土地生态质量综合评价[J]. 干旱区地理, 2022, 45(01): 164-175.

[86] 陈优良, 邹文敏, 刘星根, 等. 东江源流域不同空间尺度景观格局对水质影响分析[J/OL]. 环境科学: 1-15[2022-04-09].

[87] 刘希朝, 李效顺, 蒋冬梅. 基于土地利用变化的黄河流域景观格局及生态风险评估[J]. 农业工程学报, 2021, 37(04): 265-274.

[88] 唐亦武, 佘济云, 胡彪, 等. 海口市林分碳密度与景观格局指数耦合研究[J]. 西北林学院学报, 2020, 35(06): 168-175.

[89] Xu H Q. A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing, 2008, 29: 4269-4276.

[90] 段四波, 茹晨, 李召良, 等. Landsat卫星热红外数据地表温度遥感反演研究进展[J]. 遥感学报, 2021, 25(08): 1591-1617.

[91] Pekel JF, Cottam A, Gorelick N, et al. High-resolution mapping of global surface water and its long-term changes[J]. Nature, 2016, 540: 418-422.

[92] 刘英, 魏嘉莉, 毕银丽, 等. 神东矿区生态系统服务功能评价[J]. 煤炭学报, 2021, 46(05): 1599-1613.

[93] Zhou W, Liu G, Pan J, et al. Distribution of available soil water capacity in China. Journal of Geographical Sciences, 2005, 15(1): 3-12.

[94] 章文波, 谢云, 刘宝元. 中国降雨侵蚀力空间变化特征[J]. 山地学报, 2003(01): 33-40.

[95] Williams J R, Jones C A, Dyke P T. The EPIC model and its application[C]//Proc. Int. Symp. on minimum data sets for agrotechnology transfer. 1984: 111-121.

[96] 牛丽楠, 邵全琴, 刘国波, 等. 六盘水市土壤侵蚀时空特征及影响因素分析[J]. 地球信息科学学报, 2019, 21(11): 1755-1767.

[97] 朱文博, 张静静, 崔耀平 ,等. 基于土地利用变化情景的生态系统碳储量评估——以太行山淇河流域为例[J]. 地理学报, 2019, 74(03): 446-459.

[98] 朱超, 赵淑清, 周德成. 1997-2006年中国城市建成区有机碳储量的估算[J]. 应用生态学报, 2012, 23(05): 1195-1202.

[99] Yu G R, Li X R, Wang Q F, et al. Carbon Storage and Its Spatial Pattern of Terrestrial Ecosystem in China[J]. Journal of Resources and Ecology, 2010, 1(2): 97-109.

[100] ALAM S A, STARR M, CLARK B J F. Tree biomass and soil organic carbon densities across the Sudanese woodland savannah: A regional carbon sequestration study[J]. Journalof Arid Environments, 2013, 89: 67-76.

[101] GIARDINA C P, RYAN M G. Evidence that decomposition rates of organic carbon in mineral soil do notvary with temperature[J]. Nature, 2000, 404(6780): 61-85.

[102] 陈光水, 杨玉盛, 刘乐中, 等. 森林地下碳分配(TBCA)研究进展[J]. 亚热带资源与环境学报, 2007, 2(1): 34-42.

[103] DELANEY M, BROWN S, LUGO AE, et al. The quantity and turnover of deadwood in permanent forest plots in six life zones of Venezuela1[J]. Biotropica, 1998, 30(1): 2-11.

[104] 童昀, 马勇, 刘海猛. COVID-19疫情对中国城市人口迁徙的短期影响及城市恢复力评价[J]. 地理学报, 2020, 75(11): 2505-2520.

[105] 王劲峰, 徐成东. 地理探测器:原理与展望[J]. 地理学报, 2017, 72(01): 116-134.

[106] Fotheringham A S, Brunsdon C, Charlton M. Geographically weighted regression: The analysis of spatially vary-ing relationships[M]. New York: Wiley, 2022.

[107] 田鹏, 穆兴民, 赵广举, 等. 近549年来黄河天然径流量时间变化特征研究[J]. 水土保持学报, 2020, 34(06): 65-69.

