Classification of forest types using artificial neural networks and remote sensing data

Abstract This study classified forest types using neural network data from a forest inventory provided by the "Florestal e da Biodiversidade do Estado do Pará" (IDEFLOR-BIO), and Bands 3, 4 and 5 of TM from the Landsat satellite. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training neural networks belonging to the software tools package MATLAB(r) R2011b. The neural networks were trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and were evaluated in terms of the indicators confusion matrix, overall accuracy, the Kappa coefficient, and the receiver operating characteristics chart (ROC). The best result of classification was obtained by the probabilistic neural network of radial basis function (RBF) newpnn, with an overall accuracy of 88%, and a Kappa coefficient of 76%, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon that adopt agricultural systems with low carbon emissions.

摘要 本研究依托帕拉州森林与生物多样性管理机构（Florestal e da Biodiversidade do Estado do Pará，简称IDEFLOR-BIO）提供的森林清查数据，以及陆地卫星（Landsat）专题制图仪（Thematic Mapper, TM）的3、4、5波段数据，开展森林类型分类研究。研究团队通过QGIS 2.8.1 Wien软件提取卫星影像信息，并将其作为训练数据集，用于训练MATLAB® R2011b软件工具包中的神经网络（Neural Network）模型。本次训练的神经网络用于分类帕拉州马穆鲁-阿拉皮恩斯地块内的两类森林：低地外露冠层雨林（Rain Forest of Lowland Emerging Canopy，缩写Dbe），以及低地外露冠层雨林加开放棕榈林（Rain Forest of Lowland Emerging Canopy plus Open with palm trees，缩写Dbe + Abp）；模型评估指标包括混淆矩阵（Confusion Matrix）、总体精度（Overall Accuracy）、Kappa系数（Kappa Coefficient）以及受试者工作特征曲线（Receiver Operating Characteristics Chart，简称ROC）。实验结果表明，径向基函数（Radial Basis Function, RBF）概率神经网络（Probabilistic Neural Network，newpnn）取得了最优分类效果，其总体精度达88%，Kappa系数为76%，证实该模型为性能优异的分类器，同时证明该方法在提供生态系统服务方面具有应用潜力，尤其适用于亚马逊地区采用低碳排放农业系统的人为活动区域。