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Evaluation and Prediction of Habitat Quality in Inner Mongolia Based on CA-Markov Model and Improved Remote Sensing Ecological Index
ZHAOYueji, JIANGHuyuan, ZHANGYanbo, LIDan, XIAZeyu
Chin Agric Sci Bull ›› 2026, Vol. 42 ›› Issue (2) : 171-183.
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Abbreviation (ISO4): Chin Agric Sci Bull
Editor in chief: Yulong YIN
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Evaluation and Prediction of Habitat Quality in Inner Mongolia Based on CA-Markov Model and Improved Remote Sensing Ecological Index
Inner Mongolia is located in the upper reaches of the North China region, and its ecological environment is sensitive and fragile. In recent years, due to climate change and rapid economic growth, ecological environment problems have become increasingly prominent. Conducting ecological environment quality assessment and trend prediction in Inner Mongolia is of great significance for guiding ecological environment protection work. This article is based on the GEE platform, introducing aerosols (AOD) and desertification difference index (DDI), and constructing an improved remote sensing ecological index ARSEI to dynamically monitor and predict the ecological environment quality of Inner Mongolia from 2000 to 2023. The spatial autocorrelation of Inner Mongolia's ecological environment quality is discussed using spatial autocorrelation, and the CA-Markov model is used to predict the future ecological environment quality of Inner Mongolia. The results show that: (1) this article introduces the aerosol AOD and desertification difference index DDI to construct the ARSEI index, with a PC1 contribution of over 87%, which can better concentrate the characteristics of various ecological indicators, with little difference from RSEI, and the grading results are basically consistent. It can more accurately evaluate the ecological environment quality of Inner Mongolia and has strong applicability. (2) The ecological environment quality in Inner Mongolia from 2000 to 2023 was mainly poor and moderate, showing a decreasing trend from east to west in spatial distribution. The Inner Mongolia region experienced severe degradation from 2000 to 2005, with a degradation area accounting for 21.18% and an improvement area accounting for 8.11%. Since then, the ecological environment quality has gradually improved. (3) The global Moran index of Inner Mongolia for six years has been above 0.606, and the spatial agglomeration characteristics within the region are obvious, mainly distributed in high-high and low-low patterns. The overall trend of improving the ecological environment quality center of each level in spatial distribution is from east to west and from north to south. (4) The ARSEI prediction indicates that the ecological quality deterioration in central and western Inner Mongolia is slightly higher than improvement potential in the future. Therefore, it is crucial to prioritize ecological restoration projects in the ecologically fragile areas of this region.
Inner Mongolia / improved remote sensing ecological index / habitat quality / spatial autocorrelation / CA-Markov model
| [1] |
肖鹏, 秦富仓, 杨振奇, 等. 黄河流域内蒙古段生态空间的功能评价及管制分区研究[J]. 西南林业大学学报(自然科学), 2025, 45(3):93-101.
|
| [2] |
孟庆帅, 巩钰, 刘小燕, 等. 水资源-社会经济-生态环境耦合协调度关系研究及预测——以黄河流域内蒙古段为例[J]. 干旱区研究, 2025, 42(4):682-694.
随着经济和社会的高速发展，人类活动同自然环境之间的竞争显著增强，在社会发展与环境保护之间如何抉择成为重要问题。首先，本文构建水资源-社会经济-生态环境复合系统耦合协调度模型，明晰黄河流域内蒙古段1998—2022年系统协调发展状况，其次，利用改进LSTM（Long Short-Term Merory）模型基于四种综合调控方案对系统未来5 a发展情况进行预测。结果表明：（1） 黄河流域内蒙古段各盟市的水资源、社会经济、生态环境子系统评价指数分布区间分别为[0.47~0.57]、[0.47~0.87]、[0.42~0.58]，表明区域水资源、生态环境子系统整体协调状况处于中等水平，社会经济子系统虽相对较高但仍有发展空间。（2） 黄河流域内蒙古段各盟市水资源-社会经济-生态环境系统耦合协调度发展在0.67~0.80之间呈逐步上升状态，整体变化幅度为19%。（3） 通过对未来情景预测发现，阿拉善、巴彦淖尔与乌兰察布三盟市采用水资源-社会经济联合调控改善力度最大，乌海市采用社会经济-生态环境联合改善力度最大，包头市、呼和浩特市与鄂尔多斯市采用水资源-生态环境联合调控改善力度最大。
|
| [3] |
赵娜, 王冰, 王子昊, 等. 基于遥感生态指数的内蒙古生态环境质量时空演化及生态管理分区[J]. 环境科学, 2025, 46(7):4499-4509.
