Spectroscopy and Spectral Analysis, Volume. 37, Issue 7, 2140(2017)
Forest Stock Volume Estimation Model Using Textural and Topographic Factors of Landsat8 OLI
Forest stock volume (FSV) is an important factor in the investigation of the forest stand and the main indicator to evaluate forest. The traditional methods of forest stock volume measurement are time-consuming and low efficiency. In remote sensing multiple linear regression method the accuracy is low and it is difficult to achieve accurate forestry requirements. As a self-improvement and automatic method which using lots of training data, machine learning can approach any nonlinear system model to improve prediction accuracy. Take into account spectral factor, texture factor, topographical factors in study area JIUFENG forest. BP-FSV, LSSVM-FSV and RF-FSV multi-spectral forest volume estimation models were established using BP neural network (BP), least squares support vector machine (LSSVM), random forest (RF) method in machine learning. Ground-angle gauge plots measured data, forest resource in subcompartment inventory data for management, forest sub-compartment map, model in conjunction Landsat8 OLI multispectral remote sensing data of sub-forest types were used for forest volume inversion. Programming in Matlab 2014a realization, BP-FSV Model of BP neural network and LSSVM least squares support vector mechanism LSSVM-FSV model were compared and analyzed based on R2 and RMSE. The results showed that: the p value tested between the predicted values from BP-FSV, LSSVM-FSV and RF-FSV model and observed values is less than 005. It indicates that there is no significant differences between the predicted and observed values of forest stock volume, It shows that the predicted results with the models are ideal, and it is feasible to predict forest stock volume by the models. The model established can improve the forecasting precision of forest stock volume through inversion combining with image spectral, textural, and terrain factor. RF-FSV model in coniferous forest, broad-leaved forest and mixed forest have shown a strong predictive ability, higher than BP -FSV model, which is above or close to LSSVM-FSV model. the RF-FSV model training and predicting accuracy are the highest among the three models, RF-FSV model in the training phase R2 and RMSE is 0839 and 13953 3 in coniferous forest, in broad-leaved forest is 0924 and 7634 1, for mixed forest 0902 and 12153 9. In the prediction stage R2 and RMSE in coniferous forests is 0816 and 15630 1, in broad-leaved forest 0913 and 4890 2, in mixed forest 0865 and 9344 1, it can provide a new method for forest stock volume prediction with better prospects.
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YANG Liu, FENG Zhong-ke, YUE De-peng, SUN Jin-hua. Forest Stock Volume Estimation Model Using Textural and Topographic Factors of Landsat8 OLI[J]. Spectroscopy and Spectral Analysis, 2017, 37(7): 2140
Received: Mar. 13, 2016
Accepted: --
Published Online: Aug. 30, 2017
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