Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data

Abstract

Abstract:Urban 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.

Forest cover changes in Gorce NP (Poland) using photointerpretation of analogue photographs and GEOBIA of orthophotos and nDSM based on image-matching based approach

“…Moskal and Jakubauskas (2013), studying fire-disturbed forests using aerial photos and GEOBIA methods, achieved classification accuracies between 68% and 78% depending on the level of image analysis (three classes in each level; total nine classes). Similar results were obtained by Moskal et al (2011) for urban tree cover assessment using digital aerial imagery (GSD 1.0 m) and object-oriented classification, with tree cover classified with a user's accuracy of 80% and a producer's accuracy of 93%. Hernando et al (2012) proposed an (Object Fate Analysis) OFA-matrix, where thematic and spatial accuracies can be assessed simultaneously, as an innovative accuracy assessment method for the results of object-based classification.…”

Estimating Net Primary Production of Turfgrass in an Urban-Suburban Landscape with QuickBird Imagery

“…can be accounted for. The information derived from high resolution imagery is at the scale and resolution most pertinent for urban landscape planning and management [29]. However, extensive shadows also exist in high resolution images and create problems in directly applying imagery data to urban land use and land cover classification [30].…”

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

“…Object-based classification of land use and land cover often results in higher accuracies, when compared to pixel-based classifications [41][42][43][44]. Some studies also used hierarchical classification approaches, mainly to integrate different data types into a comprehensive mapping framework [45][46][47][48].…”