Imputing Forest Structure Attributes from Stand Inventory and Remotely Sensed Data in Western Oregon, USA

Hudak, Andrew T.; Haren, A. Tod; Crookston, Nicholas L.; Liebermann, Robert J.; Ohmann, Janet L.

doi:10.5849/forsci.12-101

Cited by 21 publications

(17 citation statements)

References 38 publications

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“…The observed and computed tree density in the study area from the UAV-derived CHM were 305 and 300 trees per hectare (TPH; trees·ha −1 ), respectively. The most accurate results in the ITD were obtained primarily in test subplots with TPH ranging from 150 to 325 trees·ha −1 (Plot FID: 4,13,16,19,22,24,25,26). On average, 93.2% of trees were detected correctly, with commission and omission errors limited to 8.7% and 6.8%, respectively, with a F-score of 0.92.…”

Section: Resultsmentioning

confidence: 99%

“…Aerial photography, light detection and ranging (LiDAR) and airborne multispectral, and hyperspectral images had been perceived as potential tools for observing forest areas and for performing broad-scale analysis of forest systems. These methods have the ability to quantify the composition and structure of the forest at different temporal and geographical scales with the support of various statistical methods, and therefore can supplement forest inventory related expeditions [14][15][16][17][18][19][20][21].…”

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confidence: 99%

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Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Mohan

Silva

Klauberg

et al. 2017

Forests

325

232

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Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Mohan

Silva

Klauberg

et al. 2017

Forests

325

232

View full text Add to dashboard Cite

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“…This approach to characterizing reference period vegetation using climate data is conceptually similar to imputation methods that assign plot‐based vegetation and other attributes to pixels that do not have plot data (Ohmann and Gregory , Hudak et al. ). The end result was a gridded dataset representing the reference period distribution of forest across the study area (e.g., Fig.…”

Section: Methodsmentioning

confidence: 99%

Living on the edge: trailing edge forests at risk of fire‐facilitated conversion to non‐forest

et al. 2019

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Forests are an incredibly important resource across the globe, yet they are threatened by climate change through stressors such as drought, insect outbreaks, and wildfire. Trailing edge forests—those areas expected to experience range contractions under a changing climate—are of particular concern because of the potential for abrupt conversion to non‐forest. However, due to plant‐climate disequilibrium, broad‐scale forest die‐off and range contraction in trailing edge forests are unlikely to occur over short timeframes (<~25–50 yr) without a disturbance catalyst (e.g., wildfire). This underscores that explicit attention to both climate and disturbance is necessary to understand how the distribution of forests will respond to climate change. As such, we first identify the expected location of trailing edge forests in the intermountain western United States by mid‐21st century. We then identify those trailing edge forests that have a high probability of stand‐replacing fire and consider such sites to have an elevated risk of fire‐facilitated transition to non‐forest. Results show that 18% of trailing edge forest and 6.6% of all forest are at elevated risk of fire‐facilitated conversion to non‐forest in the intermountain western United States by mid‐21st century. This estimate, however, assumes that fire burns under average weather conditions. For a subset of the study area (the southwestern United States), we were able to incorporate expected fire severity under extreme weather conditions. For this spatial subset, we found that 61% of trailing edge forest and 30% of all forest are at elevated risk of fire‐facilitated conversion to non‐forest under extreme burning conditions. However, due to compounding error in our process that results in unknowable uncertainty, we urge caution in a strict interpretation of these estimates. Nevertheless, our findings suggest the potential for transformed landscapes in the intermountain western United States that will affect ecosystem services such as watershed integrity, wildlife habitat, wood production, and recreation.

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“…ALS measures the three-dimensional distribution of vegetation within forest canopies and, as a result, is particularly well suited for describing structural vegetation attributes [12]. ALS has a demonstrated utility for forest inventory across a range of forest environments [13][14][15][16], and can provide a direct measurement of a range of forest inventory attributes such as tree height, location, and canopy cover, as well as providing wall-to-wall predictor variables in the form of ALS metrics that allow for the development of models to estimate for example volume, biomass, and basal area [17]. A multitude of studies have found a high correlation between field-and ALS-measured tree heights in a variety of forest environments (e.g., [6,17,18]).…”

Section: Introductionmentioning

confidence: 99%

Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling

Tompalski

Coops

White

et al. 2015

Forests

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Information on individual tree attributes is important for sustainable management of forest stands. Airborne Laser Scanning (ALS) point clouds are an excellent source of information for predicting a range of forest stand attributes, with plot and single tree volume being among the most important. Two approaches exist for estimating volume: area-based approach (ABA) and individual tree detection (ITD). The ABA is now routinely applied in operational forestry applications, and results in generalized plot-or stand-level attribute predictions. Alternatively, ITD-based estimates provide detailed information for individual trees, but are typically biased due to challenges associated with individual tree detection. In this study, we applied an ABA to estimate tree counts and individual tree volumes by downscaling plot-level predictions of total volume derived using ALS data in a highly productive and complex coastal temperate forest environment in British Columbia, Canada, characterized by large volumes and multi-species and multi-age stand structures. To do so, a two-parameter Weibull probability density function (PDF) was used to describe the within-plot tree volume distribution. The ABA approach was then used to model the total plot volume and the two Weibull PDF parameters. Next, the parameters were used to calculate mean tree volume and derive the number of trees and the individual tree volume distribution. OPEN ACCESSForests 2015, 6 2609Tree count estimates were minimally biased with RMSE of 149 trees· ha −1 or 24.4%.The volume distributions showed good agreement with reference data (mean Reynold's error index = 71.7). We conclude that the approach was suitable for enriching ABA-derived forest stand attributes in the majority of the studied forest stands; however the accuracy was lower in multi-layered stands that had a multimodal individual tree volume distribution.

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Imputing Forest Structure Attributes from Stand Inventory and Remotely Sensed Data in Western Oregon, USA

Cited by 21 publications

References 38 publications

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Living on the edge: trailing edge forests at risk of fire‐facilitated conversion to non‐forest

Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling

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