中国主要流域植被动态演变及其突变方法适宜性评估

柯浩成, 袁春慧, 王茂林, 马晓燕, 王飞, 杨陈, 李占斌, 李鹏

中国环境科学 ›› 2025, Vol. 45 ›› Issue (11) : 6262-6274.

PDF(4378 KB)
PDF(4378 KB)
中国环境科学 ›› 2025, Vol. 45 ›› Issue (11) : 6262-6274.
环境生态

中国主要流域植被动态演变及其突变方法适宜性评估

  • 柯浩成1, 袁春慧1, 王茂林1, 马晓燕1, 王飞1, 杨陈1, 李占斌2, 李鹏2
作者信息 +

Dynamic evolution of vegetation in major watersheds in China and suitability assessment of its mutation method

  • KE Hao-cheng1, YUAN Chun-hui1, WANG Mao-lin1, MA Xiao-yan1, WANG Fei1, YANG Chen1, LI Zhan-bin2, LI Peng2
Author information +
文章历史 +

摘要

基于长序列归一化植被指数(NDVI)数据,运用Theil-Sen Media趋势分析+Mann-Kendall检验、时间稳定性、有序聚类法、滑动T检验多种突变方法,研究了2000~2022年中国主要流域植被NDVI的动态变化特征和时间稳定性,并识别中国主要流域的代表性子流域.研究结果表明:时间上,中国主要流域植被NDVI值均呈显著上升趋势,其中,黄河流域增长速率最高,为0.0052a−1,松辽河流域次之,为0.0032a−1,淮河流域最小,增长速率仅为0.0010a−1.空间上,植被NDVI呈现出由东南到西北递减的趋势特征,植被变化以改善为主,占比为52.02%,且各等级植被覆盖间的流动以流向更高等级为主.中国主要流域植被稳定性的代表性子流域分别为:西南诸河流域(35# 澜沧江流域)、内陆河流域(15# 塔里木河流域)、长江流域(8# 汉江流域)、东南诸河流域(24# 闽江流域)、淮河流域(16# 沂河流域)、海河流域(32# 大清河流域)、松辽河流域(21# 松花江流域)、珠江流域(12# 西江流域)、黄河流域(7# 汾河流域).综合不同流域突变方法,得出有序聚类法和滑动T检验法更适用于大部分流域植被变化突变检测,其中东南诸河流域Pettitt检验法适宜,海河、内陆河、珠江、长江流域适宜有序聚类法,松辽河、淮河、西南诸河流域适宜有序聚类法和滑动T检验法,黄河流域适宜滑动T检验法.

Abstract

Based on the long-series normalized vegetation index (NDVI) and climate, topographic and human activity factor data, this study studied the dynamic change characteristics and temporal stability of vegetation NDVI in China's first-class watersheds from 2000~2022 by using Theil-Sen Media trend analysis+Mann-Kendall test, geographic detector and temporal stability, and identified the representative sub-watersheds of nine first-class watersheds in China. The findings indicated that: in terms of time, the NDVI values of vegetation in the nine major river basins of China showed a significant upward trend. Among them, the Yellow River Basin had the highest growth rate of 0.0052a-1, followed by the Songliao River Basin with a growth rate of 0.0032a-1, while the Huaihe River Basin had the lowest growth rate, at only 0.0010a-1. Spatially, the NDVI of vegetation exhibited a declining trend from the southeast to the northwest, and the vegetation change was mainly improved, accounting for 52.02%, and the flow between different levels of vegetation cover was mainly to the higher grades. The representative sub-basins of vegetation stability in the major river basins of China are as follows: Southwest River Basins (No. 35 Lancang River Basin), Inland River Basins (No. 15 Tarim River Basin), Yangtze River Basin (No. 8 Han River Basin), Southeast River Basins (No. 24 Min River Basin), Huai River Basin (No. 16 Yi River Basin), Hai River Basin (No. 32 Daqing River Basin), Songliao River Basin (No. 21 Songhua River Basin), Pearl River Basin (No. 12 Xijiang River Basin), and Yellow River Basin (No. 7 Fen River Basin). Based on the mutation methods of different river basins, it was concluded that the ordered clustering method and the sliding T-test were more suitable for the detection of vegetation change mutations in most of the river basins, among which the Pettt test was suitable for the southeast river basins, the Haihe River, inland rivers, Songliao River and Yangtze River basins, the Huaihe River and Southwest river basins were suitable for the ordered clustering method and the sliding T-test method, and the Yellow River basin was suitable for the sliding T-test.

