黄土高原植被抗旱性监测及影响因素分析

doi:10.12118/j.issn.1000-6060.2025.083

Abstract

Abstract:

With intensifying climate change and human activities, the increasing frequency of droughts and their impacts on the Loess Plateau of China have severely constrained regional vegetation growth. Using data from 2000 to 2022, including standardized precipitation evapotranspiration index, normalized difference vegetation index, and gross primary productivity data, three vegetation drought resistance indices were established, namely lag time, impact degree, and vegetation resilience, using the maximum correlation coefficient method. A comprehensive monitoring framework was then constructed to assess vegetation responses under drought conditions and quantify drought resistance. Results revealed three key insights. (1) The Loess Plateau exhibited the most pronounced lag time at 1 month, with maximum correlation coefficients exceeding 0.4 in 73% areas and correlation coefficients decreasing from south to north as lag increases. (2) Vegetation drought resistance ranged from 0 to 0.2 in the southern and eastern of southern temperate and northern plateau climate zones of the Loess Plateau and 0.2-0.4 in the middle temperate zone. Overall, drought resistance was highest in shrubs, followed by forest and farmland, and lowest in grassland. (3) Land surface temperature, rainfall, and soil pH showed the strongest linear correlations with vegetation drought resistance, consistent with the main influencing factors identified via optimal parameter geographic detector analyses across climate zones. This comprehensive monitoring approach enhances understanding of vegetation drought resistance patterns and provides valuable guidance for reducing ecological risk under future drought conditions.

Key words: correlation coefficient, vegetation drought resistance, drought index, remote sensing, Loess Plateau

CHENG Xi’an, NIU Quanfu, WANG Gang, SHAO Donghu, ZHU Dengfeng, WANG Zhenyu. Monitoring and influencing factors of vegetation drought resistance on the Loess Plateau[J].Arid Land Geography, 2026, 49(1): 1-12.

