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Terrain effects assessment on remotely sensed fractional vegetation cover in hilly area of southern China

  • role： First author第一作者
  • Affiliation：

    College of Resources Engineering, Longyan University, Longyan 364012, China

    Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing 100094, China

  • Email：wuzhijiefj@163.com
  • Introduction：吴志杰(1971—    )，男，副教授，研究方向为亚热带环境与资源遥感。E-mail：wuzhijiefj@163.com
WU Zhijie12,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing 100094, China

HE Guojin2,  
  • Affiliation：

    Shenzhen Institute of Building Research Company Limited, Shenzhen 518049, China

    Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China

HUANG Shaolin34,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing 100094, China

WANG Mengmeng2,  
  • Affiliation：

    College of Resources Engineering, Longyan University, Longyan 364012, China

LIN Jintang1

Resümee

Changes in Fractional Vegetation Cover (FVC) is one of the core research fields in eco-environment assessment. FVC estimation from satellite image is significantly influenced by the terrain, soil, and atmosphere. Among of them, terrain variation can arouse miscalculation of biomass information of similar kind of vegetation, and it is a main bottleneck of quantitative remote sensing of vegetation. Therefore, it is urgent to find a method of FVC estimation that can eliminate or reduce the effects of terrain variation, in order to improve the FVC estimation using the satellite data over mountainous and hilly areas.Taking Landsat 8 OLI image as a data source. Firstly, three vegetation indexes, including the normalized difference vegetation index (NDVI), Cosine-C corrected NDVI, and the normalized difference mountain vegetation Index (NDMVI), are estimated. Then FVC are calculated from three vegetation indexes, using a linear unmixing model. Finally, the impact of terrain variation of three estimation methods of FVC are assessed and compared, considering altitude, slope, aspect, and solar incident angle. five altitude ranges, (i.e. 100—400 m, 400—600 m, 600—800 m, 800—1000 m and ≥1000 m), 72 aspect ranges (from 0 to 360° with a 5° interval) , four slope ranges (i.e. 0—10°, 10°—20°, 20°—30° and ≥30°), and ten solar incident angle ranges (i.e. 0—10°, 10°—20°, …, ≥90°) are considered, and the Coefficient of Variation (CV) is used to measure the impacts.The results show that the CV is minimal when the altitude is lower than 400 m, and the CV increases with the increasing altitude in three method. The minimal CV is 0.39%, –8.15% and –3.14% for NDVI-based method, NDVI-C based method and NDMVI-based method, respectively. The CV also increases with increasing slope. The CV of NDVI-based method reaches 11.5% when the slope is larger than 30°, indicating a significant impact of terrain variation. The CV of NDMVI-based method is slightly larger than that of NDVI-C based method when the slope is lower than 10°. The CV of NDMVI is the nearest to 0 when slope ranges from 20° to 30°, indicating a little impact of terrain variation.The terrain effects are reduced for all three VI-based methods. The NDMVI-based method has a best performance, and the terrain variation has little effect on FVC estimate. However, there is over-corrected phenomenon when the Cosine-C is corrected. The NDVI-based method has the poorest result, indicating that it is heavily influenced by the terrain variation when the slope is larger than 10°. The estimated FVC in shady slope is significantly lower than that of sunny slope. Based on the analysis above, it is shown that the NDMVI-based method can effectively eliminate or reduce the effects of terrain variation to improve the FVC estimation from the remote sensing data over mountainous and hilly areas in southern China. However, only Landsat 8 image with spatial resolution of 30 m is employed in this study. The magnitude of terrain effects may also depends on the spatial resolution of image. Therefore, further studies should be focused on terrain correction for high spatial resolution satellite image.

