Skip to Main Content
Monitoring urban tree cover using object-based image analysis and public domain remotely sensed dataAuthor(s): L. Monika Moskal; Diane M. Styers; Meghan Halabisky
Source: Remote Sensing. 3(10): 2243-2262
Publication Series: Scientific Journal (JRNL)
Station: Pacific Northwest Research Station
PDF: Download Publication (1.29 MB)
DescriptionUrban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC.
- Visit PNW's Publication Request Page to request a hard copy of this publication.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationMoskal, L. Monika; Styers, Diane M.; Halabisky, Meghan. 2011. Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data. Remote Sensing. 3(10): 2243-2262.
Keywordsurban forest, object-based image analysis (OBIA), segmentation, tree canopies, tree cover assessment, hyperspatial, public domain data
- The effectiveness of texture analysis for mapping forest land using the panchromatic bands of Landsat 7, SPOT, and IRS imagery
- Building capacity for providing canopy cover and canopy height at FIA plot locations using high-resolution imagery and leaf-off LiDAR
- Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region.
XML: View XML