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Open AccessEditor’s ChoiceArticle

Estimating Coarse Woody Debris Volume Using Image Analysis and Multispectral LiDAR

1
Applied Geospatial Research Group, Geography Department, University of Calgary, Calgary, AB T2N 4V8, Canada
2
Geography Department, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
3
Geospatial Centre, Alberta Biodiversity Monitoring Institute, Edmonton, AB T6G 2E9, Canada
*
Author to whom correspondence should be addressed.
Forests 2020, 11(2), 141; https://doi.org/10.3390/f11020141
Received: 3 January 2020 / Revised: 22 January 2020 / Accepted: 23 January 2020 / Published: 25 January 2020
(This article belongs to the Special Issue Forest Resources Assessments: Mensuration, Inventory and Planning)
Coarse woody debris (CWD, parts of dead trees) is an important factor in forest management, given its roles in promoting local biodiversity and unique microhabitats, as well as providing carbon storage and fire fuel. However, parties interested in monitoring CWD abundance lack accurate methods to measure CWD accurately and extensively. Here, we demonstrate a novel strategy for mapping CWD volume (m3) across a 4300-hectare study area in the boreal forest of Alberta, Canada using optical imagery and an infra-canopy vegetation-index layer derived from multispectral aerial LiDAR. Our models predicted CWD volume with a coefficient of determination (R2) value of 0.62 compared to field data, and a root-mean square error (RMSE) of 0.224 m3/100 m2. Models using multispectral LiDAR data in addition to image-analysis data performed with up to 12% lower RMSE than models using exclusively image-analysis layers. Site managers and researchers requiring reliable and comprehensive maps of CWD volume may benefit from the presented workflow, which aims to streamline the process of CWD measurement. As multispectral LiDAR radiometric calibration routines are developed and standardized, we expect future studies to benefit increasingly more from such products for CWD detection underneath canopy cover. View Full-Text
Keywords: woody debris; woody material; boreal forest; remote sensing; GEOBIA; random forest; machine learning; LiDAR woody debris; woody material; boreal forest; remote sensing; GEOBIA; random forest; machine learning; LiDAR
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MDPI and ACS Style

Lopes Queiroz, G.; McDermid, G.J.; Linke, J.; Hopkinson, C.; Kariyeva, J. Estimating Coarse Woody Debris Volume Using Image Analysis and Multispectral LiDAR. Forests 2020, 11, 141.

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