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Mapping coarse woody debris with random forest classification of centimetric aerial imagery

Author(s): Gustavo Lopes Queiroz, Gregory J. McDermid, Guillermo Castilla, Julia Linke, Mir Mustafizur Rahman
Year Published: 2019

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.

Citation: Queiroz, Gustavo Lopes; McDermid, Gregory J.; Castilla, Guillermo; Linke, Julia; Rahman, Mir Mustafizur. 2019. Mapping coarse woody debris with random forest classification of centimetric aerial imagery. Forests 10(6):471. https://doi.org/10.3390/f10060471
Topic(s): Mapping, Fuels
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 19805
FRAMES RCS number: 58149
Record updated: Jul 17, 2019