黄河流域植被动态及其对气候变化的响应——基于气候干湿分区尺度

摘要/Abstract

图/表 12

参考文献 39

Metrics

doi:10.12118/j.issn.1000-6060.2024.416

摘要： 黄河流域作为中国重要的生态保护和经济发展区域，探究黄河流域不同干湿分区下植被变化特征对调整生态恢复以应对环境变化可能带来的威胁至关重要。基于2000—2022年黄河流域核归一化植被指数（Kernel normalized difference vegetation index，kNDVI）和关键气象驱动因子[降水量（Precipitation，PRE）、气温（Temperature，TEM）]，利用多元统计方法分析了流域内不同类型干湿分区的植被动态时空格局，并采用地理探测器模型和约束效应方法分析了黄河流域植被变化的驱动因素，找出了不同干湿区域内的植被变化与气象因子响应之间的共性和区域间的差异。结果表明：（1）黄河流域植被的kNDVI呈纬向分布，其中湿润区的年均kNDVI最高（0.49）；2000—2022年黄河流域84.58%的区域呈上升趋势，以干旱区（68.36%）和半干旱区（93.08%）的改善最为显著。（2）在黄河流域内降水量对植被的影响普遍强于气温，全流域尺度下两者的偏相关系数分别为0.36和0.19；在半干旱区内这一差异尤为突出，降水量和气温的偏相关系数分别达到0.43和0.22。（3）在空间分层异质性上，全流域尺度下降水量的q值（0.5338）大于气温的q值（0.2283）；且降水量的q值在半干旱区最高（0.4519），而气温的q值则在半湿润区最高（0.2491）。各气象因子在不同干湿分区对植被动态变化的响应呈现出不同特征的约束线。研究结果可为流域生态保护策略的调整和制定提供重要参考，对推动黄河流域高质量发展具有重要意义。

关键词: 植被变化, kNDVI, 干湿分区, 约束效应, 黄河流域

Abstract:

The Yellow River Basin, as a significant ecological protection and economic development area in China, exploring the characteristics of vegetation changes in different dry and wet zones within the basin is crucial for adjusting ecological restoration to address potential threats brought by environmental changes. Based on the kernel normalized difference vegetation index (kNDVI) and key meteorological factors [precipitation (PRE) and temperature (TEM)] from 2000 to 2022, this study utilized multivariate statistical methods to analyze the spatiotemporal patterns of vegetation dynamics in different dry and wet zones within the basin. Additionally, the Geodetector model and constrained effect method were employed to analyze the driving factors of vegetation changes in the Yellow River Basin, and to identify the commonalities and differences in the responses of vegetation changes to meteorological factors in different dry and wet zones. The results show that: (1) The kNDVI values of vegetation in the Yellow River Basin are latitudinally distributed, with the humid zone having the highest average annual kNDVI (0.49). During 2000—2022, 84.58% of the basin showed an upward trend, with the most significant improvements in the arid zone (68.36%) and semi-arid zone (93.08%). (2) Precipitation generally has a stronger influence on vegetation than temperature in the Yellow River Basin, with partial correlation coefficients of 0.36 and 0.19 at the basin scale, respectively. This difference is particularly pronounced in the semi-arid zone, where the partial correlation coefficients of precipitation and temperature reach 0.43 and 0.22, respectively. (3) In terms of spatial heterogeneity, the q value of precipitation (0.5338) is greater than that of temperature (0.2283) at the basin scale. Moreover, the q value of precipitation is highest in the semi-arid zone (0.4519), while the q value of temperature is highest in the semi-humid zone (0.2491). The responses of vegetation dynamics to various meteorological factors in different dry and wet zones exhibit distinct constraint lines. The research findings can provide important references for adjusting and formulating ecological protection strategies in the basin and are of great significance for promoting high-quality development in the Yellow River Basin.

Key words: vegetation change, kernel normalized vegetation index (kNDVI), dry and wet zone, restraint effect, Yellow River Basin

王瑞芳, 吕宝奇, 张文静. 黄河流域植被动态及其对气候变化的响应——基于气候干湿分区尺度[J]. 干旱区地理, 2025, 48(6): 973-984.

WANG Ruifang, LYU Baoqi, ZHANG Wenjing. Vegetation dynamics and their responses to climate change in the Yellow River Basin: Based on climatic wet and dry zoning scales[J]. Arid Land Geography, 2025, 48(6): 973-984.

