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Spatio-temporal dynamics of vegetation in Jungar Banner of China during 2000–2017

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Abstract

It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas. Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas. Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems. In this study, both qualitative (linear regression method and coefficient of variation (CoV)) and quantitative (spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large (Jungar Banner and three mine groups) and small (three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales. The results show that the rates of change in the average NDVI in the reclamation areas (20%–60%) and opencast coal mining excavation areas (10%–20%) were considerably higher than that in the natural areas (<7%). The vegetation in the reclamation areas experienced a trend of increase (3–5 a after reclamation)-decrease (the sixth year of reclamation)-stability. The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities. The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively. The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low (28.8%) and medium-low (10.2%) vegetation cover. The average disturbance distances of mining activities on vegetation in the three mine groups (west, southwest and east) were 800, 800 and 1000 m, respectively. The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation. We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation. Sufficient attention should be paid to the proportional allocation of plant species (herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a. Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.

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References

  • Bian Z F, Lei S G, Inyang H I, et al. 2009. Integrated method of RS and GPR for monitoring the changes in the soil moisture and groundwater environment due to underground coal mining. Environmental Geology, 57(1): 131–142.

    Google Scholar 

  • Bian Z F, Inyang H I, Daniels J L, et al. 2010. Environmental issues from coal mining and their solutions. Mining Science and Technology, 20(2): 215–223.

    Google Scholar 

  • Bian Z F, Miao X X, Lei S G, et al. 2012. The challenges of reusing mining and mineral-processing wastes. Science, 337(6095): 702–703.

    Google Scholar 

  • Brown J H, Valone T J, Curtin C G. 1997. Reorganization of an arid ecosystem in response to recent climate change. Proceedings of the National Academy of Sciences, 94(18): 9729–9733.

    Google Scholar 

  • Dong S C, Samsonov S, Yin H W, et al. 2015. Spatio-temporal analysis of ground subsidence due to underground coal mining in Huainan coalfield, China. Environmental Earth Sciences, 73(9): 5523–5534.

    Google Scholar 

  • Gurgel H C, Ferreira N J. 2003. Annual and interannual variability of NDVI in Brazil and its connections with climate. International Journal of Remote Sensing, 24(18): 3595–3609.

    Google Scholar 

  • He M X, Li H Y, Mo X Q. 2015. Optimization of application parameters of soil seed bank in vegetation recovery via response surface methodology. Ecological Engineering, 84(8): 362–369.

    Google Scholar 

  • Hu Y F, Dao R, Hu Y. 2019. Vegetation change and driving factors: Contribution analysis in the loess plateau of China during 2000–2015. Sustainability, 11(5): 1320.

    Google Scholar 

  • Huang Y F, Zhang S W, Zhang L P, et al. 2015. Research progress on conservation and restoration of biodiversity in land reclamation of opencast coal mine. Transactions of the Chinese Society for Agricultural Machinery, 46(8): 72–82. (in Chinese)

    Google Scholar 

  • Huang Y, Wang Y J, Li X S, et al. 2013. Graphic analysis of spatio-temporal effect for vegetation disturbance caused by coal mining: a case of Datong Coal Mine Area. Acta Ecologica Sinica, 33(21): 7035–7043. (in Chinese)

    Google Scholar 

  • Huang Y, Tian F, Wang Y J, et al. 2015. Effect of coal mining on vegetation disturbance and associated carbon loss. Environmental Earth Sciences, 73(5): 2329–2342.

    Google Scholar 

  • Jiang W G, Yuan L H, Wang W J, et al. 2015. Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51: 117–126.

    Google Scholar 

  • Karan S K, Samadder S R, Maiti S K. 2016. Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands. Journal of Environmental Management, 182: 272–283.

    Google Scholar 

  • Kross A, Fernandes R, Seaquist J, et al. 2011. The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests. Remote Sensing of Environment, 115(6): 1564–1575.

