TIAN Yichao, BAI Xiaoyong, WANG Shijie, QIN Luoyi, LI Yue. Spatial-temporal Changes of Vegetation Cover in Guizhou Province, Southern China[J]. Chinese Geographical Science, 2017, 27(1): 25-38. doi: 10.1007/s11769-017-0844-3
Citation: TIAN Yichao, BAI Xiaoyong, WANG Shijie, QIN Luoyi, LI Yue. Spatial-temporal Changes of Vegetation Cover in Guizhou Province, Southern China[J]. Chinese Geographical Science, 2017, 27(1): 25-38. doi: 10.1007/s11769-017-0844-3

Spatial-temporal Changes of Vegetation Cover in Guizhou Province, Southern China

doi: 10.1007/s11769-017-0844-3
Funds:  Under the auspices of National Key Research Program of China (No. 2016YFC0502300, 2016YFC0502102, 2014BAB03B00), National Key Research and Development Program (No. 2014BAB03B02), Agricultural Science and Technology Key Project of Guizhou Province of China (No. 2014-3039), Science and Technology Plan Projects of Guiyang Municipal Bureau of Science and Technology of China (No. 2012-205), Science and Technology Plan of Guizhou Province of China (No. 2012-6015), Guangxi Natural Science Foundation of China (No. 2014GXNSFBA118221)
More Information
  • Corresponding author: WANG Shijie.E-mail:wangshijie@vip.gyig.ac.cn
  • Received Date: 2016-03-29
  • Rev Recd Date: 2016-07-20
  • Publish Date: 2017-02-27
  • Guizhou Province is an important karst area in the world and a fragile ecological area in China. Ecological risk assessment is very necessary to be conducted in this region. This study investigates different characteristics of the spatial-temporal changes of vegetation cover in Guizhou Province of Southern China using the data set of SPOT VEGETATION (1999-2015) at spatial resolution of 1-km and temporal resolution of 10-day. The coefficient of variation, the Theil-Sen median trend analysis, and the Mann-Kendall test are used to investigate the spatial-temporal change of vegetation cover and its future trend. Results show that:1) the spatial distribution pattern of vegetation cover in Guizhou Plateau is high in the east whereas low in the west. The average annual normalized difference vegetation index (NDVI) from west to east is higher than that from south to north. 2) Average annual NDVI improved obviously in the past 17 years. The growth rate of average annual NDVI is 0.028/10 yr, which is slower than that of vegetation in the country (0.048/10 yr) from 1998 to 2007. Average annual NDVI in karst area is lower than that in non-karst area. However, the growing rate of average annual NDVI in karst area (0.030/10 yr) is faster than that in non-karst area (0.023/10 yr), indicating that vegetation coverage increases more rapidly in karst area. 3) Vegetation coverage in the study area is stable overall, but fluctuates in the local scales. 4) Vegetation coverage presents a continuous increasing trend. The Hurst exponent of NDVI in different vegetation types has an obvious threshold in various elevations. 5) The proportion of vegetation cover with sustainable increase is higher than that of vegetation cover with sustainable decrease. The improvement in vegetation cover may expand to most parts of the study area.