图1

表1

图2

图3

图4

图5

图6

表2

图7

图8

表3

图9

[1]	马小红, 林菲, 原黎明, 等. 2000—2020年汾河流域植被覆盖度变化及其对生态工程的响应[J]. 中国沙漠, 2023, 43(3): 86-95. doi: 10.7522/j.issn.1000-694X.2022.00144
	[Ma Xiaohong, Lin Fei, Yuan Liming, et al. Vegetation coverage change and its response to ecological protection project in Fenhe River Basin[J]. Journal of Desert Research, 2023, 43(3): 86-95. ] doi: 10.7522/j.issn.1000-694X.2022.00144
[2]	罗敏, 孟凡浩, 王云倩, 等. 气候变化下中国植被GPP与土壤水的互馈关系[J]. 地理学报, 2024, 79(1): 218-239. doi: 10.11821/dlxb202401014
	[Luo Min, Meng Fanhao, Wang Yunqian, et al. Mutual feedback relationship between vegetation GPP and soil moisture in China under climate change[J]. Acta Geographica Sinica, 2024, 79(1): 218-239. ] doi: 10.11821/dlxb202401014
[3]	吴伶, 刘湘南, 刘美玲, 等. 融合遥感时间序列时空谱信息的森林扰动检测与归因研究进展[J]. 遥感学报, 2024, 28(3): 558-575.
	[Wu Ling, Liu Xiangnan, Liu Meiling, et al. Review of the detection and attribution of multi-type forest disturbances using an ensemble of spatio-temporal-spectral information from remote sensing images[J]. National Remote Sensing Bulletin, 2024, 28(3): 558-575. ]
[4]	Zeng Y, Hao D, Huete A, et al. Optical vegetation indices for monitoring terrestrial ecosystems globally[J]. Nature Reviews Earth & Environment, 2022, 3(7): 477-493.
[5]	Tan C W, Zhang P P, Zhou X X, et al. Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law[J]. Scientific Reports, 2020, 10(1): 929, doi: 10.1038/s41598-020-57750-z.
[6]	韩万强, 靳瑰丽, 岳永寰, 等. 伊犁绢蒿荒漠草地3种主要植物光谱及植被指数改进[J]. 新疆农业科学, 2020, 57(5): 950-957. doi: 10.6048/j.issn.1001-4330.2020.05.020
	[Han Wanqiang, Jin Guili, Yue Yonghuan, et al. Spectral characteristics and modified vegetation index of three main plants of Seriphidium transiliense desert grassland[J]. Xinjiang Agricultural Sciences, 2020, 57(5): 950-957. ] doi: 10.6048/j.issn.1001-4330.2020.05.020
[7]	Gao Q, Zhu W, Schwartz M W, et al. Climatic change controls productivity variation in global grasslands[J]. Scientific Reports, 2016, 6(1): 26958, doi: 10.1038/srep26958.
[8]	康尧, 郭恩亮, 王永芳, 等. 温度植被干旱指数在蒙古高原干旱监测中的应用[J]. 应用生态学报, 2021, 32(7): 2534-2544. doi: 10.13287/j.1001-9332.202107.018
	[Kang Yao, Guo Enliang, Wang Yongfang, et al. Application of temperature vegetation dryness index for drought monitoring in Mongolian Plateau[J]. Chinese Journal of Applied Ecology, 2021, 32(7): 2534-2544. ] doi: 10.13287/j.1001-9332.202107.018
[9]	Li H, Cao Y, Xiao J, et al. A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China[J]. Scientific Data, 2024, 11(1): 527, doi: 10.1038/s41597-024-03364-3.
[10]	Jin H, Chen X, Wang Y, et al. Spatio-temporal distribution of NDVI and its influencing factors in China[J]. Journal of Hydrology, 2021, 603: 127129, doi: 10.1016/j.jhydrol.2021.127129.
[11]	Camps-Valls G, Campos-Taberner M, Moreno-Martínez Á, et al. A unified vegetation index for quantifying the terrestrial biosphere[J]. Science Advances, 2021, 7(9): 7447, doi: 10.1126/sciadv.abc7447.
[12]	Wang Q, Moreno-Martínez Á, Muñoz-Marí J, et al. Estimation of vegetation traits with kernel NDVI[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 195: 408-417.
[13]	李志杰, 黄贵超, 马訾懿, 等. 基于土地利用变化的城市生境质量时空分异格局及影响因素[J]. 中国城市林业, 2024, 22(2): 135-143.
	[Li Zhijie, Huang Guichao, Ma Ziyi, et al. Spatial and temporal variation patterns of urban habitat quality and its influencing factors based on land use change[J]. Journal of Chinese Urban Forestry, 2024, 22(2): 135-143. ]
[14]	宋进喜, 齐贵增, 佘敦先, 等. 中国植被生产力对干湿变化的响应[J]. 地理学报, 2023, 78(7): 1764-1778. doi: 10.11821/dlxb202307015