Schlüsselwort

fractional vegetation cover;NDVI;NDMVI;Cosine-C correction;terrain effect

References

  1. 1.
    Chen H F, Zhang W B and Chen F. 2008. Advances in research on application of remote sensing method to estimating vegetation coverage. Remote Sensing for Land & Resources, 75(1): 13–18
  2. 2.
    Duan S B and Yan G J. 2007. A review of models for topographic correction of remotely sensed images in mountainous area. Journal of Beijing Normal University (Natural Science), 43(3): 362–366
  3. 3.
    Hu Y F, Jiang S L, Liu Y, Li X, Wang Y T and Chen B. 2014. Temporal and spatial variation of vegetation coverage on upper Anning river based on RS. Transactions of the Chinese Society for Agricultural Machinery, 45(5): 205–215
  4. 4.
    Li M M, Wu B F, Yan C Z and Zhou W F. 2004. Estimation of vegetation fraction in the upper basin of Miyun reservoir by remote sensing. Resources Sciences, 26(4): 153–159
  5. 5.
    Ma N, Hu Y F, Zhuang D F and Zhang X L. 2012. Vegetation coverage distribution and its changes in plan blue banner based on remote sensing data and dimidiate pixel model. Scientia Geograhica Sinica, 32(2): 251–256
  6. 6.
    Proy C, Tanré D and Deschamps P Y. 1989. Evaluation of topographic effects in remotely sensed data. Remote Sensing of Environment, 30(1): 21–32
  7. 7.
    Rouse J W, Haas R H and Schell J A. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of Third Earth Resources Technology Satellite 21 Symposium Greenbelt, 1: 48–62
  8. 8.
    Teillet P M, Guindon B and Goodenough D G. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8(2): 1537–1540
  9. 9.
    Wu J, Liu M S and Li W T. 2013. Research on vegetation cover information extraction technologies under different terrain conditions. Chinese Journal of Plant Ecology, 37(1): 18–25
  10. 10.
    Wang P J, Shun R, Zhu Q J, Xie D H and Chen J M. 2006. Improvement on the abilities of BEPS under accidented Terrain. Journal of Image and Graphics, 11(7): 1017–1027
  11. 11.
    Wen J G, Liu Q H, Xiao Q, Liu Q and Li X W. 2008. Optical remote sensing reflectance calculation model of complicated mountain area. Science in China Series D: Earth Sciences, 38(11): 1419–1427
  12. 12.
    Wu Z J and Xu H Q. 2011. A new index for vegetation enhancements of mountainous regions based on satellite image data. Journal of Geo-information Science, 13(5): 656–663
  13. 13.
    Xu S, Shen R P and Yang X Y. 2012. A comparative study of different vegetation indices for estimating vegetation coverage based on the Dimidiate Pixel Model. Remote Sensing for Land & Resources, 24(4): 95–100
  14. 14.
    Yao C, Huang W and Li Y H. 2009. Evaluation of topographical influence on vegetation indices of rugged terrain. Remote Sensing Technology and Application, 24(4): 496–501
  15. 15.
    Zribia M, S Le Hegarat-Masclea and Taconeta O. 2003. Derivation of Wild Vegetation Cover Density in Semi-arid Regions: ERS2/SAR Evaluation. International Journal of Remote Sensing, 24(6): 1335–1352
  16. 16.
    Zhao S L, Yin M and Zhang W. 2002. An analysis on the spatial features of soil erosion in Shandong province base on GIS. Scientia Geograhica Sinica, 22(6): 694–699
  17. 17.
    Zhang C L, Yu H, Gong P and Ju W M. 2009. Relationship Between Land Brightness Temperature and vegetation abundance in Wuhan city. Scientia Geograhica Sinica, 29(5): 740–744
  18. 18.
    Zhang B Q, Wu P T and Zhao X N. 2011. Detecting and analysis of spatial and temporal variation of vegetation cover in the Loess Plateau during 1982–2009. Transactions of the CSAE, 27(4):287–293
  19. 19.
    Zhu G L, Liu Y B, Ju W M and Chen J M. 2013. Evaluation of topographic effects on four commonly used vegetation indices. Journal of Remote Sensing, 17(1): 222–234
  20. 20.
    Zhang C, Xu H Q, Zhang H, Tang F and Lin Z L. 2015. Fractional vegetation cover change and its ecological effect assessment in a typical reddish soil region of southeastern china: Changting County, Fujian Province. Journal of Natural Resources, 30(6): 917–927

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