|
| [4] |
|
| [5] |
张雨欣, 杜宜霖, 刘小茜, 等. 放牧活动对内蒙古草地生态系统的影响[J]. 生态学报, 2025, 45(10):4963-4974.
|
| [6] |
李文龙, 侯鹏珅, 徐芮, 等. 资源地区生态经济区划与产业功能布局研究——以内蒙古为例[J]. 生态经济, 2025, 41(4):38-48.
|
| [7] |
国务院关于《内蒙古自治区国土空间规划(2021—2035年)》的批复[EB/OL].(2023-12-21).https://www.gov.cn/zhengce/zhengceku/202312/content_6921637.htm.
|
| [8] |
刘峥延, 毛显强, 江河. “十四五”时期生态环境保护重点方向和任务研究[J]. 中国环境管理, 2019, 11(3):40-45.
|
| [9] |
颜梅春, 王元超. 区域生态环境质量评价研究进展与展望[J]. 生态环境学报, 2012, 21(10):1781-1788.
|
| [10] |
|
| [11] |
. The monthly standardized precipitation evapotranspiration index (SPEI) can\nbe used to monitor and assess drought characteristics with 1-month or\nlonger drought duration. Based on data from 1961 to 2018 at 427\nmeteorological stations across mainland China, we developed a daily SPEI\ndataset to overcome the shortcoming of the coarse temporal scale of monthly\nSPEI. Our dataset not only can be used to identify the start and end dates\nof drought events, but also can be used to investigate the meteorological,\nagricultural, hydrological, and socioeconomic droughts with a different timescales. In the present study, the SPEI data with 3-month (about 90 d)\ntimescale were taken as a demonstration example to analyze spatial distribution\nand temporal changes in drought conditions for mainland China. The SPEI\ndata with a 3-month (about 90 d) timescale showed no obvious intensifying\ntrends in terms of severity, duration, and frequency of drought events from\n1961 to 2018. Our drought dataset serves as a unique resource with daily\nresolution to a variety of research communities including meteorology,\ngeography, and natural hazard studies. The daily SPEI dataset developed is\nfree, open, and publicly available from this study. The dataset\nwith daily SPEI is publicly available via the figshare portal (Wang et al.,\n2020c), with https://doi.org/10.6084/m9.figshare.12568280.Highlights. A multi-scale daily SPEI dataset was developed across mainland China\nfrom 1961 to 2018.\nThe daily SPEI dataset can be used to identify the start and end days of the\ndrought event.\nThe developed daily SPEI dataset in this study is free, open, and\npublicly available.
|
| [12] |
刘恒, 汤弟伟, 宋鄂平, 等. 2000—2015年武陵山区植被净初级生产力的时空动态特征及其驱动因子[J]. 水土保持研究, 2020, 27(6):218-225.
|
| [13] |
|
| [14] |
刘子源, 周德成, 郝璐, 等. 基于3种遥感指数的2000—2020年尼泊尔地区植被生长动态的对比[J]. 环境科学, 2024, 45(10):5924-5930.
|
| [15] |
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(5):889-897.
|
| [16] |
田智慧, 尹传鑫, 王晓蕾. 鄱阳湖流域生态环境动态评估及驱动因子分析[J]. 环境科学, 2023, 44(2):816-827.
