Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2022, Vol. 14 Issue (3): 262-283    DOI: 10.1007/s40333-022-0011-2     CSTR: 32276.14.s40333-022-0011-2
Research article     
Spatiotemporal changes of eco-environmental quality based on remote sensing-based ecological index in the Hotan Oasis, Xinjiang
YAO Kaixuan1,2, Abudureheman HALIKE1,2,3,*(), CHEN Limei1,2, WEI Qianqian1,2
1College of Geographical Science, Xinjiang University, Urumqi 830017, China
2Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830017, China
3Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830017, China
Download: HTML     PDF(4002KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The rapid economic development that the Hotan Oasis in Xinjiang Uygur Autonomous Region, China has undergone in recent years may face some challenges in its ecological environment. Therefore, an analysis of the spatiotemporal changes in ecological environment of the Hotan Oasis is important for its sustainable development. First, we constructed an improved remote sensing-based ecological index (RSEI) in 1990, 1995, 2000, 2005, 2010, 2015 and 2020 on the Google Earth Engine (GEE) platform and implemented change detection for their spatial distribution. Second, we performed a spatial autocorrelation analysis on RSEI distribution map and used land-use and land-cover change (LUCC) data to analyze the reasons of RSEI changes. Finally, we investigated the applicability of improved RSEI to arid area. The results showed that mean of RSEI rose from 0.41 to 0.50, showing a slight upward trend. During the 30-a period, 2.66% of the regions improved significantly, 10.74% improved moderately and 32.21% improved slightly, respectively. The global Moran's I were 0.891, 0.889, 0.847 and 0.777 for 1990, 2000, 2010 and 2020, respectively, and the local indicators of spatial autocorrelation (LISA) distribution map showed that the high-high cluster was mainly distributed in the central part of the Hotan Oasis, and the low-low cluster was mainly distributed in the outer edge of the oasis. RSEI at the periphery of the oasis changes from low to high with time, with the fragmentation of RSEI distribution within the oasis increasing. Its distribution and changes are predominantly driven by anthropologic factors, including the expansion of artificial oasis into the desert, the replacement of desert ecosystems by farmland ecosystems, and the increase in the distribution of impervious surfaces. The improved RSEI can reflect the eco-environmental quality effectively of the oasis in arid area with relatively high applicability. The high efficiency exhibited with this approach makes it convenient for rapid, high frequency and macroscopic monitoring of eco-environmental quality in study area.

Key words： remote sensing-based ecological index      Google Earth Engine      spatial autocorrelation analysis      eco-environmental quality      arid area     
Received: 25 October 2021      Published: 31 March 2022
Corresponding Authors: *Abudureheman HALIKE (E-mail: ah@xju.edu.cn)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
YAO Kaixuan
Abudureheman HALIKE
CHEN Limei
WEI Qianqian
Cite this article:

YAO Kaixuan, Abudureheman HALIKE, CHEN Limei, WEI Qianqian. Spatiotemporal changes of eco-environmental quality based on remote sensing-based ecological index in the Hotan Oasis, Xinjiang. Journal of Arid Land, 2022, 14(3): 262-283.

URL:

http://jal.xjegi.com/10.1007/s40333-022-0011-2     OR     http://jal.xjegi.com/Y2022/V14/I3/262

Fig. 1 Workflow of the study. GEE, Google Earth Engine; NDVI, normalized difference vegetation index; WET, wetness; BSI, bare soil index; SI, salinity index; PCA, principal component analysis; RSEI, remote sensing-based ecological index; LUCC, land-use and land-cover change.
