Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (9): 1163-1182    DOI: 10.1007/s40333-024-0106-z     CSTR: 32276.14.s40333-024-0106-z

CSTR: 32276.14.JAL.0240106z

Research article     
A new monitoring index for ecological vulnerability and its application in the Yellow River Basin, China from 2000 to 2022
GUO Bing1, XU Mei1, ZHANG Rui2,*(), LUO Wei3
1School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
2Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3North China Institute of Aerospace Engineering, Langfang 065000, China
Download: HTML     PDF(2921KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities. However, the change mechanisms of ecological vulnerability in different sub-regions and periods vary, and the reasons for this variability are yet to be explained. Thus, in this study, we proposed a new remote sensing ecological vulnerability index by considering moisture, heat, greenness, dryness, land degradation, and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin, China from 2000 to 2022 and its driving mechanisms. The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy, at 86.36%, which indicated a higher applicability in the Yellow River Basin. From 2000 to 2022, the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03, denoting moderate vulnerability level. The intensive vulnerability area was the most widely distributed, which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province. From 2000 to 2022, the ecological vulnerability in the Yellow showed an overall stable trend, while that of the central and eastern regions showed an obvious trend of improvement. The gravity center of ecological vulnerability migrated southwest, indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin. The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index (NDVI) to temperature from 2000 to 2022, and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation, which indicated that the global climate change exerted a more significant impact on regional ecosystems. The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.

Key words： ecological vulnerability      spatio-temporal pattern      gravity center migration trajectory      interaction factors      geodetector      green index      q-value     
Received: 14 April 2024      Published: 30 September 2024
CLC:  32276.14.JAL.0240106z  
Corresponding Authors: *ZHANG Rui (zhangrui10@radi.ac.cn)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
GUO Bing
XU Mei
ZHANG Rui
LUO Wei
Cite this article:

GUO Bing, XU Mei, ZHANG Rui, LUO Wei. A new monitoring index for ecological vulnerability and its application in the Yellow River Basin, China from 2000 to 2022. Journal of Arid Land, 2024, 16(9): 1163-1182.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0106-z     OR     http://jal.xjegi.com/Y2024/V16/I9/1163

Fig. 1 Location and elevation of the Yellow River Basin, China. DEM, digital elevation model.
Data type Period Data source Resolution (m)
MODIS product data 2000-2022 National Aeronautics and Space Administration (NASA, https://ladsweb.modaps.eosdis.nasa.gov) 500
DEM 2000-2022 General Bathymetric Chart of the Oceans (GEBCO, https://www.gebco.net/data_and_products/gridded_bathymetry
_data)		 500
Soil type 2000-2022 Environmental and Scientific Data Center of the Chinese Academy of Sciences (https://www.resdc.cn) 500
Temperature 2000-2022 China Meteorological Data Service Center
(https://data.cma.cn)		 250
Precipitation 2000-2022 China Meteorological Data Service Center
(https://data.cma.cn)		 250
Vegetation coverage 2000-2022 NASA (https://ladsweb.modaps.eosdis.nasa.gov/) 250
Land use type 2000-2022 NASA (https://ladsweb.modaps.eosdis.nasa.gov/) 250
GDP density 2000-2022 Figshare (https://doi.org/10.6084/m9.figshare.17004523.v1) 500
Population density 2000-2022 Oak Ridge National Laboratory (ORNL) archive (https://landsc
an.ornl.gov)		 500
Nighttime light data 2000-2022 Harvard Dataverse (https://dataverse.harvard.edu/dataset.xhtml
?persistentId=doi:10.7910/DVN/GIYGJU)		 500
Table 1 Data sources of this study
Fig. 2 Flowchart of this study. MODIS, Moderate Resolution Imaging Spectroradiometer; DMSP/OLI, Defense Meteorological Satellite Program/Operational Linescan System; NPP/VIIRS, National Polar-orbiting Operational Environmental Satellite System Preparatory Project/Visible Infrared Imaging Radiometer Suite; PCA, principal component analysis.