[108] 吕乐婷, 任甜甜, 李赛赛, 等. 基于InVEST模型的大连市产水量时空变化分析[J]. 水土保持通报, 2019, 39(04): 144-150+157.

[109] 王秀明, 刘谞承, 龙颖贤, 等. 基于改进的InVEST模型的韶关市生态系统服务功能时空变化特征及影响因素[J]. 水土保持研究, 2020, 27(05): 381-388.

[110] 穆兴民, 赵广举, 高鹏, 等. 黄河未来输沙量态势及其适用性对策[J]. 水土保持通报, 2020, 40(05): 328-332.

[111] 方露露, 许德华, 王伦澈, 等. 长江、黄河流域生态系统服务变化及权衡协同关系研究[J]. 地理研究, 2021, 40(03): 821-838.

[112] 刘晓娜, 裴厦, 陈龙, 等. 基于InVEST模型的门头沟区生态系统土壤保持功能研究[J]. 水土保持研究, 2018, 25(06): 168-176.

[113] 杨东阳, 张骞, 苗长虹, 等. 黄河流域省区生态系统服务价值时空演变研究[J]. 黄河文明与可持续发展, 2021(01): 88-101.

[114] 付文静, 黄珺嫦, 汪松. 河南省沿黄区域土地利用变化多情景模拟及生态系统服务价值评估[J]. 河南科学, 2021, 39(12): 1994-2006.

[115] 刘凤兰. 山西省生态系统服务价值动态变化及演变趋势[J]. 国土资源科技管理, 2013, 30(05): 1-6.

[116] 张慧, 秦奇, 李艳红. 山西省土地利用变化对生态系统服务价值的影响研究[C]//. 中国地理学会2012年学术年会学术论文摘要集, 2012: 208.

[117] 王思远, 王光谦, 陈志祥. 黄河流域生态环境综合评价及其演变[J]. 山地学报, 2004(02): 133-139.

[118] 程永生, 张德元, 赵梦婵. 黄河流域生态保护和高质量发展的时空演变与驱动因素[J]. 经济体制改革, 2021(05): 61-69.

[119] 刘国彬, 上官周平, 姚文艺, 等.黄土高原生态工程的生态成效[J]. 中国科学院院刊, 2017, 32(1): 11-19.

[120] 韩静, 芮旸, 杨坤, 等. 基于地理探测器和GWR模型的中国重点镇布局定量归因[J]. 地理科学进展, 2020, 39(10): 1687-1697.

[121] 戴尔阜, 王亚慧. 横断山区产水服务空间异质性及归因分析[J]. 地理学报, 2020, 75(03): 607-619.

[122] 柳冬青, 巩杰, 张金茜, 等. 甘肃白龙江流域生态系统土壤保持功能时空变异及其影响因子[J].水土保持研究, 2018, 25(04): 98-103.

[123] 葛美玲, 封志明. 中国人口分布的密度分级与重心曲线特征分析[J]. 地理学报, 2009, 64(2): 202-210.

[124] 杨才敏, 卫正新. 山西省的自然因素与水土流失[J]. 水土保持科技情报, 2000(02): 54-56.

[125] 温媛媛, 赵军, 王炎强, 等. 基于MOD16的山西省地表蒸散发时空变化特征分析[J]. 地理科学进展, 2020, 39(02): 255-264.

[126] 吴小影, 杨山, 尹上岗, 等. 基于GTWR模型的长三角地区城市建设用地时空动态特征及其驱动机理[J]. 长江流域资源与环境, 2021, 30(11): 2594-2606.

[127] 阮晓晗, 白一茹, 王幼奇, 等. 基于地理探测器和GIS的压砂地砾石空间异质性及其影响因素分析[J/OL]. 农业资源与环境学报: 1-11[2022-03-07].

[128] 柯碧钦, 何超, 杨璐, 等. 华北地区地表臭氧时空分布特征及驱动因子研究[J/OL]. 中国环境科学: 1-13[2022-03-30].