关键词

植被动态变化 / 时间稳定性 / 突变方法适宜性 / 代表性子流域 / 中国主要流域

Key words

vegetation dynamic changes / temporal stability / suitability of mutation methods / representative sub-basins / China's first-class river basin

引用本文

导出引用
柯浩成, 袁春慧, 王茂林, 马晓燕, 王飞, 杨陈, 李占斌, 李鹏. 中国主要流域植被动态演变及其突变方法适宜性评估[J]. 中国环境科学. 2025, 45(11): 6262-6274
KE Hao-cheng, YUAN Chun-hui, WANG Mao-lin, MA Xiao-yan, WANG Fei, YANG Chen, LI Zhan-bin, LI Peng. Dynamic evolution of vegetation in major watersheds in China and suitability assessment of its mutation method[J]. China Environmental Science. 2025, 45(11): 6262-6274
中图分类号: X171   

参考文献

[1] Yan Z, Luo qi Z, Junyi W, et al. Quantitative analysis of NDVI driving factors based on the geographical detector model in the Chengdu- Chongqing region, China [J]. Ecological Indicators, 2023,155.
[2] 王永锋,靖娟利,刘海红,等.顾及时滞和累积效应的西南喀斯特地区植被变化归因分析 [J]. 环境科学, 2025,46(7):4382-4391. Wang Y F, Jing J L, Liu H H, et al. Attribution analysis of vegetation variation in the karst region of Southwest China considering time lag and accumulation effects [J]. Environmental Science, 2025,46(7): 4382-4391.
[3] Ting C, Jun X, Lei Z, et al. Quantifying the influences of natural factors and human activities on NDVI changes in the Hanjiang River Basin, China [J]. Remote Sensing, 2020,12(22):3780-3780.
[4] Lian X, Piao SL, Li Laurent, et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation [J]. Science Advances, 2020,6(1):eaax0255.
[5] Tuoku L, Wu Z, Men B. Impacts of climate factors and human activities on NDVI change in China [J]. Ecological Informatics, 2024, 81102555-.
[6] 刘 畅,任小丽,张 黎,等.三江源国家公园NPP长时序时空变化多模型集成分析 [J]. 地理学报, 2024,79(9):2356-2371. Liu C, Ren X L, Zhang L, et al. Spatio-temporal variation of net primary productivity in Three-River-Source National Park using a multi-model integration method [J]. Acta Geographica Sinica, 2024, 79(9):2356-2371.
[7] Wang B, Jia K, Wei X, et al. Generating spatiotemporally consistent fractional vegetation cover at different scales using spatiotemporal fusion and multiresolution tree methods [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020,167:214-229.
[8] Jiao W, Wang L, Smith W K, et al. Observed increasing water constraint on vegetation growth over the last three decades [J]. Nature Communications, 2021,(12):3777.
[9] Guo H, Wang X, Guo Z, et al. Assessing snow phenology and its environmental driving factors in Northeast China [J]. Remote Sensing, 2022,14(2):262.
[10] Yating R, Feng Z, Chunli Z, et al. Attribution of climate change and human activities to vegetation NDVI in Jilin Province, China during 1998~2020 [J]. Ecological Indicators, 2023,153:10415.
[11] 陈 峰,王世杰,赵晓恩,等.20世纪以来中国北方干旱半干旱区针叶树种径向生长动态变化 [J]. 地理学报, 2024,79(9):2341-2355. Chen F, Wang S J, Zhao X E, et al. Radial growth dynamics of coniferous species in arid and semi-arid areas of northern China since the 20th century [J]. Acta geographica sinica 2024,79(9):2341-2355.
[12] Yi W, Wang N, Yu H, et al. An enhanced monitoring method for spatio-temporal dynamics of salt marsh vegetation using google earth engine [J]. Estuarine, Coastal and Shelf Science, 2024,298,108658.
[13] Uribeetxebarria A, Castellxon A, Aizpurua A. A first approach to determine if it is possible to delineate in-season N fertilization maps for wheat using NDVI derived from sentinel-2 [J]. Remote Sens. -Basel, 2022,14(12):2872.
[14] 林艺真,邱炳文,陈芳鑫,等.干旱胁迫下植物抗逆性大尺度遥感监测方法 [J]. 地球信息科学学报, 2022,24(11):2225-2233. Lin Y Z, Qiu B W, Chen F X, et al. Remote sensing monitoring method for plant stress resistance under drought stress on large scale [J]. Journal of Geo-information Science, 2022,24(11):2225-2233.
