Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (10): 1720-1738    DOI: 10.1016/j.jaridl.2026.10.003     CSTR: 32276.14.JAL.20260129
Research article     
Unraveling the driving mechanisms of landscape connectivity dynamics in the Mu Us Sandy Land, northern China
JIN Xiaoze1,2,3, HE Zhaoquan1,2,3,4,*(), SHANG Xue1,2,3, WANG Xiukang1,2,3, XING Yingying1,2,3
1 School of Life Sciences, Yan'an University, Yan'an 716000, China
2 Key Laboratory of Applied Ecology of Universities in Shaanxi Province on the Loess Plateau, College of Life Sciences, Yan'an University, Yan'an 716000, China
3 Shaanxi Key Laboratory of Research and Utilization of Resource Plants on the Loess Plateau, College of Life Sciences, Yan'an University, Yan'an 716000, China
4 Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Download: HTML     PDF(5662KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The Mu Us Sandy Land, a critical agro-pastoral ecotone in northern China, served as a key region for understanding landscape connectivity dynamics under coupled climate-human pressures. This study used landscape pattern indices and the Geodetector model to systematically quantify the spatiotemporal dynamics of structural landscape connectivity in the Mu Us Sandy Land from 1990 to 2020 by integrating multi-temporal land use and meteorological data. The driving mechanisms of the landscape connectivity dynamics and present status of regional ecological restoration were analyzed. The results showed an overall decline in structural landscape connectivity over the past three decades, marked by a distinct spatial pattern of "decreasing in the east and increasing in the west". Fragmentation in the east was driven by intensive human expansion, while aggregation in the west resulted from long-term ecological restoration. Factor analysis revealed that structural landscape connectivity was jointly influenced by climatic and anthropogenic factors, with wide-ranging synergistic interactions. The dominant drivers changed temporally, i.e., in the early phases (1990-2000), natural factors (e.g., wind speed and temperature) and agricultural activities were the main drivers; in the transitional phase (2000-2010), precipitation played an important role; and in the later phases (2010-2020), the human-land synergy regime characterized by human activity factors, particularly residential density, gained predominant explanatory power. This study revealed the multifactor synergistic driving mechanisms and their temporal evolution, providing a scientific basis for adaptive ecological restoration and spatial planning in semi-arid regions.

Key words： anthropogenic driver      climate-human interactions      ecological restoration      structural landscape connectivity      Geodetector     
Received: 21 March 2026      Published: 31 October 2026
Corresponding Authors: *HE Zhaoquan (E-mail: hzq@yau.edu.cn)
About author: First author contact:

Conceptualization: HE Zhaoquan, SHANG Xue, JIN Xiaoze; Data curation: XING Yingying; Formal analysis: HE Zhaoquan, WANG Xiukang; Funding acquisition: HE Zhaoquan; Investigation: XING Yingying; Methodology: SHANG Xue; Project administration: WANG Xiukang; Resources: WANG Xiukang; Software: WANG Xiukang; Supervision: JIN Xiaoze, XING Yingying; Validation: XING Yingying; Visualization: SHANG Xue, JIN Xiaoze; Writing - original draft preparation: JIN Xiaoze; Writing - review and editing: HE Zhaoquan, SHANG Xue, XING Yingying, WANG Xiukang. All authors approved the manuscript.
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
JIN Xiaoze
HE Zhaoquan
SHANG Xue
WANG Xiukang
XING Yingying
Cite this article:

JIN Xiaoze, HE Zhaoquan, SHANG Xue, WANG Xiukang, XING Yingying. Unraveling the driving mechanisms of landscape connectivity dynamics in the Mu Us Sandy Land, northern China. Journal of Arid Land, 2026, 18(10): 1720-1738.

URL:

http://jal.xjegi.com/10.1016/j.jaridl.2026.10.003     OR     http://jal.xjegi.com/Y2026/V18/I10/1720

Fig. 1 Topographic map of the study area. (a), elevation of the Mu Us Sandy Land; (b and c), current situation of the Mu Us Sandy Land.
