Please wait a minute...


Remote Sensing Technology and Application  2022, Vol. 37 Issue (2): 389-398    DOI: 10.11873/j.issn.1004-0323.2022.2.0389
Analysis of Vegetation Cover Change and Driving Forces in Typical Resource
Chuanwu Zhao1,2(),Wei Guo1(),Yueguan Yan1,Huayang Dai1,Jian Zhang1
College of Geoscience and Surveying Engineering,China University of Mining and Technology,Beijing,Beijing 100083,ChinaInstitute of Remote Sensing Science and Engineering,Faculty of Geographical Sciences,Beijing Normal University,Beijing 100875,China
Download:  HTML  PDF (5144KB) 
Export:  BibTeX | EndNote (RIS)      

There are many researches on vegetation change in arid and semi-arid areas, but little attention has been paid on the social and economic impact of resource-based cities on vegetation change. Based on MOD13Q1 data, climate data such as rainfall and temperature, 11 socio-economic indicators such as raw coal production from 2000 to 2020, combined with GIS technology and statistical methods such as linear regression, the spatial and temporal changes of Ordos vegetation and its influencing factors were studied. The results are as follows: ①The NDVI value of Ordos ranged from 0.233 to 0.395, showing a fluctuating growth trend with a growth rate of 0.059/10 a during 2000 to 2020; the NDVI values of the eight counties under its jurisdiction also showed a fluctuating growth trend, but there were many differences among different regions. ②The vegetation in Ordos is high in the northeast and it is low in the southwest. The area of low vegetation area is 53 500 km2, accounting for 61.58% of the total area of Ordos. The area of high vegetation is only 20 000 km2. The area of the vegetation improvement is much larger than that of the vegetation degradation area. The improvement area accounts for 52.19% of the entire Ordos area, and the vegetation degradation area only accounts for 3.69%. ③The NDVI value is extremely significant positively related to rainfall, with a correlation coefficient of 0.794 (P<0.01); the correlation coefficient between the change of NDVI and the accumulated rainfall in the month is larger, and the correlation coefficient with the temperature one month ago is larger. ④The NDVI change is extremely significantly positively correlated with the 11 socioeconomic indicators, with a correlation of 0.728~0.796 (P<0.01). From 2000 to 2020, the restoration effect of Ordos vegetation is good. Rainfall and temperature are the main factors affecting the growth of vegetation in Ordos, of which rainfall dominates. The response of NDVI changes to rainfall has less obvious lag, and the response to temperature has a one-month lag. The positive effects of socio-economic development on vegetation cover outweigh the negative effects.

Key words:  Vegetation cover      Influencing factors      Ordos     
Received:  17 December 2020      Published:  17 June 2022
ZTFLH:  TP79  
Corresponding Authors:  Wei Guo     E-mail:;
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
Articles by authors
Chuanwu Zhao
Wei Guo
Yueguan Yan
Huayang Dai
Jian Zhang

Cite this article: 

Chuanwu Zhao,Wei Guo,Yueguan Yan,Huayang Dai,Jian Zhang. Analysis of Vegetation Cover Change and Driving Forces in Typical Resource. Remote Sensing Technology and Application, 2022, 37(2): 389-398.