Fig. 2 Location of the Hotan Oasis
Year Dataset Satellite Used band Number (scene) Year
combination		 Path /row Month/cloud/
spatial resolution
1990 LANDSAT/LT05/C02/
T1_L2		 Landsat 5
/TM		 SR_B (1, 2, 3, 4, 5, 7) ST_B6 4 1989/1990/1991 146/033 Jul-Aug
1995 14 1994/1995/1996 146/034 <20%
2000 LANDSAT/LE07/C02/ Landsat 7 SR_B (1, 2, 3, 4, 5, 12 1999/2000/2001 146/035 30 m
2005 T1_L2 /ETM+ 7) ST_B6 19 2004/2005/2006 147/034
2010 LANDSAT/LT05/C02/
T1_L2		 Landsat 5
/TM		 SR_B (1, 2, 3, 4, 5, 7) ST_B6 13 2009/2010/2011 147/035
2015 LANDSAT/LC08/C02/ Landsat 8 SR_B (2, 3, 4, 5, 6, 12 2014/2015/2016
2020 T1_L2 /OLI/TIRS 7) ST_B10 13 2019/2020/2021
Table 1 Basic information of Landsat data sources
PC1 1990 1995 2000 2005 2010 2015 2020
NDVI -0.48641 -0.48552 -0.49201 -0.48959 -0.49658 -0.49817 -0.50478
WET -0.47930 -0.47372 -0.47392 -0.46008 -0.48033 -0.47307 -0.47814
BSI 0.51779 0.52127 0.51721 0.52479 0.51152 0.51710 0.51375
SI 0.51534 0.51782 0.51557 0.52271 0.51092 0.51052 0.50263
Eigenvalue 0.09923 0.09809 0.09906 0.09599 0.10061 0.09903 0.10018
Percentage of eigenvalue (%) 89.63 88.67 89.59 86.94 91.52 90.60 91.59
Table 2 First principal component (PC1) of principal component analysis for RSEI in 1990, 1995, 2000, 2005, 2010, 2015 and 2020
Year Indicator PC1 PC2 PC3 PC4
1990 NDVI -0.48641 0.66914 0.10060 0.55275
WET -0.47930 -0.70834 -0.20970 0.47387
BSI 0.51779 -0.17157 0.63423 0.54792
SI 0.51534 0.14517 -0.73733 0.41194
Eigenvalue 0.09923 0.00942 0.00186 0.00020
Percentage of eigenvalue (%) 89.63 8.51 1.68 0.18
2020 NDVI -0.50478 0.55881 -0.10010 0.65032
WET -0.47814 -0.80865 -0.17005 0.29755
BSI 0.51375 -0.18263 0.54236 0.63918
SI 0.50263 -0.02139 -0.81665 0.28283
Eigenvalue 0.10018 0.00625 0.00283 0.00012
Percentage of eigenvalues (%) 91.59 5.71 2.59 0.11
Table 3 Principal component (PC) analysis for 1990 and 2020
Fig. 3 Spatial distribution of multi-year mean RSEI (remote sensing-based ecological index) in the Hotan Oasis
Fig. 4 Spatial distribution of RSEI (remote sensing-based ecological index) level in the Hotan Oasis in 1990, 1995, 2000, 2005, 2010, 2015 and 2020
Fig. 5 Percentage of different levels of RSEI (remote sensing-based ecological index) in the Hotan Oasis in 1990, 1995, 2000, 2005, 2010, 2015 and 2020
Year Index 3 (OI) 2 (GI) 1 (SI) 0 (IN) -1 (SD) -2 (GD) -3 (OD)
1990-2000 Area (km²) 0.88 36.59 547.22 3191.04 504.04 12.05 0.34
Percentage (%) 0.02 0.85 12.75 74.35 11.74 0.28 0.01
2000-2010 Area (km²) 6.79 147.91 1130.46 2641.94 355.40 9.47 0.18
Percentage (%) 0.16 3.45 26.34 61.55 8.28 0.22 0.00
2010-2020 Area (km²) 13.80 194.02 1087.26 2376.80 576.91 39.75 3.53
Percentage (%) 0.32 4.52 25.33 55.38 13.44 0.93 0.08
1990-2020 Area (km²) 114.35 460.84 1382.44 1764.33 506.54 58.32 4.23
Percentage (%) 2.66 10.74 32.21 41.11 11.80 1.36 0.10
Table 4 Area and percentage of RSEI level change during 1990-2000, 2000-2010, 2010-2020 and 1990-2020
Fig. 6 RSEI (remote sensing-based ecological index) level change during 1990-2000 (a), 2000-2010 (b), 2010-2020 (c) and 1990-2020 (d). OI, GI, SI, IN, SD, GD and OD represent that RSEI level of the area is obviously improved, generally improved, slightly improved, invariable, slightly deteriorated, generally deteriorated and obviously deteriorated, respectively. The number of 3-1, 0 and -1- -3 mean the improved, unchanged and deteriorated levels of RSEI, respectively.