[129] Rahnama M R, Sabaghi Abkooh S. The effect of air pollutant and built environment criteria on unhealthy days in Mashhad, Iran: Using OLS regression [J]. Urban Climate, 2021, 37: 100836.

[130] 解晗, 同小娟, 李俊, 等. 2000-2018年黄河流域生长季NDVI、EVI变化及其对气候因子的响应[J/OL]. 生态学报, 2022(11): 1-14[2022-03-28].

[131] Szilagyii J, Crago R, Qualls R. A Calibration-Free Formulation of the Complementary Relationship of Evaporation for Continental-Scale Hydrology[J]. Journal of Geophysical Research. Atmospheres, 2017, 122(1): 264-278.

[132] 李燕, 张凯瑞. 黄土高原不同植被覆盖/土地利用等对蒸散发量的影响[J]. 人民黄河, 2021, 43(12): 68-73.

[133] 谷佳贺, 薛华柱, 董国涛, 等. 黄河流域NDVI/土地利用对蒸散发时空变化的影响[J]. 干旱区地理, 2021, 44(01): 158-167.

[134] 于娜. 毛乌素沙地生态系统服务时空格局及其驱动力分析[D]. 北京:北京林业大学, 2018.

[135] 秦燕芹. 提高宁夏中部干旱带退耕还林成活率和保存率的技术措施[J]. 绿色科技, 2016(05): 113-114.

[136] 裴志林, 杨勤科, 王春梅, 等. 黄河上游植被覆盖度空间分布特征及其影响因素[J]. 干旱区研究, 2019, 36(03): 546-555.

[137] 陈忠, 杨志静, 宋海英. 黄河流域水土保持植被建设对策[C]//2021第九届中国水生态大会论文集, 2021: 77-81.

[138] 李晶晶, 苏鹏飞, 张建国. 黄河流域生态保护和高质量发展规划区水土流失特征与防治对策[J]. 水土保持通报, 2021, 41(05): 238-243+254+373.

[139] 刘永峰, 周子俊, 葛雷, 等. 黄河源区生态系统水源涵养功能对生态保护与修复工程响应[C]//2021第九届中国水生态大会论文集, 2021: 410-421.

[140] 毛爱华, 毛红霞, 李璇, 等.甘南黄河上游水源涵养区建设研究[J]. 资源节约与环保, 2021, (06): 37-40.

[141] 姜珊, 赵勇, 尚毅梓, 等. 中国煤炭基地水与能源协同发展评估[J]. 水电能源科学, 2016, 34(11): 40-43+67.

[142] 本刊. 山西5年治理水土流失面积1.8万余平方公里[J]. 山西水土保持科技, 2021(02): 49.

[143] 侯娟. 基于主成分分析及TOPSIS模型的山西省土地利用绩效评价研究[D]. 哈尔滨:哈尔滨师范大学, 2020.

[144] 杨静静. 煤炭资源区域产能优化配置研究[D]. 北京:中国地质大学(北京), 2017.

[145] 王春红, 郝彬彬, 孟玉双. 神东煤炭基地煤炭资源开发与区域环境协调发展[J]. 河北工程大学学报(社会科学版), 2009, 26(01): 19-20+61.

[146] 刘焱序. 陕北能源开发区生态风险与生态安全时空分析[D]. 西安:陕西师范大学, 2013.

[147] 樊华. 基于生态足迹理论的陕北生态环境可持续发展研究[D]. 北京:北京林业大学, 2010.

[148] 毕银丽, 郭晨, 肖礼, 等. 微生物复垦后土壤有机碳组分及其高光谱敏感性识别效应[J]. 煤炭学报, 2020, 45(12): 4170-4177．

[149] 闫军. 宁夏宁东能源化工基地矿区土地生态环境研究[D]. 北京:中国地质大学(北京), 2016.

[150] 蔡美峰, 吴允权, 李鹏, 等. 宁夏地区煤炭资源绿色开发现状与思路[J]. 工程科学学报, 2022, 44(01): 1-10.

[151] 范立民, 孙魁, 李成, 等. 西北大型煤炭基地地下水监测背景、思路及方法[J]. 煤炭学报, 2020, 45(01): 317-329.