URL:     OR

Fig.1  The geographical location of Ordos and the distribution of meteorological stations
Fig.2  Temporal variation of vegetation coverage in Ordos during 2000 to 2020
Fig.3  Temporal variation trends of vegetation coverage in enght counties of Ordos during 2000 to 2020
Fig.4  Spatial distribution of NDVI in Ordos during 2000 to 2020
Fig.5  Statistics of vegetation coverage level in Ordos during 2000 to 2020
Fig.6  Spatial distribution of variation trends of vegetation coverage in Ordos during 2000 to 2020
Fig.7  Statistics of variation trends of vegetation coverage variation in Ordos during 2000 to 2020
Fig.8  The relationship between NDVI and climate factors
Fig.9  The distribution of in Ordos during 2000 to 2020
Table 1  Correlation between NDVI and hydrothermal factors at monthly scale
Fig.10  Correlation between NDVI and socio-economics in Ordos
1 Sun Hongyu, Wang Changyao, Niu Zheng, et al. Analysis of the vegetation cover change and the relationship between NDVI and environmental factors by using NOAA time series data[J]. Journal of Remote Sensing, 1998, 2(3):204-210.
1 孙红雨,王长耀,牛铮,等.中国地表植被覆盖变化及其与气候因子关系——基于NOAA时间序列数据分析[J].遥感学报,1998,2(3):204-210.
2 Zhao Hongyan, Chen Ying, Zhou Yi, et al. Spatiotemporal variation of NDVI in vegetation growing season and its responses to climatic factors in mid and eastern Gansu Province from 2008 to 2016[J]. Arid Land Geography, 2019, 42(6):1427-1435.
2 赵鸿雁,陈英,周翼,等.甘肃中东部植被生长季NDVI时空变化及其对气候因子的响应[J].干旱区地理,2019,42(6):1427-1435.
3 Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems[J]. Nature, 2003,421(6918): 37-42. DOI: .
doi: 10.1038/nature01286
4 Luo Xinrui, Yang Wunian, Chen Tao. Dynamic monitoring of vegetation and its driving force analysis using remoting sensing in hilly area of Central Sichuan Province[J]. Resources and Environment in the Yangtze Basin, 2019, 28(1):103-111.
4 罗新蕊,杨武年,陈桃.川中丘陵区植被遥感动态监测及其驱动力分析[J].长江流域资源与环境,2019,28(1):103-111.
5 Emir Nikati, Shabiti Mansur, Bhatti Yusuf. Spatiotemporal variation of vegetation NDVI and its relationship with climatic factors on the Northern slope of the Tianshan Mountains[J]. Arid Zone Research, 2019, 36(5):1250-1260.
5 尼加提·伊米尔,满苏尔·沙比提,玉苏甫·买买提.天山北坡植被NDVI时空变化及其与气候因子的关系[J].干旱区研究,2019,36(5):1250-1260.
6 Zhang J, Niu J M, Bao T L, et al. Human induced dryland degradation in Ordos Plateau, China, revealed by multilevel statistical modeling of normalized difference vegetation index and rainfall time-series[J]. Journal of Arid Land, 2014, 6(2):219-229. DOI:
doi: 10.1007/s40333-013-0203-x
7 Xiu L N, Yan C Z, Li X S, et al. Monitoring the response of vegetation dynamics to ecological engineering in the Mu Us Sandy Land of China from 1982 to 2014[J]. Environmental Monitoring and Assessment,2018,190(9):543. DOI:
doi: 10.1007/ s10661-018-6931-9
8 Zheng Hailiang, Fang Shifeng, Liu Chengcheng, et al. Dynamics of monthly vegetation activity and its responses to climate change in the Qinghai-Tibet Plateau[J]. Journal of Geo-information Science, 2019, 21(2):201-214.
8 郑海亮,房世峰,刘成程,等.青藏高原月NDVI时空动态变化及其对气候变化的响应[J].地球信息科学学报,2019,21(2):201-214.
9 Zhang Jun, Yan Junping. Characteristics of NDVI changes under the different vegetation types in Shaanxi Province from 1982 to 2013[J]. Journal of Arid Land Resources and Environment, 2017, 31(4):78-92.
9 张君,延军平.1982~2013年陕西不同植被类型NDVI变化特征分析[J].干旱区资源与环境,2017,31(4):86-92.
