The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

By Joanne C. White, M. Wulder, Mikko Vastaranta, Nicholas C. Coops, Doug Pitt, Murray E. Woods. 2013

337 citations recorded by the source corpus.

Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including height, basal area, and volume). Recently, there has been increasing interest in the advanced processing of high spatial resolution digital airborne imagery to generate image-based point clouds, from which vertical information with similarities to ALS can be produced. Digital airborne imagery is typically less costly to acquire than ALS, is well understood by inventory practitioners, and in addition to enabling the derivation of height information, allows for visual interpretation of attributes that are currently problematic to estimate from ALS (such as species, health status, and maturity). At present, there are two limiting factors associated with the use of image-based point clouds. First, a DEM is required to normalize the image-based point cloud heights to aboveground heights; however DEMs with sufficient spatial resolution and vertical accuracy, particularly in forested areas, are usually only available from ALS data. The use of image-based point clouds may therefore be limited to those forest areas that already have an ALS-derived DEM. Second, image-based point clouds primarily characterize the outer envelope of the forest canopy, whereas ALS pulses penetrate the canopy and provide information on sub-canopy forest structure. The impact of these limiting factors on the estimation of forest inventory attributes has not been extensively researched and is not yet well understood. In this paper, we review the key similarities and differences between ALS data and image-based point clouds, summarize the results of current research related to the comparative use of these data for forest inventory attribute estimation, and highlight some outstanding research questions that should be addressed before any definitive recommendation can be made regarding the use of image-based point clouds for this application.

Subfields: Environmental Engineering, Geology, Insect Science

View Paper: https://www.semanticscholar.org/paper/49fc489cff0a1400ff4d8cb8512020efe8118b9b

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Citations in the corpus

Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds (2016, background): “[22], who found an increase in the canopy cover area derived from an SfM point cloud acquired from a large piloted airplane, the UAV SfM point cloud in our study has underestimated the total canopy area in comparison to both the ALS dataset and field observations.”
Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries (2017, neutral): “Its potential to characterise forest structures has long been realised but has been hampered by difficulties in the SfM algorithms to accurately perform image matching in densely vegetated areas [12,13].”
Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment (2015, methodology): “In many jurisdictions in Europe, digital aerial imagery are routinely acquired and updated by national mapping agencies [10], and many of these same jurisdictions also have high quality digital terrain models derived from ALS data [3].”
Outlook for the Next Generation’s Precision Forestry in Finland (2014, background): “ALS has been the primary data source for three-dimensional (3D) information on forest vertical structure; however, there is an increasing interest in the use of high spatial resolution digital aerial, optical satellite or radar imagery to generate 3D information analogous to ALS data [17,18] to supp”
Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification (2014, methodology): “It has been shown to be an efficient technique to characterize both forest structure [10–17], and ground topography [18–21].”
Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest (2017, background): “The area sampled per time unit for these sensors depend of many factors, such as overlap percentage, flight speed, altitude, weather conditions and aim of the study [22,90,91].”
Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests (2017, background): “Unlike airborne Lidar, digital aerial photographs cannot provide terrain height information, but they can provide the point cloud of the upper canopy surface [9], especially under dense forest canopy conditions.”
Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level (2015, background): “As the difference between ALS and image-based point clouds have been detected [54,55], we would like to point out that in the future, evaluation of the performance of image-based point clouds in tree delineation is an important line of studies that should be covered.”