  • [1] Bai X Y, Wang S J, Xiong K N, 2013. Aseessing spatial-temporal evolution processes of karst rocky desertification and indica-tions for restoration strategies. Land Degradation & Develop-ment, 24(1):47-56. doi: 10.1002/ldr.1102
    [2] Berlin G A, Linusson A C, Olsson E G A, 2000. Vegetation changes in semi-natural meadows with unchanged management in southern Sweden, 1965-1990. Acta Oecologica, 21(2):125-138. doi: 10.1016/S1146-609X(00)00117-X
    [3] Boyd D S, 1999. The relationship between the biomass of Came-roonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 μm). International Journal of Remote Sensing, 20(5):1017-1023. doi: 10.1080/014311699213055
    [4] Cao Yunfeng, Wang Zhengxing, Den Fangping, 2010. Fidelity performance of three filters for high quality NDVI time-series analysis. Remote sensing Technology and Application, 25(1):118-125. (in Chinese)
    [5] De Jong R, Verbesselt J, Zeileis A et al., 2013. Shifts in global vegetation activity trends. Remote Sensing, 5(3):1117-1133. doi: 10.3390/rs5031117
    [6] Fensholt R, Langanke T, Rusmussen K et al., 2012. Greenness in semi-arid areas across the globe 1981-2007:an earth observing satellite based analysis of trends and drivers. Remote Sensing of Environment, 121:144-158. doi:10.1016/j.rse.2012. 01.017
    [7] Fensholt R, Proud S R, 2012. Evaluation of earth observation based global long term vegetation trends-Comparing GIMMS and MODIS global NDVI time series. Remote sensing of Environment, 119:131-147. doi:10.1016/j.rse.2011. 12.015
    [8] Fensholt R, Rasmussen K, Nielsen T T et al., 2009. Evaluation of earth observation based long term vegetation trends:inter-comparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113(9):1886-1898. doi: 10.1016/j.rse.2009.04.004
    [9] Freitas S R, Mello M C S, Cruz C B M, 2005. Relationships be-tween forest structure and vegetation indices in Atlantic rain forest. Forest Ecology and Management, 218(1):353-362. doi: 10.1016/j.foreco.2005.08.036
    [10] Fuller D O, Wang Y, 2014. Recent trends in satellite vegetation index observations indicate decreasing vegetation biomass in the southeastern saline everglades wetlands. Wetlands, 34(1):67-77. doi: 10.1007/s13157-013-0483-0
    [11] Gamon J A, Field C B, Goulden M L et al., 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5(1):28-41. doi: 10.2307/1942049
    [12] Holben B N, 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11):1417-1434. doi:10.1080/0143116860 8948945
    [13] Huang Senwang, Li Xiaosong, Wu Bingfang et al., 2012. The distribution and drivers of land degradation in the Three-North Shelter Forest Region of China during 1982-2006. Acta Ge-ography Sinica, 5:005. (in Chinese)
    [14] Hurst H E, 1951. Long term storage capacity of reservoirs. Reston:Transactions of the American Society of Civil Engineer, 116:770-799.
    [15] Jeong S J, Ho C H, Gim H J et al., 2011. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008. Global Change Biology, 17(7):2385-2399. doi:10.1111/j.1365-2486. 2011.02397.x
    [16] Jing Juanli, Wang Yongfeng, 2014. Temporal and spatial variation of vegetation cover in Southwest China karst area during 1998-2012. Research of Soil and Water Conseration, 21(4):163-167. (in Chinese)
    [17] Jugder D, Shinoda M, Sugimoto N et al., 2011. Spatial and tem-poral variations of dust concentrations in the Gobi Desert of Mongolia. Global and Planetary Change, 78(1):14-22. doi: 10.1016/j.gloplacha.2011.05.003
    [18] Li F, Zeng Y, Li X S et al., 2014. Remote sensing based monitor-ing of interannual variations in vegetation activity in China from 1982 to 2009. Science China Earth Sciences, 57(8):1800-1806. doi: 10.1007/s11430-014-4883-7
    [19] Li Hao, Cai Yunlong, Chen Ruishan et al., 2011. Effect assessment of the project of grain for green in the karst region in Southwestern China:a case study of Bijie Prefecture. Acta Ecologica Sinica, 31(12):3255-3264. (in Chinese)
    [20] Li Shuangcheng, Zhao Zhiqiang, Gao Yang et al., 2008. Determin-ing the predictability and the spatial pattern of urban vegetation using recurrence quantification analysis:a case study of Shenzhen City. Geographical Research, 27(6):1243-1252. (in Chinese)
    [21] Liang Shuang, Peng Shushi, Lin Xin et al., 2013. NDVI-based spatial-temporal change in grassland growth of China from 1982 to 2010. Acta Scientiarum Naturalium Universitatis Pe-kinensis, 49(2):311-320. (in Chinese)
