Vegetation Dynamics and Potential Factors Driving Mechanisms in the Tarim River Basin
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摘要: 塔里木河流域气候极端干旱，探究该区域植被动态特征并定量评价潜在驱动因素的作用强度对维持生态系统功能、实现可持续发展至关重要.基于长时间序列NDVI数据集、气候数据、背景数据及土地利用数据，探究了塔里木河流域2000-2020年植被时空变化趋势和空间自相关性，并利用地理探测器定量评估了潜在驱动因素对NDVI变化的作用强度.结果发现：2000-2020年平均NDVI为0.159，52.63%的区域呈显著增长趋势，增长速率为0.02/10a，NDVI的全局莫兰指数呈波动上升趋势，表现为空间集聚性.土地利用转化、土壤类型、距人造地表距离是塔里木河流域植被变化的主要驱动因素，对NDVI变化的解释力分别为22.20%、8.57%、8.28%.降水是塔里木河流域北部的主导气候因素，气温在西部和南部对NDVI变化的解释力更强，距冰川和积雪的距离是影响NDVI变化不可忽略的因素.任意两因素的交互作用可以提高对NDVI变化的解释力，其中在流域尺度上土地利用转化∩土壤类型（q=29.44%）解释力最强，而在子流域尺度上，解释力最强的交互组合及强度存在差异.本文结果有助于提高对塔里木河流域NDVI变化机制的认识，为干旱半干旱地区生态保护提供科学依据.Abstract: Given the extreme aridity of the Tarim River basin, it is critical to explore the characteristics of vegetation dynamics in the region and to quantitatively evaluate the strength of potential drivers to maintain ecosystem function and achieve sustainable development. The spatial and temporal trends and spatial autocorrelation of vegetation cover in the Tarim River basin from 2000 to 2020 are explored, based on long time series NDVI datasets, climate data, background data and land use data, and the strength of potential driving factors on NDVI changes is quantitatively assessed, using geographical detector. It is found that the average NDVI from 2000 to 2020 was 0.159, and 52.63% of the area showed a significant growth trend with a growth rate of 0.02/10a, and the global Moran's index of NDVI showed a fluctuating upward trend and exhibited a spatial agglomeration. Land use conversion, soil type, and distance from the man-made surface were the main driving factors of vegetation change in the Tarim River basin, with explanatory power of 22.20%, 8.57%, and 8.28% for NDVI change, respectively. Precipitation is the dominant climatic factor in the northern part of the Tarim River basin, temperature has a stronger explanatory power for NDVI changes in the west and south, and distance to glaciers and snow is a factor affecting NDVI changes that cannot be ignored. The interaction of any two factors can improve the explanatory power of NDVI changes, among which the strongest explanatory power is found in land use conversion ∩ soil type (q=29.44%) at the watershed scale, while there are differences in the combination and intensity of the strongest explanatory interactions at the sub-basin scale. The results help to improve the understanding of NDVI change mechanisms in the Tarim River basin and provide a scientific basis for ecological conservation in arid and semi-arid regions.
