An Automatic Extraction Approach to Fractional Vegetation Cover of Saline Land with Digital Images

  • 1. Northeast Institute of Geography and Agricultural Ecology, CAS, Changchun 130012, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Changchun Jingyuetan Remote Sensing Test Site of Chinese Academy of Sciences, Changchun 130012, China

Received date: 2012-01-04

  Revised date: 2013-02-21

  Online published: 2013-08-08


Fractional vegetation cover is an important variable in ecological environment and a key parameter in remote sensing estimation, which is needed in the modeling of the land-atmosphere exchanges of momentum, energy, water, and trace gases. Determination of fractional vegetation cover exactly is necessary for studies on plant transpiration, ground surface evaporation, soil degradation and salinization. Excess green, highlighting vegetation and inhibiting the interference of soil and shadow, was used as a contrast enhancement for identifying plants from soil regions. This study uses modified excess green index to extract fractional vegetation cover by analyzing RGB color features of corn, sorghum, mung beans and weeds growing in saline land in western Jilin Province. The digital images are geometrically corrected in order to eliminate distortion. The automatic extraction approach using modified excess green indexes which is about 40 for vegetation growing on the saline land of western Jilin Province accurately distinguishes vegetation from soil, derives plant and soil binary images, then calculates fractional covers of the four vegetation types. This paper chooses maximum likelihood method to contrast the results of MExG automatic classification. The covers of corn, sorghum, mung beans and weeds calculated by these two methods were compared by visual interpretation and t-test. The visual interpretation shows a very high probability. The t-tests indicate that the means of the four vegetation types extracted by maximum likelihood and MExG automatic classification show a high consistency. In addition, the true values of corn, sorghum, mung bean are obtained by digitizing these images using ArcGIS software to validate MExG approach. The accuracy of MExG method can reach 99%. The results show that the MExG automatic approach which achieves good classification results and is less labor and time intensive than maximum likelihood, can be a viable ground-based method to validate fractional cover products generated by remote sensing.

Cite this article

DING Yan-Ling, DIAO Kai, LI Xiao-Feng, ZHENG Xin-Meng . An Automatic Extraction Approach to Fractional Vegetation Cover of Saline Land with Digital Images[J]. Journal of Geo-information Science, 2013 , 15(4) : 618 -624 . DOI: 10.3724/SP.J.1047.2013.00618


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