title: [Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].
abstract: Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.
authors.affiliation: The First Institute of Photo-Grammetry and Remote Sensing, State Bureau of Surveying and Mapping, Xi'an 710054, China. top_speed2@163.com
authors.forename: Yin-Xi
authors.initials: YX
authors.lastname: Gong
authors.affiliation: Nanjing Forest Police College, Nanjing 210046, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: He
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Yan
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Zhong-Ke
authors.initials: ZK
authors.lastname: Feng
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Meng-Lei
authors.initials: ML
authors.lastname: Cao
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Institute of GIS, RS&GPS, Beijing Forestry University, Beijing 100083, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Miao
authors.affiliation: The Research Center for Forest Ecology, Beijing Forestry University, Beijing 100083, China.
authors.forename: Jin-Long
authors.initials: JL
authors.lastname: Zhao
mesh_terms: D002681:China; D065928:Forests; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D058998:Remote Sensing Technology; D013057:Spectrum Analysis