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Key words:
- Tarim River basin /
- NDVI /
- geographical detector /
- driving factors /
- vegetation /
- climate change
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图 1 塔里木河流域地理位置及气候特征
Fig. 1. Geographical location and climatic characteristics of the Tarim River basin
图 2 多年平均NDVI空间分布格局
a.空间分布；b.各级NDVI在子流域的占比
Fig. 2. Spatial distribution pattern of multi-year average NDVI
图 3 2000-2020年塔里木河流域NDVI时空变化趋势
a.NDVI变化斜率；b.NDVI变化显著性；c.各子流域平均NDVI变化斜率；d.各子流域NDVI变化显著性占比
Fig. 3. Spatial and temporal trends of NDVI in the Tarim River basin from 2000 to 2020
图 4 2000-2020年塔里木河流域NDVI空间自相关特征
Fig. 4. Spatial autocorrelation characteristics of NDVI in the Tarim River basin from 2000 to 2020
图 5 塔里木河流域NDVI变化的因子探测结果
Fig. 5. Factor detection results of NDVI changes in the Tarim River basin
图 6 塔里木河流域NDVI变化的因子交互探测结果
“↑”和“↑↑”分别表示双因子增强和非线性增强
Fig. 6. Factor interaction detection results of NDVI changes in the Tarim River basin
表 1 数据来源及说明
Table 1. Data sources and descriptions
产品 数据 编码 时间分辨率 空间分辨率 来源 MOD13 A3 NDVI Y month 1 km https://www.earthdata.nasa.gov/ 降水 累计降水 X1 month 1 km http://www.tpdc.ac.cn 气温 平均气温 X2 month 1 km http://www.geodata.cn/ DEM 海拔 X3 / 90 m https://www.gscloud.cn/ DEM 坡度 X4 / 90 m 由DEM数据处理得到 DEM 坡向 X5 / 90 m 由DEM数据处理得到 土壤类型 土壤类型 X6 / 1 km http://www.ncdc.ac.cn/portal/ GLOBELAND30 距水体距离 X7 10 a 30 m 由土地利用转化数据处理得到 GLOBELAND30 距冰川和积雪距离 X8 10 a 30 m 由土地利用转化数据处理得到 GLOBELAND30 距人造地表距离 X9 10 a 30 m 由土地利用转化数据处理得到 GLOBELAND30 土地利用 X10 10 a 30 m http://www.globallandcover.com/ GLOBELAND30 土地利用转化 X11 10 a 30 m 由土地利用转化数据处理得到 
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表 2 两个解释变量之间的交互作用及交互影响
Table 2. Interaction between the two explanatory variables and interaction effects
交互结果 交互作用 q(X1∩X2) < Min (q(X1), q(X2)) 非线性减弱 Min (q(X1), q(X2)) < q(X1∩X2) < Max (q(X1),
q(X2))单因子非线性减弱 q(X1∩X2) > Max (q(X1), q(X2)) 双因子增强 q(X1∩X2)=q(X1)+q(X2) 独立 q(X1∩X2) > q(X1)+q(X2) 非线性增强
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表 3 各子流域典型因子交互作用结果
Table 3. Results of typical factor interactions in each sub-basin
流域 交互类型1 q(%) 交互类型2 q(%) 交互类型3 q(%) 塔里木河流域 土地利用转化∩土壤类型 29.44 土地利用转化∩海拔 29.16 土地利用转化∩降水 27.3 开孔河 土地利用转化∩降水 40.95 土地利用转化∩距冰川和积雪距离 38.4 土地利用转化∩土壤类型 37.09 渭干河 土地利用转化∩坡向 34.64 土地利用转化∩土壤类型 33.66 土地利用转化∩降水 33.09 塔里木河干流 土地利用转化∩坡向 46.12 土地利用转化∩距冰川和积雪距离 43.56 土地利用转化∩降水 42.6 阿克苏河 土地利用转化∩土壤类型 32.66 土地利用转化∩坡向 29.35 土地利用转化∩距冰川和积雪距离 27.53 喀什噶尔河 土地利用转化∩土壤类型 29.69 土地利用转化∩海拔 28.92 土地利用转化∩坡向 28.54 叶尔羌河 土地利用转化∩土壤类型 22.46 土地利用转化∩海拔 21.58 土地利用转化∩距冰川和积雪距离 20.55 和田河 土地利用转化∩海拔 36.02 土地利用转化∩气温 31.42 土地利用转化∩距人造地表距离 30.11 克里雅河 土地利用转化∩海拔 46.4 土地利用转化∩土壤类型 43.94 土地利用转化∩气温 42.12 车尔臣河 土地利用转化∩海拔 32.5 土地利用转化∩气温 29.91 土地利用转化∩土壤类型 26.04
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Chu, H. S., Venevsky, S., Wu, C., et al., 2019. NDVI-Based Vegetation Dynamics and Its Response to Climate Changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of the Total Environment, 650: 2051-2062. https://doi.org/10.1016/j.scitotenv.2018.09.115 Guo, M. D., 2021. Vegetation Autumn Phenology and its Responses Toenvironmental Changes in the Mid-High Latitudes of the Northern Hemisphere Based on Remote Sensing Data (Dissertation). University