    Google Scholar 

  • Kusimi J M. 2008. Assessing land use and land cover change in the Wassa West District of Ghana using remote sensing. GeoJournal, 71(4): 249–259.

    Google Scholar 

  • Latifovic R, Fytas K, Chen J, et al. 2005. Assessing land cover change resulting from large surface mining development. International Journal of Applied Earth Observation and Geoinformation, 7(1): 29–48.

    Google Scholar 

  • Lei S G. 2010. Monitoring and analyzing the mining impacts on key environmental elements in water shortage mining area. Journal of China Coal Society, 62–63. (in Chinese)

  • Lei S G, Bian Z F, Daniels J L, et al. 2010. Spatio-temporal variation of vegetation in an arid and vulnerable coal mining region. Mining Science and Technology, 20(3): 485–490.

    Google Scholar 

  • Lei S G, Bian Z F. 2014. Research progress on the environment impacts from underground coal mining in arid western area of China. Acta Ecologica Sinica, 34(11): 2837–2843. (in Chinese)

    Google Scholar 

  • Lei S G, Ren L X, Bian Z F. 2016. Time-space characterization of vegetation in a semiarid mining area using empirical orthogonal function decomposition of MODIS NDVI time series. Environmental Earth Sciences, 75(6): 516.

    Google Scholar 

  • Li J, Zipper C E, Li S, et al. 2015. Character analysis of mining disturbance and reclamation trajectory in surface coal-mine area by time-series NDVI. Transactions of the Chinese Society of Agricultural Engineering, 31(16): 251–257. (in Chinese)

    Google Scholar 

  • Liao C H, Liu X H. 2010. Identifying the mining impact range on the vegetation of Yangquan coal mining region by using 3S technology. Journal of Natural Resources, 25(2): 185–191. (in Chinese)

    Google Scholar 

  • Liu D F, Quan Y, Ren Z P, et al. 2016. Assessment of heavy metal contamination in soil associated with Chinese coal-fired power plants: a case study in Xilingol, Inner Mongolia. International Journal of Sustainable Development & World Ecology, 24(5): 439–443.

    Google Scholar 

  • Liu X Y, Zhou W, Bai Z K. 2016. Vegetation coverage change and stability in large open-pit coal mine dumps in China during 1990–2015. Ecological Engineering, 95: 447–451.

    Google Scholar 

  • Liu Y, Lei S G, Cheng L S, et al. 2018. Effects of soil water content on stomatal conductance, transpiration, and photosynthetic rate of Caragana korshinskii under the influence of coal mining subsidence. Acta Ecologica Sinica, 38(9): 3069–3077. (in Chinese)

    Google Scholar 

  • Liu Y, Lei S G, Cheng L S, et al. 2019. Leaf photosynthesis of three typical plant species affected by the subsidence cracks of coal mining: a case study in the semiarid region of Western China. Photosynthetica, 57(1): 75–85.

    Google Scholar 

  • Loupasakis C, Angelitsa V, Rozos D, et al. 2014. Mining geohazards-land subsidence caused by the dewatering of opencast coal mines: The case study of the Amyntaio coal mine, Florina, Greece. Natural Hazards, 70(1): 675–691.

    Google Scholar 

  • Lovich J E, Bainbridge D. 1999. Anthropogenic degradation of the southern California desert ecosystem and prospects for natural recovery and restoration. Environmental Management, 24(3): 309–326.

    Google Scholar 

  • Milich L, Weiss E. 2000. GAC NDVI interannual coefficient of variation (CoV) images: Ground truth sampling of the Sahel along north-south transects. International Journal of Remote Sensing, 21(2): 235–260.

    Google Scholar 

  • Neshat A, Pradhan B, Dadras M. 2014. Groundwater vulnerability assessment using an improved DRASTIC method in GIS. Resources, Conservation and Recycling, 86(5): 74–86.

    Google Scholar 

  • Nordberg M L, Evertson J. 2005. Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery. Land Degradation & Development, 16(2): 139–149.