Figures/Tables 10

Fig. 1

Fig. 2

Fig. 3

Tab. 1

Fig. 4

Fig. 5

Tab. 2

Fig. 6

Tab. 3

Fig. 7

References 35

[1]	Dai A, Trenberth K E, Qian T. A global dataset of palmer drought severity index for 1870—2002: Relationship with soil moisture and effects of surface warming[J]. Journal of Hydrometeorology, 2004, 5(6): 1117-1130. doi: 10.1175/JHM-386.1
[2]	史培军, 李宁, 叶谦, 等. 全球环境变化与综合灾害风险防范研究[J]. 地球科学进展, 2009, 24(4): 428-435. doi: 10.11867/j.issn.1001-8166.2009.04.0428
	[Shi Peijun, Li Ning, Ye Qian, et al. Research on global environmental change and integrated disaster risk governance[J]. Advances in Earth Science, 2009, 24(4): 428-435.] doi: 10.11867/j.issn.1001-8166.2009.04.0428
[3]	何翠荣, 窦彩霞, 刘金付. 干旱灾害对粮食安全的影响及应对措施[J]. 现代农业科技, 2010(15): 332-333.
	[He Cuirong, Dou Caixia, Liu Jinfu. Impact of drought disaster on food security and countermeasures[J]. Modern Agricultural Science and Technology, 2010(15): 332-333.]
[4]	Duca M. Physiology of plant resistance to unfavorable environmental factors[M]. Cham: Springer International Publishing, 2015: 271-308.
[5]	刘斌, 孙艳玲, 王中良, 等. 华北地区植被覆盖变化及其影响因子的相对作用分析[J]. 自然资源学报, 2015, 30(1): 12-23.
	[Liu Bin, Sun Yanling, Wang Zhongliang, et al. Analysis of the vegetation cover change and the relative role of its influencing factors in north China[J]. Journal of Natural Resources, 2015, 30(1): 12-23.] doi: 10.11849/zrzyxb.2015.01.002
[6]	杨舒畅, 杨恒山. 1982—2013年内蒙古地区干旱变化及植被响应[J]. 自然灾害学报, 2019, 28(1): 175-183.
	[Yang Shuchang, Yang Hengshan. Drought evolution and vegetation response in Inner Mongolia from 1982 to 2013[J]. Journal of Natural Disasters, 2019, 28(1): 175-183.]
[7]	王凯悦, 陈芳泉, 黄五星. 植物干旱胁迫响应机制研究进展[J]. 中国农业科技导报, 2019, 21(2): 19-25. doi: 10.13304/j.nykjdb.2018.0115
	[Wang Kaiyue, Chen Fangquan, Huang Wuxing. Research advance on drought stress response mechanism in plants[J]. Journal of Agricultural Science and Technology, 2019, 21(2): 19-25.] doi: 10.13304/j.nykjdb.2018.0115
[8]	拉本, 胡娟, 张旭萍. 干旱胁迫对植物生理的影响以及分子机制的响应研究进展[J]. 青海草业, 2022, 31(4): 31-35.
	[La Ben, Hu Juan, Zhang Xuping. Research progress on the effect of drought on plant physiology and the response of molecular mechanism[J]. Qinghai Prataculture, 2022, 31(4): 31-35.]
[9]	Doughty C E, Metcalfe D B, Girardin C A J, et al. Drought impact on forest carbon dynamics and fluxes in Amazonia[J]. Nature, 2015, 519: 78-82. doi: 10.1038/nature14213
[10]	Mckee T B, Doesken N J, Kleist J R. The relationship of drought frequency and duration to time scales[R]. Boston: Massachusetts, American Meteorological Society, 1993.
[11]	Wu P T, Jin J M, Zhao X N. Impact of climate change and irrigation technology advancement on agricultural water use in China[J]. Climatic Change, 2010, 100: 797-805. doi: 10.1007/s10584-010-9860-3
[12]	Vicente-Serrano S M, Beguería S, López-Moreno J I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index[J]. Journal of Climate, 2010, 23(7): 1696-1718. doi: 10.1175/2009JCLI2909.1
[13]	王林, 陈文. 标准化降水蒸散指数在中国干旱监测的适用性分析[J]. 高原气象, 2014, 33(2): 423-431. doi: 10.7522/j.issn.1000-0534.2013.00048
	[Wang Lin, Chen Wen. Applicability analysis of standardized precipitation evapotranspiration index in drought monitoring in China[J]. Plateau Meteorology, 2014, 33(2): 423-431.] doi: 10.7522/j.issn.1000-0534.2013.00048
[14]	汪士为, 吴伟. 气候因素与黄土高原植被的时间滞后关系分析[J]. 草业科学, 2025, 42(2): 329-339.
	[Wang Shiwei, Wu Wei. Analysis of time lag relationships between climate factors and vegetation on the Loess Plateau[J]. Pratacultural Science, 2025, 42(2): 329-339.]
[15]	韩东, 王鹏新, 张悦, 等. 农业干旱卫星遥感监测与预测研究进展[J]. 智慧农业, 2021, 3(2): 1-14.
	[Han Dong, Wang Pengxin, Zhang Yue, et al. Progress of agricultural drought monitoring and forecasting using satellite remote sensing[J]. Smart Agriculture, 2021, 3(2): 1-14.] doi: 10.12133/j.smartag.2021.3.2.202104-SA002
[16]	李家誉, 佘敦先, 张利平, 等. 黄土高原植被变化对气象干旱多尺度响应特征与机制[J]. 水土保持学报, 2022, 36(6): 280-289.
	[Li Jiayu, She Dunxian, Zhang Liping, et al. Multi-scale response characteristics and mechanism of vegetation to meteorological drought on the Loess Plateau[J]. Journal of Soil and Water Conservation, 2022, 36(6): 280-289.]
[17]	姚远, 陈曦, 钱静. 遥感数据在农业旱情监测中的应用研究进展[J]. 光谱学与光谱分析, 2019, 39(4): 1005-1012.
	[Yao Yuan, Chen Xi, Qian Jing. Advance in agricultural drought monitoring using remote sensing data[J]. Spectroscopy and Spectral Analysis, 2019, 39(4): 1005-1012.] doi: 10.3964/j.issn.1000-0593(2019)04-1005-08
[18]	谢宝妮. 黄土高原近30年植被覆盖变化及其对气候变化的响应[D]. 咸阳: 西北农林科技大学, 2016.
	[Xie Baoni. Changes in vegetation cover and its response to climate change in the Loess Plateau in the last 30 years[D]. Xianyang: Northwest Agriculture and Forestry University, 2016.]
