• DocumentCode
    2318322
  • Title

    The quantitative estimation of periurban vegetation ecology using hyperspectral remote sensing

  • Author

    Lu Xia ; Hu Zhenqi ; Guo Shuyan

  • Author_Institution
    Sch. of Geodesy & Geomatics Eng., Huaihai Inst. of Technol., Lianyungang, China
  • fYear
    2009
  • fDate
    20-22 May 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The environment of the mountainous area is one of important parts in urban ecological and economic system. It has an impact on the stabilization of urban ecological environment. Mentougou District in Beijing, a mountainous area, had abundant mineral resources originally. Many years´ exploitation activities made the mineral resources exhausted. It is an inevitable objective law that each mining site has to face. Of course, we know that it brought enormous benefits for the local government and the mine group, while it also had caused tremendous environmental damage. In order to realize the periurban ecological barrier of Beijing, the damaged ecosystem must be restored and the rapid succession of the architecture of plant community should be finished successfully. The paper took the Wangpingcun coal mine as a case study, the estimation models of the biophysical and biochemical parameters of vegetation were established by hyperspectral remote sensing technology. The fresh weight estimation model of vegetation was built based on the vegetation index of R752/R548 by thrice function method. The estimation model of chlorophyll concentration (SPAD) was established through linear four-point interpolation technique. The total nitrogen content estimation model was created based on derivative spectrum among the absorption bands ranging from 350 nm to 2500 nm through the multivariate regression modelling. Based on the construction of above models, the spatial distribution maps of biophysical and biochemical parameters of vegetation were extracted by the parameter mapping and regularities of distribution were revealed. The research results showed that the precisions of above estimation models were very high. The multiple determinant coefficients (R2) of fresh weight and SPAD and the total nitrogen content estimation models of vegetation were 0.883, 0.814 and 0.795 respectively. The spatial distribution maps indicated that the chlorophyll concentration and fresh weight - of vegetation around the coal wastes pile were the minimum. The more the SPAD and fresh weight became, the farther the distance away from the coal wastes pile became. The regularity of distribution of the total nitrogen content was completely opposite to the fresh weight and SPAD. It is concluded that estimating biophysical and biochemical parameters of vegetation using hyperspectral remote sensing is completely possible. It has theoretical significance for restoring service function of ecosystem and transforming regional industrial structure.
  • Keywords
    ecology; geochemistry; minerals; nitrogen; remote sensing; soil pollution; vegetation; Beijing; China; Mentougou District; N; Wangpingcun coal mine; absorption bands; abundant mineral resources; biochemical parameter; biophysical parameter; chlorophyll concentration; damaged ecosystem; economic system; exploitation activities; four-point interpolation technique; hyperspectral remote sensing; mine group; periurban vegetation ecology estimation; plant community; thrice function method; total nitrogen content estimation model; tremendous environmental damage; urban ecological barrier; urban ecological system; vegetation index; wavelength 350 nm to 2500 nm; weight estimation model; Biological system modeling; Ecosystems; Environmental economics; Environmental factors; Hyperspectral sensors; Local government; Mineral resources; Nitrogen; Remote sensing; Vegetation mapping;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Urban Remote Sensing Event, 2009 Joint
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3460-2
  • Electronic_ISBN
    978-1-4244-3461-9
  • Type

    conf

  • DOI
    10.1109/URS.2009.5137469
  • Filename
    5137469