Journal Article

Merged Airborne LiDAR and Hyperspectral Data for Tree Species Classification in Puer's Mountainous Area / 机载LiDAR和高光谱融合实现普洱山区树种分类

廖声熙, 刘怡君, 庞勇, 刘鲁霞, 陈博伟, 荚文

2016

Overview

[Objective] To classify the tree species in Puer's mountainous area by remote sensing image, and to search an efficient way to forest management planning.[Method] The AISA Eagle II hyperspectral data and airborne LiDAR taken in April of 2014 were merged, and based on Canopy Height Model (CHM) derived from airborne LiDAR point cloud data, the vertical structure data of target species were obtained. Then, the Principal Component Analysis (PCA) transformation was used to reduce the noise and dimension of hyperspectral image. Finally, the Support Vector Machine (SVM) approach was used to classify the main tree species of Pu'er city.[Result] (1) The main tree species of Puer are Pinus kesiya Royle ex Gord. var. langbianensis (A.Chev.) Gaussen, Betula alnoides Buch.-Ham. ex D. Don, Castanopsis hystrix A.DC, Schima superba Gardn. et Champ and so on. (2) It showed that the total accuracy and kappa coefficient are 80.54%, and 0.78, which are 6.55% and 0.08 higher compared with the classification accuracies without CHM

• Publisher: Chinese Academy of Forestry
• Subject: Accuracy / Classification / Forest management / Hyperspectral imaging / Image classification / Lidar / Management planning / Mountain regions / Mountainous areas / Mountains / Noise reduction / Plant species / Principal components analysis / Rainforests / Remote sensing / Satellites / Species / Species classification / Support vector machines / Trees