[19]	王宗明, 张柏. 西北黄土高原区生态恢复重建与农业可持续发展[J]. 农业系统科学与综合研究, 2003(2): 112-115.
	[Wang Zongming, Zhang Bai. Ecological restoration and reconstruction and agriculture sustainable development in Loess Plateau of northwest China[J]. System Sciences and Comprehensive Studies in Agriculture, 2003(2): 112-115.]
[20]	胡春宏, 张治昊. 论黄河河道平衡输沙量临界阈值与黄土高原水土流失治理度[J]. 水利学报, 2020, 51(9): 1015-1025.
	[Hu Chunhong, Zhang Zhihao. Discussing of the critical threshold of equilibrium sediment transport in the Yellow River and the degree of soil erosion control in the Loess Plateau[J]. Journal of Hydraulic Engineering, 2020, 51(9): 1015-1025.]
[21]	Yang J, Huang X. The 30 m annual land cover datasets 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
[22]	山建安, 朱睿, 尹振良, 等. 基于CMIP6模式的中国西北地区干旱时空变化[J]. 干旱区研究, 2024, 41(5): 717-729. doi: 10.13866/j.azr.2024.05.01
	[Shan Jian’an, Zhu Rui, Yin Zhenliang, et al. Spatial and temporal variation of drought in northwest China based on CMIP6 model[J]. Arid Zone Research, 2024, 41(5): 717-729.] doi: 10.13866/j.azr.2024.05.01
[23]	夏浩铭, 赵晓阳, 焦文哲, 等. 2001—2020年中国1 km分辨率多时间尺度SPEI数据集[DB/OL]. [2023-07-23]. https://cstr.cn/15732.11.sciencedb.ecodb.00090.
	[Xia Haoming, Zhao Xiaoyang, Jiao Wenzhe, et al. SPEI dataset with 1 km resolution and multiple time scales in China from 2001 to 2020[DB/OL]. [2023-07-23]. https://cstr.cn/15732.11.sciencedb.ecodb.00090. ]
[24]	王东. 黄土高原干旱时空特征及对植被生长潜在风险评估[D]. 兰州: 兰州大学, 2023.
	[Wang Dong. Spatiotemporal characteristics of drought on the Loess Plateau and potential risk assessment for vegetation growth[D]. Lanzhou: Lanzhou University, 2023.]
[25]	张更喜, 粟晓玲, 郝丽娜, 等. 基于NDVI和scPDSI研究1982—2015年中国植被对干旱的响应[J]. 农业工程学报, 2019, 35(20): 145-151.
	[Zhang Gengxi, Su Xiaoling, Hao Lina, et al. Response of vegetation to drought based on NDVI and scPDSI data sets from 1982 to 2015 across China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(20): 145-151.]
[26]	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. doi: 10.12082/dqxxkx.2022.220152
	[Lin Yizhen, Qiu Bingwen, Chen Fangxin, 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.]
[27]	Song Y Z, Wang J F, Ge Y, et al. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data[J]. GIScience & Remote Sensing, 2020, 57(5): 593-610.
[28]	周孝明, 张喆, 张越, 等. 基于TVDI的近20 a吐鲁番市干旱及影响因素分析[J]. 干旱区地理, 2024, 47(12): 2104-2114. doi: 10.12118/j.issn.1000-6060.2024.234
	[Zhou Xiaoming, Zhang Zhe, Zhang Yue, et al. TVDI-based analysis of drought and influencing factors in Turpan City in the last 20 years[J]. Arid Land Geography, 2024, 47(12): 2104-2114.] doi: 10.12118/j.issn.1000-6060.2024.234
[29]	杨蔓红, 尹宁玲, 左金友, 等. 基于植被指数的生态恢复力评价及影响因素研究——以武陵山片区为例[J]. 江西农业学报, 2024, 36(3): 93-101.
	[Yang Manhong, Yin Ningling, Zuo Jinyou, et al. Evaluation and influencing factors of ecological resilience evaluation and influencing factors of ecological resilience[J]. Acta Agriculturae Jiangxi, 2024, 36(3): 93-101.]
[30]	焦盼盼. 水分变化对黄土高原典型土壤有机碳矿化影响的微生物作用机制[D]. 北京: 中国科学院大学, 2023.
	[Jiao Panpan. Mechanisms of microbial effects of water change on organic carbon mineralization of typical soil on the Loess Plateau[D]. Beijing: University of Chinese Academy of Sciences, 2023.]
[31]	Ocheltree T W, Nippert J B, Prasad P V. A safety vs efficiency trade-off identified in the hydraulic pathway of grass leaves is decoupled from photosynthesis, stomatal conductance and precipitation[J]. New Phytologist, 2016, 210(1): 97-107. doi: 10.1111/nph.13781 pmid: 26680276
[32]	孙明伟. 不同光合类型植物对干旱-复水的光合生理响应及生长适应策略[D]. 长春: 东北师范大学, 2021.
	[Sun Mingwei. Photosynthetic physiology and growth adaptation strategies of plants with different photosynthetic types to drought rewatering[D]. Changchun: Northeast Normal University, 2021.]
[33]	史尚渝, 王飞, 金凯, 等. 黄土高原地区植被指数对干旱变化的响应[J]. 干旱气象, 2020, 38(1): 1-13.
	[Shi Shangyu, Wang Fei, Jin Kai, et al. Response of vegetation index to meteorological drought over Loess Plateau[J]. Journal of Arid Meteorology, 2020, 38(1): 1-13.]
[34]	Camps-Valls G, Campos-Taberner M, Moreno-Martínez A, et al. A unified vegetation index for quantifying the terrestrial biosp-here[J]. Science Advances, 2021, 7(9): eabc7447, doi: 10.1126/sciadv.abc7447.
[35]	张更喜, 王慧敏, 粟晓玲, 等. 复合干热胁迫下黄土高原夏季植被脆弱性评估[J]. 农业工程学报, 2024, 40(6): 339-346.
	[Zhang Gengxi, Wang Huimin, Su Xiaoling, et al. Assessing the vegetation vulnerability of Loess Plateau under compound dry and hot climates[J]. Transactions of the Chinese Society of Agricultural Engineering, 2024, 40(6): 339-346.]

