A Comparison of Point Clouds Derived from Stereo Imagery and Airborne Laser Scanning for the Area-Based Estimation of Forest Inventory Attributes in Boreal Ontario

Pitt, Doug; Woods, Murray; Penner, Margaret

doi:10.1080/07038992.2014.958420

Cited by 59 publications

(52 citation statements)

References 25 publications

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“…Ground sampling is expensive and plot size is generally balanced by the number of samples. Prediction unit size is a function of the spatial resolution desired for the inventory attributes, and ~400 m 2 has been found to be a suitable size for both ALS [2] and IPC [3] predictions in the boreal forest. Larger prediction units may be used, but as the size increases, so does the cost of calibration and validation.…”

Section: Discussionmentioning

confidence: 99%

“…The study area and data were used in a previous study [3] to estimate traditional forest inventory attributes.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, ALS-like point clouds have been derived from stereo imagery using pixel matching. A previous study compared ALS and IPC for predicting forest inventory attributes in the Ontario boreal forest [3]. Comparable accuracies were obtained for predictions of forest inventory attributes including basal area, merchantable stem volume, top height and quadratic mean Dbh, but they found some loss of precision with the IPC using an area-based modeling approach.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have shown that these data may be augmented with area-based estimates of growing stock (basal area, volume) and average tree size (height, diameter, volume) derived from either Airborne Laser Scanning (ALS) [1,2] or stereo image point clouds (IPC) [3], to facilitate tactical (5-year) and operational (1-year) planning of forest operations. Since harvesting operations have become more mechanized and processing facilities are increasingly optimized for particular products and sizes of raw materials, there is considerable interest in adding information to inventories on the size assortment of the stems.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

Penner¹,

Woods

Pitt

2015

Forests

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Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within a stand. Adding size class distribution information to area-based inventories helps to bridge the gap between area-and tree-based inventories. This study examines the potential of ALS and stereo-imagery point clouds to predict size class distributions in a boreal forest. With an accurate digital terrain model, both ALS and imagery point clouds can be used to estimate size class distributions with comparable accuracy. Nonparametric imputations were generally superior to parametric imputations; this may be related to the limitation of using a unimodal Weibull function on a relatively small prediction unit (e.g., 400 m 2 ).

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Section: Discussionmentioning

confidence: 99%

“…The study area and data were used in a previous study [3] to estimate traditional forest inventory attributes.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

Penner¹,

Woods

Pitt

2015

Forests

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“…While several researchers have reported on the accuracy and practical advantages of PPCs when exploited through the ABA [37][38][39][40][41][42][43], only recently has the possibility of applying ITC approaches to PPCs started to emerge [44,45]; see also [46] for a precursor hybrid approach. Although some non-forest centric studies have documented the accuracy of PPCs with the most advanced image matching algorithms on different surfaces [47][48][49], it is still unclear if the current generation of PPCs provide 3D data sufficient to ensure proper tree delineation, if they are precise enough to reflect the height distribution of single trees, and if they contain reliable 3D and reflectance features for identifying tree species.…”

Section: Introductionmentioning

confidence: 99%

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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Analysis of structure from motion and airborne laser scanning features for the evaluation of forest structure

et al. 2022

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Airborne Laser Scanning (ALS) is widely extended in forest evaluation, although photogrammetry-based Structure from Motion (SfM) has recently emerged as a more affordable alternative. Return cloud metrics and their normalization using different typologies of Digital Terrain Models (DTM), either derived from SfM or from private or free access ALS, were evaluated. In addition, the influence of the return density (0.5–6.5 returns m-2) and the sampling intensity (0.3–3.4%) on the estimation of the most common stand structure variables were also analysed. The objective of this research is to gather all these questions in the same document, so that they serve as support for the planning of forest management. This study analyses the variables collected from 60 regularly distributed circular plots (r = 18 m) in a 150-ha of uneven-aged Scots pine stand. Results indicated that both ALS and SfM can be equally used to reduce the sampling error in the field inventories, but they showed differences when estimating the stand structure variables. ALS produced significantly better estimations than the SfM metrics for all the variables of interest, as well as the ALS-based normalization. However, the SfM point cloud produced better estimations when it was normalized with its own DTM, except for the dominant height. The return density did not have significant influence on the estimation of the stand structure variables in the range studied, while higher sampling intensities decreased the estimation errors. Nevertheless, these were stabilized at certain intensities depending on the variance of the stand structure variable.

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A Comparison of Point Clouds Derived from Stereo Imagery and Airborne Laser Scanning for the Area-Based Estimation of Forest Inventory Attributes in Boreal Ontario

Cited by 59 publications

References 25 publications

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

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

Analysis of structure from motion and airborne laser scanning features for the evaluation of forest structure

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