Fig. 7 Global Moran's I scatterplot of RSEI (remote sensing-based ecological index) level change in the Hotan Oasis in 1990 (a), 2000 (b), 2010 (c) and 2020 (d). H-H, L-L, H-L and L-H are high-high cluster, low-low cluster, high-low outlier and low-high outlier, respectively.
Fig. 8 LISA (local indicators of spatial autocorrelation) clustering map of RSEI in the Hotan Oasis in 1990 (a), 2000 (b), 2010 (c) and 2020 (d)
Fig. 9 Spatial distribution map of LUCC (land-use and land-cover change) in the Hotan Oasis in 2000 and 2020 (a, b, c, d, e and f are the Google Earth images of six types of typical land surfaces. Boxes are the location of these land surfaces)
Fig. 10 Three-dimensional scatter plots of NDVI, WET and RSEI (a); and of BSI, SI and RSEI (b). NDVI, normalized difference vegetation index; WET, wetness; RSEI, remote sensing-based ecological index; BSI, bare soil index; SI, salinity index;
Fig. 11 Correlation matrix of each indicator and RSEI in 1990 (a) and 2020 (b). NDVI, normalized difference vegetation index; WET, wetness; BSI, bare soil index; SI, salinity index; RSEI, remote sensing-based ecological index.
Fig. 12 Comparison among indicators. a, b and c represent different sampling locations; 6 and 7 represents the standard false color image and true color image of Sentinel 2, respectively. NDVI, normalized difference vegetation index; WET, wetness; BSI, bare soil index; SI, salinity index; RSEI, remote sensing-based ecological index.
Year Total population (10,000 persons) Total power of
agricultural machinery (10,000 kW)		 Cultivated
land area (1000 hm2)		 Grain production (t) Gross
regional product (10,000 CNY)		 Primary industry GRP (10,000 CNY) Secondary industry GRP (10,000 CNY) Tertiary industry GRP (10,000 CNY)
1992 90.10 17.50 81.84 387,358 81,029 50,761 11,648 18,620
1995 96.60 18.40 82.64 404,569 160,780 105,131 21,612 34,035
2000 110.00 23.00 91.17 509,114 178,411 101,566 22,614 54,231
2005 120.00 26.00 91.41 579,342 321,140 134,080 75,448 111,612
2010 135.00 33.00 97.11 674,272 696,522 210,118 141,565 344,839
2015 155.00 54.00 - - 1,612,653 403,842 266,778 942,033
2019 171.28 - 123.25 - 2,630,573 451,317 422,743 1,756,513
Table 5 Socio-economic data of the Hotan Oasis
[1]   Alexander C. 2020. Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). International Journal of Applied Earth Observation and Geoinformation, 86: 102013, doi: 10.1016/j.jag.2019.102013.
doi: 10.1016/j.jag.2019.102013
[2]   Anselin L. 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27(2): 93-115.
doi: 10.1111/gean.1995.27.issue-2
[3]   Baig M H A, Zhang L F, Shuai T, et al. 2014. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5): 423-431.
doi: 10.1080/2150704X.2014.915434
[4]   Cao R, Chen Y, Shen M G, et al. 2018. A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter. Remote Sensing of Environment, 217: 244-257.
doi: 10.1016/j.rse.2018.08.022
[5]   Chen J, Shen M G, Zhu X L, et al. 2009. Indicator of flower status derived from in situ hyperspectral measurement in an alpine meadow on the Tibetan Plateau. Ecological Indicators, 9(4): 818-823.
doi: 10.1016/j.ecolind.2008.09.009
[6]   Chen M X, Ye C, Lu D D, et al. 2019. Cognition and construction of the theoretical connotations of new urbanization with Chinese characteristics. Journal of Geographical Sciences, 29(10): 1681-1698.