|
| [17] |
杨泽康, 田佳, 李万源, 等. 黄河流域生态环境质量时空格局与演变趋势[J]. 生态学报, 2021, 41(19):7627-7636.
|
| [18] |
汪孝贤, 张秀霞, 李旺平, 等. 基于遥感生态指数(RSEI)改进模型的祁连山国家级自然保护区生态环境质量评价[J]. 生态与农村环境学报, 2023, 39(7):853-863.
|
| [19] |
张伟, 杜培军, 郭山川, 等. 改进型遥感生态指数及干旱区生态环境评价[J]. 遥感学报, 2023, 27(2):299-317.
|
| [20] |
张静, 杨丽萍, 贡恩军, 等. 基于谷歌地球引擎和改进型遥感生态指数的西安市生态环境质量动态监测[J]. 生态学报, 2023, 43(5):2114-2127.
|
| [21] |
张靓涵, 张未, 赵安周, 等. 融合PM2.5浓度遥感生态指数的峰峰矿区生态环境质量时空特征及驱动力分析[J]. 环境科学, 2024, 45(10):5900-5911.
|
| [22] |
Over the last few years, under the combined effects of climate change and human factors, the ecological environment of coal mining areas has undergone tremendous changes. Therefore, the rapid and accurate quantitative assessments of the temporal and spatial evolution of the ecological environment quality is of great significance for the ecological restoration and development planning of coal mining areas. This study applied the ecological environment index after topographic correction to improve the remote sensing ecological index (RSEI). Based on a series of Landsat images, the ecological environment quality of Yangquan Coal Mine in Shanxi Province from 1987 to 2020 was monitored and evaluated by an improved remote sensing ecological index. The results show that after topographic correction, the topographic effect of the remote sensing ecological index was greatly reduced, and its practicability was improved. From 1987 to 2020, the ecological environment quality of Yangquan Coal Mine was improved, and the mean of the RSEI increased from 0.4294 to 0.6379. The ecological environment quality of the six coal mines in the study area was improved. Among the six coal gangue dumps, the ecological environmental quality of D1, D2, D3, and D4 has improved, and the ecological environment quality of D5 and D6 worsened. The percentages of improved, unchanged, and degraded ecological environment quality in the entire coal mining area were 77.08%, 0.99%, and 21.93%, respectively. The global Moran’s index was between 0.7929 and 0.9057, and it was shown that there was a strong positive correlation between the ecological environmental qualities of the study area, and that its spatial distribution was clustered rather than random. The LISA cluster map showed that the aggregation and dispersion degree of ecological environment quality was mainly high–high clustering and low–low clustering over the whole stage. During the study period, temperature and precipitation had limited impacts on the ecological environment quality of Yangquan Coal Mine, while the coal mining activities and urbanization construction seriously affected the local ecological environment quality and the implementation of ecological restoration policies, regulations, and measures was the main reason for the improvement of the ecological environment quality.
|
| [23] |
Lanzhou City is located in the semi-arid region of northwest China, which experiences serious desertification. Moreover, the high intensity of land development, with the accelerated industrialization and urbanization, causes increasingly aggravated conflict between humans and the environment. Exploring the response of the ecological environment quality to the natural environment and anthropogenic activities is important to protect the sustainable development of urban economic construction and the environment. Based on the Google Earth Engine (GEE) platform, this paper constructed a modified Remote Sensing Ecological Index (MRSEI) model which could reflect the ecological environment quality by integrating the desertification index (DI) into the Remote Sensing Ecological index (RSEI) model. This paper explores the spatiotemporal variation in the environmental quality from 2000 to 2020 in Lanzhou, China, and analyzes the natural and anthropogenic factors affecting the environment quality in terms of temperature, precipitation, gross domestic product (GDP), land use, night lighting, and population. The results showed that the mean value of MRSEI ranged from 0.254 to 0.400. The area undergoing fast growth in ecological quality was in the northwestern part of Lanzhou, and the area of decrease was in the central part. Various factors have different degrees of influence on the ecosystem, with temperature, precipitation, and land use having a greater impact, and GDP and population having a limited impact. Precipitation and temperature showed a strong impact when interacting with other factors, demonstrating that precipitation and temperature were also key factors affecting MRSEI. Overall, climate change and the implementation of ecological restoration projects have led to an improvement in the quality of the ecological environment in Lanzhou. This study provides a reference for understanding the spatiotemporal changes in the ecological environment in semi-arid Lanzhou and is conducive to formulating proper protection strategies.