Vulnerability level Number of samples
Slight Mild Moderate Intensive Severe Sum
Slight vulnerability 47 2 0 0 0 49
Mild vulnerability 3 39 2 1 0 45
Moderate vulnerability 2 4 54 3 2 65
Intensive vulnerability 0 1 3 57 5 66
Severe vulnerability 0 0 0 2 49 51
Sum 52 46 59 63 56 276
Table 2 Error matrix of remote sensing ecological vulnerability index in the Yellow River Basin in 2022
Fig. 3 Spatial distribution of average remote sensing ecological vulnerability index in the Yellow River Basin from 2000 to 2022
Fig. 4 Change rate of ecological vulnerability in the Yellow River Basin from 2000 to 2022
Fig. 5 Migration trajectory of the gravity center of ecological vulnerability in the Yellow River Basin from 2000 to 2022
Fig. 6 Gravity center distribution of ecological vulnerability in the Yellow River Basin from 2000 to 2022
Fig. 7 Eco-geographical division of the Yellow River Basin
Year Area Single factor q-value Interaction factors q-value
2000 Yellow River Basin NDVI 0.784 Temperature∩NDVI 0.884
Region Ⅰ NDVI 0.707 Temperature∩NDVI 0.856
Region Ⅱ Temperature 0.561 Temperature∩NDVI 0.711
Region Ⅲ NDVI 0.754 Temperature∩NDVI 0.856
2010 Yellow River Basin NDVI 0.688 Temperature∩NDVI 0.817
Region Ⅰ NDVI 0.614 Temperature∩NDVI 0.714
Region Ⅱ NDVI 0.588 Temperature∩NDVI 0.617
Region Ⅲ NDVI 0.658 Temperature∩NDVI 0.762
2022 Yellow River Basin Temperature 0.677 Temperature∩precipitation 0.749
Region Ⅰ Temperature 0.609 Temperature∩NDVI 0.568
Region Ⅱ Temperature 0.602 Temperature∩precipitation 0.613
Region Ⅲ Temperature 0.597 Temperature∩precipitation 0.650
Table 3 q-value of dominant single factor and interaction factors of ecological vulnerability in different regions of the Yellow River Basin in 2000, 2010, and 2022
Fig. 8 q-values of dominant interaction factors in different regions of the Yellow River Basin in 2000 (a, b, c, and d), 2010 (e, f, g, and h), and 2022 (i, j, k, and l). X1, DEM; X2, slope; X3, soil type; X4, temperature; X5, precipitation; X6, normalized difference vegetation index (NDVI); X7, land use type; X8, gross domestic product (GDP) density; X9, population density.
[1]   Ankush R, Saurabh K G, Suraj K S, et al. 2023. Predicting future land use utilizing economic and land surface parameters with ANN and Markov chain models. Earth, 4(3): 728-751.
[2]   Bai X, Zhang Z W, Li Z, et al. 2023. Spatial heterogeneity and formation mechanism of eco-environmental quality in the Yellow River Basin. Sustainability, 15(14): 10878, doi: 10.3390/su151410878.
[3]   Cai Z C, Zhang Z, Zhao F, et al. 2023. Assessment of eco-environmental quality changes and spatial heterogeneity in the Yellow River Delta based on the remote sensing ecological index and geo-detector model. Ecological Informatics, 77: 102203, doi: 10.1016/j.ecoinf.2023.102203.
[4]   Cheng Y, Zhang L J, Zhang Z Q, et al. 2022. Spatiotemporal variation and influence factors of vegetation cover in the Yellow River Basin (1982-2021) based on GIMMS NDVI and MOD13A1. Water, 14(20): 3274-3274.
[5]   Du W P, Hao Z X, Bai M X, et al. 2023. Spatiotemporal variation in the Meteorological drought comprehensive index in the Beijing-Tianjin-Hebei Region during 1961-2023. Water, 15(24): 4230, doi: 10.3390/w15244230.
[6]   Duo L H, Wang J Q, Zhang F Q, et al. 2023. Assessing the spatiotemporal evolution and drivers of ecological environment quality using an enhanced remote sensing ecological index in Lanzhou City, China. Remote Sensing, 15(19): 4704, doi: 10.3390/rs15194704.
