Please wait a minute...
Journal of Arid Land  2021, Vol. 13 Issue (1): 40-55    DOI: 10.1007/s40333-021-0052-y
Research article     
Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin
WANG Jie, LIU Dongwei*(), MA Jiali, CHENG Yingnan, WANG Lixin
School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Download: HTML     PDF(945KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia. It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin. In this study, the arid remote sensing ecological index (ARSEI) for large-scale arid areas was developed, which coupled the information of the greenness index, the salinity index, the humidity index, the heat index, and the land degradation index of arid areas. The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019. The results show that the greenness index, the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin, while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment. The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement, followed by deterioration, and finally further improvement. The spatial variation of these changes was significant. From 2000 to 2019, grassland and wasteland (saline alkali land and sandy land) in the central and western parts of the basin had the worst ecological environment quality. The areas with poor ecological environment quality are mainly distributed in rivers, wetlands, and cultivated land around lakes. During the period from 2000 to 2019, except for the surrounding areas of the Aral Sea, the ecological environment quality in other areas of the Aral Sea Basin has been improved in general. The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were -0.593 and 0.524, respectively. Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin. However, human activities had a greater impact. The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas; it is very suitable for the study of the eco-environmental quality in arid areas.



Key wordseco-environmental quality      arid remote sensing ecological index      Moderate Resolution Imaging Spectroradiometer (MODIS)      landscape changes      remote sensing monitoring      Central Asia     
Received: 13 May 2020      Published: 10 January 2021
Corresponding Authors:
About author: *LIU Dongwei (E-mail: liudw@imu.edu.cn)
Cite this article:

WANG Jie, LIU Dongwei, MA Jiali, CHENG Yingnan, WANG Lixin. Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin. Journal of Arid Land, 2021, 13(1): 40-55.

URL:

http://jal.xjegi.com/10.1007/s40333-021-0052-y     OR     http://jal.xjegi.com/Y2021/V13/I1/40

Fig. 1 Overview and land cover types of the study area. ASB, Aral Sea Basin.
Product name Product function Temporal resolution Spatial resolution
MOD09A1 Surface reflectance 8 d 500 m
MOD11A2 Surface temperature 8 d 1000 m
MOD13A3 Vegetation index Monthly 1000 m
MCD12Q1 Land cover type Yearly 500 m
ASTER-GDEM Digital elevation 30 m
Table 1 Product names, product functions, and temporal and spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) data
Land use type Vegetation coverage Land degradation intensity
Slope<5° 5°≤Slope<8° 8°≤Slope<15° 15°≤Slope<25° 25°≤Slope<35° Slope≥35°
Forest and grassland >75% Slight Slight Slight Slight Slight Slight
60%-75% Slight Mild Mild Mild Moderate Moderate
45%-60% Slight Mild Mild Moderate Moderate Intense
30%-45% Slight Mild Moderate Moderate Intense Extreme
<30% Slight Moderate Moderate Intense Extreme Violent
Farmland - Slight Mild Moderate Intense Extreme Violent
Table 2 Land degradation intensity of different land use types with different levels of coverage and slope
Land degradation index Land degradation intensity
Slight Mild Moderate Intense Extreme Violent
Value 0.0513 0.0412 0.1302 0.2650 0.5123 0.6342
Table 3 Land degradation index values of different land degradation intensities
Year PC G H S He LD Accumulated contribution rate (%)