of Electronic Science and Technology of China, Chengdu (in Chinese with English abstract). Huang, S. Z., Zheng, X. D., Ma, L., et al., 2020. Quantitative Contribution of Climate Change and Human Activities to Vegetation Cover Variations Based on GA-SVM Model. Journal of Hydrology, 584: 124687. https://doi.org/10.1016/j.jhydrol.2020.124687 Jeong, S. J., Ho, C. H., Gim, H. J., et al., 2011. Phenology Shifts at Start vs. End of Growing Season in Temperate Vegetation over the Northern Hemisphere for the Period 1982-2008. Global Change Biology, 17(7): 2385-2399. https://doi.org/10.1111/j.1365-2486.2011.02397.x Jiang, N., Zhang, Q. Q., Zhang, S. C., et al., 2022. Spatial and Temporal Evolutions of Vegetation Coverage in the Tarim River Basin and Their Responses to Phenology. CATENA, 217: 106489. https://doi.org/10.1016/j.catena.2022.106489 Li, R. H., Chen, N. C., Zhang, X., et al., 2020. Quantitative Analysis of Agricultural Drought Propagation Process in the Yangtze River Basin by Using Cross Wavelet Analysis and Spatial Autocorrelation. Agricultural and Forest Meteorology, 280: 107809. https://doi.org/10.1016/j.agrformet.2019.107809 Li, X., Zhang, G. Z., Chen, Y. H., et al., 2022. Vegetation Cover Change and Driving Factors in the Agro-Pastoral Ecotone of Liaohe River Basin of China from 2010 to 2019. Transactions of the Chinese Society of Agricultural Engineering, 38(22): 63-72 (in Chinese with English abstract). Liu, C. L., Li, W. L., Wang, W. Y., et al., 2021a. Quantitative Spatial Analysis of Vegetation Dynamics and Potential Driving Factors in a Typical Alpine Region on the Northeastern Tibetan Plateau Using the Google Earth Engine. CATENA, 206: 105500. https://doi.org/10.1016/j.catena.2021.105500 Liu, C. X., Wu, X. L., Wang, L., 2019. Analysis on Land Ecological Security Change and Affect Factors Using RS and GWR in the Danjiangkou Reservoir Area, China. Applied Geography, 105: 1-14. https://doi.org/10.1016/j.apgeog.2019.02.009 Liu, Q. H., Liu, L. S., Zhang, Y. L., et al., 2021b. Identification of Impact Factors for Differentiated Patterns of NDVI Change in the Headwater Source Region of Brahmaputra and Indus, Southwestern Tibetan Plateau. Ecological Indicators, 125: 107604. https://doi.org/10.1016/j.ecolind.2021.107604 Liu, Y., Li, Y., Li, S. C., et al., 2015. Spatial and Temporal Patterns of Global NDVI Trends: Correlations with Climate and Human Factors. Remote Sensing, 7(10): 13233-13250. https://doi.org/10.3390/rs71013233 Meng, X. Y., Gao, X., Li, S. Y., et al., 2020. Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982-2015. Remote Sensing, 12(4): 603. https://doi.org/10.3390/rs12040603 Mu, B. H., Zhao, X., Wu, D. H., et al., 2021. Vegetation Cover Change and Its Attribution in China from 2001 to 2018. Remote Sensing, 13(3): 496. https://doi.org/10.3390/rs13030496 Mu, Y. R., Shen, W., 2022. Mathematical Problems in Engineering Landscape Ecological Security Assessment and Ecological Pattern Optimization of Inland River Basins in Arid Regions: A Case Study in Tarim River Basin. Mathematical Problems in Engineering, 2022: 9476860. https://doi.org/10.1155/2022/9476860 Nan, F. S., Li, Z. X., Zhang, X. P., et al., 2023. Particle Size Fractal Characteristics of Soils in Desert-Steppe Transition Zone along the Northern Bank of Yellow River Basin in Lanzhou. Earth Science, 48(3): 1195-1204 (in Chinese with English abstract). Nemani, R. R., Keeling, C. D., Hashimoto, H., et al., 2003. Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999. Science, 300(5625): 1560-1563. https://doi.org/10.1126/science.1082750 Peng, W. F., Kuang, T. T., Tao, S., 2019. Quantifying Influences of Natural Factors on Vegetation NDVI Changes Based on Geographical Detector in Sichuan, Western China. Journal of Cleaner Production, 233: 353-367. https://doi.org/10.1016/j.jclepro.2019.05.355 Ren, H. R., Shang, Y. J., Zhang, S., 2020. Measuring the Spatiotemporal Variations of Vegetation Net Primary Productivity in Inner Mongolia Using Spatial Autocorrelation. Ecological Indicators, 112: 106108. https://doi.org/10.1016/j.ecolind.2020.106108 Wang, B., Xu, G. C., Li, P., et al., 2020a. Vegetation Dynamics and Their Relationships with Climatic Factors in the Qinling Mountains of China. Ecological Indicators, 108: 105719. https://doi.org/10.1016/j.ecolind.2019.105719 Wang, C. Y., Wang, J. N., Cui, X., et al., 2021. Spatio-Temporal Change in Vegetation Patterns and Its Climatic Drivers in the Core Region of Three Parallel Rivers in Southeast Tibet. Geographical Research, 40(11): 3191-3207 (in Chinese with English abstract). doi: 10.11821/dlyj020201123 Wang, H., Liu, X. M., Zhao, C. Y., et al., 2021a. Spatial-Temporal Pattern Analysis of Landscape Ecological Risk Assessment Based on Land Use/Land Cover Change in Baishuijiang National Nature Reserve in Gansu Province, China. Ecological Indicators, 124: 107454. https://doi.org/10.1016/j.ecolind.2021.107454 Wang, J. F., Li, X. H., Christakos, G., et al., 2010. Geographical Detectors-Based Health Risk Assessment and Its Application in the Neural Tube Defects Study of the Heshun Region, China. International Journal of Geographical Information Science, 24(1): 107-127. https://doi.org/10.1080/13658810802443457 Wang, J. M., Ouyang, J. M., Zhang, M., 2020b. Spatial Distribution Characteristics of Soil and Vegetation in a Reclaimed Area in an Opencast Coalmine. CATENA, 195: 104773. https://doi.org/10.1016/j.catena.2020.104773 Wang, X., Huo, A. D., Lü, J. Q., et al., 2023. Dynamic Changes and Driving Factors of Vegetation Coverage in the Mainstream of Tarim River, China. Transactions of the Chinese Society of Agricultural Engineering, 39(8): 284-292 (in Chinese with English abstract). Wang, Y., Xia, T. T., Shataer, R., et al., 2021b. Analysis of Characteristics and Driving Factors of Land-Use Changes in the Tarim River Basin from 1990 to 2018. Sustainability, 13(18): 10263. https://doi.org/10.3390/su131810263 Wang, Y., Hao, L. N., Xu, Q., et al., 2023. Spatio-Temporal Variations of Vegetation Coverage and Its Geographical Factors Analysis on the Loess Plateau from 2001 to 2019. Acta Ecologica Sinica, 43(6): 2397-2407 (in Chinese with English abstract). Xu, L., Gao, G. Y., Wang, X. F., et al., 2023. Quantifying the Contributions of Climate Change and Human Activities to Vegetationgreening in the Drylands of Northern China. Acta Ecologica Sinica, 43(17): 7274-7283 (in Chinese