Optimum Non-parametric Method For Forest Above Ground Biomass Estimation Based On Remote Sensing Data

In the last years, the remote sensing community has devoted particular attention to the estimation of forest above ground biomass (AGB) via the analysis of multisource remote sensing data. A major observation in previous research on forest AGB estimation is that conventional parametric statistical pattern recognition methods are not appropriate in forest AGB estimation using multisource remote sensing data, since they cannot be modeled by a convenient multivariate statistical model. In these situations, the use of nonlinear regression techniques based on machine learning methodologies, such as K nearest neighbours(KNN), neural networks (NN), can represent an effective approach to solve such estimation problems. Another promising alternative nonparametric method to neural networks and KNN is the Support Vector Machine(SVM), originating from statistical learning theory, have provided capacities to deal with the few ground measurements, nonlinear, overfitting, highly dimension problems and have already proven their usefullness in many literature. However, few studies have investigated the potential of applying SVM to estimate forest AGB and the peculiarities of the nonparametric methods to improve the robustness and precision of the estimation process.This paper completely appraised various methods on forest AGB estimation and summerized the research status of the nonparametric methods, such as SVM, for biophysical variable estimation from remotely sensed images. On the basis of systematicly and intensely investigation and optimization of some nonlinear tools, the optimized neural networks and KNN algorithms on forest AGB estimation is presented. Furthermore, the technique flow of SVM for estimating forest AGB is introduced. To increase the performance of the algorithms in terms of estimation accuracy and robustness, the algorithm of SVM is modified and combined with random feature selection for optimizing the estimation results.The main works and results are as follows: (1) The method of back propagation neural network (BPNN) with single hidden layer and radial basis function neural network(RBFNN) are designed using the SPOT5 spectral reflectivity, LiDAR points cloud statistical variables and some remote sensing factors such as texture and vegetation index. The results clearly demonstrate that estimation accuracies increased by feature selection based on the random forest(RF) algorithm. Furthermore, compared to BPNN, the RBFNN model provided more accurate, improved robust result on the considered case.(2) The optimal KNN algorithm is established to estimate the forest AGB by using SPOT5 spectral reflectivity, LiDAR points cloud statistical variables and some remote sensing factors such as texture and vegetation index. The results show that the optimal KNN model, based on the selection of features and the determination of optimal parameters, provides a solution to deal with such problems and represents a promising method to address the complex and important problem of containing kinds of noise in training samples. In order to be compared with other nonparametric methods in this paper, the feature selection algorithm is RF too.(3) This paper introduces the basic flow of SVM for estimating forest AGB, which includes: feature selection, the determination of optimal parameter, the automatic optimization of kernel function and so on. In the stage of feature selection, the RF model provide better results compared to the typical F-score method. For the automatic optimization of kernel function, we developed the method of selecting optimal kernel automatically from four common kernels, which are linear kernel, polynomial kernel, RBF kernel and sigmoid kernel, abiding by root mean squared error (RMSE) minimum principle.(4) A novel approach to the estimation of forest AGB from multisource remote sensing images based on the composed model of SVM and random feature selection has been presented. The composed model, on the basis of RF feature selection, trains a group of SVM models by random feature selection technique and aims at exploting the peculiarities of an ensemble of SVM to improve the robustness and accuracy of the estimation process. The defining of optimal parameter and the automatic optimization of kernel function are handled by the single SVM in composed model. Three result combination strategies are adopted which are average-based method, weight-based method and selection-based method. Results show that: the composed model is more effective than regular SVM and other nonparametric methods to estimate forest AGB whether the training data are uncertainty or not, since it has the characteristic of self-adapted to some extent.The innovations in the thesis are as follows:(1) Introduce the basic flow of using SVM to estimate forest AGB and propose a novel approach to estimate forest AGB from multisource remote sensing images by combining SVM and random feature selection technique.The gain of the proposed algorithm is noticeable especially significant when working with very reduced training sets and different noise sources.(2) Implement the optimal estimation model based on KNN for estimating forest AGB from multisource remote sensing images. The optimal model improves the accuracy further by combining the RF feature selection method.(3) Use the RF algorithm to select features in the three kinds of nonparametric estimation method and validate the effectiveness by comparing to the result of typical F-score algorithm.