doi: 10.1007/s11442-019-1685-z
[7]   Chu H S, Venevsky S, Wu C, et al. 2019. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of the Total Environment, 650: 2051-2062.
doi: 10.1016/j.scitotenv.2018.09.115
[8]   Cook M, Schott J R, Mandel J, et al. 2014. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sensing, 6(11): 11244-11266.
doi: 10.3390/rs61111244
[9]   Deng X Z, Huang J K, Rozelle S, et al. 2015. Impact of urbanization on cultivated land changes in China. Land Use Policy, 45: 1-7.
doi: 10.1016/j.landusepol.2015.01.007
[10]   Ding Y, Abdirahman H, Chen X Y, et al. 2020. Spatial-temporal changes in vegetation characteristics and climate in Hotan Prefecture. Acta Ecologica Sinica, 40(4): 1258-1268. (in Chinese)
[11]   Dong D W, Abdirahman H, Wang D D, et al. 2019. Spatio-temporal variations in vegetation cover in Hotan Oasis from 1994 to 2016. Acta Ecologica Sinica, 39(10): 3710-3719. (in Chinese)
[12]   Fang H L, Zhang Y G, Wei S S, et al. 2019. Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern China. Remote Sensing of Environment, 233: 111377, doi: 10.1016/j.rse.2019.111377.
doi: 10.1016/j.rse.2019.111377
[13]   Feidas H, Karagiannidis A, Keppas S, et al. 2014. Modeling and mapping temperature and precipitation climate data in Greece using topographical and geographical parameters. Theoretical and Applied Climatology, 118(1): 133-146.
doi: 10.1007/s00704-013-1052-4
[14]   Fung T, Siu W. 2000. Environmental quality and its changes, an analysis using NDVI. International Journal of Remote Sensing, 21(5): 1011-1024.
doi: 10.1080/014311600210407
[15]   Getis A. 2007. Reflections on spatial autocorrelation. Regional Science and Urban Economics, 37(4): 491-496.
doi: 10.1016/j.regsciurbeco.2007.04.005
[16]   Guo H, Bao A M, Liu AT, et al. 2019. Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI): Evaluation in Central Asia. Remote Sensing of Environment, 231: 111220, doi: 10.1016/j.rse.2019.111220.
doi: 10.1016/j.rse.2019.111220
[17]   Guo H W, Ling H B, Xu H L, et al. 2016. Study of suitable oasis scales based on water resource availability in an arid region of China: a case study of Hotan River Basin. Environmental Earth Sciences, 75(11): 1-14.
doi: 10.1007/s12665-015-4873-x
[18]   Guo Y H, Abdirahman H, Wei T B, et al. 2021. The ecosystem service value evaluation of Hotan area based on land use changes. Acta Ecologica Sinica, 41(16): 6363-6372. (in Chinese)
[19]   Hao L N, Su X L, Singh V P, et al. 2019. Suitable oasis and cultivated land scales in arid regions based on ecological health. Ecological Indicators, 102: 33-42.
doi: 10.1016/j.ecolind.2019.01.053
[20]   Hassan M A, Yang M J, Rasheed A, et al. 2019. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 282: 95-103.
doi: 10.1016/j.plantsci.2018.10.022
[21]   Huang C, Wylie B, Yang L, et al. 2002. Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. Remote Sensing Letters, 23(8): 1741-1748.
[22]   Jia H W, Yan C Z, Xing X G, et al. 2021. Evaluation of ecological environment in the Dulan County based on the modified remote sensing ecological index model. Journal of Desert Research, 41(2): 181-190. (in Chinese)
[23]   Jiang C L, Wu L, Liu D, et al. 2019. Dynamic monitoring of eco-environmental quality in arid desert area by remote sensing: Taking the Gurbantunggut Desert China as an example. Chinese Journal of Applied Ecology, 30(3): 877-883. (in Chinese)
[24]   Jiang L L, Jiapaer G, Bao A M, et al. 2019. Assessing land degradation and quantifying its drivers in the Amudarya River delta. Ecological Indicators, 107: 105595, doi: 10.1016/j.ecolind.2019.105595.
