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Journal of Arid Land  2022, Vol. 14 Issue (11): 1196-1211    DOI: 10.1007/s40333-022-0085-x
Research article     
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
WANG Jinjie1,2,3, DING Jianli1,2,3,*(), GE Xiangyu1,2,3, QIN Shaofeng1,2,3, ZHANG Zhe1,2,3
1College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800046, China
2Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
3Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China
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The ecological quality of inland areas is an important aspect of the United Nations Sustainable Development Goals (UN SDGs). The ecological environment of Northwest China is vulnerable to changes in climate and land use/land cover, and the changes in ecological quality in this arid region over the last two decades are not well understood. This makes it more difficult to advance the UN SDGs and develop appropriate measures at the regional level. In this study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) products to generate remote sensing ecological index (RSEI) on the Google Earth Engine (GEE) platform to examine the relationship between ecological quality and environment in Xinjiang during the last two decades (from 2000 to 2020). We analyzed a 21-year time series of the trends and spatial characteristics of ecological quality. We further assessed the importance of different environmental factors affecting ecological quality through the random forest algorithm using data from statistical yearbooks and land use products. Our results show that the RSEI constructed using the GEE platform can accurately reflect the ecological quality information in Xinjiang because the contribution of the first principal component was higher than 90.00%. The ecological quality in Xinjiang has increased significantly over the last two decades, with the northern part of this region having a better ecological quality than the southern part. The areas with slightly improved ecological quality accounted for 31.26% of the total land area of Xinjiang, whereas only 3.55% of the land area was classified as having a slightly worsen (3.16%) or worsen (0.39%) ecological quality. The vast majority of the deterioration in ecological quality mainly occurred in the barren areas Temperature, precipitation, closed shrublands, grasslands and savannas were the top five environmental factors affecting the changes in RSEI. Environmental factors were allocated different weights for different RSEI categories. In general, the recovery of ecological quality in Xinjiang has been controlled by climate and land use/land cover during the last two decades and policy-driven ecological restoration is therefore crucial. Rapid monitoring of inland ecological quality using the GEE platform is projected to aid in the advancement of the comprehensive assessment of the UN SDGs.

Key wordsecological quality      land use/land cover      spatiotemporal change      remote sensing ecological index (RSEI)      Google Earth Engine      Xinjiang     
Received: 10 July 2022      Published: 30 November 2022
Corresponding Authors: DING Jianli     E-mail:
Cite this article:

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. Journal of Arid Land, 2022, 14(11): 1196-1211.

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Fig. 1 Overview of Xinjiang Uygur Autonomous Region (Xinjiang) in Northwest China. Note that the figure is based on the standard map (新S(2021)023) of the Map Service System ( marked by the Xinjiang Uygur Autonomous Region Platform for Common Geospatial Information Services, and the standard map has not been modified. DEM, digital elevation model.
Indicator Source Product Spatial resolution Temporal resolution
Greenness EVI MOD13A1 0.5 km 16 d
Dryness NDBSI MOD09A1 0.5 km 8 d
Wetness Wet MOD09A1 0.5 km 8 d
Heat LST MOD11A2 1.0 km 8 d
Table 1 Main information of the selected four indictors for the construction of remote sensing ecological index (RSEI)
Fig. 2 Schematic diagram of the change trend analysis of remote sensing ecological index (RSEI). DRSEI, difference of RSEI between 2020 and 2000.
DRSEI Change trend DRSEI Change trend
3.0 Significantly improved -1.0 Slightly worsen
2.0 Improved -2.0 Worsen
1.0 Slightly improved -3.0 Significantly worsen
0.0 Stable
Table 2 Classification of the change trend of RSEI
Fig. 3 Spatial and temporal distribution of ecological quality in Xinjiang from 2000 to 2020 (a-u). Note that the figures are based on the standard map (新S(2021)023) of the Map Service System ( marked by the Xinjiang Uygur Autonomous Region Platform for Common Geospatial Information Services, and the standard map has not been modified.
Fig. 4 Change of ecological quality in Xinjiang from 2000 to 2020. (a), change trend of mean RSEI (RSEImean); (b), area statistics under the five categories of RSEI (excellent, good, moderate, fair and poor).
Fig. 5 Spatial distribution of changes in ecological quality in Xinjiang during 2000-2020. Note that the figure is based on the standard map (新S(2021)023) of the Map Service System ( marked by the Xinjiang Uygur Autonomous Region Platform for Common Geospatial Information Services, and the standard map has not been modified.
Fig. 6 Spatial distribution of the changes in ecological quality within barren and non-barren areas in Xinjiang during 2000-2020. Note that the figure is based on the standard map (新S(2021)023) of the Map Service System ( marked by the Xinjiang Uygur Autonomous Region Platform for Common Geospatial Information Services, and the standard map has not been modified.
Fig. 7 Importance of factors influencing the level of ecological quality in Xinjiang during 2000-2020. (a)-(f) are the importance of different influencing factors on average, excellent, good, moderate, fair and poor RSEI, respectively. T, temperature; P, precipitation; CS, closed shrublands; GL, grasslands; SV, savannas; WB, water bodies; AOD, aerosol optical depth; VM, vegetation mosaics; ENF, evergreen needleleaf forest; OS, open shrublands; TAW, total water resources; PSI, permanent snow and ice; DNF, deciduous needleleaf forest; MF, mixed forests; PW, permanent wetlands; GDP, gross domestic product; CL, croplands; BR, barren; EC, energy consumption; POP, population; DBF, deciduous broadleaf forest; EP, energy production; WS, woody savannas; UBL, urban and built-up lands.
Fig. 8 Changes in ecological quality in national nature reserves in Xinjiang during 2000-2020. 1, Altun Mountains National Nature Reserve; 2, Bayanbulak National Nature Reserve; 3, Ebinur Lake Wetland National Nature Reserve; 4, Ganjiahu Haloxylon Forest National Nature Reserve; 5, Hanas National Nature Reserve; 6, Lop Nur Wild Camel National Nature Reserve; 7, Tarim Populus euphratica Forest National Nature Reserve; 8, Tomur National Nature Reserve; 9, West Tianshan Mountains National Nature Reserve. The data were from the China Nature Reserve Specimen Resource Sharing Platform (
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