    [22] Lioubimtseva E, Cole R, Adams J M et al., 2005. Impacts of cli-mate and land-cover changes in arid lands of Central Asia. Journal of Arid Environments, 62(2):285-308. doi:10.1016/j. jaridenv.2004.11.005
    [23] Mandelbrot B B, Wallis J R, 1969. Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Research, 5(5):967-988. doi: 10.1029/WR005i005p00967
    [24] Meng Jijun, Wang Jun, 2007. The response of vegetation dynamics to climate change in the southwestern karst region of China since the early 1980s. Geographical Research, 26(5):857-866. (in Chinese)
    [25] Miao L, Jiang C, Xue B et al., 2014. Vegetation dynamics and factor analysis in arid and semi-arid Inner Mongolia. Envi-ronmental Earth Sciences, 73(5):2343-2352. doi: 10.1007/s12665-014-3582-1
    [26] Milich L, Weiss E, 2000. GAC NDVI inter annual coefficient of variation (CoV) images:ground truth sampling of the Sahel along north-south transects. International Journal of Remote Sensing, 21(2):235-260. doi: 10.1080/014311600210812
    [27] Mohammat A, Wang X, Xu X et al., 2013. Drought and spring cooling induced recent decrease in vegetation growth in Inner Asia. Agricultural and Forest Meteorology, 178:21-30. doi: 10.1016/j.agrformet.2012.09.014
    [28] Park H S, Sohn B J, 2010. Recent trends in changes of vegetation over East Asia coupled with temperature and rainfall variations. Journal of Geophysical Research:Atmospheres, 115:(D14). doi: 10.1029/2009JD012752
    [29] Piao S, Wang X, Ciais P et al., 2011. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Global Change Biology, 17(10):3228-3239. doi: 10.1111/j.1365-2486.2011.02419.x
    [30] Pouliot D, Latifovic R, Olthof I, 2009. Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985-2006. International Journal of Remote Sensing, 30(1):149-168. doi: 10.1080/01431160802302090
    [31] Qiu Haijun, Cao Mingming, 2011. Spatial and temporal variations in vegetation cover in China based on SPOT vegetation data. Resources Science, 33(2), 335-340. (in Chinese)
    [32] Schweers W, Bai Z, Campbell E et al., 2011. Identification of potential areas for biomass production in China:Discussion of a recent approach and future challenges. Biomass and Bioenery, 35(5):2268-2279. doi: 10.1016/j.biombioe.2011.02.034
    [33] Tucker C J, Justice C O, Prince S D, 1986. Monitoring the grass-lands of the Sahel 1984-1985. International Journal of Remote Sensing, 7(11):1571-1581. doi:10.1080/014311686089 48954
    [34] Tucker C J, Newcomb W W, Los S O et al., 1991. Mean and in-ter-year variation of growing-season normalized difference vegetation index for the Sahel 1981-1989. International Journal of Remote Sensing, 12 (6):1133-1135. doi:10.1080/0143 1169108929717
    [35] Wang Q, Adiku S, Tenhunen J et al., 2005. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote sensing of Environment, 94(2):244-255. doi:10.1016/j.rse. 2004.10.006
    [36] Wang S J, Liu Q M, Zhang D F, 2004. Karst rock desertification in Southwestern China:geomorphology, landuse, impact and rehabilitation. Land Degradation & Development, 15:115-121. doi: 10.1002/ldr.592.
    [37] Wang Zhengxing, Liu Chuang, Huete Alfredo, 2003. From AVHRR-NDVI to MODIS-EVI:advances in vegetation index research. Acta Ecologica Sinica, 23(5):979-987. (in Chinese)
    [38] Wu Z T, Wu J J, Liu J H et al., 2013. Increasing terrestrial vegeta-tion activity of ecological restoration program in the Bei-jing-Tianjin Sand Source Region of China. Ecological Engi-neering, 52:37-50. doi: 10.1016/j.ecoleng.2012.12.040
    [39] Xu E Q, Zhang H, Li M, 2013. Mining spatial information to investigate the evolution of karst rocky desertification and its human driving forces in Changshun, China. Science of the Total Environment, 458:419-426. doi:10.1016/j.scitotenv.2013. 04.048
    [40] Xu Jianhua, 2002. Mathematical Methods in Modern Geography. Beijing:Higher Education Press, 2002:37-41. (in Chinese)
    [41] Xue Y, Liu S, Zhang L et al., 2013. Integrating fuzzy logic with piecewise linear regression for detecting vegetation greenness change in the Yukon River Basin, Alaska. International Journal of Remote Sensing, 34(12):4242-4263. doi:10.1080/014 31161.2013.775532
    [42] Yue S, Pilon P, Cavadias G, 2002. Power of the Mann-Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series. Journal of Hydrology, 259(1):254-271. doi: 10.1016/S0022-1694(01)00594-7