|
| [24] |
王经宇, 杨丽萍, 王美, 等. 陕北黄土高原生态环境质量时空变化监测及驱动力分析[J]. 环境科学, 2025, 46(7):4522-4533.
|
| [25] |
Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.
|
| [26] |
|
| [27] |
孙桂凯, 王国帅, 魏义熊, 等. 基于改进遥感生态指数的岩溶山区生态环境质量评价——以澄碧河流域为例[J]. 水土保持通报, 2021, 41(2):230-239,353.
|
| [28] |
|
| [29] |
王永祥, 徐园园, 杨佳嘉, 等. 基于Landsat的重庆市生态环境质量动态监测及其时空格局演变分析[J]. 生态学报, 2023, 43(15):6278-6292.
|
| [30] |
张京新, 谷雨鑫, 沈佳琦, 等. 黄河流域生态环境质量时空变化及驱动因素分析[J]. 环境科学, 2025, 46(2):956-971.
|
| [31] |
|
| [32] |
徐智超, 刘华民, 韩鹏, 等. 内蒙古生态安全时空演变特征及驱动力[J]. 生态学报, 2021, 41(11):4354-4366.
|
| [33] |
张树礼, 李现华, 郭二果, 等. 我国北方重要生态安全屏障建设的战略举措[J]. 内蒙古大学学报(自然科学版), 2024, 55(6):561-569.
|
| [34] |
万虹麟, 霍飞, 牛玉芬, 等. 顾及PM2.5浓度遥感生态指数模型的沧州市区生态环境质量动态监测分析[J]. 地球物理学进展, 2021, 36(3):953-960.
|
| [35] |
王旺盛. 我国北方重要生态安全屏障建设的战略思考[M]. 呼和浩特: 内蒙古人民出版社,2024:15-39.
|
| [36] |
焦晓萌, 张兵, 高阳, 等. 利用改进型遥感生态指数的岩溶区生态环境质量评价[J]. 测绘通报, 2024(4):54-60.
本文选取绿度(NDMVI)、湿度(Wet)、石漠化(RDI)和热度(LST)4个指数,构建了改进型遥感生态指数(IRSEI),以实现县域岩溶地区的生态环境质量评价。基于2003与2023年的县域尺度遥感影像与其他地理信息数据,通过若干方面定量对比IRSEI与RSEI指数。结果表明,IRSEI包含了更丰富的山地植被与石漠化状况信息,能够更好地表征和评价岩溶地区生态环境质量,适合作为岩溶地区生态环境质量评价的定量指标。研究区在2003—2023年间,生态环境质量改善和退化情况均有发生,但生态环境质量总体下降了7.46%,生态等级由良变为中等。从空间分布来看,罗平县研究区生态质量差的区域主要分布在北部,生态环境质量优的区域主要分布在中部。
|
| [37] |
刘晓茜, 解云虎, 海春兴. 基于荒漠化差值指数的荒漠绿洲过渡带土地沙化研究[J]. 内蒙古师范大学学报(自然科学汉文版), 2020, 49(1):59-67.
|
| [38] |
叶博文, 孙标, 史小红, 等. 巴彦淖尔市土地利用变化及其对生态环境质量的影响[J]. 环境科学, 2024, 45(12):6899-6909.
|
| [39] |
何孙鹏, 甄江红, 贺静, 等. 内蒙古生态环境质量评价与预警研究[J]. 阴山学刊(自然科学版), 2015, 29(2):46-51.
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