    Google Scholar 

  • Pandey B, Agrawal M, Singh S. 2014. Coal mining activities change plant community structure due to air pollution and soil degradation. Ecotoxicology, 23(8): 1474–1483.

    Google Scholar 

  • Paulina K, Torbern T, Rasmus F. 2017. Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the northern hemisphere boreal zone. Remote Sensing, 9(5): 485.

    Google Scholar 

  • Pei F S, Wu C J, Liu X P, et al. 2018. Monitoring the vegetation activity in China using vegetation health indices. Agricultural and Forest Meteorology, 248: 215–227.

    Google Scholar 

  • Perez L, Nelson T, Coops N C, et al. 2016. Characterization of spatial relationships between three remotely sensed indirect indicators of biodiversity and climate: a 21years’ data series review across the Canadian boreal forest. International Journal of Digital Earth, 9(7): 676–696.

    Google Scholar 

  • Prosperbasommi L, Guan Q F, Cheng D D, et al. 2016. Dynamics of land use change in a mining area: a case study of Nadowli District, Ghana. Journal of Mountain Science, 13(4): 633–642.

    Google Scholar 

  • Qian T N, Bagan H, Kinoshita T, et al. 2014. Spatial-temporal analyses of surface coal mining dominated land degradation in Holingol, Inner Mongolia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5): 1675–1687.

    Google Scholar 

  • Sen S, Zipper C E, Wynne R H, et al. 2012. Identifying revegetated mines as disturbance/recovery trajectories using an interannual Landsat chronosequence. Photogramm Engineering & Remote Sensing, 78(3): 223–235.

    Google Scholar 

  • Sun J, Qin X J, Yang J. 2016. The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau. Environmental Monitoring and Assessment, 188(1): 20.

    Google Scholar 

  • Sun Z D, Chang N B, Opp C. 2010. Using SPOT-VGT NDVI as a successive ecological indicator for understanding the environmental implications in the Tarim River Basin, China. Journal of Applied Remote Sensing, 4(1): 043554, https://doi.org/10.1117/1.3518454.

    Google Scholar 

  • Tian H J, Cao C X, Dai S M, et al. 2014. Analysis of vegetation fractional cover in Jungar Banner based on time-series remote sensing data. Journal of Geo-information Science, 16(1): 126–133. (in Chinese)

    Google Scholar 

  • Tian H J, Cao C X, Chen W, et al. 2015. Response of vegetation activity dynamic to climatic change and ecological restoration programs in Inner Mongolia from 2000 to 2012. Ecological Engineering, 82(4): 276–289.

    Google Scholar 

  • Ussiri D A N, Lal R. 2005. Carbon sequestration in reclaimed minesoils. Critical Reviews in Plant Sciences, 24(3): 151–165.

    Google Scholar 

  • Ussiri D A N, Lal R, Jacinthe P A. 2006. Soil properties and carbon sequestration of afforested pastures in reclaimed minesoils of Ohio. Soil Science Society of America Journal, 70(5): 1797–1806.

    Google Scholar 

  • Vicente-Serrano S M, Cuadrat-Prats J M, Romo A. 2006. Aridity influence on vegetation patterns in the middle Ebro Valley (Spain): Evaluation by means of AVHRR images and climate interpolation techniques. Journal of Arid Environments, 66(2): 353–375.

    Google Scholar 

  • Wang C J, Jia D, Lei S G, et al. 2017. Analysis of dynamic characteristics of vegetation in semi-arid mining area based on time trajectory segmentation algorithm. Journal of China Coal Society, 42(2): 477–483. (in Chinese)

    Google Scholar 

  • Wang J M, Guo L L, Bai Z K, et al. 2013. Succession law of reclaimed soil and vegetation on opencast coal mine dump of loess area. Transactions of the Chinese Society of Agricultural Engineering, 29(21): 223–232. (in Chinese)

    Google Scholar 

  • Wang T. 2016. Vegetation NDVI change and its relationship with climate change and human activities in Yulin, Shaanxi Province of China. Journal of Geoscience and Environment Protection, 4(10): 28–40.