Criteria for judgment Interaction
q(X1∩X2)<Min(q(X1), q(X2)) Nonlinear attenuation
Min(q(X1), q(X2))<q(X1∩X2)<Max(q(X1), q(X2)) One-factor nonlinear attenuation
q(X1∩X2)>Max(q(X1), q(X2)) Two-factor enhancement
q(X1∩X2)=q(X1)+q(X2) Independent
q(X1∩X2)>q(X1)+q(X2) Nonlinear enhancement
Table 1 Types of interaction between two covariates
Fig. 2 Spatial distribution in temperature (T; a), precipitation (P; b), and wind speed (WS; c)
Fig. 3 Trends in T (a), P (b), and WS (c)
Variable Sen's slope p-value of MK test H index Persistence characteristic
P 1.576 mm/a 0.395 0.545 Persistence
T 0.030°C/a 0.030 0.595 Persistence
WS 0.005 m/(s•a) 0.174 0.697 Persistence
Note: P, precipitation; T, temperature; WS, wind speed; H index, Hurst index; MK test, Mann-Kendall test.
Table 2 Trend and persistence characteristic of meteorological factors
Fig. 4 Spatial distribution and area proportion of land use types in the Mu Us Sandy Land from 1990 to 2020. (a), 1990; (b), 1995; (c), 2000; (d), 2005; (e), 2010; (f), 2015; (g), 2020.
Fig. 5 String diagram of land use type transition in the Mu Us Sandy Land in different periods. (a), 1990-1995; (b), 1995-2000; (c), 2000-2005; (d), 2005-2010; (e), 2010-2015; (f), 2015-2020; (g), 1990-2020; (h), 2000-2020.
Type Grassland Cropland Construction land Woodland Water body Unused land Loss
(km2)
Grassland 15,978.070 260.277 222.630 205.957 25.526 1235.679 1950.069
Cropland 251.084 2615.525 65.247 51.768 17.575 50.446 2800.561
Construction land 2.092 4.743 124.228 0.232 0.482 1.184 130.869
Woodland 71.108 36.449 26.727 698.905 2.409 26.906 791.396
Water body 31.461 16.072 6.079 0.726 471.536 50.688 545.101
Unused land 1687.359 178.737 238.697 61.018 43.315 13,589.940 14,111.710
Gain 2043.104 2851.526 460.978 812.649 535.317 13,719.164 -
Table 3 Land use conversion matrix in the Mu Us Sandy Land from 1990 to 2020
Fig. 6 Landscape indices in the Mu Us Sandy Land from 1990 to 2020. H', Shannon's diversity index; DIVISION, landscape division index; CONTAG, contagion index; LSI, landscape shape index; ED, edge density.
Fig. 7 Spatial variation of structural landscape connectivity in the Mu Us Sandy Land from 1990 to 2020. (a), ED; (b), LSI; (c), CONTAG; (d), DIVISION; (e), H'.
Region ED LSI CONTAG DIVISION H' Trend direction
East 25.736→29.681 4.083→4.562 56.149→55.773 0.669→0.698 0.892→0.973 Fragmentation
West 17.261→18.492 3.124→3.284 55.451→57.695 0.565→0.554 0.727→0.740 Aggregation
Note: ED, edge density; LSI, landscape shape index; CONTAG, contagion index; DIVISION, landscape division index; H', Shannon's diversity index. All inter-regional differences were statistically significant at P<0.050 level.
Table 4 Comparison of structural landscape connectivity indices between eastern and western Mu Us Sandy Land from 1990 to 2020
Fig. 8 Principal component analysis (PCA) of structural landscape connectivity index. (a), 1990; (b), 1995; (c), 2000; (d), 2005; (e), 2010; (f), 2015; (g), 2020. PC, principal component.