	[Song Jinxi, Qi Guizeng, She Dunxian, et al. Response of vegetation productivity to wet and dry changes in China[J]. Acta Geographica Sinica, 2023, 78(7): 1764-1778. ] doi: 10.11821/dlxb202307015
[15]	孙善磊, 孙杰, 李洪利, 等. 典型干湿区植被与气候因子的相互响应关系研究[J]. 安徽农业科学, 2010, 38(9): 4713-4716.
	[Sun Shanlei, Sun Jie, Li Hongli, et al. Interactions between vegetation and climatic factors in typical arid and humid regions[J]. Journal of Anhui Agriculture Science, 2010, 38(9): 4713-4716. ]
[16]	韩云环, 马柱国, 李明星, 等. 中国不同干湿区植被变化及其与气候因子的关系[J]. 大气科学, 2023, 47(6): 1680-1692.
	[Han Yunhuan, Ma Zhuguo, Li Mingxing, et al. Vegetation changes and their relationship with climate factors in different dry/wet areas over China[J]. Chinese Journal of Atmospheric Sciences, 2023, 47(6): 1680-1692. ]
[17]	田茜, 杨芳, 王召欢, 等. 陆地生态系统土壤CO₂排放对模拟增温的响应特征及影响因素[J]. 生态学报, 2024, 44(5): 1928-1939.
	[Tian Qian, Yang Fang, Wang Zhaohuan, et al. Response of soil CO₂emission in terrestrial ecosystems to the simulated warming and its influencing factors[J]. Acta Ecologica Sinica, 2024, 44(5): 1928-1939. ]
[18]	He B, Chen A, Jiang W, et al. The response of vegetation growth to shifts in trend of temperature in China[J]. Journal of Geographical Sciences, 2017, 27(7): 801-816. doi: 10.1007/s11442-017-1407-3
[19]	Zhang J Y, Yin Y H, Li B Y. A new scheme for climate regionalization in China[J]. Acta Geographica Sinica, 2010, 65(1): 3-12. doi: 10.11821/xb201001002
[20]	Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021, 13(8): 3907-3925. doi: 10.5194/essd-13-3907-2021
[21]	傅楷翔, 贾国栋, 余新晓, 等. 基于改进遥感生态指数的吐鲁番-哈密地区生态环境质量评价及驱动机制分析[J]. 生态学报, 2024, 44(9): 3911-3923.
	[Fu Kaixiang, Jia Guodong, Yu Xinxiao, et al. Evaluation of ecological environment quality and analysis of driving mechanism in Tulufan-Hami region based on improved remote sensing ecological indices[J]. Acta Ecologica Sinica, 2024, 44(9): 3911-3923. ]
[22]	刘泽, 陈建平. 北京植被时空变化与气候因子相关性[J]. 地质通报, 2021, 40(12): 2159-2166.
	[Liu Ze, Chen Jianping. Correlation between temporal-spatial changes of vegetation and climate factors in Beijing[J]. Geological Bulletin of China, 2021, 40(12): 2159-2166. ]
[23]	吴大放, 马佩芳, 李龙, 等. 基于地理探测器的区域土地利用转型时空演变及因子探测——以珠海市为例[J]. 华南师范大学学报(自然科学版), 2023, 55(4): 50-61.
	[Wu Dafang, Ma Peifang, Li Long, et al. Spatiotemporal variation and factor detection of land use transition based on geodetector: A case study of Zhuhai[J]. Journal of South China Normal University (Natural Science Edition), 2023, 55(4): 50-61. ]
[24]	Zhang Z, Song Y, Wu P. Robust geographical detector[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 109: 102782, doi: 10.1016/j.jag.2022.102782.
[25]	An Q, Yuan X, Zhang X, et al. Spatio-temporal interaction and constraint effects between ecosystem services and human activity intensity in Shaanxi Province, China[J]. Ecological Indicators, 2024, 160: 111937, doi: 10.1016/j.ecolind.2024.111937.
[26]	薛联青, 肖颖, 刘远洪, 等. 黄河流域植被水分利用效率对干旱的时空累积响应[J]. 水资源保护, 2023, 39(4): 32-41.
	[Xue Lianqing, Xiao Ying, Liu Yuanhong, et al. Spatiotemporal accumulation response of vegetation water use efficiency to drought in the Yellow River Basin[J]. Water Resources Protection, 2023, 39(4): 32-41. ]
[27]	Bai X, Fan Z, Yue T. Dynamic pattern-effect relationships between precipitation and vegetation in the semi-arid and semi-humid area of China[J]. Catena, 2023, 232: 107425, doi: 10.1016/j.catena.2023.107425.
[28]	郭富印, 刘晓煌, 张文博, 等. 2000—2040年黄河流域(河南段)生境质量时空格局演变及驱动力分析[J]. 现代地质, 2024, 38(3): 599-611.
	[Guo Fuyin, Liu Xiaohuang, Zhang Wenbo, et al. Evolution of the spatial and temporal patterns of habitat qualiy and analysis of the driving forces in Yellow River Basin (Henan section) from 2000 to 2040[J].Geoscience, 2024, 38(3): 599-611. ]