[7]   Guo Y, Zhang L F, He Y, et al. 2024. LSTM time series NDVI prediction method incorporating climate elements: A case study of Yellow River Basin, China. Journal of Hydrology, 629: 130518, doi: 10.1016/j.jhydrol.2023.130518.
[8]   He B S, Han F, Han J L, et al. 2023a. The ecological evolution analysis of heritage sites based on the remote sensing ecological index—A case study of Kalajun-Kuerdening World Natural Heritage Site. Remote Sensing, 15(5): 1179-1179.
[9]   He X R, Cai C Y, Tang J X, et al. 2023b. Analysis of coupling coordination and obstacle factors between tourism development and ecosystem services value: A case study of the Yellow River Basin, China. Ecological Indicators, 157: 111234, doi: 10.1016/j.ecolind.2023.111234.
[10]   Hou K, Tao W D, Wang L M, et al. 2020. Study on hierarchical transformation mechanisms of regional ecological vulnerability and its applicability. Ecological Indicators, 114: 106343, doi: 10.1016/j.ecolind.2020.106343.
[11]   Hu W, Liu J S. 2023. The coupling and coordination of urban modernization and low-carbon development. Sustainability, 15(19): 14335, doi: 10.3390/su151914335.
[12]   Huang J, Zhong P S, Zhang J Z, et al. 2023. Spatial-temporal differentiation and driving factors of ecological resilience in the Yellow River Basin, China. Ecological Indicators, 154: 110763, doi: 10.1016/j.ecolind.2023.110763.
[13]   Kang N. 2023. Ecological vulnerability assessment based on 'nature-humanity-response' model. Environment and Development, 35(1): 52-57. (in Chinese)
[14]   Kong X L, Han M, Li Y L, et al. 2024. Spatial differentiation and formation mechanism of ecological sensitivity in large river basins: A case study of the Yellow River Basin, China. Ecological Indicators, 158: 111571, doi: 10.1016/j.ecolind.2024.111571.
[15]   Krzyśko M, Nijkamp P, Ratajczak W, et al. 2024. Spatio-temporal principal component analysis. Spatial Economic Analysis, 19(1): 8-29.
[16]   Li H C, Guan Q Y, Sun Y F, et al. 2022. Spatiotemporal analysis of the quantitative attribution of soil water erosion in the upper reaches of the Yellow River Basin based on the RUSLE-TLSD model. Catena, 212: 106081, doi: 10.1016/j.catena.2022.106081.
[17]   Lin Z Q, Liu Y Y, Wen Z M, et al. 2023. Spatial-temporal variation characteristics and driving factors of net primary production in the Yellow River Basin over multiple time scales. Remote Sensing, 15(22): 5273, doi: 10.3390/rs15225273.
[18]   Liu X Q, Liu Y S. 2009. Research on the sustainable development of vulnerable ecological region based on AHP—A case study of Yulin City. Journal of Arid Land Resources and Environment, 23(5): 19-23. (in Chinese)
[19]   Lu Q G, Zhang Y D, Song B C, et al. 2023. The responses of ecological indicators to compound extreme climate indices in southwestern China. Ecological Indicators, 157: 11253, doi: 10.1016/j.ecolind.2023.111253.
[20]   Niu W T, Shi J Y, Xu Z Z, et al. 2022. Evaluating the sustainable land use in ecologically fragile regions: A case study of the Yellow River Basin in China. International Journal of Environmental Research and Public Health, 19(6): 3222, doi: 10.3390/ijerph19063222.
[21]   Niu H P, Xiu Z Y, Xiao D Y. 2024. Impact of land-use change on ecological vulnerability in the Yellow River Basin based on a complex network model. Ecological Indicators, 166: 112212, doi: 10.1016/j.ecolind.2024.112212.
[22]   Ren H Y, Zhou X, Zhang D, et al. 2018. Changes of ecological risk in Karst mountain city during rapid urbanization—A case study in Huaxi District, Guiyang. Journal of Guizhou Normal University (Natural Sciences), 36(5): 16-23. (in Chinese)
[23]   Shan Y, Wang N L. 2023. Spatiotemporal evolution and the influencing factors of China's high-tech industry GDP using a geographical detector. Sustainability, 15(24): 16678, doi: 10.3390/su152416678.