doi: 10.1016/j.ecolind.2019.105595
[25]   Jing Y Q, Zhang F, He Y F, et al. 2020. Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China. Ecological Indicators, 110: 105874, doi: 10.1016/j.ecolind.2019.105874.
doi: 10.1016/j.ecolind.2019.105874
[26]   Joshi P P, Wynne R H, Thomas V A, et al. 2019. Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8. International Journal of Applied Earth Observation and Geoinformation, 82: 101898, doi: 10.1016/j.jag.2019.101898.
doi: 10.1016/j.jag.2019.101898
[27]   Kganyago M, Mhangara P, Alexandridis T, et al. 2020. Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape. Remote Sensing Letters, 11(10): 883-892.
doi: 10.1080/2150704X.2020.1767823
[28]   Khan N M, Rastoskuev V V, Sato Y, et al. 2005. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77(1-3): 96-109.
doi: 10.1016/j.agwat.2004.09.038
[29]   Legendre P. 1993. Spatial autocorrelation: trouble or new paradigm?. Ecology, 74(6): 1659-1673.
doi: 10.2307/1939924
[30]   Li R H, Chen N C, Zhang X, et al. 2020. Quantitative analysis of agricultural drought propagation process in the Yangtze River Basin by using cross wavelet analysis and spatial autocorrelation. Agricultural and Forest Meteorology, 280: 107809, doi: 10.1016/j.agrformet.2019.107809.
doi: 10.1016/j.agrformet.2019.107809
[31]   Li Z Y, Han Y, Hao T Y. 2020. Assessing the consistency of remotely sensed multiple drought indices for monitoring drought phenomena in continental China. IEEE Transactions on Geoscience and Remote Sensing, 58(8): 5490-5502.
doi: 10.1109/TGRS.36
[32]   Liu C, Yang M H, Hou Y T, et al. 2021. Spatiotemporal evolution of island ecological quality under different urban densities: A comparative analysis of Xiamen and Kinmen Islands, southeast China. Ecological Indicators, 124: 107438, doi: 10.1016/j.ecolind.2021.107438.
doi: 10.1016/j.ecolind.2021.107438
[33]   Liu Y, Xue J, Gui D W, et al. 2018. Agricultural oasis expansion and its impact on oasis landscape patterns in the southern margin of Tarim Basin, Northwest China. Sustainability, 10(6): 1957.
doi: 10.3390/su10061957
[34]   Mildrexler D J, Zhao M S, Running S W. 2009. Testing a MODIS global disturbance index across North America. Remote Sensing of Environment, 113(10): 2103-2117.
doi: 10.1016/j.rse.2009.05.016
[35]   Moran P A P. 1948. The interpretation of statistical maps. Journal of the Royal Statistical Society: Series B (Methodological), 10(2): 243-251.
[36]   Mutanga O, Kumar L. 2019. Google earth engine applications. Remote Sensing, 11(5): 591.
doi: 10.3390/rs11050591
[37]   Pekel J F, Cottam A, Gorelick N, et al. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422.
doi: 10.1038/nature20584
[38]   Rhee J, Im J, Carbone G J. 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114(12): 2875-2887.
doi: 10.1016/j.rse.2010.07.005
[39]   Sokal R R, Oden N L. 1978. Spatial autocorrelation in biology: Methodology. Biological Journal of the Linnean Society, 10(2): 199-228.
doi: 10.1111/bij.1978.10.issue-2
[40]   Wang J, Ma J L, Xie F F, et al. 2020. Improvement of remote sensing ecological index in arid regions: Taking Ulan Buh Desert as an example. Chinese Journal of Applied Ecology, 31(11): 3795-3804. (in Chinese)
[41]   Wang J, Liu D W, Ma J L, et al. 2021. Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin. Journal of Arid Land, 13(1): 40-55.
doi: 10.1007/s40333-021-0052-y
[42]   Wang L C, Jiao L, Lai F B, et al. 2019. Evaluation of ecological changes based on a remote sensing ecological index in a Manas Lake wetland. Acta Ecologica Sinica, 39(8): 2963-2972. (in Chinese)
[43]   Wang L P, Wang S F, Zhang L D, et al. 2020. Assessing the spatial pattern of irrigation demand under climate change in arid area. ISPRS International Journal of Geo-Information, 9(9): 506.
