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Remote Sens. 2019, 11(6), 661; https://doi.org/10.3390/rs11060661

Potential of Modern Photogrammetry Versus Airborne Laser Scanning for Estimating Forest Variables in a Mountain Environment

1
Chair of Remote Sensing and Landscape Information System, Institute of Forest Sciences, Faculty of Environment and Natural Resources, University of Freiburg, 79106 Freiburg, Germany
2
Department of Forestry, Shaheed Benazir Bhutto University, 18050 Sheringal, Dir Upper, Khyber Pakhtunkhwa, Pakistan
3
UNIQUE forestry and land use GmbH, 79098 Freiburg, Germany
4
Forest Research Institute, Baden-Wurttemberg (FVA), 79100 Freiburg, Germany
5
Remote Sensing and Geoinformation, JOANNEUM RESEARCH, 8010 Graz, Austria
*
Authors to whom correspondence should be addressed.
Received: 9 February 2019 / Revised: 14 March 2019 / Accepted: 16 March 2019 / Published: 19 March 2019
(This article belongs to the Section Forest Remote Sensing)
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Abstract

Digital stereo aerial photographs are periodically updated in many countries and offer a viable option for the regular update of information on forest variables. We compared the potential of image-based point clouds derived from three different sets of aerial photographs with airborne laser scanning (ALS) to assess plot-level forest attributes in a mountain environment. The three data types used were (A) high overlapping pan-sharpened (80/60%); (B) high overlapping panchromatic band (80/60%); and (C) standard overlapping pan-sharpened stereo aerial photographs (60/30%). We used height and density metrics at the plot level derived from image-based and ALS point clouds as the explanatory variables and Lorey’s mean height, timber volume, and mean basal area as the response variables. We obtained a RMSE = 8.83%, 29.24% and 35.12% for Lorey’s mean height, volume, and basal area using ALS data, respectively. Similarly, we obtained a RMSE = 9.96%, 31.13%, and 35.99% and RMSE = 11.28%, 31.01%, and 35.66% for Lorey’s mean height, volume and basal area using image-based point clouds derived from pan-sharpened stereo aerial photographs with 80/60% and 60/30% overlapping, respectively. For image-based point clouds derived from a panchromatic band of stereo aerial photographs (80%/60%), we obtained an RMSE = 10.04%, 31.19% and 35.86% for Lorey’s mean height, volume, and basal area, respectively. The overall findings indicated that the performance of image-based point clouds in all cases were as good as ALS. This highlights that in the presence of a highly accurate digital terrain model (DTM) from ALS, image-based point clouds offer a viable option for operational forest management in all countries where stereo aerial photographs are updated on a routine basis. View Full-Text
Keywords: forest inventory attribute; Lorey’s mean height; timber volume; basal area; ALS LiDAR; stereo aerial photographs forest inventory attribute; Lorey’s mean height; timber volume; basal area; ALS LiDAR; stereo aerial photographs
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Ullah, S.; Dees, M.; Datta, P.; Adler, P.; Schardt, M.; Koch, B. Potential of Modern Photogrammetry Versus Airborne Laser Scanning for Estimating Forest Variables in a Mountain Environment. Remote Sens. 2019, 11, 661.

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