Year Value T P WS DEM Slope Aspect LUCC CLR RD NL
1990 q 0.166 0.084 0.182 0.151 0.064 0.004 0.113 0.174 0.106 0.025
P 0.000 0.000 0.000 0.000 0.000 0.501 0.000 0.000 0.000 0.000
1995 q 0.251 0.118 0.161 0.150 0.055 0.002 0.130 0.181 0.155 0.031
P 0.000 0.000 0.000 0.000 0.000 0.850 0.000 0.000 0.000 0.000
2000 q 0.194 0.263 0.168 0.155 0.054 0.003 0.070 0.130 0.107 0.059
P 0.000 0.000 0.000 0.000 0.000 0.709 0.000 0.000 0.000 0.000
2005 q 0.186 0.338 0.078 0.155 0.054 0.003 0.075 0.131 0.088 0.051
P 0.000 0.000 0.000 0.000 0.000 0.709 0.000 0.000 0.000 0.000
2010 q 0.218 0.259 0.126 0.178 0.055 0.003 0.075 0.127 0.153 0.074
P 0.000 0.000 0.000 0.000 0.000 0.720 0.000 0.000 0.000 0.000
2015 q 0.249 0.165 0.068 0.185 0.057 0.003 0.080 0.131 0.162 0.142
P 0.000 0.000 0.000 0.000 0.000 0.691 0.000 0.000 0.000 0.000
2020 q 0.274 0.185 0.020 0.207 0.067 0.004 0.086 0.135 0.232 0.150
P 0.000 0.000 0.000 0.000 0.000 0.605 0.000 0.000 0.000 0.000
Note: T, temperature; P, precipitation; WS, wind speed; DEM, digital elevation model; LUCC, land use/cover change; CLR, cropland ratio; RD, residential density; NL, nighttime light.
Table 5 Factor detector of the Geodetector (GD)
Fig. 9 Interaction result of the Geodetector (GD). (a), 1990; (b), 1995; (c), 2000; (d), 2005; (e), 2010; (f), 2015; (g), 2020. NL, nighttime light; RD, residential density; CLR, cropland ratio; LUCC, land use/cover change; DEM, digital elevation model.
Fig. 10 Conceptual diagram of the driving mechanisms of structural landscape connectivity in the Mu Us Sandy Land from 1990 to 2020. Arrows indicate causal relationships and bidirectional arrows indicate synergistic interaction.
[1]   Armando S, Javier M, Carmen C V, et al. 2023. Modelling the effects of climate and land-cover changes on the potential distribution and landscape connectivity of three earth snakes (Genus Conopsis, Günther 1858) in central Mexico. The Science of Nature, 110(6): 52, doi: 10.1007/s00114-023-01880-7.
[2]   Bishop-Taylor R, Tulbure M G, Broich M. 2017. Impact of hydroclimatic variability on regional-scale landscape connectivity across a dynamic dryland region. Ecological Indicators, 94: 142-150.
doi: 10.1016/j.ecolind.2017.07.029
[3]   Chen H J, Ge J Y, He W, et al. 2025. Quantifying urban vitality in Guangzhou through multi-source data: a comprehensive analysis of land use change, streetscape elements, POI distribution, and smartphone-GPS data. Land, 14(6): 1309, doi: 10.3390/land14061309.
[4]   Clauzel C, Jeliazkov A, Mimet A, et al. 2018. Coupling a landscape-based approach and graph theory to maximize multispecific connectivity in bird communities. Landscape and Urban Planning, 179: 1-16.
doi: 10.1016/j.landurbplan.2018.07.002
[5]   Dong F F, Qin F C, Dong X Y, et al. 2024. Spatiotemporal evolution and drivers of ecological quality in the Tengger Desert (2001-2021). Land, 13(11): 1838, doi: 10.3390/land13111838.