    [43] Zhang H, Song T, Wang K L et al., 2014. Biomass and carbon storage in an age-sequence of Cyclobalanopsis glauca planta-tions in southwest China. Ecological Engineering, 73:184-191. doi: 10.1016/j.ecoleng.2014.09.008
    [44] Zhang Geli, Xu Xingliang, Zhou Caiping, 2011. Responses of vegetation changes to climatic variations in Hulun Buir grass-land in past 30 years. Acta Geographica Sinica, 66(1):41-58. (in Chinese)
    [45] Zhang X Y, Goldberg M, Tarpley D et al., 2010. Drought-induced vegetation stress in southwestern North America. Environ-mental Research Letters, 5(2):024008. doi: 10.1088/1748-9326/5/2/024008
    [46] Zhang Y L, Gao J, Liu L et al., 2013. NDVI-based vegetation changes and their responses to climate change from 1982 to 2011:A case study in the Koshi River Basin in the middle Himalayans. Global and Planetary Change, 108:139-148. doi: 10.1016/J.gloplacha.2013.06.012
    [47] Zhang Yuandong, Zhang Xiaohe, Liu Shirong, 2011. Correlation analysis on normalized difference vegetation index of different vegetation and climatic factors in Southwest China. Chinese Journal of Applied Ecology, 22(2):323-330. (in Chinese)
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Spatial-temporal Changes of Vegetation Cover in Guizhou Province, Southern China

doi: 10.1007/s11769-017-0844-3
Funds:  Under the auspices of National Key Research Program of China (No. 2016YFC0502300, 2016YFC0502102, 2014BAB03B00), National Key Research and Development Program (No. 2014BAB03B02), Agricultural Science and Technology Key Project of Guizhou Province of China (No. 2014-3039), Science and Technology Plan Projects of Guiyang Municipal Bureau of Science and Technology of China (No. 2012-205), Science and Technology Plan of Guizhou Province of China (No. 2012-6015), Guangxi Natural Science Foundation of China (No. 2014GXNSFBA118221)
    Corresponding author: WANG Shijie.E-mail:wangshijie@vip.gyig.ac.cn

Abstract: Guizhou Province is an important karst area in the world and a fragile ecological area in China. Ecological risk assessment is very necessary to be conducted in this region. This study investigates different characteristics of the spatial-temporal changes of vegetation cover in Guizhou Province of Southern China using the data set of SPOT VEGETATION (1999-2015) at spatial resolution of 1-km and temporal resolution of 10-day. The coefficient of variation, the Theil-Sen median trend analysis, and the Mann-Kendall test are used to investigate the spatial-temporal change of vegetation cover and its future trend. Results show that:1) the spatial distribution pattern of vegetation cover in Guizhou Plateau is high in the east whereas low in the west. The average annual normalized difference vegetation index (NDVI) from west to east is higher than that from south to north. 2) Average annual NDVI improved obviously in the past 17 years. The growth rate of average annual NDVI is 0.028/10 yr, which is slower than that of vegetation in the country (0.048/10 yr) from 1998 to 2007. Average annual NDVI in karst area is lower than that in non-karst area. However, the growing rate of average annual NDVI in karst area (0.030/10 yr) is faster than that in non-karst area (0.023/10 yr), indicating that vegetation coverage increases more rapidly in karst area. 3) Vegetation coverage in the study area is stable overall, but fluctuates in the local scales. 4) Vegetation coverage presents a continuous increasing trend. The Hurst exponent of NDVI in different vegetation types has an obvious threshold in various elevations. 5) The proportion of vegetation cover with sustainable increase is higher than that of vegetation cover with sustainable decrease. The improvement in vegetation cover may expand to most parts of the study area.

TIAN Yichao, BAI Xiaoyong, WANG Shijie, QIN Luoyi, LI Yue. Spatial-temporal Changes of Vegetation Cover in Guizhou Province, Southern China[J]. Chinese Geographical Science, 2017, 27(1): 25-38. doi: 10.1007/s11769-017-0844-3
Citation: TIAN Yichao, BAI Xiaoyong, WANG Shijie, QIN Luoyi, LI Yue. Spatial-temporal Changes of Vegetation Cover in Guizhou Province, Southern China[J]. Chinese Geographical Science, 2017, 27(1): 25-38. doi: 10.1007/s11769-017-0844-3
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