Biophysical Parameter Estimation of a Pine Plantation from Satellite Images Using Artificial Neural Networks
نویسنده
چکیده
One non destructive method of biomass quantization involves exploiting biophysical parameters of trees such as diameter at breast height (DBH), height, basal area, volume and stocking. Generally, these parameters are estimated through model functions or algorithms which transform a set of remote sensing observations into biophysical measurements. Several studies have investigated the estimation of biomass parameters using low to high resolution optical digital images, but few studies have compared the performances of different advanced classification methods for estimating biomass variables. In this study, biophysical parameters including basal area, volume and stocking are estimated using different textural attributes calculated from SPOT 5 images over a Pinus radiata plantation in Australia. Two different neural networks including multilayer perceptron (MLP) with three different activation functions and radial basis function (RBF) neural networks are applied to analyze the relationship between the plot level biophysical information and the remotely sensed data. The results showed the capability of SPOT-5 data for use for biophysical parameters of Pinus radiata forest especially when MLP
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Utilizing satellite images and artificial neural networks in estimation of vegetation fraction in arid regions
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