Volume IV-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 3-6, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-3-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 3-6, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-3-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2017

19 Oct 2017

CANOPY SURFACE RECONSTRUCTION AND TROPICAL FOREST PARAMETERS PREDICTION FROM AIRBORNE LASER SCANNER FOR LARGE FOREST AREA

Z. Chen1, Z. Yang1, Y. Chen1, C. Wang2, J. Qian1, Q. Yang1, X. Chen1, and J. Lei1 Z. Chen et al.
  • 1Forestry Research Institute of Hainan Province HaiKou, HaiNan, 571100, China
  • 2Hainan Geomatics Center, National Administration of Surveying, Mapping and Geoinformation of China, HaiKou, HaiNan, 570203, China

Keywords: Canopy Height Model, CHM, LiDAR, Tree Mean Height

Abstract. Canopy height model(CHM) and tree mean height are critical forestry parameters that many other parameters such as growth, carbon sequestration, standing timber volume, and biomass can be derived from. LiDAR is a new method used to rapidly estimate these parameters over large areas. The estimation of these parameters has been derived successfully from CHM. However, a number of challenges limit the accurate retrieval of tree height and crowns, especially in tropical forest area. In this study, an improved canopy estimation model is proposed based on dynamic moving window that applied on LiDAR point cloud data. DEM, DSM and CHM of large tropical forest area can be derived from LiDAR data effectively and efficiently.