[6]   Duan H C, Wang T, Xue X, et al. 2012. Spatial-temporal evolution of aeolian desertification and landscape pattern in Horqin Sandy Land: a case study of Naiman Banner in Inner Mongolia. Acta Geographica Sinica, 67(7): 917-928. (in Chinese)
doi: 10.11821/xb201207005
[7]   Fang F H, Li Y L, Li Y Q, et al. 2025. Changes in landscape patterns and their driving factors of the typical dune alternated with meadow area in the Horqin Sandy Land. Journal of Desert Research, 45(4): 285-294. (in Chinese)
doi: 10.7522/j.issn.1000-694X.2025.00181
[8]   Foley J A, Defries R, Asner G P, et al. 2005. Global consequences of land use. Science, 309(5734): 570-574.
doi: 10.1126/science.1111772 pmid: 16040698
[9]   Guan B C, Gao J J, Chen W, et al. 2021. The effects of climate change on landscape connectivity and genetic clusters in a small subtropical and warm-temperate tree. Frontiers in Plant Science, 12: 671336, doi: 10.3389/fpls.2021.671336.
[10]   Haddad N M, Brudvig L A, Clobert J, et al. 2015. Habitat fragmentation and its lasting impact on Earth's ecosystems. Science Advances, 1(2): e1500052, doi: 10.1126/sciadv.1500052.
[11]   Han J X, Dalaibaatar E. 2023. A study on the influencing factors of China's ecological footprint based on EEMD-GeoDetector. Sustainability, 15(8): 6680, doi: 10.3390/SU15086680.
[12]   Han X Y, Jia G P, Yang G, et al. 2020. Spatiotemporal dynamic evolution and driving factors of desertification in the Mu Us Sandy Land in 30 years. Scientific Reports, 10: 21734, doi: 10.1038/s41598-020-78665-9.
[13]   Haverland M B, Veech J A. 2017. Examining the occurrence of mammal species in natural areas within a rapidly urbanizing region of Texas, USA. Landscape and Urban Planning, 157: 221-230.
doi: 10.1016/j.landurbplan.2016.06.001
[14]   Ji X Y, Yang J Z, Liu J Y, et al. 2023. Analysis of spatial-temporal changes and driving forces of desertification in the Mu Us Sandy Land from 1991 to 2021. Sustainability, 15(13): 10399, doi: 10.3390/su151310399.
[15]   Johst K, Drechsler M, van Teeffelen A J A, et al. 2011. Biodiversity conservation in dynamic landscapes: trade-offs between number, connectivity and turnover of habitat patches. Journal of Applied Ecology, 48(5): 1227-1235.
doi: 10.1111/jpe.2011.48.issue-5
[16]   Korpach A M, Garroway C J, Mills A M, et al. 2022. Urbanization and artificial light at night reduce the functional connectivity of migratory aerial habitat. Ecography, 2022(8): e05581, doi: 10.1111/ecog.05581.
[17]   Kubiak E J. 2025. Hydrological drought trends and seasonality in selected polish catchments between 1993 and 2022 using a threshold based approach. Scientific Reports, 15: 40454, doi: 10.1038/s41598-025-24133-1.
[18]   Lavagnini I, Badocco D, Pastore P, et al. 2011. Theil-Sen nonparametric regression technique on univariate calibration, inverse regression and detection limits. Talanta, 87: 180-188.
doi: 10.1016/j.talanta.2011.09.059 pmid: 22099666
[19]   Li C X, Lei L T, Jia X, et al. 2025a. Accuracy and applicability assessment of vegetation types of the land cover products produced in China. Ecological Indicators, 174: 113506, doi: 10.1016/j.ecolind.2025.113506.
[20]   Li H C, Chen J J, Ling M, et al. 2025b. A framework for dynamic assessment of soil erosion and detection of driving factors in alpine grassland ecosystems using the RUSLE-InVEST (SDR) model and Geodetector: a case study of the source region of the Yellow River. Ecological Informatics, 85: 102928, doi: 10.1016/J.ECOINF.2024.102928.
[21]   Li J M, Liang L E, Chao Y, et al. 2025c. Construction of the ecological security pattern of Mu Us sandy land on the basis of the ''Source-Resistance-Corridor'' theory. Ecological Indicators, 171: 113162, doi: 10.1016/j.ecolind.2025.113162.