[15] 协子昂,张 超,冯绍元,等.植被物候遥感监测研究进展 [J]. 遥感技术与应用, 2023,38(1):1-14. Xie Z A, Zhang C, Feng S Y, et al. Reviews of methods for vegetation phenology monitoring from remote sensing data [J]. Remote Sensing Technology and Application, 2023,38(1):1-14.
[16] Tian J, Han Z B, Bogena H R, et al. Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas [J]. Hydrology and Earth System Sciences, 2020,24(9):4659-4674.
[17] Yang W J, Zhao Y, Wang Q M, et al. Climate, CO2, and anthropogenic drivers of accelerated vegetation greening in the Haihe River Basin [J]. Remote Sensing, 2022,14:268.
[18] 秦肖伟,程 博,杨志平,等.基于时序遥感影像的西南山区地块尺度作物类型识别 [J]. 地球信息科学学报, 2023,25(3):654-668. QinX W, Cheng B, Yang Z P, et al. Identification of parcel-scale crop types in southwestern mountainous area based on time series remote sensing images [J]. Journal of Geo-information Science, 2023,25(3): 654-668.
[19] Ma B, Wang S, Mupenzi C, et al. Quantitative contributions of climate change and human activities to vegetation changes in the Upper White Nile River. Remote Sensing, 2021,13:3648.
[20] Liu C, Liu J, Zhang Q, et al. Attribution of NDVI dynamics over the globe [J]. Remote Sens.-Basel, 2022,14(11):2706.
[21] Vachaud G A, Silans A P D, Balabanis P, et al. Temporal stability of spatially measured soil water probability density function1 [J]. Soil Science Society of America Journal, 1985,49(4):822-828.
[22] 刘登峰,田富强,黄 强.基于代表性单元的流域水沙耦合模型及其应用 [J]. 水力发电学报, 2015,34(4):22-30. Liu D F, Tian F Q, Huang Q.Tsinghua hydrological model based on representative elementary watershed coupling sediment and its application [J]. Journal of hydroelectric engineering, 2015,34(4):22-30.
[23] 刘 源,田 杰,王水献.祁连山草地坡面土壤水时空动态及时间稳定性 [J]. 高原气象, 2024,43(5):1249-1258. Liu Y, Tian J, Wang S X. Spatiotemporal dynamics and temporal stability of soil moisture on grassland slopes in Qilian Mountains [J]. Plateau Meteorology, 2024,43(5):1249-1258.
[24] 梁朝文,马海华.民勤荒漠区不同林龄人工梭梭林土壤水分时间稳定性研究 [J]. 水利水电技术(中英文), 2025,56(2):147-157. Liang C W, Ma H H. Study on temporal stability of soil moisture in Haloxylonammodendronplantationsof different ages in the Minqin desert area]. Water Resources and Hydropower Engineering, 2025,56 (2):147-157.
[25] Zhou X, Hu K, Xiao H, et al. Effects of vegetation on the spatiotemporal distribution of soil water content in re-vegetated slopes using temporal stability analysis [J]. Catena, 2023,234.
[26] ZhangJ Y, Zhao H L. Review on the study of vegetation stability [J]. cje, 2003,(4):42-48.
[27] 申子傲,吴 静,李纯斌.基于生态水文最优性理论的河西内陆河流域植被覆盖模拟 [J]. 草业学报, 2024,33(9):15-27. Shen Z A, Wu J, Li C B. Simulation of vegetation cover in the inland river basin of Hexi based on the theory of ecohydrological optimality [J]. Acta Prataculturae Sinica, 2024,33(9):15−27
[28] He Z, Yue T, Chen Y, et al. Analysis of spatial and temporal changes in vegetation cover and driving forces in the Yan River Basin, Loess Plateau [J]. Remote Sensing, 2023,15(17):4240.
[29] 寇杰锋,杨 超,张莹莹,等.黄河中下游伊洛河流域2002~2021年植被覆盖度时空变化及影响因素分析 [J]. 灌溉排水学报, 2024,43(1): 89-96. KouJ F, Yang C, Zhang Y Y, et al. Spatio-temporal variation of fractional vegetation cover and its influencing factors in the Yiluo River basin of the middle and lower reaches of the Yellow River from 2002 to 2021 [J]. Journal of Irrigation and Drainage, 2024,43(1):89-96.
[30] 侯 迎,黄欣慧,褚 阳,等.黄河“几字弯”大气水分亏缺及其影响因素的时空变化特征 [J]. 中国环境科学, 2025,45(2):637647. Hou Y, Huang X H, Chu Y, et al. Spatiotemporal variability of atmospheric moisture deficit and its influencing factors in the “Jizi Bay” of the Yellow River [J]. China Environmental Science, 2025, 45(2):637647.