doi: 10.3390/ijgi9090506
[44]   Wang Y G. 2021. Evaluation of lake wetland ecotourism resources based on remote sensing ecological index. Arabian Journal of Geosciences, 14(7): 1-8.
doi: 10.1007/s12517-020-06304-8
[45]   Wang Z X, Liu C, Alfredo H. 2003. From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index research. Acta Ecologica Sinica, 23(5): 979-987. (in Chinese)
[46]   Whitney K, Scudiero E, El-Askary H M, et al. 2018. Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA. Ecological Indicators, 93: 889-898.
doi: 10.1016/j.ecolind.2018.05.069
[47]   Xiong Y, Xu W H, Lu N, et al. 2021. Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan Province, China. Ecological Indicators, 125: 107518, doi: 10.1016/j.ecolind.2021.107518.
doi: 10.1016/j.ecolind.2021.107518
[48]   Xu H Q. 2005. A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5): 589-595. (in Chinese)
[49]   Xu H Q. 2013. A remote sensing urban ecological index and its application. Acta Ecologica Sinica, 33(24): 7853-7862. (in Chinese)
[50]   Xu H Q, Wang M Y, Shi T T, et al. 2018. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecological Indicators, 93: 730-740.
doi: 10.1016/j.ecolind.2018.05.055
[51]   Xu H Q, Wang Y F, Guan H, et al. 2019. Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sensing, 11(20): 2345.
doi: 10.3390/rs11202345
[52]   Xue D P, Xue J, Dai H, et al. 2021. Analysis of spatial and temporal pattern changes and driving factors of Hotan Oasis. Journal of Desert Research, 41(4): 59-69. (in Chinese)
[53]   Xue J, Su B F. 2017. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017: 1-17, doi: 10.1155/2017/1353691.
[54]   Yang Q Q, Xu G L, Li A J, et al. 2021. Evaluation and trade-off of ecosystem services in the Qingyijiang River Basin. Acta Ecologica Sinica, 41(23): 9315-9327. (in Chinese)
[55]   Yu G M, Zhang S, Yu Q W, et al. 2014. Assessing ecological security at the watershed scale based on RS/GIS: A case study from the Hanjiang River Basin. Stochastic Environmental Research and Risk Assessment, 28(2): 307-318.
doi: 10.1007/s00477-013-0750-x
[56]   Yu R H, Liu T X, Xu Y P, et al. 2010. Analysis of salinization dynamics by remote sensing in Hetao Irrigation District of North China. Agricultural Water Management, 97(12): 1952-1960.
doi: 10.1016/j.agwat.2010.03.009
[57]   Yu T, Jiapaer G, Bao A M, et al. 2021. Using synthetic remote sensing indicators to monitor the land degradation in a salinized area. Remote Sensing, 13(15): 2581, doi: 10.3390/rs13152851.
doi: 10.3390/rs13132581
[58]   Zhang T Y, Wang L, Wang H, et al. 2017. Assessment of soil salinization ecological environment change in the Manas river basin using remote sensing technology. Acta Ecologica Sinica, 37(9): 3009-3018. (in Chinese)
[59]   Zhao G, Gao H L, Cai X M, 2020. Estimating lake temperature profile and evaporation losses by leveraging MODIS LST data. Remote Sensing of Environment, 251: 112104, doi: 10.1016/j.rse.2020.112104.
doi: 10.1016/j.rse.2020.112104
[60]   Zheng Z H, Wu Z F, Chen Y B, et al. 2020. Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years. Ecological Indicators, 119: 106847, doi: 10.1016/j.ecolind.2020.106847.
[61]   Zhu J W, Xie X T, Li X H. 2017. A solution to the problem of ecological environmental carrying capacity evaluation: A case study of Henan Province. Acta Ecologica Sinica, 37(21): 7039-7047. (in Chinese)
[1] YANG Jianhua, LI Yaqian, ZHOU Lei, ZHANG Zhenqing, ZHOU Hongkui, WU Jianjun. Effects of temperature and precipitation on drought trends in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(8): 1098-1117.
