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Citation:

Merged Airborne LiDAR and Hyperspectral Data for Tree Species Classification in Puer's Mountainous Area

  • Received Date: 2015-12-21
  • [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. The mapping accuracy of the main tree species reached as high as 90.24%.[Conclusion] It is proved that this method is feasible for the identification of tree species in mountainous areas, and is a feasible way to improve total accuracy with merged LiDAR and hyperspectral data.
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Merged Airborne LiDAR and Hyperspectral Data for Tree Species Classification in Puer's Mountainous Area

  • 1. Research Institute of Resource Insects, Chinese Academy of Forestry, Kunming 650224, Yunnan, China
  • 2. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Abstract: [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. The mapping accuracy of the main tree species reached as high as 90.24%.[Conclusion] It is proved that this method is feasible for the identification of tree species in mountainous areas, and is a feasible way to improve total accuracy with merged LiDAR and hyperspectral data.

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