[24]   Shi X X, Li J, Liu J F, et al. 2008. Eco-environment vulnerability evaluation based on successive projection pursuit model: a case study of Selangor, Malaysia. Journal of Natural Disasters, 17(6): 129-133.
[25]   Sun P J, Xiu C L. 2011. Study on the vulnerability of economic development in mining cities based on the PSE Model. Geographical Research, 30(2): 301-310. (in Chinese)
[26]   Sun Y F, Wang N L. 2022. Sustainable urban development of the π-shaped curve area in the Yellow River Basin under ecological constraints: A study based on the improved ecological footprint model. Journal of Cleaner Production, 337: 130452, doi: 10.1016/j.jclepro.2022.130452.
[27]   Tanago I G, Urquijo J, Blauhut V, et al. 2016. Learning from experience: a systematic review of assessments of vulnerability to drought. Natural Hazards, 80(2): 951-973.
[28]   Wang K L, Zhou J, Tan M L, et al. 2024a. Impacts of vegetation restoration on soil erosion in the Yellow River Basin, China. Catena, 234: 107547, doi: 10.1016/j.catena.2023.107547.
[29]   Wang Q X, Zhang P Y, Chang Y H, et al. 2024b. Landscape pattern evolution and ecological risk assessment of the Yellow River Basin based on optimal scale. Ecological Indicators, 158: 111381, doi: 10.1016/j.ecolind.2023.111381.
[30]   Wang X L, Zhang S R, Zhao X, et al. 2023a. Exploring the relationship between the eco-environmental quality and urbanization by utilizing sentinel and Landsat data: A case study of the Yellow River Basin. Remote Sensing, 15(3): 743, doi: 10.3390/rs15030743.
[31]   Wang X R, Duan L R, Zhang T J, et al. 2023b. Ecological vulnerability of China's Yellow River Basin: evaluation and socioeconomic driving factors. Environmental Science and Pollution Research, 30(54): 115915-115928.
[32]   Wang Y, Xia W T, Liang T G, et al. 2010. Spatial and temporal dynamic changes of net primary product based on MODIS vegetation index in Gannan grassland. Acta Prataculturae Sinica, 19(1): 201-210. (in Chinese)
[33]   Wang Z Y, Xiong H X, Zhang F W, et al. 2024c. Sustainable development assessment of ecological vulnerability in arid areas under the influence of multiple indicators. Journal of Cleaner Production, 436: 140629, doi: 10.1016/j.jclepro.2024.140629.
[34]   Wei S F, Dong Y, Qiu Y X, et al. 2023. Temporal and spatial analysis of vegetation cover change in the Yellow River Delta based on Landsat and MODIS time series data. Environmental Monitoring and Assessment, 195(9): 1057, doi: 10.1007/s10661-023-11652-5.
[35]   Wen B Q, Huang C L, Zhou C, et al. 2023a. Spatiotemporal dynamics and driving factors of soil erosion in the Beiluo River Basin, Loess Plateau, China. Ecological Indicators, 155: 110976, doi: 10.1016/j.ecolind.2023.110976.
[36]   Wen G C, Wu B J, Wang L, et al. 2023b. Analysis of eco-environmental geological problems and their driving forces in the Henan section of the Yellow River Basin, China. Sustainability, 15(8): 6818, doi: 10.3390/su15086818.
[37]   Wu H, Fang S M, Zhang C, et al. 2022. Exploring the impact of urban form on urban land use efficiency under low-carbon emission constraints: A case study in China's Yellow River Basin. Journal of Environmental Management, 311: 114866, doi: 10.1016/j.jenvman.2022.114866.
[38]   Xia T T, Wang Y, Zhang S. 2023. Spatio-temporal coupling analysis of differences in regional grain-economy-population and water resources. Atmosphere, 14(3): 431, doi: 10.3390/atmos14030431.
