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

Forests

GregoryJ. McDermid¹  MirMustafizur Rahman¹  Julia Linke¹  Gustavo Lopes Queiroz¹  Guillermo Castilla² 

[1] Applied Geospatial Research Group, Geography Department, University of Calgary, Calgary, AB T2N 4V8, Canada;Northern Forestry Centre, Canadian Forest Service, Edmonton, AB T6H 3S5, Canada;

关键词: coarse woody debris;    coarse woody material;    large woody debris;    random forest classification;    GEOBIA;    aerial image;    LiDAR;    segmentation;   

DOI  :  10.3390/f10060471

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown