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Journal of Arid Land
Journal of Arid Land  2021, Vol. 13 Issue (5): 534-547    DOI: 10.1007/s40333-021-0007-3     CSTR: 32276.14.s40333-021-0007-3
Research article     
Vegetation dynamics of coal mining city in an arid desert region of Northwest China from 2000 to 2019
ZHOU Siyuan, DUAN Yufeng, ZHANG Yuxiu*(), GUO Jinjin
School of Chemical & Environmental Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
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Abstract  

Coal mining has led to serious ecological damages in arid desert region of Northwest China. However, effects of climatic factor and mining activity on vegetation dynamics and plant diversity in this region remain unknown. Wuhai City located in the arid desert region of Northwest China is an industrial city and dominated by coal mining. Based on Landsat data and field investigation in Wuhai City, we analyzed the vegetation dynamics and the relationships with climate factors, coal mining activity and ecological restoration projects from 2000 to 2019. Results showed that vegetation in Wuhai City mostly consisted of desert plants, such as Caragana microphylla, Tetraena mongolica and Achnatherum splendens. And the vegetation fractional coverage (VFC) and greenness rate of change (GRC) showed that vegetation was slightly improved during the study period. Normalized difference vegetation index (NDVI) was positively correlated with annual mean precipitation, relative humidity and annual mean temperature, indicating that these climate factors might play important roles in the improved vegetation. Vegetation coverage and plant diversity around the coal mining area were reduced by coal mining, while the implementation of ecological restoration projects improved the vegetation coverage and plant diversity. Our results suggested that vegetation in the arid desert region was mainly affected by climate factors, and the implementation of ecological restoration projects could mitigate the impacts of coal mining on vegetation and ecological environment.

Key words： climate factor      ecological restoration      normalized difference vegetation index (NDVI)      plant diversity      Wuhai City     
Received: 10 September 2020      Published: 10 May 2021
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About author: *ZHANG Yuxiu (E-mail: zhangyuxiu@cumtb.edu.cn)
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Siyuan ZHOU
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Jinjin GUO
Cite this article:

ZHOU Siyuan, DUAN Yufeng, ZHANG Yuxiu, GUO Jinjin. Vegetation dynamics of coal mining city in an arid desert region of Northwest China from 2000 to 2019. Journal of Arid Land, 2021, 13(5): 534-547.

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http://jal.xjegi.com/10.1007/s40333-021-0007-3     OR     http://jal.xjegi.com/Y2021/V13/I5/534

