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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (3): 231-252    DOI: 10.1007/s40333-023-0053-0     CSTR: 32276.14.s40333-023-0053-0
Research article     
Spatial-temporal changes and driving factors of eco- environmental quality in the Three-North region of China
LONG Yi1,2,3, JIANG Fugen1,2,3, DENG Muli1,2,3, WANG Tianhong1,2,3, SUN Hua1,2,3,*()
1Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
2Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China
3Key Laboratory of National Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China
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Abstract  

Eco-environmental quality is a measure of the suitability of the ecological environment for human survival and socioeconomic development. Understanding the spatial-temporal distribution and variation trend of eco-environmental quality is essential for environmental protection and ecological balance. The remote sensing ecological index (RSEI) can quickly and objectively quantify eco-environmental quality and has been extensively utilized in regional ecological environment assessment. In this paper, Moderate Resolution Imaging Spectroradiometer (MODIS) images during the growing period (July-September) from 2000 to 2020 were obtained from the Google Earth Engine (GEE) platform to calculate the RSEI in the three northern regions of China (the Three-North region). The Theil-Sen median trend method combined with the Mann-Kendall test was used to analyze the spatial-temporal variation trend of eco-environmental quality, and the Hurst exponent and the Theil-Sen median trend were superimposed to predict the future evolution trend of eco-environmental quality. In addition, ten variables from two categories of natural and anthropogenic factors were analyzed to determine the drivers of the spatial differentiation of eco-environmental quality by the geographical detector. The results showed that from 2000 to 2020, the RSEI in the Three-North region exhibited obvious regional characteristics: the RSEI values in Northwest China were generally between 0.2 and 0.4; the RSEI values in North China gradually increased from north to south, ranging from 0.2 to 0.8; and the RSEI values in Northeast China were mostly above 0.6. The average RSEI value in the Three-North region increased at an average growth rate of 0.0016/a, showing the spatial distribution characteristics of overall improvement and local degradation in eco-environmental quality, of which the areas with improved, basically stable and degraded eco-environmental quality accounted for 65.39%, 26.82% and 7.79% of the total study area, respectively. The Hurst exponent of the RSEI ranged from 0.20 to 0.76 and the future trend of eco-environmental quality was generally consistent with the trend over the past 21 years. However, the areas exhibiting an improvement trend in eco-environmental quality mainly had weak persistence, and there was a possibility of degradation in eco-environmental quality without strengthening ecological protection. Average relative humidity, accumulated precipitation and land use type were the dominant factors driving the spatial distribution of eco-environmental quality in the Three-North region, and two-factor interaction also had a greater influence on eco-environmental quality than single factors. The explanatory power of meteorological factors on the spatial distribution of eco-environmental quality was stronger than that of topographic factors. The effect of anthropogenic factors (such as population density and land use type) on eco-environmental quality gradually increased over time. This study can serve as a reference to protect the ecological environment in arid and semi-arid regions.

Key words： eco-environmental quality      remote sensing ecological index      Google Earth Engine      Hurst exponent      geographical detector      Three-North region of China     
Received: 07 July 2022      Published: 31 March 2023
Corresponding Authors: * SUN Hua (E-mail: sunhua@csuft.edu.cn)
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LONG Yi
JIANG Fugen
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Cite this article:

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. Journal of Arid Land, 2023, 15(3): 231-252.

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http://jal.xjegi.com/10.1007/s40333-023-0053-0     OR     http://jal.xjegi.com/Y2023/V15/I3/231

