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Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Sami Ullah (1)   , Petra Adler (2), Matthias Dees (1), Pawan Datta (1), Holger Weinacker (1), Barbara Koch (1)

iForest - Biogeosciences and Forestry, Volume 10, Issue 1, Pages 273-280 (2017)
doi: https://doi.org/10.3832/ifor2077-009
Published: Feb 23, 2017 - Copyright © 2017 SISEF

Research Articles


Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plot-level height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey’s mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALS-derived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.

  Keywords


Forest Inventory, Canopy Height Model, Stereo Aerial Photographs, LiDAR, Semi-Global Matching (SGM), enhanced Automatic Terrain Extraction (eATE)

Authors’ address

(1)
Sami Ullah
Matthias Dees
Pawan Datta
Holger Weinacker
Barbara Koch
Chair of Remote Sensing and Landscape Information System, Institute of Forest Sciences, Faculty of Environment and Natural Resources, University of Freiburg (Germany)
(2)
Petra Adler
Forest Research Institute, Baden-Württemberg - FVA (Germany)

Corresponding author

 
Sami Ullah
samidawar@gmail.com

Citation

Ullah S, Adler P, Dees M, Datta P, Weinacker H, Koch B (2017). Comparing image-based point clouds and airborne laser scanning data for estimating forest heights. iForest 10: 273-280. - doi: 10.3832/ifor2077-009

Academic Editor

Matteo Garbarino

Paper history

Received: Apr 06, 2016
Accepted: Oct 30, 2016

First online: Feb 23, 2017
Publication Date: Feb 28, 2017
Publication Time: 3.87 months

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