An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

Richardson, J.; Moskal, L. Monika

doi:10.3390/rs8090778

Cited by 15 publications

(5 citation statements)

References 23 publications

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“…The present study mapped individual CWD pieces, analogous to the results of Richardson and Moskal [14] and Duan et al [16], but was also able to map snags obtaining 95.5% completeness and 91.6% correctness in the calibration area and 65.7% completeness and 88.8% correctness in the verification area. These are comparable to the results of Bütler and Schlaepfer [28] and Stereńczak et al [15].…”

Section: General Accuracy and Transferabilitysupporting

confidence: 74%

Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery

Queiroz

McDermid

Castilla

et al. 2019

Forests

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Coarse woody debris (CWD; large parts of dead trees) is a vital element of forest ecosystems, playing an important role in nutrient cycling, carbon storage, fire fuel, microhabitats, and overall forest structure. However, there is a lack of effective tools for identifying and mapping both standing (snags) and downed (logs) CWD in complex natural settings. We applied a random forest machine learning classifier to detect CWD in centimetric aerial imagery acquired over a 270-hectare study area in the boreal forest of Alberta, Canada. We used a geographic object-based image analysis (GEOBIA) approach in the classification with spectral, spatial, and LiDAR (light detection and ranging)-derived height predictor variables. We found CWD to be detected with great accuracy (93.4 ± 4.2% completeness and 94.5 ± 3.2% correctness) when training samples were located within the application area, and with very good accuracy (84.2 ± 5.2% completeness and 92.2 ± 3.2% correctness) when training samples were located outside the application area. The addition of LiDAR-derived variables did not increase the accuracy of CWD detection overall (<2%), but aided significantly (p < 0.001) in the distinction between logs and snags. Foresters and researchers interested in CWD can take advantage of these novel methods to produce accurate maps of logs and snags, which will contribute to the understanding and management of forest ecosystems.

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Section: General Accuracy and Transferabilitysupporting

confidence: 74%

Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery

Queiroz

McDermid

Castilla

et al. 2019

Forests

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“…Challenges in CWD field sampling techniques (Ståhl et al 2001;Woldendorp et al 2004), as well as the relevance of dead wood in a host of forest ecosystem functions (Woodall et al 2019), have sparked an interest in applying remotely sensed data for this purpose. For example, CWD objects have recently been detected with encouraging results using LiDAR point clouds (Polewski et al 2015;Joyce et al 2019), aerial photography by means of Unmanned Aerial Vehicles (UAV) (Inoue et al 2014;Duan et al 2017;Panagiotidis et al 2019), or using LiDAR combined with multispectral aerial photography (Richardon and Moskal 2016;Stereńczak et al 2017;Lopes Queiroz et al 2019). Alternatively, models can be established leveraging spectral and structural relationships between plot-level observations of CWD volume and LiDAR or winter Landsat scenes (Sumnall et al 2016;Wolter et al 2017).…”

Section: Remote Sensing Of Coarse Woody Debrismentioning

confidence: 99%

Archaeological documentation of wood caribou fences using unmanned aerial vehicle and very high-resolution satellite imagery in the Mackenzie Mountains, Northwest Territories

Sluijs

MacKay

Andrew³

et al. 2020

J. Unmanned Veh. Sys.

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Indigenous peoples of Canada’s North have long made use of boreal forest products, with wooden drift fences to direct caribou movement towards kill sites as unique examples. Caribou fences are of archaeological and ecological significance, yet sparsely distributed and increasingly at risk to wildfire. Costly remote field logistics requires efficient prior fence verification and rapid on-site documentation of structure and landscape context. Unmanned aerial vehicle (UAV) and very high-resolution (VHR) satellite imagery were used for detailed site recording and detection of coarse woody debris (CWD) objects under challenging Subarctic alpine woodlands conditions. UAVs enabled discovery of previously unknown wooden structures and revealed extensive use of CWD (n = 1745, total length = 2682 m, total volume = 16.7 m3). The methodology detected CWD objects much smaller than previously reported in remote sensing literature (mean 1.5 m long, 0.09 m wide), substantiating a high spatial resolution requirement for detection. Structurally, the fences were not uniformly left on the landscape. Permafrost patterned ground combined with small CWD contributions at the pixel level complicated identification through VHR data sets. UAV outputs significantly enriched field techniques and supported a deeper understanding of caribou fences as a hunting technology, and they will aid ongoing archaeological interpretation and time-series comparisons of change agents.

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“…However, the efficacy of new methods requires testing and comparison with accepted methods. Data derived from manual digitization of logs from aerial imagery (Richardson andMoskai 2016, Blanchard et al 2011) or mapping of woody debris in the field (Woldendorp et al 2004, Pesonen et al 2009 have been used to verify or test the efficacy of other automated or field sample-based methods, respectively, for determining down woody debris. Using remote sensing technologies, Trofymow et al (2014) presented a method to determine piled woody residue volumes in four cutblocks in the Oyster River area of Vancouver Island, using a combination of LiDAR-based measurements of bulk volumes of all residue piles and scaled log volumes in sample piles.…”

Section: R a F Tmentioning

confidence: 99%

Comparison of geospatial and ground-based methods for determining postharvest dispersed woody residues

2019

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Postharvest woody residues are measured to estimate billable waste, bioenergy potential, fuel loadings, and carbon budgets. In fall 2014, a waste and residue survey (WRS) established twenty-nine 0.4 ha plots in the dispersed residue stratum on two cutblocks on Vancouver Island, British Columbia, and measured woody residue “logs” ≥ 10 cm inside-bark diameter and ≥ 20 cm in length. A line-intersect sampling (LIS), in spring 2015, measured all woody debris ≥ 10 cm diameter outside bark (DOB) on 18 plots. High-resolution (2 cm) photography was acquired in summer 2015, orthophotomosaics were prepared and analyzed for residue “logs” ≥ 10 cm DOB in 29 plots using semi-automated “log” delineation (SLD) and manual heads-up “log” digitization (MLD). After adjustment for bark thickness, SLD values were still higher than WRS values, due to inclusion of non-log pieces, though MLD values were not. LIS values were not different from WRS values once adjusted for bark thickness, transect overlaps, and decayed or non-log pieces excluded. The LIS and preharvest forest cover species composition differed from the WRS. While the SLD geospatial method can census ≥ 10 cm diameter residues in entire cutblocks, it was biased. Field-based methods may be required to correct SLD bias and measure species composition to determine bark thickness and wood densities to calculate biomass from residue volumes.

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An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

Cited by 15 publications

References 23 publications

Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery

Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery

Archaeological documentation of wood caribou fences using unmanned aerial vehicle and very high-resolution satellite imagery in the Mackenzie Mountains, Northwest Territories

Comparison of geospatial and ground-based methods for determining postharvest dispersed woody residues

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