Using Multi-spectral Remote Sensing Data to Extract and Analyze the Vegetation Change of the Western Gurbantunggut Desert

  • 1. College of Life Sciences, Shihezi University, Shihezi 832000, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China

Received date: 2011-02-22

  Revised date: 2011-05-30

  Online published: 2011-06-15


Acquiring vegetation information is a key step in monitoring and evaluating arid land cover, even though it is difficult to extract desert vegetation information due to the confounding effort of soil or sand. There are many limiting factors for estimating vegetation growth in arid landscapes. Nevertheless, the growth of perennial vegetation reflects comprehensive vegetation conditions and can represent vegetation cover during a certain period. So in this study, we analyzed three years of Landsat images (1989, 2000 and 2007) that covered a typical portion of the Gurbantunggut Desert to estimate the vegetation change that has occurred. After comparing different methods, we finally chose the U-min method (one of the spectral mixture analysis methods) as the best way to determine the fraction of vegetation information of Landsat images. The RGB composition method was used to monitor the changes in vegetation abundance. In the end, we also comprehensively estimated vegetation changes using annual average time-series precipitation data and other's NDVI research results. The main results showed that: (1) a significant linear relationship exists between vegetation cover and vegetation fraction, with a correlation coefficient of 0.858. So the vegetation cover percent can be expressed by vegetation fraction extracted from remote sense images. (2) The area with improved vegetation cover accounted for 41.47% of the whole study area, while these patches with degraded vegetation cover accounting for 16.51%. However, it should be noted that these degraded vegetation pixels were distributed more sporadically. (3) During the study period, improved vegetation regions were mainly located in the semi-fixed dunes. At the same time, the analysis results also showed that the vegetation was distributed extensively in the arid land, regions including vegetation grown occupied almost 90% of the whole study area, and the number of improved vegetation pixels was greater than the number of degraded vegetation pixels during the period of 20 years.

Cite this article

CUI Yaoping, LIU Tong, ZHAO Zhiping, LI Jia . Using Multi-spectral Remote Sensing Data to Extract and Analyze the Vegetation Change of the Western Gurbantunggut Desert[J]. Journal of Geo-information Science, 2011 , 13(3) : 305 -312 . DOI: 10.3724/SP.J.1047.2011.00305


[1] 方精云, 刘国华, 徐嵩龄. 我国森林植被的生物量和净生产量[J]. 生态学报, 1996, 16(5): 497-508.

[2] IPCC, WGI. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[M]. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 2007,976-996.

[3] Yohe G W, Malone E, Brenkert A, et al. A Synthetic Assessment of the Global Distribution of Vulnerability to Climate Change from the IPCC Perspective That Reflects Exposure and Adaptive Capacity . Center for International Earth Science Information Network (CIESIN), Columbia University, New York, 2006.

[4] 钱亦兵, 吴兆宁, 张立运, 等. 古尔班通古特沙漠短命植物的空间分布特征[J]. 科学通报, 2007,19(52):2299-2306.

[5] 崔耀平, 王让会, 刘彤, 等. 基于光谱混合分析的干旱荒漠区植被遥感信息提取研究——以古尔班通古特沙漠西缘为例[J]. 中国沙漠, 2010, 30(2): 334-341.

[6] 张立运, 陈昌笃. 论古尔班通古特沙漠植物多样性的一般特点[J]. 生态学报, 2002,22(11):1923-1932.

[7] 贾亚敏, 刘彤, 骆郴,等. 新疆莫索湾南缘沙漠四种灌木空间异质性的对比[J]. 干旱区研究, 2008, 25(2): 225-230.

[8] Chavez P, Mackinnon D. Image-based Atmospheric Corrections: Revisited and Improved[J]. Photogrammetric Engineering and Remote Sensing, 1996, 62(9): 1025-1036.

[9] Elmore A J, Mustard J F, Manning S J, et al. Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index[J]. Remote Sensing of Environment, 2000, 73(1): 87-102.

[10] Collado A D, Chuvieco E, Camarasa A. Satellite Remote Sensing Analysis to Monitor Desertification Processes in the Crop-rangeland Boundary of Argentina[J]. Journal of Arid Environments, 2002, 52(1): 121-133.

[11] Small C. Estimation of Urban Vegetation Abundance by Spectral Mixture Analysis[J]. International Journal of Remote Sensing, 2001, 22(7): 1305-1334.

[12] 李本纲, 陶澍. 一种利用多时相TM影像分析地表植被变化的新方法: 以敦煌地区绿洲植被变化分析为例[J]. 遥感学报, 2000, 4(4):295-298.

[13] 施雅风, 沈永平, 李栋梁, 等. 中国西北气候有暖干向暖湿转型的特征和趋势探讨[J]. 第四纪研究, 2003, 23(2): 152-164.

[14] 李珍存, 马明国, 张峰, 等. 中国西北地区植被NDVI的时空变化及其影响因子分析[J]. 地球信息科学学报, 2010, 12(3): 315-321.

[15] 方精云, 朴世龙, 贺金生, 等. 近20年来中国植被活动在增强[J]. 中国科学C辑:生命科学, 2003, 33(6): 229-240.