Assessing Dead Wood by Airborne Laser Scanning

Maltamo, Matti; Kallio, Eveliina; Bollandsås, Ole Martin; Næsset, Erik; Pesonen, Annukka

doi:10.1007/978-94-017-8663-8_19

Cited by 26 publications

(23 citation statements)

References 63 publications

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“…No ecological study has provided a clear threshold for distinguishing between fine and coarse woody debris [41], even though the choice of a specific definition can have a strong effect on deadwood quantification. Indeed, testing the exclusion of different deadwood elements from the total amount derived from the available NFI data, it is possible to see how adding just stumps can cause a 44% increase in volume [42].…”

Section: Deadwoodmentioning

confidence: 99%

“…The predictive power of ABA models mainly increases with stand maturity, but the accuracy of the models for predicting CWD volume using characteristics of living trees as predictors has been rather poor [41].…”

Section: Area-based Approachmentioning

confidence: 99%

“…However, not enough information is available on how the existing ones work within different forest types. The need to investigate this aspect has been underlined by different authors [19,41,67,69,74,117], though only a few have analyzed benchmarks [68,118,119]. Furthermore, these are limited to the extraction of inventory parameters on living trees whereas, as concerns deadwood, only the sampling methods related to CWD are compared [120].…”

Section: Critical Points and Future Perspectivesmentioning

confidence: 99%

“…The majority of studies dealing with CWD modeling obtained good results but, according to Maltamo et al [41], this is due to their setting in natural areas where mortality conditions are more homogenous; these authors indeed point out how it is much harder to statistically model CWD in managed forests where management operations have altered its presence across the landscape. Nevertheless, for Downed Deadwood (DDW), area-based techniques and the associated measurements of DDW cover should be pursued, as single-tree log detection techniques are still problematic.…”

Section: Critical Points and Future Perspectivesmentioning

confidence: 99%

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Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

2018

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LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future.

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Section: Deadwoodmentioning

confidence: 99%

Section: Area-based Approachmentioning

confidence: 99%

Section: Critical Points and Future Perspectivesmentioning

confidence: 99%

Section: Critical Points and Future Perspectivesmentioning

confidence: 99%

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Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

2018

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“…For automated analyses, ALS data in combination with CIR aerial imagery showed the best results to date with OA of about 0.90 [38][39][40]. Detection of standing deadwood from ALS data was also tested and it delivered heterogeneous results, depending on forest type and detection method [41,42], with OAs from 0.65-0.73 [43][44][45] to 0.86-0.92 [35,46].…”

Section: Introductionmentioning

confidence: 99%

Detection of Standing Deadwood from Aerial Imagery Products: Two Methods for Addressing the Bare Ground Misclassification Issue

Zielewska-Büttner

Adler

Kolbe

et al. 2020

Forests

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Deadwood mapping is of high relevance for studies on forest biodiversity, forest disturbance, and dynamics. As deadwood predominantly occurs in forests characterized by a high structural complexity and rugged terrain, the use of remote sensing offers numerous advantages over terrestrial inventory. However, deadwood misclassifications can occur in the presence of bare ground, displaying a similar spectral signature. In this study, we tested the potential to detect standing deadwood (h > 5 m) using orthophotos (0.5 m resolution) and digital surface models (DSM) (1 m resolution), both derived from stereo aerial image matching (0.2 m resolution and 60%/30% overlap (end/side lap)). Models were calibrated in a 600 ha mountain forest area that was rich in deadwood in various stages of decay. We employed random forest (RF) classification, followed by two approaches for addressing the deadwood-bare ground misclassification issue: (1) post-processing, with a mean neighborhood filter for “deadwood”-pixels and filtering out isolated pixels and (2) a “deadwood-uncertainty” filter, quantifying the probability of a “deadwood”-pixel to be correctly classified as a function of the environmental and spectral conditions in its neighborhood. RF model validation based on data partitioning delivered high user’s (UA) and producer’s (PA) accuracies (both > 0.9). Independent validation, however, revealed a high commission error for deadwood, mainly in areas with bare ground (UA = 0.60, PA = 0.87). Post-processing (1) and the application of the uncertainty filter (2) improved the distinction between deadwood and bare ground and led to a more balanced relation between UA and PA (UA of 0.69 and 0.74, PA of 0.79 and 0.80, under (1) and (2), respectively). Deadwood-pixels showed 90% location agreement with manually delineated reference to deadwood objects. With both alternative solutions, deadwood mapping achieved reliable results and the highest accuracies were obtained with deadwood-uncertainty filter. Since the information on surface heights was crucial for correct classification, enhancing DSM quality could substantially improve the results.

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From model selection to maps: A completely design‐based data‐driven inference for mapping forest resources

et al. 2022

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A completely data‐driven, design‐based sampling strategy is proposed for mapping a forest attribute within the spatial units tessellating a survey region. Based on sample data, a model is selected, and model parameters are estimated using least‐squares criteria for predicting the attribute of interest within units as a linear function of a set of auxiliary variables. The spatial interpolation of residuals arising from model predictions is performed by inverse distance weighting. The leave‐one‐out cross validation procedure is adopted for selecting the smoothing parameter used for interpolation. The densities of the attributes of interest within units are estimated by summing predictions and interpolated residuals. Finally, density estimates are rescaled to match the total estimate over the survey region obtained by the traditional regression estimator with the total estimate obtained from the map as the sum of the density estimates within units. A bootstrap procedure accounts for the uncertainty. The consistency of the strategy is proven by incorporating previous results. A simulation study is performed and an application for mapping wood volume densities in the forest estate of Rincine (Central Italy) is described.

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Assessing Dead Wood by Airborne Laser Scanning

Cited by 26 publications

References 63 publications

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

Detection of Standing Deadwood from Aerial Imagery Products: Two Methods for Addressing the Bare Ground Misclassification Issue

From model selection to maps: A completely design‐based data‐driven inference for mapping forest resources

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