滞时/月	高原气候区				南温带气候区				中温带气候区
滞时/月	农田	森林	灌木	草地	农田	森林	灌木	草地	农田	森林	灌木	草地
1	0.461	0.345	0.377	0.439	0.454	0.352	0.321	0.472	0.456	0.402	0.380	0.479
3	0.332	0.348	0.336	0.348	0.384	0.374	0.339	0.372	0.334	0.331	0.345	0.345
6	0.358	0.349	0.339	0.339	0.369	0.361	0.353	0.322	0.286	0.292	0.332	0.296
9	0.411	0.366	0.374	0.381	0.325	0.336	0.317	0.327	0.295	0.323	0.353	0.295

气候区	农田	森林	灌木	草地
高原气候区	-0.014	0.051	0.037	0.001
南温带气候区	-0.016	0.011	0.023	-0.024
中温带气候区	-0.026	0.004	0.034	-0.036

探测因子	高原气候区		南温带气候区		中温带气候区
探测因子	方法	分类级别	方法	分类级别	方法	分类级别
有效水含量	等间距法	3	几何间隔法	4	几何间隔法	4
土壤类型	自然断点法	5	自然断点法	5	几何间隔法	5
有机碳含量	分位数法	5	分位数法	5	分位数法	5
酸碱度	分位数法	5	等间距法	5	等间距法	4
LST	分位数法	5	自然断点法	5	自然断点法	5
降雨量	标准差法	5	分位数法	5	等间距法	5
PET	分位数法	5	分位数法	4	等间距法	5
夜间灯光指数	几何间隔法	4	标准差法	5	标准差法	5
人口密度	分位数法	5	等间距法	5	自然断点法	5
DEM	几何间隔法	4	标准差法	5	等间距法	5
坡度	几何间隔法	5	等间距法	5	分位数法	5
坡向	标准差法	5	几何间隔法	4	等间距法	5

Monitoring and influencing factors of vegetation drought resistance on the Loess Plateau

