Forest Stock Volume Estimation Model Using Textural and Topographic Factors of Landsat8 OLI
YANG Liu1，2, FENG Zhong-ke1*, YUE De-peng1, SUN Jin-hua3
1. Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China
2. College of Tourism and Planning, Pingdingshan University, Pingdingshan 467002, China
3. College of Geoscience and Surveying Engineering, China University of Mining and Techology, Beijing 100083, China
Abstract：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 0.05. 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 0.839 and 13.953 3 in coniferous forest, in broad-leaved forest is 0.924 and 7.634 1, for mixed forest 0.902 and 12.153 9. In the prediction stage R2 and RMSE in coniferous forests is 0.816 and 15.630 1, in broad-leaved forest 0.913 and 4.890 2, in mixed forest 0.865 and 9.344 1, it can provide a new method for forest stock volume prediction with better prospects.
杨 柳，冯仲科，岳德鹏，孙金华. 结合纹理因子和地形因子的森林蓄积量多光谱估测模型[J]. 光谱学与光谱分析, 2017, 37(07): 2140-2145.
YANG Liu, FENG Zhong-ke, YUE De-peng, SUN Jin-hua. Forest Stock Volume Estimation Model Using Textural and Topographic Factors of Landsat8 OLI. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(07): 2140-2145.
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