[2] LU Haitian, ZHAO Ruifeng, ZHAO Liu, LIU Jiaxin, LYU Binyang, YANG Xinyue. Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China[J]. Journal of Arid Land, 2024, 16(6): 798-815.
[3] TANG Xiaoyan, FENG Yongjiu, LEI Zhenkun, CHEN Shurui, WANG Jiafeng, WANG Rong, TANG Panli, WANG Mian, JIN Yanmin, TONG Xiaohua. Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact[J]. Journal of Arid Land, 2024, 16(4): 580-601.
[4] LI Wenye, ZHANG Jianfeng, SONG Shuangshuang, LIANG Yao, SUN Baoping, WU Yi, MAO Xiao, LIN Yachao. Combination of artificial zeolite and microbial fertilizer to improve mining soils in an arid area of Inner Mongolia, China[J]. Journal of Arid Land, 2023, 15(9): 1067-1083.
[5] LONG Yi, JIANG Fugen, DENG Muli, WANG Tianhong, SUN Hua. Spatial-temporal changes and driving factors of eco- environmental quality in the Three-North region of China[J]. Journal of Arid Land, 2023, 15(3): 231-252.
[6] ZHANG Shubao, LEI Jun, TONG Yanjun, ZHANG Xiaolei, LU Danni, FAN Liqin, DUAN Zuliang. Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China[J]. Journal of Arid Land, 2023, 15(10): 1245-1268.
[7] WANG Yuxia, ZHANG Jing, YU Xiaojun. Effects of mulch and planting methods on Medicago ruthenica seed yield and soil physical-chemical properties[J]. Journal of Arid Land, 2022, 14(8): 894-909.
[8] GE Qianqian, XU Wenjie, FU Meichen, HAN Yingxin, AN Guoqiang, XU Yuetong. Ecosystem service values of gardens in the Yellow River Basin, China[J]. Journal of Arid Land, 2022, 14(3): 284-296.
[9] LI Feng, LI Yaoming, ZHOU Xuewen, YIN Zun, LIU Tie, XIN Qinchuan. Modeling and analyzing supply-demand relationships of water resources in Xinjiang from a perspective of ecosystem services[J]. Journal of Arid Land, 2022, 14(2): 115-138.
[10] CHEN Limei, Abudureheman HALIKE, YAO Kaixuan, WEI Qianqian. Spatiotemporal variation in vegetation net primary productivity and its relationship with meteorological factors in the Tarim River Basin of China from 2001 to 2020 based on the Google Earth Engine[J]. Journal of Arid Land, 2022, 14(12): 1377-1394.
[11] LI Xiu, ZHAI Juntuan, LI Zhijun. Morphological and physiological differences in heteromorphic leaves of male and female Populus euphratica Oliv.[J]. Journal of Arid Land, 2022, 14(12): 1456-1469.
[12] WANG Jinjie, DING Jianli, GE Xiangyu, QIN Shaofeng, ZHANG Zhe. Assessment of ecological quality in Northwest China (2000-2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality[J]. Journal of Arid Land, 2022, 14(11): 1196-1211.
[13] WEI Yajuan, DANG Xiaohong, WANG Ji, GAO Junliang, GAO Yan. Response of C:N:P in the plant-soil system and stoichiometric homeostasis of Nitraria tangutorum leaves in the oasis-desert ecotone, Northwest China[J]. Journal of Arid Land, 2021, 13(9): 934-946.
[14] Benjamin DAVIDSON, Elli GRONER. An arthropod community beyond the dry limit of plant life[J]. Journal of Arid Land, 2021, 13(6): 629-638.
[15] Abdulrahim M AL-ISMAILI, Moustafa A FADEL, Hemantha JAYASURIYA, L H Janitha JEEWANTHA, Adel AL-MAHDOURI, Talal AL-SHUKEILI. Potential reduction in water consumption of greenhouse evaporative coolers in arid areas via earth-tube heat exchangers[J]. Journal of Arid Land, 2021, 13(4): 388-396.