Fig. 1 Location of study area and distribution of sampling site in Wuhai City (a, b and c). Area M (d) is near to open-pit coal mine. Area M includes the core reserve area (M1), reserve area (M2) and marginal area (M3) of T. mongolica and open-pit coal mine (CM). BG, botanical garden; DR, dump waste reclamation area; TmD, T. mongolica disturbance area; W, wetland; CR, abandoned coking vegetation restoration area; SR, subsidence restoration area; NS, natural shrub vegetation area; TmN, T. mongolica Nature Reserve; GM, Gander Mountain.
Remote sensing type Date (mm/dd/yy) Remote sensing type Date (mm/dd/yy)
Landsat TM 08/29/2000 Landsat TM 06/18/2011
Landsat TM 08/19/2002 Landsat OLI 07/28/2014
Landsat TM 08/15/2003 Landsat OLI 05/30/2016
Landsat TM 06/01/2005 Landsat OLI 05/17/2017
Landsat TM 08/14/2006 Landsat OLI 08/29/2018
Landsat TM 08/10/2007 Landsat OLI 06/19/2019
Landsat TM 06/28/2009
Table 1 Remote sensing images used in this study
Level Value of VFC Description
Low coverage 0.00-0.05 Moderate desertification land, rock, buildings, opencast mine, waste dump and low yield grassland
Medium-low coverage 0.05-0.15 Medium-low yield grassland, industrial land and sporadic vegetation
Medium coverage 0.15-0.30 Unutilized land, medium-low yield grassland and medium-low shrubland
Medium-high coverage 0.30-0.60 Medium-high yield grassland, medium-high shrubland and sparse woodland
High coverage 0.60-1.00 High-yield grassland, dense shrubland, reclaimed dump, cropland and dense woodland
Table 2 Classification of vegetation fractional coverage (VFC) in Wuhai City from 2000 to 2019
Fig. 2 Spatiotemporal distribution of vegetation fractional coverage (VFC) in Wuhai City. (a), 2000; (b), 2006; (c), 2009; (d), 2011; (e), 2019.
Fig. 3 Area changes in vegetation fractional coverage (VFC) in Wuhai City from 2000 to 2019
Value of GRC Category 2000-2009 2009-2019 2000-2019
Area (km2) Percentage of total area (%) Area (km2) Percentage of total area (%) Area
(km2)		 Percentage of total area (%)
< -0.10 Severe degradation 57.82 3.30 66.09 3.77 6.84 0.39
-0.10- -0.05 Moderate degradation 960.02 54.73 24.08 1.37 22.56 1.29
-0.05-0.00 Slight degradation 631.30 35.99 26.01 1.48 102.29 5.83
0.00-0.05 Slight improvement 49.26 2.81 121.68 6.94 1572.91 89.68
0.05-0.10 Moderate improvement 17.97 1.02 1457.92 83.10 46.33 2.64
>0.10 Significant improvement 35.86 2.04 58.23 3.32 0.18 0.01
Table 3 Dynamics of greenness rate of change (GRC) during three stages in Wuhai City
Fig. 4 Dynamics of annual land cover classification in Wuhai City. (a), 2000; (b), 2006; (c), 2009; (d), 2011; (e), 2019.
Fig. 5 Area of land cover and coal output in Wuhai City from 2000 to 2019
Fig. 6 Change in vegetation fractional coverage (VFC) over a chronological sequence in Wuhai City (a) and area M (b) and area percentage of different VFC levels in M1 (core reserve area), M2 (reserve area), M3 (marginal area) and CM (open-pit coal mine) in 2000, 2009 and 2018 (c)
Fig. 7 Normalized difference vegetation index (NDVI) change at sampling sites in Wuhai City from 2000 to 2019. BG, botanical garden; CR, abandoned coking plant restoration area; SR, subsidence restoration area; DR, dump waste reclamation area; TmD, T. mongolica disturbance area; NS, natural shrub vegetation area; GM, Gander Mountain; TmN, T. mongolica Nature Reserve; W, wetland.
Sampling site Species number Dominant species
BG 15 Elaeagnus angustifolia; Populus alba var. pyramidalis; Platycladus orientalis
SR1 14 Populus alba var. pyramidalis; Amorpha fruticose; Cynanchum chinense
SR2 15 Pinus sylvestris var. mongolica; Picea asperata; Ulmus pumila
CR 8 Elaeagnus angustifolia; Syringa vulgaris; Amorpha fruticosa
DR1 9 Medicago sativa; Limonium bicolor; Amorpha fruticosa
DR2 7 Artemisia salsoloides; Medicago sativa; Alhagi camelorum
TmD1 12 Achnatherum splendens; Tetraena mongolica; Convolvulus tragacanthoides
TmD2 7 Artemisia salsoloides; Convolvulus tragacanthoides; Achnatherum splendens
TmD3 9 Caragana microphylla; Cynanchum hancockianum; Achnatherum splendens
TmD4 10 Tetraena mongolica; Zygophyllum xanthoxylon; Achnatherum splendens
TmD5 9 Tetraena mongolica; Zygophyllum xanthoxylon; Achnatherum splendens
TmD6 7 Tetraena mongolica; Salsola passerina; Achnatherum splendens
NS1 10 Achnatherum splendens; Ammopiptanthus mongolicus; Caragana microphylla
NS2 11 Zygophyllum xanthoxylon; Nitraria sibirica; Cynanchum chinense
GM1 11 Caragana microphylla; Picea asperata; Pinus sylvestris var. mongolica
GM2 10 Caragana microphylla; Picea asperata; Pinus sylvestris var. mongolica
TmN1 12 Tetraena mongolica; Achnatherum splendens; Reaumuria soongarica
TmN2 11 Tetraena mongolica; Achnatherum splendens; Reaumuria soongarica
W1 10 Phragmites australis; Sophora alopecuroides; Tamarix chinensis
W2 10 Phragmites australis; Echinochloa crusgalli; Tamarix chinensis
Table 4 Plant community composition in each sampling site
Fig. 8 Plant alpha diversity indices, coverage, occurrence of T. mongolica (a) and beta diversity indices (b) at sampling sites in Wuhai City from 2000 to 2019. BG, botanical garden; CR, abandoned coking plant restoration area; SR, subsidence restoration area; DR, dump waste reclamation area; TmD, T. mongolica disturbance area; NS, natural shrub vegetation area; GM, Gander Mountain; TmN, T. mongolica Nature Reserve; W, wetland.
Fig. 9 Inter-annual normalized difference vegetation index (NDVI, a), annual precipitation (AP, b), relative humidity (RH, c), mean temperature (MT, d), strong wind frequency (W%, e) and annual average wind speed (WS, f) in Wuhai City from 2000 to 2019. The linear trend (red dashed lines) is based on ordinary least squares regression, while the nonlinear trend (blue solid lines) is fitted by LOWESS (locally weighted scatter point smoothing).
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