[29]	Li C, Fu B, Wang S, et al. Drivers and impacts of changes in China’s drylands[J]. Nature Reviews Earth & Environment, 2021, 2(12): 858-873.
[30]	Liu L, Gou X, Wang X, et al. Relationship between extreme climate and vegetation in arid and semi-arid mountains in China: A case study of the Qilian Mountains[J]. Agricultural and Forest Meteorology, 2024, 348: 109938, doi: 10.1016/j.agrformet.2024.109938.
[31]	姜凯升, 刘宇, 刘英, 等. 西北地区植被覆盖度与气候因子响应关系探究[J]. 测绘科学, 2023, 48(6): 172-180.
	[Jiang Kaisheng, Liu Yu, Liu Ying, et al. Research on the relationship between vegetation coverage and climate factor response in northwest China[J]. Science of Surveying and Mapping, 2023, 48(6): 172-180. ]
[32]	Sun G Q, Li L, Li J, et al. Impacts of climate change on vegetation pattern: Mathematical modeling and data analysis[J]. Physics of Life Reviews, 2022, 43: 239-270.
[33]	王春雅, 王金牛, 崔霞, 等. 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[J]. 地理研究, 2021, 40(11): 3191-3207. doi: 10.11821/dlyj020201123
	[Wang Chunya, Wang Jinniu, Cui Xia, et al. Spatio-temporal change in vegetation patterns and its climatic drivers in the core region of Three Parallel Rivers in southeast Tibet[J]. Geographical Research, 2021, 40(11): 3191-3207. ]
[34]	Wang K, Zhou J, Tan M L, et al. Impacts of vegetation restoration on soil erosion in the Yellow River Basin, China[J]. Catena, 2024, 234: 107547, doi: 10.1016/j.catena.2023.107547.
[35]	Zhu Y, Zhang Y, Zhang Z, et al. Converted vegetation type regulates the vegetation greening effects on land surface albedo in arid regions of China[J]. Agricultural and Forest Meteorology, 2022, 324: 109119, doi: 10.1016/j.agrformet.2022.109119.
[36]	Yin Y, Deng H, Ma D. Complex effects of moisture conditions and temperature enhanced vegetation growth in the arid/humid transition zone in northern China[J]. Science of the Total Environment, 2022, 805: 150152, doi: 10.1016/j.scitotenv.2021.150152.
[37]	薛联青, 王文壮, 刘远洪, 等. 黄河流域植被总初级生产力对持续性干旱水分亏缺的响应[J]. 水资源保护, 2024, 40(3): 44-51.
	[Xue Lianqing, Wang Wenzhuang, Liu Yuanhong, et al. Response of gross primary productivity of vegetation to persistent drought-induced water deficit in the Yellow River Basin[J]. Water Resources Protection, 2024, 40(3): 44-51. ]
[38]	Bita C, Gerats T. Plant tolerance to high temperature in a changing environment: Scientific fundamentals and production of heat stress-tolerant crops[J]. Frontiers in Plant Science, 2013, 4: 273, doi: 10.3389/fpls.2013.00273. pmid: 23914193
[39]	Xu H J, Wang X P, Zhao C Y. Drought sensitivity of vegetation photosynthesis along the aridity gradient in northern China[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102418, doi: 10.1016/j.jag.2021.102418.

Just accepted

Online first

Just accepted

Online first

Viewed

Full text

From	Others	local

Times	13	18
Rate	42%	58%

Abstract

101

Just accepted	Online first	Issue

0	0	101

From	Others	local

Times	76	25
Rate	75%	25%

Cited

Web of Science	Crossref	ScienceDirect	Search for Citations in Google Scholar >>


This page requires you have already subscribed to WoS.

Shared

数据集	数据类型	分辨率/m	年份	数据来源
降水量	栅格	1000	2000—2022	资源环境数据云平台（https://www.resdc.cn/）
气温	栅格	1000	2000—2022	资源环境数据云平台（https://www.resdc.cn/）
高程	栅格	90	2019—2021	地理空间数据云（https://www.gscloud.cn/）
土地利用类型	栅格	30	2000—2022	国家地球系统科学数据中心（https://www.geodata.cn/）

S_kNDVI	Z_s值	kNDVI趋势	面积占比/%
≥0.0005	≥1.96	显著上升	65.36
≥0.0005	-1.96~1.96	轻微上升	19.22
-0.0005~0.0005	-1.96~1.96	无变化	8.80
≤-0.0005	-1.96~1.96	轻微下降	4.75
≤-0.0005	≤-1.96	显著下降	1.87