2000 PC1 -0.232 -0.332 0.232 0.623 -0.669 80.879
2005 PC1 -0.284 -0.323 0.226 0.733 -0.527 82.818
2010 PC1 -0.325 -0.365 0.372 0.696 -0.526 83.783
2015 PC1 -0.277 -0.321 0.275 0.667 -0.613 82.418
2019 PC1 -0.389 -0.382 0.366 0.620 -0.563 82.151
Table 4 Contribution weights of the five indices in the arid remote-sensing ecological index (ARSEI) in 2000, 2005, 2010, 2015, and 2019
Level 2000 2005 2010 2015 2019
Area
(×104 km2)
P (%) Area
(×104 km2)
P (%) Area
(×104 km2)
P (%) Area
(×104 km2)
P (%) Area
(×104 km2)
P (%)
Worst 135.114 70.797 125.376 65.694 127.723 66.924 132.463 69.408 123.783 64.860
Poor 39.128 20.502 42.916 22.487 41.547 21.770 42.597 22.320 43.177 22.624
General 15.529 8.137 20.318 10.646 19.965 10.461 14.728 7.717 22.516 11.798
Good 1.036 0.543 2.181 1.143 1.565 0.820 1.010 0.529 1.326 0.695
Best 0.041 0.022 0.057 0.030 0.048 0.025 0.050 0.026 0.046 0.024
Total 190.848 100.001 190.848 100.000 190.848 100.000 190.848 100.000 190.848 100.001
Table 5 Areas and percentages of the different eco-environmental quality classifications in the Aral Sea Basin in 2000, 2005, 2010, 2015, and 2019 determined using the ARSEI
Fig. 2 Area percentages of the different grades of the eco-environmental quality change in the Aral Sea Basin determined using the arid remote sensing ecological index (ARSEI) variance in 2000-2005, 2005-2010, 2010-2015, 2015-2019, and 2000-2019. U3, significantly better; U2, obviously better; U1, slightly better; D1, slightly worse; D2, obviously worse; D3, significantly worse.
Fig. 3 Eco-environmental quality classification of the Aral Sea Basin obtained using the ARSEI in 2000 (a), 2005 (b), 2010 (c), 2015 (d), and 2019 (e)
Index Variance of the eco-environmental quality
2000-2005 2005-2010 2010-2015 2015-2019 2000-2019
G 0.299 0.262 0.495 0.442 0.257
H 0.444 0.415 0.449 0.405 0.524
He -0.722 -0.517 -0.774 -0.682 -0.593
S -0.300 -0.263 -0.496 -0.443 -0.258
LD 0.179 0.137 0.068 0.133 0.191
PRE 0.500 0.131 0.186 - 0.272
TEM -0.485 -0.527 -0.405 - -0.472
EVA -0.173 -0.099 -0.417 - -0.230
IWD_CON - -0.476 -0.534 - -0.505
IWD_WIN - -0.487 -0.520 - -0.504
Table 6 Correlation coefficients between the variance of the eco-environmental quality and the indices in the Aral Sea Basin in 2000-2005, 2005-2010, 2010-2015, 2015-2019, and 2000-2019 based on Pearson correlation analysis
Fig. 4 Changes in the intensity of the regional eco-environmental quality in the Aral Sea Basin determined using the ARSEI in 2000-2019. U3, significantly better; U2, obviously better; U1, slightly better; D1, slightly worse; D2, obviously worse; D3, significantly worse.
[1]   Aladin N. 2008. Reclaiming the Aral Sea. Scientific American, 298(4): 64-71.
pmid: 18380143
[2]   Allbed A, Kumar L, Aldakheel Y Y J G. 2014. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma, 230-231: 1-8.
[3]   Alves T L B, Azevedo P V D, Santos C A C D, et al. 2015. Influence of climate variability on land degradation (desertification) in the watershed of the upper Paraíba River. Theoretical Applied Climatology, 34(3): 1-11.
[4]   Baig M H A, Zhang L, 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.
[5]   Bennett J. 2003. Opportunities for increasing water productivity of CGIAR crops through plant breeding and molecular biology. In: Kijne J W, Barker R, Molden D. Water Productivity in Agriculture: Limits and Opportunities for Improvement. Colombo: International Water Management Institute Press, 103-126.
[6]   Breyfogle N. 2018. Eurasian Environments: Nature and Ecology in Imperial Russian and Soviet History. Pittsburgh University of Pittsburgh Press, 33-34.
[7]   Chai L H, Lha D. 2018. A new approach of deriving indicators and comprehensive measure for ecological environmental quality assessment. Ecological Indicators, 85: 716-728.
[8]   Dagani R. 1990. Stronger U.S. role in Great Lakes cleanup urged. Chemical & Engineering News, 68(4): 6 doi: 10.1021/cen-v068n004.p006a.