[22]   Li P X, Liu C G, Sun W. 2021. Quantifying changes of landscape connectivity based on ecological process simulation in a rapidly urbanized city: Nanjing, China. Environmental Earth Sciences, 80: 644, doi: 10.1007/s12665-021-09938-3.
[23]   Liang P, Yang X P. 2016. Landscape spatial patterns in the Maowusu (Mu Us) Sandy Land, northern China and their impact factors. CATENA, 145: 321-333.
doi: 10.1016/j.catena.2016.06.023
[24]   Liang L E, Chao Y, Wang X H, et al. 2025. Seasonal climate change characteristics of the Mu Us Sandy Land based on long time scale. Environmental Monitoring and Assessment, 197: 771, doi: 10.1007/s10661-025-14204-1.
pmid: 40536528
[25]   Lin M, Hou L Z, Qi Z M, et al. 2022. Impacts of climate change and human activities on vegetation NDVI in China's Mu Us Sandy Land during 2000-2019. Ecological Indicators, 142: 109164, doi: 10.1016/j.ecolind.2022.109164.
[26]   Lin J, Bo W H, Dong X P, et al. 2025. Evolution of vegetation cover and impacts of climate change and human activities in arid regions of Northwest China: a Mu Us Sandy Land case. Environment, Development and Sustainability, 27: 18977-18996.
doi: 10.1007/s10668-024-04704-4
[27]   Liu J, Liu H M, Zhuo Y, et al. 2017. Dynamics and driving forces of landscape patterns in Mu Us Sandy Land, from 1990 to 2014. Pratacultural Science, 34(2): 255-263. (in Chinese)
[28]   Liu J Y, Jia N, Kuang W H, et al. 2018. Spatio-temporal patterns and characteristics of land-use change in China during 2010-2015. Acta Geographica Sinica, 73(5): 789-802.
doi: 10.11821/dlxb201805001
[29]   Liu Q F, Zhang Q, Yan Y Z, et al. 2020. Ecological restoration is the dominant driver of the recent reversal of desertification in the Mu Us Desert (China). Journal of Cleaner Production, 268: 122241, doi: 10.1016/j.jclepro.2020.122241.
[30]   Mantyka-Pringle C S, Visconti P, Di Marco M, et al. 2015. Climate change modifies risk of global biodiversity loss due to land-cover change. Biological Conservation, 187: 103-111.
doi: 10.1016/j.biocon.2015.04.016
[31]   McGuire J L, Lawler J J, McRae B H, et al. 2016. Achieving climate connectivity in a fragmented landscape. Proceedings of the National Academy of Sciences of the United States of America, 113: 7195-7200.
[32]   Mestre F, Risk B B, Mira A, et al. 2017. A metapopulation approach to predict species range shifts under different climate change and landscape connectivity scenarios. Ecological Modelling, 359: 406-414.
doi: 10.1016/j.ecolmodel.2017.06.013
[33]   O'Connell E, O'Donnell G, Koutsoyiannis D, et al. 2023. On the spatial scale dependence of long-term persistence in global annual precipitation data and the Hurst phenomenon. Water Resources Research, 59(4): e2022WR033133, doi: 10.1029/2022WR033133.
[34]   Pelorosso R, Noce S, De Notaris C, et al. 2025. The nexus between connectivity, climate, and land use: a scenario assessment of bio-energy landscape connectivity in central Italy. Journal of Environmental Management, 376: 124521, doi: 10.1016/j.jenvman.2025.124521.
[35]   Peng J, Xu D M, Tang H, et al. 2025. A landscape ecological approach to spatial conservation planning-ecological security pattern. Trends in Ecology & Evolution, 40(10): 1010-1022.
doi: 10.1016/j.tree.2025.07.014
[36]   Raven P H, Wagner D L. 2021. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proceedings of the National Academy of Sciences of the United States of America, 118(2): e2002548117, doi: 10.1073/pnas.2002548117.
