Next Article in Journal
Reassimilation of Leaf Internal CO2 Contributes to Isoprene Emission in the Neotropical Species Inga edulis Mart.
Next Article in Special Issue
Application of Least-Cost Movement Modeling in Planning Wildlife Mitigation Measures along Transport Corridors: Case Study of Forests and Moose in Lithuania
Previous Article in Journal
Laminated Veneer Lumber with Non-Wood Components and the Effects of Selected Factors on Its Bendability
Previous Article in Special Issue
High Precision Altimeter Demonstrates Simplification and Depression of Microtopography on Seismic Lines in Treed Peatlands
Open AccessArticle

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

1
Applied Geospatial Research Group, Geography Department, University of Calgary, Calgary, AB T2N 4V8, Canada
2
Northern Forestry Centre, Canadian Forest Service, Edmonton, AB T6H 3S5, Canada
*
Author to whom correspondence should be addressed.
Forests 2019, 10(6), 471; https://doi.org/10.3390/f10060471
Received: 10 May 2019 / Revised: 24 May 2019 / Accepted: 26 May 2019 / Published: 30 May 2019
(This article belongs to the Special Issue Forest Biodiversity Conservation with Remote Sensing Techniques)
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. View Full-Text
Keywords: coarse woody debris; coarse woody material; large woody debris; random forest classification; GEOBIA; aerial image; LiDAR; segmentation coarse woody debris; coarse woody material; large woody debris; random forest classification; GEOBIA; aerial image; LiDAR; segmentation
Show Figures

Graphical abstract

MDPI and ACS Style

Lopes Queiroz, G.; McDermid, G.J.; Castilla, G.; Linke, J.; Rahman, M.M. Mapping Coarse Woody Debris with Random Forest Classification of Centimetric Aerial Imagery. Forests 2019, 10, 471.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
  • Supplementary File 1:

    ZIP-Document (ZIP, 2434 KB)

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3233072
    Link: https://github.com/silverlq/RF_CWD
    Description: Source code and input files used to generate results described in paper.
Back to TopTop