[31] Pinna D, Sozzi M, Pornaro C, et al. Modelling vegetation phenology in alpine grasslands of North-East Italy integrating microclimatic and meteorological Drivers: A 23-Years Time-Series analysis [J]. Computers and Electronics in Agriculture, 2025,232:110154-110154.
[32] 杜中曼,马文明,周青平,等.基于遥感技术的植被识别方法研究进展 [J]. 生态科学, 2022,41(6):222-229. DuZ M, MaW M, ZhouQ P, et al. Research progress on vegetation identification methods based on remote sensing technology [J]. Ecological Science, 2022,41(6):222-229.
[33] Gocic M and Trajkovic S. Analysis of changes in meteorological variables using Mann-Kendall and Sen¢s slope estimator statistical tests in Serbia [J]. Global & Planetary Change, 2013,100:172-182.
[34] Yanlin W, Xiaofeng L, Lingjia G, et al. Spatiotemporal changes of snow depth in Western Jilin, China from 1987 to 2018 [J]. Chinese Geographical Science, 2024,34(2):357-368.
[35] Chebana, Fateh et al. Testing for multivariate trends in hydrologic frequency analysis [J]. Journal of Hydrology, 2013,486:519-530.
[36] Wang T, Wedin D A, Franz T E, Hiller J. Effect of vegetation on the temporal stability of soil moisture in grass-stabilized semi-arid sand dunes [J]. Hydrol (amst), 2015,521:447-459.
[37] Gao L, Shao M. Temporal stability of soil water storage in diverse soil layers [J]. Catena (amst), 2012,95:24-32.
[38] 钱筱暄.不同突变检验方法在萧濉新河流域径流序列中的研究 [J]. 世界生态学, 2024,13(2):274-281. Qian X X. Study on runoff sequence of Xiaosuixin River Basin by different mutation test methods [J]. International Journal of Ecology, 2024,13(2):274-281.
[39] 中国气象局.2022年全国生态气象公报 [R]. 北京:中国气象局, 2023,(36). China Meteorological Administration. 2022 National Ecological Meteorological Bulletin. Report. Beijing: China Meteorological Administration, 2023,(36).
[40] 孙应龙,韩佳芮,陈紫璇,等.2000~2021年长江中游流域气候对植被和生态系统服务功能的影响分析 [J]. 环境生态学, 2024,6(6):99- 108.37. Sun Y L, HanJ R, ChenZ X, et al. Impacts of climate on vegetation and ecosystem services in the middle reaches of the Yangtze River from 2000 to 2021 [J]. Environmental Ecology, 2024,6(6):99-108.37.
[41] 易 扬,胡昕利,史明昌,等.基于MODIS NDVI的长江中游区域植被动态及与气候因子的关系 [J]. 生态学报, 2021,41(19):7796-7807. Yi Y, Hu X L, Shi M C, et al. Vegetation dynamics and its relationship with climate factors in the middle reaches of the Yangtze River Based on MODIS NDVI [J]. Acta Ecologica Sinica, 2021,41(19):7796-7807.
[42] Hu Y, Li H, Wu D, et al. LAI-indicated vegetation dynamic in ecologically fragile region: A case study in the Three-North Shelter Forest program region of China [J]. Ecological Indicators, 2021, 120106932-.
[43] Xie S, Mo X, Hu S, et al. Contributions of climate change, elevated atmospheric CO2 and human activities to ET and GPP trends in the Three-North Region of China [J]. Agricultural and Forest Meteorology, 2020,295.
[44] Liu D, Chen J, Ouyang Z. Responses of landscape structure to the ecological restoration programs in the farming-pastoral ecotone of Northern China [J]. Science of the Total Environment, 2020,710136311.
[45] Sun Z H, Mao Z A, Yang L Y,et al. Impacts of climate change and afforestation on vegetation dynamic in the Mu Us Desert, China [J]. Ecological Indicators, 2021,129.
[46] Zhu Z C, Piao S L, Myneni R B, et al. Greening of the earth and its drivers [J]. Nature Climate Change, 2016,6(8):791-795.
[47] Yao Y T, Wang X H, Li Y, et al. Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years [J]. Global Change Biology, 2018,24(1):184-196.
[48] 钟超慧,陈甲豪,杨 涵,等.基于核归一化植被指数数据的中国西部四省自然保护区保护成效评估 [J]. 生态学报, 2025,45(3):1-11. Zhong C H, Chen J H, Yang H, et al. Assessment of conservation effectiveness in nature eserves of four provinces in western China based on kernel Normalized Difference Vegetation Index data [J]. Acta Ecologica Sinica, 2025,45(3):1-11.