    Google Scholar 

  • Wang Y C, Bian Z F, Lei S G, et al. 2017. Investigating spatial and temporal variations of soil moisture content in an arid mining area using an improved thermal inertia model. Journal of Arid Land, 9(5): 712–726.

    Google Scholar 

  • Weiss E, Marsh S E, Pfirman E S. 2001. Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia’s rangelands. International Journal of Remote Sensing, 22(6): 1005–1027.

    Google Scholar 

  • Wu L X, Ma B D, Liu S J. 2009. Analysis to vegetation coverage change in Shendong mining area with SPOT NDVI data. Journal of China Coal Society, 34(9): 1217–1222. (in Chinese)

    Google Scholar 

  • Wu W C, Pauw E D. 2010. Policy Impacts on Land Degradation: Evidence Revealed by Remote Sensing in Western Ordos, China. Netherland: Springer, 219–233.

    Google Scholar 

  • Xia L, Wang F, Mu X M, et al. 2015. Water use efficiency of net primary production in global terrestrial ecosystems. Journal of Earth System Science, 124(5): 921–931.

    Google Scholar 

  • Yan E P, Wang G X, Lin H, et al. 2015. Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series. International Journal of Remote Sensing, 36(2): 489–512.

    Google Scholar 

  • Yang Y J, Erskine P D, Lechner A M, et al. 2018. Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm. Journal of Cleaner Production, 178: 353–362.

    Google Scholar 

  • Yao F, Guli J P, Bao A M, et al. 2013. Damage assessment of the vegetable types based on remote sensing in the open coal mine of arid desert area. China Environmental Science, 33(4): 707–713. (in Chinese)

    Google Scholar 

  • Zhang D S, Fan G W, Ma L Q, et al. 2009. Harmony of large-scale underground mining and surface ecological environment protection in desert district — a case study in Shendong mining area, northwest of China. Procedia Earth and Planetary Science, 1(1): 1114–1120.

    Google Scholar 

  • Zhang S W, Ning H R, Xu D L, et al. 2016. Analysis of spatio-temporal evolution and driving factors of vegetation fraction for opencast coal mine in grassland area. Transactions of the Chinese Society of Agricultural Engineering, 32(17): 233–241. (in Chinese)

    Google Scholar 

  • Zhang Z T, Wang J M, Li B. 2019. Determining the influence factors of soil organic carbon stock in opencast coal-mine dumps based on complex network theory. Catena, 173: 433–444.

    Google Scholar 

  • Zhao Z Q, Bai Z K, Zhang Z, et al. 2012. Population structure and spatial distributions patterns of 17 years old plantation in a reclaimed spoil of Pingshuo opencast mine, China. Ecological Engineering, 44: 147–151.

    Google Scholar 

  • Zoungrana J B, Conrad C, Thiel M, et al. 2018. MODIS NDVI trends and fractional land cover change for improved assessments of vegetation degradation in Burkina Faso, West Africa. Journal of Arid Environments, 153: 66–75.

    Google Scholar 

Download references

Acknowledgements

The research was supported by the National Key Research and Development Program of China (2016YFC0501107), the Project of Ordos Science and Technology Program (2017006) and the Special Project of Science and Technology Basic Work of Ministry of Science and Technology of China (2014FY110800). We would like to thank the anonymous reviewers for their useful comments and suggestions, which improve the quality of this manuscript. We would also like to thank the editors for their contributions to this manuscript.

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Correspondence to Shaogang Lei.

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Li, X., Lei, S., Cheng, W. et al. Spatio-temporal dynamics of vegetation in Jungar Banner of China during 2000–2017. J. Arid Land 11, 837–854 (2019). https://doi.org/10.1007/s40333-019-0067-9

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