[9]   Deng H J, Chen Y N. 2017. Influences of recent climate change and human activities on water storage variations in Central Asia. Journal of Hydrology, 544: 46-57.
doi: 10.1016/j.jhydrol.2016.11.006
[10]   Deng M L, Long A H. 2011a. Evolution of hydrologic and water resources and ecological crisis in the Aral Sea Basin. Journal of Glaciology and Geocryology, 33(6): 1363-1375. (in Chinese)
[11]   Deng M L, Long A H. 2011b. Water resources issue among the Central Asian countries around the Aral Sea: conflict and cooperation. Journal of Glaciology and Geocryology, 33(6): 1376-1390. (in Chinese)
[12]   Dong Z, Wang Z, Liu D, et al. 2014. Mapping wetland areas using Landsat-derived NDVI and LSWI: a case study of West Songnen Plain, Northeast China. Journal of the Indian Society of Remote Sensing, 42(3): 569-576.
[13]   Fan C, Xia B C, Qin J Q. 2013. An integrated assessment model of county level eco-environmental quality based on RS and GIS: A case study of Huidong County, Guangdong Province of South China. Chinese Journal of Ecology, 32(3): 719-725. (in Chinese)
[14]   Guo B, Zang W, Han B, et al. 2020a. Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images. Land Degradation & Development, 31(12): 1573-1592.
[15]   Guo B, Zang W, Luo W, et al. 2020b. Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image. Geomatics, Natural Hazards and Risk, 11(1): 288-300.
[16]   Hu X S, Xu H Q. 2018. A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China. Ecological Indicators, 89: 11-21.
[17]   Hu X S, Xu H Q. 2019. A new remote sensing index based on the pressure-state-response framework to assess regional ecological change. Environmental Science and Pollution Research, 26(6): 5381-5393.
doi: 10.1007/s11356-018-3948-0 pmid: 30607851
[18]   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. Journal of Applied Ecology, 30(3): 877-883. (in Chinese)
doi: 10.13287/j.1001-9332.201903.008 pmid: 30912380
[19]   Jiang L H, Wang W, Yang X, et al. 2010. Classification methods of remote sensing image based on decision tree technologies. In: Li D, Liu Y, Chen Y. Computer and Computing Technologies in Agriculture IV. CCTA 2010. IFIP Advances in Information and Communication Technology, Vol 344. Berlin: Springer, 353-358.
[20]   Jiang L L, JIAPAER G L, Bao A, et al. 2017. Vegetation dynamics and responses to climate change and human activities in Central Asia. Science of the Total Environment, 599-600: 967-980.
[21]   Kang Y, Cai H J, Song S B. 2012. Application of improved fuzzy matter-element model for assessing eco-environmental quality in arid area. Disaster Advances, 5(4): 637-642.
[22]   Keith D A, Rodriguez J P, Rodriguez C K M, et al. 2013. Scientific foundations for an IUCN Red List of ecosystems. PLoS ONE, 8(5): e62111, doi: 10.1371/journal.pone.0062111.
doi: 10.1371/journal.pone.0065850 pmid: 23741516
[23]   Khamzina A, Lamers J P A, Vlek P L G, et al. 2008. Tree establishment under deficit irrigation on degraded agricultural land in the lower Amu Darya River region, Aral Sea Basin. Forest Ecology, 255(1): 168-178.
[24]   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.
[25]   Kumar R. 2002. Aral Sea: Environmental tragedy in Central Asia. Economic and Political Weekly, 37(37): 14-20.
[26]   Langer M, Westermann S, Boike J. 2010. Spatial and temporal variations of summer surface temperatures of wet polygonal tundra in Siberia - implications for MODIS LST based permafrost monitoring. Remote Sensing of Environment, 114(9): 2059-2069.
[27]   Li L, Zhang H T. 2008. Assessment model of townlet eco-environmental quality based on BP-artificial neural network. Journal of Applied Ecology, 19(12): 2693-2698. (in Chinese)
pmid: 19288725
[28]   Mamat Z, Halik Ü, Keyimu M, et al. 2018. Variation of the floodplain forest ecosystem service value in the lower reaches of Tarim River, China. Land Degradation & Development, 29(1): 47-57.
