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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (8): 920-939    DOI: 10.1007/s40333-023-0065-9     CSTR: 32276.14.s40333-023-0065-9
Research article     
Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index
ZHAO Xiaohan1, HAN Dianchen1, LU Qi2, LI Yunpeng3, ZHANG Fangmin1,*()
1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
2Institute of Desertification, Chinese Academy of Forestry, Beijing 100091, China
3Ecology and Agricultural Meteorology Center of Inner Mongolia Autonomous Region, Hohhot 010051, China
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Abstract  

Otindag Sandy Land in China is an important ecological barrier to Beijing; the changes in its ecological quality are major concerns for sustainable development and planning of this area. Based on principal component analysis and path analysis, we first generated a modified remote sensing ecological index (MRSEI) coupled with satellite and ground observational data during 2001-2020 that integrated four local indicators (greenness, wetness, and heatness that reflect vegetation status, water, and heat conditions, respectively, as well as soil erosion). Then, we assessed the ecological quality in Otindag Sandy Land during 2001-2020 based on the MRSEI at different time scales (i.e., the whole year, growing season, and non-growing season). MRSEI generally increased with an upward rate of 0.006/a during 2001-2020, with clear seasonal and spatial variations. Ecological quality was significantly improved in most regions of Otindag Sandy Land but degraded in the southern part. Regions with ecological degradation expanded to 18.64% of the total area in the non-growing season. The area with the worst grade of MRSEI shrunk by 15.83% of the total area from 2001 to 2020, while the area with the best grade of MRSEI increased by 9.77% of the total area. The temporal heterogeneity of ecological conditions indicated that the improvement process of ecological quality in the growing season may be interrupted or deteriorated in the following non-growing season. The implementation of ecological restoration measures in Otindag Sandy Land should not ignore the seasonal characteristics and spatial heterogeneity of local ecological quality. The results can explore the effectiveness of ecological restoration and provide scientific guides on sustainable development measures for drylands.

Key words： ecological quality      modified remote sensing ecological index      principal component analysis      path analysis      Otindag Sandy Land      dryland ecosystem     
Received: 28 January 2023      Published: 31 August 2023
Corresponding Authors: * ZHANG Fangmin (E-mail: Fmin.zhang@nuist.edu.cn)
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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. Journal of Arid Land, 2023, 15(8): 920-939.

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http://jal.xjegi.com/10.1007/s40333-023-0065-9     OR     http://jal.xjegi.com/Y2023/V15/I8/920

Fig. 1 Spatial distribution of different land cover types in Otindag Sandy Land in 2020 (a) and land cover transformation from 2001 to 2020 (b). The 1-km gridded land use cover datasets in 2000 and 2020 were derived from the Resource and Environment Science and Data Center (http://www.resdc.cn).
Fig. 2 Flowchart for constructing modified remote sensing ecological index (MRSEI) and spatiotemporal analysis of ecology quality with MRSEI in Otindag Sandy Land. MOD13A3, Monthly Normalized Difference Vegetation Index (NDVI) products from the Moderate-resolution Imaging Spectroradiometer (MODIS); DEM, digital elevation model; GEE, Google Earth Engine; ANUSPLIN, Australian National University Spline; NDVI, Normalized Difference Vegetation Index; ET0, total potential evapotranspiration; Ig, greenness index; Iw, wetness index; Ih, heatness index; Is, soil erosion index; PCA, principal component analysis; RSEI, remote sensing ecological index; ARSEI, arid remote sensing ecological index.
Fig. 3 Contribution rates of the first principal component (PC1) of MRSEI indicators (greenness, wetness, heatness, and soil erosion) at the whole year, growing season, and non-growing season scales from 2001 through 2020
Year Time scale PC1 load Eigenvalue Accumulated contribution
rate (%)
Ig Iw Ih Is
2001 Whole year 0.32 0.54 -0.78 0.04 0.14 87.72
Growing season 0.40 0.57 -0.72 0.06 0.11 84.10
Non-growing season 0.31 0.47 -0.82 0.06 0.06 74.46
2020 Whole year 0.37 0.73 -0.57 0.06 0.08 73.95
Growing season 0.44 0.73 -0.52 0.06 0.08 74.53
Non-growing season 0.24 0.72 -0.65 0.06 0.06 73.96
Table 1 PC1 of MRSEI indicators (Ig, Iw, Ih, and Is) in 2001 and 2020
Fig. 4 Path analysis of MRSEI and its four indicators (Ig, Iw, Ih, and Is) in Otindag Sandy Land at the whole year (a), growing season (b), and non-growing season (c) scales from 2001 through 2020. Value around the double arrows indicates the correlation coefficient between independent variables, while value around the single arrow represents the path coefficient between independent and dependent variables. *, passing the 0.05 significant test; **, passing the 0.01 significant test.
Fig. 5 Spatial distributions of multi-year average MRSEI and the corresponding MRSEI change trend at the whole year (a1 and a2), growing season (b1 and b2), and non-growing season (c1 and c2) scales from 2001 to 2020
Fig. 6 Area proportions of different MRSEI grades in Otindag Sandy Land at the whole year (a), growing season (b), and non-growing season (c) scales from 2001 to 2020
Worst Poor General Good Best Total (2001)
Worst 8462.95 18,262.89 8648.95 458.00 0.00 35,832.79
Poor 12.00 3571.98 21,380.88 684.00 0.00 25,648.86
General 0.00 9.00 17,351.23 21,541.88 65.00 38,967.11
Good 0.00 0.00 2254.99 22,205.87 17,675.90 42,136.76
Best 0.00 0.00 0.00 850.00 29,364.83 30,214.83
Total (2020) 8474.95 21,843.87 49,636.05 45,739.75 47,105.73 172,800.35
Table 2 Area transfer matrix of MRSEI with different grades in Otindag Sandy Land from 2001 to 2020 (unit: km2)
Fig. 7 Temporal change of MRSEI in Otindag Sandy Land (a) and its barycenter movements (b-d) at the whole year, growing season, and non-growing season scales from 2001 to 2020
Fig. 8 Relationships of the slope of MRSEI with the slopes of precipitation (a1) and air temperature (b1), and spatial distributions of changes in precipitation (a2) and air temperature (b2), as well as multi-year average MRSEI over different land cover types (c1) and MRSEI changes with transformation of land cover types (c2). Bars mean standard errors; dot means the average of the slope of MRSEI. Colors in Figure 8a1 and b1 correspond to color classes in Figure 8a2 and b2, respectively.
Fig. 9 Local validation of MRSEI in capturing the texture information of water bodies (a and b) and topography (a and c) between MRSEI and real scenes of surface in 2020. TCC is the true color composite image (RGB 432) of Landsat 8 filtered; it was calculated by the median composite method of the images with the cloud cover of less than 5% in the growing season (from May to September) in 2020 by the Google Earth Engine.
Fig. 10 Comparison between the contribution rates of PC1 of RSEI, MRSEI, and ARSEI indicators in the growing seasons of 2001, 2005, 2010, 2015, and 2020 (a) and zonal distribution and temporal changes (the inset) of RSEI, MRSEI, and ARSEI (b). *, passing the 0.05 significant test. Bars mean standard errors and dot means the average of slope.
Fig. S1 Spatial distributions of MRSEI at the whole year (a1-a5), growing season (b1-b5), and non-growing season (c1-c5) scales in 2001, 2005, 2010, 2015, and 2020 in Otindag Sandy Land
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