Mapping canopy height using a combination of digital stereo‐photogrammetry and lidar

St-Onge, Benoît; Vega, Cédric; Fournier, Richard; Hu, Yuan

doi:10.1080/01431160701469040

Cited by 146 publications

(112 citation statements)

References 37 publications

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“…These point clouds have been shown to be very similar to those created using lidar, especially when based on high overlap photos acquired from unmanned aerial vehicles [32]. However, photogrammetric results generally appear smoother than corresponding point clouds [33], may not describe the full 3D scene because of occlusions effects, and may contain artefacts caused by mismatch [34].…”

Section: Introductionmentioning

confidence: 93%

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

St-Onge

Audet

Bégin

2015

Forests

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Photogrammetric point clouds (PPC) obtained by stereomatching of aerial photographs now have a resolution sufficient to discern individual trees. We have produced such PPCs of a boreal forest and delineated individual tree crowns using a segmentation algorithm applied to the canopy height model derived from the PPC and a lidar terrain model. The crowns were characterized in terms of height and species (spruce, fir, and deciduous). Species classification used the 3D shape of the single crowns and their reflectance properties. The same was performed on a lidar dataset. Results show that the quality of PPC data generally approaches that of airborne lidar. For pixel-based canopy height models, viewing geometry in aerial images, forest structure (dense vs. open canopies), and composition (deciduous vs. conifers) influenced the quality of the 3D reconstruction of PPCs relative to lidar. Nevertheless, when individual tree height distributions were analyzed, PPC-based results were very similar to those extracted from lidar. The random forest classification (RF) of individual trees performed better in the lidar case when only 3D metrics were used (83% accuracy for lidar, 79% for PPC). However, when 3D and intensity or multispectral data were used together, the accuracy of PPCs (89%) surpassed that of lidar (86%).

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Section: Introductionmentioning

confidence: 93%

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

St-Onge

Audet

Bégin

2015

Forests

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“…Evidently, there is a need to compare the capabilities of ALS and image-based point clouds in the area-based approach for forest attribute estimation across a range of forest types and stand conditions. St-Onge et al [45] compared image-based and ALS CHMs and found that the ALS CHM had a larger standard deviation than the image-based CHM, but that the mean and maximum heights, and 95th and 99th percentiles of height differed by less than 1.69 m and were strongly correlated (with r up to 0.95 for the 95th percentile). These results are notable considering that the image-based CHM in this case was built from the stereo-matching of digital images and not using the more advanced SGM approach to image matching and DSM generation.…”

Section: Area-based Approach For Estimating Forest Inventory Attributesmentioning

confidence: 99%

“…While technological innovations can improve image matching capacity, issues of sun angle and viewing geometry will be a factor in any optical image product that is acquired and used for point cloud and DSM generation [45]. Several issues that confound image matching, including issues specific to forest environments, are summarized in Table 2.…”

Section: Image-based Point Cloudsmentioning

confidence: 99%

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

White

Wulder

Vastaranta

et al. 2013

Forests

276

217

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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 OPEN ACCESSForests 2013, 4 519 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.

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“…The acquisition of aerial stereo imagery has a long tradition in many countries and such images are used to update topographic maps and for orthophoto production. There are a variety of studies based on digital aerial images for canopy height generation which have been mainly carried out in Canada [12,13], Germany [14][15][16], Sweden [17] and Finland [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

2015

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Surface models provide key knowledge of the 3-d structure of forests. Aerial stereo imagery acquired during routine mapping campaigns covering the whole of Switzerland (41,285 km 2 ), offers a potential data source to calculate digital surface models (DSMs). We present an automated workflow to generate a nationwide DSM with a resolution of 1 × 1 m based on photogrammetric image matching. A canopy height model (CHM) is derived in combination with an existing digital terrain model (DTM). ADS40/ADS80 summer images from 2007 to 2012 were used for stereo matching, with ground sample distances (GSD) of 0.25 m in lowlands and 0.5 m in high mountain areas. Two different image matching strategies for DSM calculation were applied: one optimized for single features such as trees and for abrupt changes in elevation such as steep rocks, and another optimized for homogeneous areas such as meadows or glaciers. The country was divided into 165,500 blocks, which were matched independently using an automated workflow. The completeness of successfully matched points was high, 97.9%. To test the accuracy of the derived DSM, two reference data sets were used: (1) topographic survey points (n = 198) and (2) stereo measurements (n = 195,784) within the framework of the Swiss National Forest Inventory (NFI), in order to distinguish various land cover types. An overall median accuracy of 0.04 m with a normalized median absolute deviation (NMAD) of 0.32 m was found using the topographic survey points. The agreement between the stereo measurements and the values of the DSM revealed acceptable NMAD values between 1.76 and 3.94 m for forested areas. A good correlation (Pearson's r = 0.83) was found between terrestrially OPEN ACCESSRemote Sens. 2015, 7 4344 measured tree height (n = 3109) and the height derived from the CHM. Optimized image matching strategies, an automatic workflow and acceptable computation time mean that the presented approach is suitable for operational usage at the nationwide extent. The CHM will be used to reduce estimation errors of different forest characteristics in the Swiss NFI and has high potential for change detection assessments, since an aerial stereo imagery update is available every six years.

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Mapping canopy height using a combination of digital stereo‐photogrammetry and lidar

Cited by 146 publications

References 37 publications

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

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

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

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