with English abstract). Zhang, H., Xue, L. Q., Wei, G. H., et al., 2020. Assessing Vegetation Dynamics and Landscape Ecological Risk on the Mainstream of Tarim River, China. Water, 12(8): 2156. https://doi.org/10.3390/w12082156 Zhang, J., Chen, H. S., Fu, Z. Y., et al., 2021a. Effects of Vegetation Restoration on Soil Properties along an Elevation Gradient in the Karst Region of Southwest China. Agriculture, Ecosystems & Environment, 320: 107572. https://doi.org/10.1016/j.agee.2021.107572 Zhang, M., Wang, J. M., Li, S. J., 2019. Tempo-Spatial Changes and Main Anthropogenic Influence Factors of Vegetation Fractional Coverage in a Large-Scale Opencast Coal Mine Area from 1992 to 2015. Journal of Cleaner Production, 232: 940-952. https://doi.org/10.1016/j.jclepro.2019.05.334 Zhang, Q. F., Sun, C. J., Chen, Y. N., et al., 2022a. Recent Oasis Dynamics and Ecological Security in the Tarim River Basin, Central Asia. Sustainability, 14(6): 3372. https://doi.org/10.3390/su14063372 Zhang, Q., Yang, J. H., Duan, X. Y., et al., 2022b. The Eastward Expansion of the Climate Humidification Trend in Northwest China and the Synergistic Influences on the Circulation Mechanism. Climate Dynamics, 59(7): 2481-2497. https://doi.org/10.1007/s00382-022-06221-4 Zhang, Q., Yang, J. H., Wang, W., et al., 2021b. Climatic Warming and Humidification in the Arid Region of Northwest China: Multi-Scale Characteristics and Impacts on Ecological Vegetation. Journal of Meteorological Research, 35(1): 113-127. https://doi.org/10.1007/s13351-021-0105-3 Zhao, R. F., Chen, Y. N., Shi, P. J., et al., 2013. Land Use and Land Cover Change and Driving Mechanism in the Arid Inland River Basin: A Case Study of Tarim River, Xinjiang, China. Environmental Earth Sciences, 68(2): 591-604. https://doi.org/10.1007/s12665-012-1763-3 Zhao, Y., Li, Z. X., Li, Z. P., et al., 2023. Temporal and Spatial Variation of Wet Deposition of Nitrogen in the Source Region of the Yangtze River. Earth Science, 48(3): 1179-1194 (in Chinese with English abstract). Zhou, Y., Li, X. H., Liu, Y. S., 2020. Land Use Change and Driving Factors in Rural China during the Period 1995-2015. Land Use Policy, 99: 105048. https://doi.org/10.1016/j.landusepol.2020.105048 Zhu, L. J., Meng, J. J., Zhu, L. K., 2020. Applying Geodetector to Disentangle the Contributions of Natural and Anthropogenic Factors to NDVI Variations in the Middle Reaches of the Heihe River Basin. Ecological Indicators, 117: 106545. https://doi.org/10.1016/j.ecolind.2020.106545 郭梦迪, 2021. 北半球中高纬植被秋季物候遥感提取及其环境响应机制(硕士学位论文). 成都: 电子科技大学. 李霞, 张国壮, 陈永昊, 等, 2022. 农牧交错带辽河流域2010—2019年植被覆盖变化及驱动因素分析. 农业工程学报, 38(22): 63-72. 南富森, 李宗省, 张小平, 等, 2023. 黄河北岸兰州段荒漠‒草原过渡带土壤粒径分形特征. 地球科学, 48(3): 1195-1204. doi: 10.3799/dqkx.2022.238 王春雅, 王金牛, 崔霞, 等, 2021. 藏东南三江并流核心区植被时空动态变化及其气候驱动力分析. 地理研究, 40(11): 3191-3207. 王星, 霍艾迪, 吕继强, 等, 2023. 塔里木河干流植被覆盖度动态变化及驱动因素分析. 农业工程学报, 39(8): 284-292. 王一, 郝利娜, 许强, 等, 2023. 2001—2019年黄土高原植被覆盖度时空演化特征及地理因子解析. 生态学报, 43(6): 2397-2407. 许丽, 高光耀, 王晓峰, 等, 2023. 气候变化和人类活动对中国北方旱区植被变绿的定量贡献. 生态学报, 43(17): 7274-7283. 赵越, 李宗省, 李中平, 等, 2023. 长江源区大气氮湿沉降时空变化特征. 地球科学, 48(3): 1179-1194. doi: 10.3799/dqkx.2022.319 -
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