Forest Volume Estimation Based On BP Neural Network

This paper focuses on forestry county-level city in Zhejiang Province (Longquan City) as the study area, theforest reserves as the monitoring indicators, in order to effectively estimate forest reserves, we request theintegration of DEM (digital elevation model) and Forest Resource Inventory data to establish independent variablefactors set, which covering topography, climate, soil, forest structure characteristics. The target variable (reservesvolume) uses the BP neural network model for estimation. The main contents and results are as follows:(1)With the DEM of30m resolution as data source, through ArcGIS9.3extracted or calculated6factors ofthe data, included slope, aspect, elevation, surface curvature, solar radiation, topographic wetness index (TWI);The forest resource inventory data have5factors of the data, obtained soil thickness, A layer thickness, age,canopy density, accumulation per unit volume, etc.(2) Based on the principles of scientific, systemic, economical and practical guidance, unit volume formonitoring indicators, Set up an independent variable factors which contain soil layer thickness, A layer thickness,elevation, slope, aspect, solar radiation, topographic wetness index, surface curvature, age, crown density10impact factors etc.(3)Take the above10impact factors as the input layer vector, the average unit volume as the output layervector. In MATLAB R2011a, we should build BP neural network model estimate the reserves of dominant treespecies like cunninghamia lanceolata, pinus massoniana, hard-broadleaf species, taiwanensis etc. The BP neuralnetwork model number of neurons in each layer configuration is as follows:10input layer, hidden layer21, anoutput layer. On this basis, the2007Forest Resource Inventory Data for the study areadivide into groups to train and simulate. The results showed that: the LARE of each dominant tree species isfrom28.61%to84.78%, the average is47.06%; GRE is from6.35%to12.62%, the average of9.49%. From thesimulation results, the GRE of each dominant tree species is less15%, which means the simulation unit ofprecision is more than85%.This show that Although the average of individual average relative error is47.06%,Considering the prediction of volume of forest resources are usually based on a wide range of groups (such as anadministrative region or type of advantage tree) for the unit, in this paper, BP neural network to estimate forestvolume has high reference value.