Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1)
  • Open Access

    ARTICLE

    Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm

    Liang Wanga,b, Jiping Liua,b, Shenghua Xub, Jinjin Dongc, Yi Yangd

    Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 391-398, 2018, DOI:10.1080/10798587.2017.1296660

    Abstract Forest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in More >

Displaying 1-10 on page 1 of 1. Per Page