[49] 阎世杰,王 欢,焦珂伟.京津冀地区植被时空动态及定量归因 [J]. 地球信息科学学报, 2019,21(5):767-780. Yan S J, Wang H, Jiao K W. Spatiotemporal dynamic of NDVI in the Beijing-Tianjin-Hebei region based on MODIS data and quantitative attribution [J]. Journal of Geo-information Science, 2019,21(5):767-780.
[50] Wang B, Xu G, Li P, et al. Vegetation dynamics and their relationships with climatic factors in the Qinling Mountains of China [J]. Ecol. Indic., 2020a,108,105719.
[51] Liu C, Zhang X, Wang T, et al. Detection of vegetation coverage changes in the Yellow River Basin from 2003 to 2020 [J]. Ecological Indicators, 2022,138:108818-.
[52] Meng Z, Liu M, Gao C, et al. Greening and browning of the coastal areas in China’s mainland: Spatial heterogeneity, seasonal variation and its influential factors [J]. Ecological Indicators, 2020,110:105888-.
[53] Yan W, Wang H, Jiang C, et al. Satellite view of vegetation dynamics and drivers over Southwestern China [J]. Ecological Indicators, 2021.
[54] 吕 妍,张 黎,闫慧敏,等.中国西南喀斯特地区植被变化时空特征及其成因 [J]. 生态学报, 2018,38(24):8774-8786. Lv Y, Zhang L, Yan H M, et al. Spatial and temporal patterns of changing vegetation and the influence of environmental factors in the karst region of Southwest China [J]. Acta Ecologica Sinica, 2018,38 (24):8774-8786.
[55] Ji G, Song H, Wei H, et al. Attribution analysis of climate and anthropic factors on runoff and vegetation changes in the source area of the Yangtze River from 1982 to 2016 [J]. Land, 2021,10(6):612.
[56] Chen W, Yuan Y, Liu Z, et al. Ecosystem health in the Yangtze and Yellow River basins in China: Regional differences and driving forces [J]. Ecological Indicators, 2024,166:112422.
[57] Qin Y, Yang D, Lei H, et al. Comparative analysis of drought based on precipitation and soil moisture indices in Haihe basin of North China during the period of 1960~2010 [J]. Journal of Hydrology, 2015, 526:55-67.
[58] Chu H, Venevsky S, Wu C, et al. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River basin from 1982 to 2015 [J]. Sci. Total Environ., 2019,650:2051-2062.
[59] Zhang W Q, Dai L, Yan Y G, et al. Multiscale geographically weighted regression-based analysis of vegetation driving factors and mining-induced quantification in the Fengfeng District, China [J]. Ecol. Inform., 2024,80:102506.
[60] 陈文裕,夏丽华,徐国良,等.2000~2020年珠江流域NDVI动态变化及影响因素研究 [J]. 生态环境学报, 2022,31(7):1306-1316. ChenW Y, Xia L H, XuG L, et al. Dynamic variation of NDVI and its influencing factors in the Pearl River Basin from 2000 to 2020 [J]. Ecology and Environment, 2022,31(7):1306-1316.
[61] 刘芃凯,饶良懿.海河流域植被变化时空分异及影响因素探测 [J]. 环境科学, 2025,46(4):2352-2362. Liu F K, Rao L Y. Detection of the spatial and temporal differentiation and influencing factors of vegetation changes in the Haihe River Basin [J]. Environmental Science, 2025,46(4):2352-2362.
[62] 李 霞,张国壮,陈永昊,等.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 [J]. 农业工程学报, 2022,38(22):63-72. Li X, Zhang G Z, Chen Y H, et al. Vegetation cover change and driving factors in the agro-pastoral ecotone of Liaohe River Basin of China from 2010 to 2019 [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022,38(22):63-72.
[63] Nan L, Yang M, Wang H, et al. Assessment of spatio-temporal dynamic vegetation evolution and its driving mechanism on the Kubuqi Desert using multi-source satellite remote sensing [J]. Remote Sensing, 2024,16(24).

基金

甘肃省科技计划资助项目(25JRRA096);甘肃省重点研发计划项目(22YF7FA165,23YF7FA0062)

PDF(4378 KB)

Accesses

Citation

Detail

段落导航
相关文章

/

〈 〉