[37]   Sang Y F, Wang Z G, Liu C M. 2014. Comparison of the MK test and EMD method for trend identification in hydrological time series. Journal of Hydrology, 510: 293-298.
doi: 10.1016/j.jhydrol.2013.12.039
[38]   Tischendorf L, Fahrig L. 2000. On the usage and measurement of landscape connectivity. Oikos, 90(1): 7-19.
doi: 10.1034/j.1600-0706.2000.900102.x
[39]   Tu W N, Crowther T W, Du Y Y, et al. 2025. Wilderness quality, habitat connectivity, and the effectiveness of protected areas diminish as human activities intensify. Conservation Letters, 18(5): e13149, doi: 10.1111/conl.13149.
[40]   Wang C X, Liu Y X, Chen J S, et al. 2025. Turning points of the relationship between human activity and environmental quality in China. Sustainable Cities and Society, 119: 106123, doi: 10.1016/j.scs.2025.106123.
[41]   Wang J F, Xu C D. 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116-134. (in Chinese)
doi: 10.11821/dlxb201701010
[42]   Wang N, Xie J C, Han J C, et al. 2014. A comprehensive framework on land-water resources development in Mu Us Sandy Land. Land Use Policy, 40: 69-73.
doi: 10.1016/j.landusepol.2013.09.022
[43]   Williams S H, Scriven S A, Burslem D F R P, et al. 2020. Incorporating connectivity into conservation planning for the optimal representation of multiple species and ecosystem services. Conservation Biology, 34(4): 934-942.
doi: 10.1111/cobi.13450 pmid: 31840279
[44]   Xu Z W, Lu H Y. 2021. Aeolian environmental change studies in the Mu Us Sandy Land, north-central China: theory and recent progress. Acta Geographica Sinica, 76(9): 2203-2223. (in Chinese)
doi: 10.11821/dlxb202109012
[45]   Zhang H D, Wei W, Xue S. 2015. Analysis on the variation of temperature and precipitation in Dingxi based on R/S and Mann-Kendall test. Research of Soil and Water Conservation, 22(6): 183-189. (in Chinese)
[46]   Zhang J X, Gong J, Liu D Q. 2018. Dynamics and driving factors of landscape fragmentation based on Geodetector in the Bailongjiang watershed of Gansu Province. Geographical Science, 38(8): 1370-1378. (in Chinese)
[47]   Zhang L X, Ren Z H, Chen B, et al. 2024a. A prolonged artificial nighttime-light dataset of China (1984-2020). Scientific Data, 11: 414, doi: 10.1038/s41597-024-03223-1.
[48]   Zhang Q M, Jia B Q, Li T, et al. 2024b. Research on the sustainability of "greening" process in the Mu Us Sandy Land based on the spatiotemporal stability of ecological land. PLoS ONE, 19(2): e0292469, doi: 10.1371/journal.pone.0292469.
[49]   Zhang W T, Fan Y R, Qiao Q, et al. 2025. Delineating ecological restoration zoning integrating functional and structural models in Horqin Sandy Land, China. Forests, 16(11): 1616, doi: 10.3390/f16111616.
[50]   Zheng X, Zhu J J, Wang G G, et al. 2025. Assessing the ecological effects of the world's largest forestry eco-engineering: Three-North Protective Forest Program within the initially scheduled range from 1978 to 2022. Science China Life Sciences, 68: 314-327.
doi: 10.1007/s11427-024-2705-4
[51]   Zhou S Q, Jing Y D, Zhang Q F, et al. 2013. Vegetation landscape pattern change and characteristics of spatial distribution in south edge of Mu Us Sandy Land. Acta Ecologica Sinica, 33(12): 3774-3782. (in Chinese)
doi: 10.5846/stxb
[52]   Zhu H Y, Li X B. 2003. Discussion on the index method of regional land use change. Acta Geographica Sinica, 58(5): 643-650. (in Chinese)
doi: 10.11821/xb200305001
[1] HUANG Kepan, XU Haojie, LIU Zhifei, WANG Dawei. Enhanced InVEST modeling reveals divergent trajectories of carbon storage in the eastern Qinghai-Xizang Plateau during 1990-2100[J]. Journal of Arid Land, 2026, 18(8): 1283-1303.