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 35

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZHANG Chunyan, LI Yanying, WU Wen, CHEN Jing, MA Xingwei, NIE Xin. Causes and transport characteristics of two strong sandstorms in summer and autumn in the Hexi Corridor [J]. Arid Land Geography, 2025, 48(8): 1363-1373.
[2]	ZHANG Xinhan, ZHAO Wenting, JIAO Juying, MA Xiaowu, YANG Bo, LING Qi. Spatiotemporal evolution characteristics of extreme precipitation events on the Loess Plateau from 1960 to 2023 [J]. Arid Land Geography, 2025, 48(7): 1153-1166.
[3]	WANG Xi, LI Wei, ZHU Tao, JIN Wenzhe, SUN Jianfu. Progress and hotspot analysis of remote sensing research in the Ebinur Lake area based on bibliometrics [J]. Arid Land Geography, 2025, 48(4): 689-703.
[4]	YU Kaixin, LI Jifeng, ZHEN Tianle, ZHANG Xialei, LI Huiru, GUO Zhongling, CHANG Chunping, ZHAO Xueqing. Estimation of crop stubble biomass in the Bashang region of Zhangjiakou by combining optics and radar remote sensing [J]. Arid Land Geography, 2025, 48(3): 455-466.
[5]	HUANG Xueyu, XIU Lina, LU Zhixiang. Effects and driving factors of ecosystem service trade-offs in the Longdong Loess Plateau, China [J]. Arid Land Geography, 2025, 48(3): 480-493.
[6]	ZHANG Jie, QU Jianjun, CHEN Hai, SHI Jinxin, MA Yuhe, LIU Di. Spatial and temporal evolution and influencing factors of ecological security pattern based on circuit theory: A case of Loess Plateau in northern Shaanxi [J]. Arid Land Geography, 2025, 48(3): 494-505.
[7]	TANG Yandong, ZANG Cuiping, YU Yunpeng, YU Qinglin. Developmental characteristics and influencing factors of gullies based on fractal theory in eastern Gansu Province [J]. Arid Land Geography, 2025, 48(2): 223-233.
[8]	LIU Wei, LING Hongbo, GONG Yanming, CHEN Fulong, SHAN Qianjuan. Evaluation of ecological environment quality in the mainstream of Tarim River based on improved remote sensing ecological index [J]. Arid Land Geography, 2025, 48(2): 271-282.
[9]	JIA Ruonan, WU Zuobin. Optimization of “ecological-production-living” spaces for urban areas in Loess Plateau hilly and gully region by the dual-guidance between constraints and growth: A case of the central urban area of Mizhi County [J]. Arid Land Geography, 2025, 48(2): 333-344.
[10]	DENG Wenbin, SONG Sen, YI Hongmei. Spatiotemporal dynamics of ecological quality in the Zhundong region based on an arid modified remote sensing ecological index model [J]. Arid Land Geography, 2025, 48(12): 2073-2086.
[11]	GUO Jiali, MA Yonggang, PAN Heng, LI Na, SUN Changning, SUN Qian, ZHOU Wenchang, DANG Yuxuan. Comparative analysis of satellite monitoring of soil salinization in Ebinur Lake during spring and summer [J]. Arid Land Geography, 2025, 48(12): 2143-2157.
[12]	GAO Ruixiang, LUO Geping, ZHANG Wenqiang, XIE Mingjuan, WANG Yuangang. Ecosystem carbon flux inversion method combining LSTM and fuzzy mathematics [J]. Arid Land Geography, 2025, 48(12): 2210-2219.
[13]	CHEN Xianlin, YAN Zhiyuan, LIU Quanming, LI Ruiping, ZHANG Shengwei. Spatiotemporal characteristics of drought in Inner Mongolia based on GEE and optimal remote sensing drought index [J]. Arid Land Geography, 2025, 48(11): 1903-1912.
[14]	ZHANG Yin, SUN Congjian, LIU Geng, CHAO Jinlong, GENG Tianwei, LIU Honghong. Spatiotemporal changes and driving factors of ecological environment quality in the Shanxi section of the Yellow River Basin from 2000 to 2023 [J]. Arid Land Geography, 2025, 48(11): 1983-1994.
[15]	Adiljan PARHAT, LI Gangyong, CHEN Chunbo, PENG Jian. Spatio-temporal shifts in ecological habitat quality of the oasis-desert critical zone on the northern slope of Kunlun Mountains: Taking the Yutian Oasis in Hotan Prefecture as an example [J]. Arid Land Geography, 2025, 48(10): 1721-1735.