10 Wang Juan, Li Baolin, Yu Wanli. Analysis of vegetation trend and their causes during recent 30 years in Inner Mongolia Autonomous Region[J]. Journal of Arid Land Resources and Environment, 2012, 26(2):125-138.
10 王娟,李宝林,余万里.近30年内蒙古自治区植被变化趋势及影响因素分析[J].干旱区资源与环境,2012,26(2):132-138.
11 Yao Xueru, Liu Huamin, Pei Hao, et al. Variation of vegetation and its driving factors in Erdos Plateau from 1982 to 2006[J]. Bulletin of Soil and Water Conservation, 2012, 32(3):220-233.
11 姚雪茹,刘华民,裴浩,等.鄂尔多斯高原1982-2006年植被变化及其驱动因子[J].水土保持通报,2012,32(3):225-230.
12 Hu Junde, Lu Baisui, Chula Sa, et al. Vegetation dynamics and its responses to drought in Ordos Plateau[J]. Science of Surveying and Mapping, 2018, 43(4):45-58.
12 胡君德,李百岁,萨楚拉,等.2000~2012年鄂尔多斯高原植被动态及干旱响应[J].测绘科学,2018,43(4):49-58.
13 ERDUN Grile, Bao Gang, Bao Yulong, et al. Vegetation coverage changes in west Ordos National Natural Reserves during the period from 2001 to 2013[J]. Research of Soil and Water Conservation,2016,23(1):110-119.
13 额尔敦格日乐,包刚,包玉龙,等.2001~2013年西鄂尔多斯国家级自然保护区植被覆盖变化[J].水土保持研究,2016,23(1):110-116,2.
14 Yue Y J, Li M, Zhu A X, et al. Land degradation monitoring in the Ordos Plateau of China using an expert knowledge and BP-ANN-Based Approach[J]. Sustainability,2016,8(11):1174-1194. DOI: .
doi: 10.3390/su8111174
15 Ma Q M, Long Y P, Jia X P, et al. Vegetation response to climatic variation and human activities on the Ordos Plateau from 2000 to 2016[J]. Environmental Earth Sciences, 2019, 78(24):709-713. DOI: .
doi: 10.1007/s12665-019-8732-z
16 Zeng X J, Liu Z F, He C Y, et al. Quantifying surface coal-mining patterns to promote regional sustainability in Ordos, Inner Mongolia[J]. Sustainability,2018,10(4):1135-1151. DOI: .
doi: 10.3390/su10041135
17 Li Xiaoguang, Liu Huamin, Wang Lixin, et al. Vegetation cover change and its relationship between climate and human activities in Ordos Plateau[J]. Chinese Journal of Agrometeorology, 2014, 35(4):470-485.
17 李晓光,刘华民,王立新,等.鄂尔多斯高原植被覆盖变化及其与气候和人类活动的关系[J].中国农业气象,2014,35(4):470-476.
18 Xiao Y, Wang R, Wang F, et al. Investigation on spatial and temporal variation of coupling coordination between socioeconomic and ecological environment: A case study of the Loess Plateau,China[J]. Ecological Indicators,2022, 136: 108667. DOI: .
doi: 10.1016/j.ecolind.2022.108667
19 Ma Q, He C Y, Fang X N. A rapid method for quantifying landscape-scale vegetation disturbances by surface coal mining in arid and semiarid regions[J]. Landscape Ecology, 2018,33(12):2061-2070. DOI: .
doi: 10.1007/s10980-018-0726-9
20 Li Lili, Wang Dawei, Han Tao. Spatial-temporal dynamics of vegetation coverage and responding to climate change in Shiyang River Basin during 2000~2015[J]. Journal of Desert Research, 2018, 38(5):1108-1118.
20 李丽丽,王大为,韩涛.2000~2015年石羊河流域植被覆盖度及其对气候变化的响应[J].中国沙漠,2018,38(5):1108-1118.
21 ORDOS Statistics Bureau. Ordos statistical yearbook[M]. Beijing: China Statistics Press, 2000~2019.
21 鄂尔多斯统计局.鄂尔多斯统计年鉴[M].北京:中国统计出版社,2000-2019.
22 Yang G, Shen H F, Zhang L P, et al. Xinghua Li. A moving weighted harmonic analysis method for reconstructing high-quality spot vegetation NDVI time-series data[J].IEEE Tra-nsactions on Geoscience and Remote Sensing, 2015,53(11):6008-6021. DOI: .
doi: 10.1109/TGRS.2015.2431315
23 Shen H F, Li X H, Cheng Q, et al. Missing information reconstruction of remote sensing data: A technical review[J]. IEEE Geoscience and Remote Sensing Magazine, 2015, 3(3):61-85. DOI: .
