An Accuracy Assessment of Tree Detection Algorithms in Juniper Woodlands
This research provides a comprehensive accuracy assessment of five methods for classifying western juniper (Juniperus occidentalis) canopy cover from 1 m, 4-band National Agriculture Imagery Program (NAIP) imagery. Two object-oriented classification approaches (image segmentation
and spatial wavelet analysis, (SWA)) are compared to three pixel based classification approaches (random forests, Iterative Self-Organizing Data Analysis (ISODATA), and maximum likelihood). Methods are applied to approximately 250 km2 in the intermountain western USA. A robust suite of statistical
approaches, which offer an alternative to traditional kappa-based methods, are utilized to determine equivalence between methods and overall effectiveness. Object-oriented approaches have the highest overall accuracy among the assessed methods. Each of the methods varied considerably in cover
class accuracy. SWA has the highest class accuracy when juniper canopy cover is low (0 to 40 percent cover), ISODATA performs best at moderate cover (60 to 80 percent) and maximum likelihood performs best at higher cover (60 to 100 percent cover).
Document Type: Research Article
Publication date: 01 July 2014
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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