[29]   McDermid S S, Winter J. 2017. Anthropogenic forcings on the climate of the Aral Sea: A regional modeling perspective. Anthropocene, 20: 48-60.
[30]   Micklin P P. 2004. The Aral Sea Crisis. In: Nihoul J C J, Zavialov P O, Micklin P P. Dying and Dead Seas Climatic Versus Anthropic Causes. Berlin: Springer Science & Business Media Press, 99-123.
[31]   Micklin P P. 2007. The Aral Sea disaster. Annual Review of Earth and Planetary Sciences, 35(1): 47-72.
[32]   Mladenova I E, Jackson T J, Njoku E, et al. 2014. Remote monitoring of soil moisture using passive microwave-based techniques—Theoretical basis and overview of selected algorithms for AMSR-E. Remote Sensing of Environment, 144: 197-213.
[33]   Mo K, Chen Q, Chen C, et al. 2019. Spatiotemporal variation of correlation between vegetation cover and precipitation in an arid mountain-oasis river basin in northwest China. Journal of Hydrology, 574: 138-147.
[34]   Ning L, Wang J, Fen Q. 2020. The improvement of ecological environment index model RSEI. Arabian Journal of Geosciences, 13(12): 132-137.
[35]   Nishikawa H, Yasuda I. 2011. Long-term variability of winter mixed layer depth and temperature along the Kuroshio jet in a high-resolution ocean general circulation model. Journal of Oceanography, 67(4): 503-518.
[36]   Nõges P, VandeBund W, Cardoso A C, et al. 2007. Impact of climatic variability on parameters used in typology and ecological quality assessment of surface waters—implications on the Water Framework Directive. Hydrobiologia, 584(1): 373-379.
[37]   Nyimbili P H, Erden T, Karaman H. 2018. Integration of GIS, AHP and TOPSIS for earthquake hazard analysis. Natural Hazards, 92(3): 1523-1546.
[38]   Park S K, Marmur A, Russell A G. 2013. Environmental risk assessment: Comparison of receptor and air quality models for source apportionment. Human and Ecological Risk Assessment: An International Journal, 19(5): 1385-1403.
[39]   Posthuma L, Suter G W. 2011. Ecological risk assessment of diffuse and local soil contamination using species sensitivity distributions. In: Swartjes F A. Dealing with Contaminated Sites. Dordrecht: Springer, 625-691.
[40]   Rastorgueff P A, Bellan S D, Bianchi C N, et al. 2015. An ecosystem-based approach to evaluate the ecological quality of Mediterranean undersea caves. Ecological Indicators, 54: 137-152.
[41]   Shan W, Jin X, Ren J, et al. 2019. Ecological environment quality assessment based on remote sensing data for land consolidation. Journal of Cleaner Production, 239: 118126, doi: 10.1016/j.jclepro.2019.118126.
[42]   Shobairi S O R, Usoltsev V A, Chasovskikh V. 2018. Dynamic estimation model of vegetation fractional coverage and drivers. International Journal of Advanced and Applied Sciences, 5(3): 60-66.
[43]   Smiraglia D, Ceccarelli T, Bajocco S, et al. 2015. Linking trajectories of land change, land degradation processes and ecosystem services. Environmental Research, 147(5): 590-600.
[44]   Song Y S, Du C, Yang C, et al. 2012. Ecological environmental quality evaluation of Yellow River Basin. Procedia Engineering, 28: 754-758.
doi: 10.1016/j.proeng.2012.01.803
[45]   Sun Q, Zhao K, Zhu L, et al. 2015. A comprehensive evaluation index system for rural environmental quality. Journal of Ecology and Rural Environment, 31(1): 39-43. (in Chinese)
[46]   Touge Y, Tanaka K, Nakakita E. 2015. Estimation of climate change impacts on water balance in the Aral Sea basin using terrestrial water circulation model. Journal of Japan Society of Civil Engineers, Series G (Environmental Research), 71(5): I_183-I_188, doi: 10.2208/jscejer.71.I_183.