doi: 10.1109/MGRS.2015.2441912
24 Liu Minxia, Zhao Ruidong, Shao Peng, et al. Temporal and spatial variation of vegetation coverage and its driving forces in the Loess Plateau from 2001 to 2015[J]. Arid Land Geography, 2018, 41(1):99-108.
24 刘旻霞,赵瑞东,邵鹏,等.近15 a黄土高原植被覆盖时空变化及驱动力分析[J].干旱区地理,2018,41(1):99-108.
25 Guo W, Zhang Y H, Gao L. Using VIIRS-DNB and landsat data for impervious surface area mapping in an arid/semiarid region[J]. Remote Sensing Letters,2018,9(6):587-596. DOI:
doi: 10.1080/2150704X.2018.1455234
26 Li Jing, Cui Lvyuan, Yan Xiaoxiao, et al. Comparative analysis of long-term trends on fraction of vegetation coverage in grassland mining area[J]. Bulletin of Surveying and Mapping, 2019(8):130-134+157.
26 李晶,崔绿园,闫萧萧,等.草原矿区长时序植被覆盖度变化趋势对比分析[J].测绘通报,2019(8):130-134+157.
27 Zhang W, Wang L, Xiang F F, et al. Vegetation dynamics and the relations with climate change at multiple time scales in the Yangtze River and Yellow River Basin, China[J]. Ecological Indicators,2020,110:105892. DOI: .
doi: 10.1016/j.ecolind. 2019.105892
28 A D, Zhao W J, Qu X Y, et al. Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 53: 103-117. DOI: .
doi: 10.1016/j.jag.2016.08.008
29 Meng X Y, Gao X, Li S Y, et al. I Spatial and temporal characteristics of vegetation NDVI changes and the driving forces in Mongolia during 1982~2015[J]. Remote Sensing, 2020, 12(4), 603. DOI: .
doi: 10.3390/rs12040603
30 Nie T, Dong G T, Jiang X H, et al. Spatio-temporal changes and driving forces of vegetation coverage on the Loess Plateau of Northern Shaanxi[J]. Remote Sensing, 2021, 13(4):613. DOI: .
doi: 10.3390/rs13040613
[1] Xue Xingyu,Liu Hongyu. Study on the Classification Approaches of Yancheng Coastal Wetlands based on ALOS Image[J]. Remote Sensing Technology and Application, 2012, 27(2): 248 -255 .
[2] Zhu Jing,Tang Chuan. An Overview of Remote Sensing Applications for Landslide Research in China[J]. Remote Sensing Technology and Application, 2012, 27(3): 458 -464 .
[3] . [J]. Remote Sensing Technology and Application, 1989, 4(2): 39 -43 .
[4] ZHOU De-min, GONG Hui-li, HU Jin-ming, WU Feng-lin. Application of Satellite Remote Sensing Technology to Wetland Research[J]. Remote Sensing Technology and Application, 2006, 21(6): 577 -583 .
[5] Xue Dongjian,Zhang Donghui,He Zhengwei,Zhang Xuefeng. Application of Multi\|source Remote Sensing Image Fusion in Geohazard Investigation[J]. Remote Sensing Technology and Application, 2011, 26(5): 664 -669 .
[6] CHEN Ding-gui, ZHOU De-min, LU Xian-guo, WANG Run-hua. A Study on Classification of Wetland Communities in Honghe National Nature Reserve by Remote Sensing[J]. Remote Sensing Technology and Application, 2007, 22(4): 485 -491 .
[7] Ding Yi,Zhang Jie,Ma Yi,Jiang Tao,Wang Qiang,Shan Chunzhi. A Remote Sensing Classification Methods of Coastal Wetlands Considering the Distance to Main Water[J]. Remote Sensing Technology and Application, 2013, 28(5): 785 -790 .
[8] . [J]. Remote Sensing Technology and Application, 1994, 9(2): 67 -68 .
[9] . [J]. Remote Sensing Technology and Application, 2005, 20(1): 1 -5 .
[10] JIANG Hui,ZHOU Wen-bin,LIU Yao. Research and Application of the Poyang Lake Wet LandClassification Using Remote Sensing[J]. Remote Sensing Technology and Application, 2008, 23(6): 648 -652 .