Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

Rahlf, Johannes; Breidenbach, Johannes; Solberg, Svein; Astrup, Rasmus

doi:10.3390/f6114059

Cited by 35 publications

(31 citation statements)

References 28 publications

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“…The abovementioned studies applying DSI have mainly focused on predicting the forest inventory attributes at a plot or grid level comparable to ABA [34]. However, recent research has also utilized individual tree detection from DSI [35][36][37]. St-Onge et al [35] compared tree detection and tree-level height estimates based on DSI to ALS, and obtained similar results with both data sets.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of High-Altitude Aerial Image-Based Digital Surface Models in Detecting Individual Tree Crowns in Mature Boreal Forests

Tanhuanpää

Saarinen

Kankare

et al. 2016

Forests

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Height models based on high-altitude aerial images provide a low-cost means of generating detailed 3D models of the forest canopy. In this study, the performance of these height models in the detection of individual trees was evaluated in a commercially managed boreal forest. Airborne digital stereo imagery (DSI) was captured from a flight altitude of 5 km with a ground sample distance of 50 cm and corresponds to regular national topographic airborne data capture programs operated in many countries. Tree tops were detected from smoothed canopy height models (CHM) using watershed segmentation. The relative amount of detected trees varied between 26% and 140%, and the RMSE of plot-level arithmetic mean height between 2.2 m and 3.1 m. Both the dominant tree species and the filter used for smoothing affected the results. Even though the spatial resolution of DSI-based CHM was sufficient, detecting individual trees from the data proved to be demanding because of the shading effect of the dominant trees and the limited amount of data from lower canopy levels and near the ground.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of High-Altitude Aerial Image-Based Digital Surface Models in Detecting Individual Tree Crowns in Mature Boreal Forests

Tanhuanpää

Saarinen

Kankare

et al. 2016

Forests

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“…Our results showed lower accuracies as the relative RMSE with ITC was 41.73% and 47.64% with spectral features and vegetation indices, respectively, and with semi-ITC the corresponding values were 46.68% and 51.95%. In [72] the relative RMSE was 26% for quadratic mean diameter.…”

Section: Discussionmentioning

confidence: 99%

“…Especially with ITC and spectral features, we were able to obtain similar results for deciduous trees (relative RMSE 89.07%) to theirs (relative RMSE ranging from 42.00% to 81.98%). In [72], photogrammetric point clouds were utilized to predict total stem volume and stem number at plot level with semi-ITC. The relative RMSE was 46% for stem density, but it was 25% and 30% for volume estimates depending on modelling technique (i.e., multivariate or univariate kNN).…”

Section: Discussionmentioning

confidence: 99%

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

et al. 2018

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Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.

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“…This systematic error can be considerably reduced with the semi-ITC approach as demonstrated in our study. Comparing the semi-ITC and ABA inventory approaches, Breidenbach et al and Rahlf et al [37,61] showed that the semi-ITC approach provided prediction accuracies that were higher or similar to the ABA, while in the study of Ørka et al [23], the ABA performed better than the semi-ITC approach. Based on a trade-off between the goodness of fit and the systematic error, the compared total and species-specific results suggest that the semi-ITC approach in total outperformed the other approaches.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data

et al. 2017

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Abstract:Fusion of ALS and hyperspectral data can offer a powerful basis for the discrimination of tree species and enables an accurate prediction of species-specific attributes. In this study, the fused airborne laser scanning (ALS) data and hyperspectral images were used to model and predict the total and species-specific volumes based on three forest inventory approaches, namely the individual tree crown (ITC) approach, the semi-ITC approach, and the area-based approach (ABA). The performances of these inventory approaches were analyzed and compared at the plot level in a complex Alpine forest in Italy. For the ITC and semi-ITC approaches, an ITC delineation algorithm was applied. With the ITC approach, the species-specific volumes were predicted with allometric models for each crown segment and aggregated to the total volume. For the semi-ITC and ABA, a multivariate k-most similar neighbor method was applied to simultaneously predict the total and species-specific volumes using leave-one-out cross-validation at the plot level. In both methods, the ALS and hyperspectral variables were important for volume modeling. The total volume of the ITC, semi-ITC, and ABA resulted in relative root mean square errors (RMSEs) of 25.31%, 17.41%, 30.95% of the mean and systematic errors (mean differences) of 21.59%, −0.27%, and −2.69% of the mean, respectively. The ITC approach achieved high accuracies but large systematic errors for minority species. For majority species, the semi-ITC performed slightly better compared to the ABA, resulting in higher accuracies and smaller systematic errors. The results indicated that the semi-ITC outperformed the two other inventory approaches. To conclude, we suggest that the semi-ITC method is further tested and assessed with attention to its potential in operational forestry applications, especially in cases for which accurate species-specific forest biophysical attributes are needed.

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Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

Cited by 35 publications

References 28 publications

Evaluating the Performance of High-Altitude Aerial Image-Based Digital Surface Models in Detecting Individual Tree Crowns in Mature Boreal Forests

Evaluating the Performance of High-Altitude Aerial Image-Based Digital Surface Models in Detecting Individual Tree Crowns in Mature Boreal Forests

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data

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