[47]   Wang J, Song P, Wang Z, et al. 2015. A combined model for regional eco-environmental quality evaluation based on particle swarm optimization-radial basis function network. Arabian Journal for Science and Engineering, 41(4): 1483-1493.
[48]   Wang X, Cao L. 2013. Change study of Hongze Lake wetland based on knowledge engineer. Journal of Henan Normal University (Natural Science Edition), 41(5): 148-151. (in Chinese)
[49]   Wang Y, Kong J L, Yang L Y, et al. 2019. Remote Sensing Inversion of Soil Moisture in Vegetation-Sparse Arid Areas based on SVR. Journal of Geo-information Science, 21(8): 1275-1283. (in Chinese)
[50]   Wang Z, Chang S L, Shi Q D, et al. 2010. Markov process of vegetation cover change in arid area of Northwest China based on FVC index. Journal of Applied Ecology, 21(5): 1129-1136. (in Chinese)
pmid: 20707091
[51]   Wei W, Shi P J, Zhou J J, et al. 2013. Assessment of eco-environmental quality of Shiyang River Basin based on GIS and combination weighting method. Advanced Materials Research, 864-867: 1302-1306. (in Chinese)
[52]   Wu J L, Ma L, Abuduwaili J. 2009. Lake surface change of the Aral Sea and its environmental effects in the arid region of the Central Asia. Arid Land Geography, 32(3): 418-422. (in Chinese)
[53]   Wu T, Sang S, Wang S, et al. 2020. Remote sensing assessment and spatiotemporal variations analysis of ecological carrying capacity in the Aral Sea Basin. Science of the Total Environment, 735: 139562, doi: 10.1016/j.scitotenv.2020.139562.
[54]   Xu H Q. 2013. A remote sensing index for assessment of regional ecological changes. China Environmental Science, 33(5): 889-897. (in Chinese)
[55]   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.
[56]   Xu W Q. 2019. Basic geographic dataset of resources and environment in Central and Western Asia Region. National Tibetan Plateau Data Center. [2020-04-15]. doi: 10.11888/Geogra.tpdc.270491.
[57]   Yang X W, Wang N L, Chen A A. 2019. The relationship between human activities, climate change and area variation of the Aral Sea in the arid Central Asia. Journal of Glaciology and Geocryology, 191: 104566, doi: 10.1016/j.catena.2020.104566.
[58]   Ying X, Zeng G, Chen G, et al. 2007. Combining AHP with GIS in synthetic evaluation of eco-environment quality—A case study of Hunan Province, China. Ecological Modelling, 209(2-4): 97-109.
[59]   Zhang J L, Wang Y J, Ding W Q. 2010. Eco-environmental quality evaluation of Manasi River Basin in recent 30 years. Meteorological and Environmental Research, 1(4): 109-112.
[60]   Zhao W C, Qi X Q, Ye X. 2018. Impact of terrain on vegetation coverage estimation method. Journal of Anhui Agricultural Sciences, 46(36): 38-41. (in Chinese)
[61]   Zheng M, Zhu M L, Wang Y, et al. 2018. Eco-environment status evaluation and change analysis of Qinghai based on national geographic conditions census data. In: Proceedings of the ISPRS Technical Commission III Midterm Symposium on "Developments, Technologies and Applications in Remote Sensing". Beijing: National Geomatics Center of China, 2453-2457.
[62]   Zheng P, Deng Z D, Wang D Q, et al. 2015. Study on predicting shallow groundwater in semi-arid area based on soil humidity index of TM data: Taking Chaoyang City as a study case. Journal of China Hydrology, 35(5): 23-29. (in Chinese)
[63]   Zhou K F, Zhang Q, Chen Q, et al. 2006. Characteristics and trends of eco-environmental changes in arid areas of Central Asia. Scientia Sinica (Terrae), 36(z1): 133-139. (in Chinese)
[64]   Zhu H, Wang J L, Cheng F, et al. 2020. Monitoring and evaluation of eco-environmental quality of lake basin regions in Central Yunnan Province, China. Journal of Applied Ecology, 31(4): 1289-1297. (in Chinese)
doi: 10.13287/j.1001-9332.202004.011 pmid: 32530204
[1] WANG Min, CHEN Xi, CAO Liangzhong, KURBAN Alishir, SHI Haiyang, WU Nannan, EZIZ Anwar, YUAN Xiuliang, Philippe DE MAEYER. Correlation analysis between the Aral Sea shrinkage and the Amu Darya River[J]. Journal of Arid Land, 2023, 15(7): 757-778.
