基于多源遥感的塔克拉玛干沙漠边缘生态屏障植被演替格局
Patterns of Vegetation Succession in the Ecological Barrier along the Taklamakan Desert Based on Multi-Source Remote Sensing
摘要: 塔克拉玛干沙漠是中国最大的流动性沙漠，评估其边缘“绿色长城”生态屏障的长期演替格局，对极端干旱区生态工程的可持续管理具有重要意义。本研究以塔克拉玛干沙漠边缘生态屏障为对象，构建1990~2024年多源归一化植被指数(NDVI)数据集，以联合国防治荒漠化公约(UNCCD)推荐的土地生产力退化监测框架为核心方法，按工程建设进程分为四个阶段识别绿化核心区与退化区的时空格局。结果表明：(1) 1990~2024年研究区植被演替呈显著四阶段特征，整体经历了由“块”到“片”再到“带”的空间扩张过程，2017年后扩张明显放缓。(2) 绿化成效具有显著空间非均质性，北缘和西缘优于南缘和东南缘。(3) 国家生态工程政策与区域水资源禀赋共同塑造了植被演替的时空格局。本研究旨在为客观评价“绿色长城”生态成效与优化后续治理策略提供遥感定量依据。
Abstract: The Taklamakan Desert is the largest mobile desert in China. Assessing the long-term succession patterns of the “Great Green Wall” ecological barrier along its margins is of great significance for the sustainable management of ecological projects in extremely arid regions. This study focuses on the ecological barrier along the Taklamakan Desert. A multi-source Normalized Difference Vegetation Index (NDVI) dataset spanning from 1990 to 2024 was constructed, and the land productivity degradation monitoring framework recommended by the United Nations Convention to Combat Desertification (UNCCD) was adopted as the core methodology. The study period was divided into four stages according to the progression of project construction to identify the spatiotemporal patterns of core greening areas and degraded areas. The results show that: (1) Vegetation succession in the study area from 1990 to 2024 exhibited distinct four-stage characteristics, undergoing an overall spatial expansion process from “patches” to “clusters” and further to “belts”, with expansion notably decelerating after 2017. (2) Greening effectiveness displayed significant spatial heterogeneity, with the northern and western margins outperforming the southern and southeastern margins. (3) National ecological project policies and regional water resource endowments jointly shaped the spatiotemporal patterns of vegetation succession. This study aims to provide a quantitative remote sensing basis for objectively evaluating the ecological effectiveness of the “Great Green Wall” and optimizing subsequent governance strategies.
文章引用：王籽福. 基于多源遥感的塔克拉玛干沙漠边缘生态屏障植被演替格局[J]. 自然科学, 2026, 14(3): 319-326. https://doi.org/10.12677/ojns.2026.143036

参考文献

[1] 王涛, 吴薇, 王熙章. 沙质荒漠化的遥感监测与评估——以中国北方沙质荒漠化区内的实践为例[J]. 第四纪研究, 1998(2): 108-118.
[2] 金凯, 王飞, 韩剑桥, 等. 1982-2015年中国气候变化和人类活动对植被NDVI变化的影响[J]. 地理学报, 2020, 75(5): 961-974.
[3] Peng, L., Wan, Y., Shi, H., Anwaier, A. and Shi, Q. (2023) Influence of Climate, Topography, and Hydrology on Vegetation Distribution Patterns-Oasis in the Taklamakan Desert Hinterland. Remote Sensing, 15, Article 5299. [Google Scholar] [CrossRef] 
[4] Ding, Y., Feng, Y., Chen, K. and Zhang, X. (2024) Analysis of Spatial and Temporal Changes in Vegetation Cover and Its Drivers in the Aksu River Basin, China. Scientific Reports, 14, Article No. 10165. [Google Scholar] [CrossRef] [PubMed]
[5] Sims, N., Newnham, G., England, J., Guerschman, J., Cox, S., et al. (2021) Good Practice Guidance. Sdg Indicator 15.3.1: Proportion of Land That Is Degraded over Total Land Area. Version 2.0.
[6] Reith, J., Ghazaryan, G., Muthoni, F. and Dubovyk, O. (2021) Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3. Remote Sensing, 13, Article 1754. [Google Scholar] [CrossRef] 
[7] Du, Z., Yu, L., Chen, X., Gao, B., Yang, J., Fu, H., et al. (2024) Land Use/cover and Land Degradation across the Eurasian Steppe: Dynamics, Patterns and Driving Factors. Science of the Total Environment, 909, Article 168593. [Google Scholar] [CrossRef] [PubMed]
[8] Zhao, L., Jia, K., Liu, X., Li, J. and Xia, M. (2023) Assessment of Land Degradation in Inner Mongolia between 2000 and 2020 Based on Remote Sensing Data. Geography and Sustainability, 4, 100-111. [Google Scholar] [CrossRef] 
[9] Didan, K. (2015) MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 [Data Set]. NASA Land Processes Distributed Active Archive Center. [Google Scholar] [CrossRef] 
[10] Earth Resources Observation and Science (EROS) Center (2020) Landsat 4-5 Thematic Mapper Level-2, Collection 2 [Dataset]. U.S. Geological Survey. [Google Scholar] [CrossRef] 
[11] Wang, D., Morton, D., Masek, J., Wu, A., Nagol, J., Xiong, X., et al. (2012) Impact of Sensor Degradation on the MODIS NDVI Time Series. Remote Sensing of Environment, 119, 55-61. [Google Scholar] [CrossRef] 
[12] Twedt, K.A., Xiong, X., Geng, X., Wilson, T. and Mu, Q. (2023) Impact of Satellite Orbit Drift on MODIS Earth Scene Observations Used in Calibration of the Reflective Solar Bands. Earth Observing Systems XXVIII, 12685, Article 1268500. [Google Scholar] [CrossRef] 
[13] Earth Resources Observation and Science (EROS) Center (2020) Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor Level-2, Collection 2 [Dataset]. U.S. Geological Survey. [Google Scholar] [CrossRef] 
[14] 陈亚宁, 李卫红, 徐海量, 等. 塔里木河下游地下水位对植被的影响[J]. 地理学报, 2003(4): 542-549.
[15] 罗敏, 古丽·加帕尔, 郭浩, 等. 2000-2013年塔里木河流域生长季NDVI时空变化特征及其影响因素分析[J]. 自然资源学报, 2017, 32(1): 50-63.
[16] 陈亚宁, 徐长春, 杨余辉, 等. 新疆水文水资源变化及对区域气候变化的响应[J]. 地理学报, 2009, 64(11): 1331-1341.
[17] 何宝忠, 丁建丽, 张喆, 等. 新疆植被覆盖度趋势演变实验性分析[J]. 地理学报, 2016, 71(11): 1948-1966.
[18] 马森, 张代青, 张惠, 等. 塔里木河流域NDVI时空变化特征及其影响因素分析[J]. 高原山地气象研究, 2023, 43(4): 76-83.
[19] 孙帆, 王弋, 陈亚宁. 塔里木盆地荒漠-绿洲过渡带动态变化及其影响因素[J]. 生态学杂志, 2020, 39(10): 3397-3407.