[2] MA Zhenrong, WANG Yongdong, ZHOU Zhibin, CHEN Yusen. Ecozone-dependent reorganization of multifactor interactions controls actual evapotranspiration across arid mountain-basin systems[J]. Journal of Arid Land, 2026, 18(8): 1354-1377.
[3] ZHAO Dan, YANG Xiya, GAO Yukun, PAN Jing, YOU Quangang, XUE Xian. Incorporating spatial autocorrelation into soil salinity models: Insights from the Minqin Oasis and its desert-oasis transition zone in Northwest China[J]. Journal of Arid Land, 2026, 18(7): 1135-1158.
[4] CHENG Ruixuan, QIN Ke, YIN Sijiang. Surface water dynamics and driving factors in the Huangshui River Basin of the Xining-Haidong Corridor in China from 2000 to 2024 based on Google Earth Engine[J]. Journal of Arid Land, 2026, 18(7): 1192-1212.
[5] LYU Xin, LI Xiaobing, WANG Kai, CAO Wanyu, ZHANG Chenhao. Anthropogenic activities amplify spatiotemporal variations in regional ecological security patterns dominated by natural factors: Evidence from the West Liaohe River Basin, China[J]. Journal of Arid Land, 2026, 18(5): 752-773.
[6] LIU Yuhan, ZHAO Yuanyuan, GAO Guanglei, DING Guodong, LI Ning. Spatiotemporal patterns and driving forces of dust weather events in Central Asia from 2000 to 2020[J]. Journal of Arid Land, 2026, 18(1): 1-16.
[7] XI Ruiyun, PEI Tingting, CHEN Ying, XIE Baopeng, HOU Li, WANG Wen. Spatiotemporal dynamic and drivers of ecological environmental quality on the Chinese Loess Plateau: Insights from kRSEI model and climate-human interaction analysis[J]. Journal of Arid Land, 2025, 17(7): 958-978.
[8] HE Yuanrong, CHEN Yuhang, ZHONG Liang, LAI Yangfeng, KANG Yuting, LUO Ming, ZHU Yunfei, ZHANG Ming. Spatiotemporal evolution of ecological environment quality and its drivers in the Helan Mountain, China[J]. Journal of Arid Land, 2025, 17(2): 224-244.
[9] Haoriwa, Zhalagahu, ZHOU Ruiping. Spatiotemporal variation of drought and its influential factors in the Yellow River Basin, China based on vegetation health index[J]. Journal of Arid Land, 2025, 17(10): 1361-1377.
[10] CHENG Lanhua, YANG Xianming, PAN Xumei, AN Jingfeng. Identification and classification of ecological restoration areas in the territorial land space of the Qaidam Basin, China[J]. Journal of Arid Land, 2025, 17(10): 1402-1424.
[11] PAN Meihui, CHEN Qing, LI Chenlu, LI Na, GONG Yifu. Spatiotemporal variation and influencing factors of desertification sensitivity on the Qinghai-Xizang Plateau, China[J]. Journal of Arid Land, 2025, 17(1): 58-73.
[12] 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[J]. Journal of Arid Land, 2024, 16(9): 1163-1182.
[13] 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.
[14] SHEN Jianxiang, WANG Xin, WANG Lei, WANG Jiahui, QU Wenjie, ZHANG Xue, CHANG Xuanxuan, YANG Xinguo, CHEN Lin, QIN Weichun, ZHANG Bo, NIU Jinshuai. Spatiotemporal characteristics of seed rain and soil seed bank of artificial Caragana korshinskii Kom. forest in the Tengger Desert, China[J]. Journal of Arid Land, 2024, 16(4): 550-566.
[15] 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.