[2] LI Wen, MU Guijin, YE Changsheng, XU Lishuai, LI Gen. Aeolian activity in the southern Gurbantunggut Desert of China during the last 900 years[J]. Journal of Arid Land, 2023, 15(6): 649-666.
[3] 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.
[4] Omobayo G ZOFFOUN, Chabi A M S DJAGOUN, Etotépé A SOGBOHOSSOU. Distribution patterns of fire regime in the Pendjari Biosphere Reserve, West Africa[J]. Journal of Arid Land, 2023, 15(10): 1160-1173.
[5] YAN Xue, LI Lanhai. Spatiotemporal characteristics and influencing factors of ecosystem services in Central Asia[J]. Journal of Arid Land, 2023, 15(1): 1-19.
[6] YAO Linlin, ZHOU Hongfei, YAN Yingjie, LI Lanhai, SU Yuan. Projection of hydrothermal condition in Central Asia under four SSP-RCP scenarios[J]. Journal of Arid Land, 2022, 14(5): 521-536.
[7] 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[J]. Journal of Arid Land, 2022, 14(3): 262-283.
[8] SONG Yujia, LIU Xijun, XIAO Wenjiao, ZHANG Zhiguo, LIU Pengde, XIAO Yao, LI Rui, WANG Baohua, LIU Lei, HU Rongguo. Neoproterozoic I-type granites in the Central Tianshan Block (NW China): geochronology, geochemistry, and tectonic implications[J]. Journal of Arid Land, 2022, 14(1): 82-101.
[9] YIN Hanmin, Jiapaer GULI, JIANG Liangliang, YU Tao, Jeanine UMUHOZA, LI Xu. Monitoring fire regimes and assessing their driving factors in Central Asia[J]. Journal of Arid Land, 2021, 13(5): 500-515.
[10] LIU Pengde, LIU Xijun, XIAO Wenjiao, ZHANG Zhiguo, SONG Yujia, XIAO Yao, LIU Lei, HU Rongguo, WANG Baohua. Geochronology, geochemistry, and Sr-Nd isotopes of Early Carboniferous magmatism in southern West Junggar, northwestern China: Implications for Junggar oceanic plate subduction[J]. Journal of Arid Land, 2021, 13(11): 1163-1182.
[11] Sanim BISSENBAYEVA, Jilili ABUDUWAILI, Assel SAPAROVA, Toqeer AHMED. Long-term variations in runoff of the Syr Darya River Basin under climate change and human activities[J]. Journal of Arid Land, 2021, 13(1): 56-70.
[12] SONG Yongyong, XUE Dongqian, DAI Lanhai, WANG Pengtao, HUANG Xiaogang, XIA Siyou. Land cover change and eco-environmental quality response of different geomorphic units on the Chinese Loess Plateau[J]. Journal of Arid Land, 2020, 12(1): 29-43.
[13] Jiaxiu LI, Yaning CHEN, Zhi LI, Xiaotao HUANG. Low-carbon economic development in Central Asia based on LMDI decomposition and comparative decoupling analyses[J]. Journal of Arid Land, 2019, 11(4): 513-524.
[14] Yang YU, Yuanyue PI, Xiang YU, Zhijie TA, Lingxiao SUN, DISSE Markus, Fanjiang ZENG, Yaoming LI, Xi CHEN, Ruide YU. Climate change, water resources and sustainable development in the arid and semi-arid lands of Central Asia in the past 30 years[J]. Journal of Arid Land, 2019, 11(1): 1-14.
[15] Jinping LIU, Wanchang ZHANG, Tie LIU. Monitoring recent changes in snow cover in Central Asia using improved MODIS snow-cover products[J]. Journal of Arid Land, 2017, 9(5): 763-777.