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.
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.
Fig. 1Location 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. 2Spatiotemporal distribution of vegetation fractional coverage (VFC) in Wuhai City. (a), 2000; (b), 2006; (c), 2009; (d), 2011; (e), 2019.
Fig. 3Area 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. 4Dynamics of annual land cover classification in Wuhai City. (a), 2000; (b), 2006; (c), 2009; (d), 2011; (e), 2019.
Fig. 5Area of land cover and coal output in Wuhai City from 2000 to 2019
Fig. 6Change 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. 7Normalized 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
Table 4 Plant community composition in each sampling site
Fig. 8Plant 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. 9Inter-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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