Classifying Forest Structure of Red-Cockaded Woodpecker Habitat Using Structure from Motion Elevation Data Derived from sUAS Imagery

  1. Lawrence, Brett
  2. González Aguilera, Diego ed. lit. 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revue:
Drones

ISSN: 2504-446X

Année de publication: 2022

Volumen: 6

Número: 1

Pages: 26

Type: Article

DOI: 10.3390/DRONES6010026 GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Drones

Résumé

Small unmanned aerial systems (sUAS) and relatively new photogrammetry software solutions are creating opportunities for forest managers to perform spatial analysis more efficiently and cost-effectively. This study aims to identify a method for leveraging these technologies to analyze vertical forest structure of Red-cockaded Woodpecker habitat in Montgomery County, Texas. Traditional sampling methods would require numerous hours of ground surveying and data collection using various measuring techniques. Structure from Motion (SfM), a photogrammetric method for creating 3-D structure from 2-D images, provides an alternative to relatively expensive LIDAR sensing technologies and can accurately model the high level of complexity found within our study area’s vertical structure. DroneDeploy, a photogrammetry processing app service, was used to post-process and create a point cloud, which was later further processed into a Canopy Height Model (CHM). Using supervised, object-based classification and comparing multiple classifier algorithms, classifications maps were generated with a best overall accuracy of 84.8% using Support Vector Machine in ArcGIS Pro software. Appropriately sized training sample datasets, correctly processed elevation data, and proper image segmentation were among the major factors impacting classification accuracy during the numerous classification iterations performed.

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