[39]   Xiao H, Shao H Y, Long J M, et al. 2023. Spatial-temporal pattern evolution and geological influence factors analysis of ecological vulnerability in western Sichuan mountain region. Ecological Indicators, 155: 110980, doi: 10.1016/j.ecolind.2023.110980.
[40]   Xu Y Z, Hao S, Cui Y H, et al. 2023. Analysis of the spatiotemporal expansion and pattern evolution of urban areas in Anhui Province, China, based on nighttime light data. Ecological Indicators, 157: 11283, doi: 10.1016/j.ecolind.2023.111283.
[41]   Yang X L, Feng Z W, Chen Y Y. 2023. Evaluation and obstacle analysis of high-quality development in Yellow River Basin and Yangtze River Economic Belt, China. Humanities and Social Sciences Communications, 10(1): 757, doi: 10.1057/s41599-023-02278-6.
[42]   Yang Y H, Qin T L, Yan D H, et al. 2024. Analysis of the evolution of ecosystem service value and its driving factors in the Yellow River Source Area, China. Ecological Indicators, 158: 111344, doi: 10.1016/j.ecolind.2023.111344.
[43]   You G X, Chen T Y, Shen P X, et al. 2023. Designing an ecological network in Yichang central city in China based on habitat quality assessment. Sustainability, 15(10): 8313, doi: 10.3390/su15107835.
[44]   Yu S, Yang L L, Song Z J, et al. 2023. Measurement of land ecological security in the middle and lower reaches of the Yangtze River Base on the PSR Model. Sustainability, 15(19): 14098, doi: 10.3390/su151914098.
[45]   Yu T T, Leng H, Yuan Q, et al. 2022a. Vulnerability evaluation of villages based on social-ecological indicators and strategies for improvement: Case study of villages in the Northeast China Plain. Journal of Urban Planning and Development, 148(1): 04021059, doi: 10.1061/(asce)up.1943-5444.0000770.
[46]   Yu Z, Chen X Z, Zhou G Y, et al. 2022b. Natural forest growth and human induced ecosystem disturbance influence water yield in forests. Communications Earth Environment, 3(1): 148, doi: 10.1038/s43247-022-00483-w.
[47]   Yuan D H, Du M R, Yan C L, et al. 2024. Coupling coordination degree analysis and spatiotemporal heterogeneity between water ecosystem service value and water system in Yellow River Basin cities. Ecological Informatics, 79: 102440, doi: 10.1016/j.ecoinf.2023.102440.
[48]   Zhang Q, Wang G, Yuan R Y, et al. 2022. Dynamic responses of ecological vulnerability to land cover shifts over the Yellow River Basin, China. Ecological Indicators, 144: 109554, doi: 10.1016/j.ecolind.2022.109554.
[49]   Zhang J, Liu Y, Liu C, et al. 2023a. Study on the spatial and temporal evolution of high-quality development in nine provinces of the Yellow River Basin. Sustainability, 15(8): 6975, doi: 10.3390/su15086975.
[50]   Zhang M N, Qi S Z. 2023. The spatio-temporal evolution and driving factors of high-quality development in the Yellow River Basin during the period of 2010-2022. Sustainability, 15(18): 13512, doi: 10.3390/su151813512.
[51]   Zhang Y L, Wang Y L, Fu B J, et al. 2023b. Identification of critical ecological areas using the ecosystem multifunctionality-stability-integrity framework: A case study in the Yellow River Basin, China. Journal of Environmental Management, 348: 119296, doi: 10.1016/j.jenvman.2023.119296.
[52]   Zhang Y M. 2018. The evaluation of ecological vulnerability in western China: An exploratory analysis based on 45 cities in western China. Journal of Statistics and Information, 33(8): 74-84. (in Chinese)
[53]   Zhang Z W, Chang T Y, Qiao X N, et al. 2021. Eco-economic coordination analysis of the Yellow River Basin in China: Insights from major function-oriented zoning. Sustainability, 13(5): 2715, doi: 10.3390/su13052715.
[54]   Zhao Y X. 2003. Research of regionalism by ecological fragility based on condition of soil erosion in Shanxi Province. Journal of Soil and Water Conservation, 17(4): 71-74. (in Chinese)
[55]   Zhao Z Z, Ma Q, Wang Y, et al. 2024. Analysis and prediction of ecological vulnerability of the central plains urban agglomeration based on the SRP Model. Environmental Science, doi: 10.13227/j.hjkx.202403011.
[56]   Zuo L, Sun L G, Lu J J, et al. 2022. MODIS-based comprehensive assessment and spatial-temporal change monitoring of ecological quality in Beijing-Tianjin-Hebei region. Remote Sensing for Natural Resources, 34(2): 203-214. (in Chinese)
[1] CHEN Jiazhen, KASIMU Alimujiang, REHEMAN Rukeya, WEI Bohao, HAN Fuqiang, ZHANG Yan. Temporal and spatial variation and prediction of water yield and water conservation in the Bosten Lake Basin based on the PLUS-InVEST model[J]. Journal of Arid Land, 2024, 16(6): 852-875.
[2] ZHANG Mingyu, CAO Yu, ZHANG Zhengyong, ZHANG Xueying, LIU Lin, CHEN Hongjin, GAO Yu, YU Fengchen, LIU Xinyi. Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(3): 373-395.
[3] WANG Yinping, JIANG Rengui, YANG Mingxiang, XIE Jiancang, ZHAO Yong, LI Fawen, LU Xixi. Spatiotemporal characteristics and driving mechanisms of land use/land cover (LULC) changes in the Jinghe River Basin, China[J]. Journal of Arid Land, 2024, 16(1): 91-109.
[4] LIN Yanmin, HU Zhirui, LI Wenhui, CHEN Haonan, WANG Fang, NAN Xiongxiong, YANG Xuelong, ZHANG Wenjun. Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin, China[J]. Journal of Arid Land, 2024, 16(1): 110-130.
[5] WU Jingyan, LUO Jungang, ZHANG Han, YU Mengjie. Driving forces behind the spatiotemporal heterogeneity of land-use and land-cover change: A case study of the Weihe River Basin, China[J]. Journal of Arid Land, 2023, 15(3): 253-273.
[6] YUAN Shuai, LIU Yongqiang, QIN Yan, ZHANG Kun. Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China[J]. Journal of Arid Land, 2023, 15(11): 1315-1339.
[7] LIU Yifeng, GUO Bing, LU Miao, ZANG Wenqian, YU Tao, CHEN Donghua. Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019[J]. Journal of Arid Land, 2023, 15(1): 91-108.
[8] DONG Jianhong, ZHANG Zhibin, LIU Benteng, ZHANG Xinhong, ZHANG Wenbin, CHEN Long. Spatiotemporal variations and driving factors of habitat quality in the loess hilly area of the Yellow River Basin: A case study of Lanzhou City, China[J]. Journal of Arid Land, 2022, 14(6): 637-652.
[9] ZHANG Zhen, GU Zhengnan, Hu Kehong, XU Yangyang, ZHAO Jinbiao. Spatial variability between glacier mass balance and environmental factors in the High Mountain Asia[J]. Journal of Arid Land, 2022, 14(4): 441-454.
[10] BAI Jie, LI Junli, BAO Anmin, CHANG Cun. Spatial-temporal variations of ecological vulnerability in the Tarim River Basin, Northwest China[J]. Journal of Arid Land, 2021, 13(8): 814-834.
[11] FENG Jian, ZHAO Lingdi, ZHANG Yibo, SUN Lingxiao, YU Xiang, YU Yang. Can climate change influence agricultural GTFP in arid and semi-arid regions of Northwest China?[J]. Journal of Arid Land, 2020, 12(5): 837-853.
[12] Wei JIAO, Yaning CHEN, Weihong LI, Chenggang ZHU, Zhi LI. Estimation of net primary productivity and its driving factors in the Ili River Valley, China[J]. Journal of Arid Land, 2018, 10(5): 781-793.
[13] WU Yanfeng, Batur BAKE, ZHANG Jusong, Hamid RASULOV. Spatio-temporal patterns of drought in North Xinjiang, China, 1961–2012 based on meteorological drought index[J]. Journal of Arid Land, 2015, 7(4): 527-543.