Diane M. Styers

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

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.

Urbanization and Atmospheric Deposition: Use of Bioindicators in Determining Patterns of Land-Use Change in West Georgia

Changes in land use disrupt ecosystem patterns and processes and serve as precursors to other biotic and abiotic stressors. Forest ecosystems in the urban core typically differ structurally and functionally from those in rural areas. The overall objective of the study was to determine concentrations of selected air-borne contaminants (N, S, and heavy metals) over space and time and relate these to land-use changes. Elemental concentrations in lichens, soils, and tree cores were examined from 36 plots distributed along an urban-to-rural gradient surrounding Columbus, GA, USA. In situ lichen tissue exhibited the most significant differences among land-use types, with Cu, N, Pb, S, and Zn concentrations all significantly greater at urban sites. Lichen transplants revealed differences in concentrations between species, but not between land-use types. No discernable trends were observed regarding concentrations in soil and tree core data. Lichens appear to be a sensitive indicator of land-use change in this particular case study.

Evaluation of the contribution of LiDAR data and postclassification procedures to object-based classification accuracy

Abstract Object-based image analysis (OBIA) is becoming an increasingly common method for producing land use/land cover (LULC) classifications in urban areas. In order to produce the most accurate LULC map, LiDAR data and postclassification procedures are often employed, but their relative contributions to accuracy are unclear. We examined the contribution of LiDAR data and postclassification procedures to increase classification accuracies over using imagery alone and assessed sources of error along an ecologically complex urban-to-rural gradient in Olympia, Washington. Overall classification accuracy and user’s and producer’s accuracies for individual classes were evaluated. The addition of LiDAR data to the OBIA classification resulted in an 8.34% increase in overall accuracy, while manual postclassification to the imagery + LiDAR classification improved accuracy only an additional 1%. Sources of error in this classification were largely due to edge effects, from which multiple different types of errors result.

Using big data to engage undergraduate students in authentic science

ABSTRACT The abundance of freely available, scientific big data sets can facilitate discovery-based authentic science projects saving time, money, and effort. This is especially true of remotely sensed data, as there is global coverage of Earths surface spanning several decades that can be used for a multitude of applications. In this article, I present three different case studies in which a project-based learning model was successfully integrated into undergraduate courses using big data to support authentic science. I illustrate this process, its implementation, and a timeline for use in both introductory and advanced undergraduate remote sensing courses. By participating in these projects, students learn the skills to link ground observation data with large, public-domain geospatial datasets to answer site- to landscape-level questions about the natural and built environment. In their multiscalar analysis of environmental data, students are forced to acknowledge the different yet overlapping operational scales of various social and ecological processes that drive landscape changes affecting Earths resources. Student feedback from these courses has been positive, with participants indicating the projects gave them practical experience using geospatial technologies in real-world applications in natural resource management. From a teaching and learning perspective, the benefits of such an undertaking far outweigh the challenges, and I encourage others to consider a shift from traditional classroom practices to this more rewarding model of discovery.

Determination of Alterations in Forest Condition Using Various Measures of Land Use Change along an Urban-Rural Gradient in the West Georgia Piedmont, USA

Our overall goal was to examine forest condition across different land use types through measurement of various biotic, abiotic, and anthropogenic variables. Thirty-six permanent 0.05-ha circular plots were established along an urban-rural gradient near Columbus, Ga, USA. In general, forest structure did not differ by land use type for the majority of variables measured. However, urban forests contained less total tree and hardwood species than developing or rural areas. Regarding forest condition, no differences were observed for pest or disease incidence by land use, but more mechanical injury (broken branches, wounds, etc.) was found in urban locales. Lichens were the most sensitive indicator of possible changes in forest condition. Lichen incidence, abundance, and species richness were the greatest in rural forests and the least in urban locations. These factors were related to several indicators of urbanization such as housing density and distance from roads. In this case study subtle, but significant changes in forest structure and condition may have resulted from alterations in land use patterns.