Fig. 1 Location of the study area (Three-North region) in China (a) and topography of the study area (b). The Three-North region includes Northwest China, North China and Northeast China, covering 13 provinces, autonomous regions and municipalities. SAR, special administrative region.
Function Data name Spatial resolution Time
resolution		 Time span Source
RSEI calculation MOD09A1 V6 0.5 km 8 d 2000-2020
(growing period)		 GEE (https://developers.google.com)
MOD11A2 V6 1.0 km 8 d
Factor response analysis Cumulative precipitation 1.0 km Monthly 2000, 2005, 2010, 2015 and 2020 (growing period) National Earth System Science Data Center and the National Science & Technology Infra-structure of China (http://www.geodata.cn)
Average temperature 1.0 km Monthly
Average relative humidity 1.0 km Monthly
Potential evapotranspiration 1.0 km Monthly
Elevation 1.0 km 2000 A Big Earth Data Platform for Three Poles (http://poles.tpdc.ac.cn/)
Slope 1.0 km 2000
Aspect 1.0 km 2000
Nighttime-light 1.0 km Annual 2000, 2005, 2010, 2015 and 2020 A Big Earth Data Platform for Three Poles (http://poles.tpdc.ac.cn/)
Population density 1.0 km Annual 2000, 2005, 2010, 2015 and 2020 WorldPop (https://www.worldpop.org)
Land use type 1.0 km Quinquennial 2000, 2005, 2010, 2015 and 2020 Resource and Environment Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn)
Table 1 Detailed description of the data used in the study
Classification standard Variation trend of eco-environmental quality
β< -0.0005 and |Z|>1.96 Significant degradation
β< -0.0005 and |Z|≤1.96 Slight degradation
-0.0005≤β≤0.0005 Basically stable
β>0.0005 and |Z|≤1.96 Slight improvement
β>0.0005 and |Z|>1.96 Significant improvement
Table 2 Classification standard for the trend analysis of eco-environmental quality based on the Theil-Sen median (β) and Mann-Kendall (MK) test results
Classification standard Future trend of eco-environmental quality
β>0.0005 and 0.65<H≤1.00 Strong persistent improvement
β>0.0005 and 0.50<H≤0.65 Weak persistent improvement
β< -0.0005 and 0.00≤H<0.35 Strong anti-sustained degradation
β< -0.0005 and 0.35≤H<0.50 Weak anti-sustained degradation
-0.0005≤β≤0.0005 Essentially constant
β< -0.0005 and 0.65<H≤1.00 Strong persistent degradation
β< -0.0005 and 0.50<H≤0.65 Weak persistent degradation
β>0.0005 and 0.00≤H<0.35 Strong anti-sustained improvement
β>0.0005 and 0.35≤H<0.50 Weak anti-sustained improvement
Table 3 Classification standard for the future trend analysis of eco-environmental quality based on the Hurst exponent and the Theil-Sen median trend
Classification standard Interaction type
q(X1∩X2)<Min[q(X1), q(X2)] Nonlinear-weaken
Min[q(X1), q(X2)]<q(X1∩X2)<Max[q(X1), q(X2)] Uni-weaken
q(X1∩X2)>Max[q(X1), q(X2)] Bi-enhance
q(X1∩X2)=q(X1)+q(X2) Independent
q(X1∩X2)>q(X1)+q(X2) Nonlinear-enhance
Table 4 Classification standard of the interaction type of factors
Fig. 2 Spatial distribution of eco-environmental quality (as indicated by the RSEI) in the Three-North region in 2000 (a), 2002 (b), 2004 (c), 2006 (d), 2008 (e), 2010 (f), 2012 (g), 2014 (h), 2016 (i), 2018 (j) and 2020 (k)
Fig. 3 Percentage of the area with different eco-environmental quality degrees (as indicated by the RSEI values) and the mean RSEI in Northwest China (a), North China (b), Northeast China (c) and the Three-North region (d) from 2000 to 2020
Variation trend Northwest China North China Northeast China Three-North region
Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)		 Area
(km2)		 Percentage
(%)
Significant degradation 41,949 1.39 21,793 1.44 11,238 1.43 75,190 1.41
Slight degradation 227,387 7.56 78,656 5.19 32,731 4.16 339,363 6.38
Basically stable 1,026,679 34.13 246,460 16.26 151,471 19.25 1,426,634 26.82
Slight improvement 1,051,168 34.94 677,542 44.70 256,214 32.55 1,987,808 37.38
Significant improvement 661,078 21.98 491,153 32.41 335,402 42.61 1,489,976 28.01
Table 5 Statistical results of the variation trend in eco-environmental quality from 2000 to 2020
Fig. 4 Spatial distribution of the variation trend of eco-environmental quality in the Three-North region from 2000 to 2020. The pie chart shows the percentage of the area occupied by different variation trends of eco-environmental quality. (a), (b) and (c) show the partial enlargement of the spatial distribution of the variation trend of eco-environmental quality.
Fig. 5 Spatial distribution of the long-term correlation of eco-environmental quality in time series (indicated by the Hurst exponent (H); a) and the future trend of eco-environmental quality (b) in the Three-North region
Fig. 6 Explanatory power q values of cumulative precipitation (X1), average temperature (X2), average relative humidity (X3), potential evapotranspiration (X4), elevation (X5), slope (X6), aspect (X7), nighttime-light (X8), population density (X9), and land use type (X10) in 2000, 2005, 2010, 2015 and 2020
Fig. 7 Interaction detection and ecological detection results of cumulative precipitation (X1), average temperature (X2), average relative humidity (X3), potential evapotranspiration (X4), elevation (X5), slope (X6), aspect (X7), nighttime-light (X8), population density (X9) and land use type (X10) in 2000 (a), 2005 (b), 2010 (c), 2015 (d) and 2020 (e). The ↑ means that the interaction between two influencing factors is nonlinear enhance effect and no ↑ means that the interaction between two influencing factors is bi-enhance effect.
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[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] ZHAO Xiaohan, HAN Dianchen, LU Qi, LI Yunpeng, ZHANG Fangmin. Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index[J]. Journal of Arid Land, 2023, 15(8): 920-939.
[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] XU Mengran, ZHANG Jing, LI Zhenghai, MO Yu. Attribution analysis and multi-scenario prediction of NDVI drivers in the Xilin Gol grassland, China[J]. Journal of Arid Land, 2022, 14(9): 941-961.
[8] 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.
[9] 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.
[10] 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.
[11] WU Shupu, GAO Xin, LEI Jiaqiang, ZHOU Na, GUO Zengkun, SHANG Baijun. Ecological environment quality evaluation of the Sahel region in Africa based on remote sensing ecological index[J]. Journal of Arid Land, 2022, 14(1): 14-33.
[12] 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[J]. Journal of Arid Land, 2021, 13(1): 40-55.
[13] 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.