Combination of Artificial Neural Network with Multispectral Remote Sensing Data as Applied in Site Quality Evaluation in Inner Mongolia

While abundant ground surface and site information is included in multispectral remote sensing data, traditional site quality evaluation system mainly uses artificial ground surface survey data. To construct an effective site quality evaluation system, this paper, with Wangyedian Forest Farm in Inner Mongolia as the object of study, has adopted an improved back propagation artificial neural network (BPANN) model based on a combination of multispectral remote sensing and surface survey data of the zone. With dahurian larch as an example, a neural network model based on a combination of remote sensing spectrum factor, site index and site factors has been constructed, which, applied in the site quality evaluation of sub compartments of the studied zone, has led to an optimized geographical position prediction model with an accuracy of 95.36%, and an increase of 9.83% as compared with neural network model based on traditional sub compartment survey data. The result indicates that multispectral remote sensing data is very suitable for forest site quality evaluation. Besides, the improved BP neural system features ideal accuracy of prediction, which testifies to the effectiveness and advantage of the methodology described in this paper.