Emily Hoffhine Wilson

Detection of forest harvest type using multiple dates of Landsat TM imagery

Abstract A simple and relatively accurate technique for classifying time-series Landsat Thematic Mapper (TM) imagery to detect levels of forest harvest is the topic of this research. The accuracy of multidate classification of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were compared and the effect of the number of years (1–3, 3–4, 5–6 years) between image acquisition on forest change accuracy was evaluated. When Landsat image acquisitions were only 1–3 years apart, forest clearcuts were detected with producers accuracy ranging from 79% to 96% using the RGB-NDMI classification method. Partial harvests were detected with lower producers accuracy (55–80%) accuracy. The accuracy of both clearcut and partial harvests decreased as time between image acquisition increased. In all classification trials, the RGB-NDMI method produced significantly higher accuracies, compared to the RGB-NDVI. These results are interesting because the less common NDMI (using the reflected middle infrared band) outperformed the more popular NDVI. In northern Maine, industrial forest landowners have shifted from clearcutting to partial harvest systems in recent years. The RGB-NDMI change detection classification applied to Landsat TM imagery collected every 2–3 years appears to be a promising technique for monitoring forest harvesting and other disturbances that do not remove the entire overstory canopy.

Development of a geospatial model to quantify, describe and map urban growth

Abstract In the United States, there is widespread concern about understanding and curbing urban sprawl , which has been cited for its negative impacts on natural resources, economic health, and community character. There is not, however, a universally accepted definition of urban sprawl. It has been described using quantitative measures, qualitative terms, attitudinal explanations, and landscape patterns. To help local, regional and state land use planners better understand and address the issues attributed to sprawl, researchers at NASAs Northeast Regional Earth Science Applications Center (RESAC) at The University of Connecticut have developed an urban growth model. The model, which is based on land cover derived from remotely sensed satellite imagery, determines the geographic extent, patterns, and classes of urban growth over time. Input data to the urban growth model consist of two dates of satellite-derived land cover data that are converted, based on user-defined reclassification options, to just three classes: developed, non-developed, and water. The model identifies three classes of undeveloped land as well as developed land for both dates based on neighborhood information. These two images are used to create a change map that provides more detail than a traditional change analysis by utilizing the classes of non-developed land and including contextual information. The change map becomes the input for the urban growth analysis where five classes of growth are identified: infill , expansion , isolated , linear branch , and clustered branch . The output urban growth map is a powerful visual and quantitative assessment of the kinds of urban growth that have occurred across a landscape. Urban growth further can be characterized using a temporal sequence of urban growth maps to illustrate urban growth dynamics. Beyond analysis, the ability of remote sensing-based information to show changes to a communitys landscape, at different geographic scales and over time, is a new and unique resource for local land use decision makers as they plan the future of their communities.

Multitemporal spectroradiometry-guided object-oriented classification of salt marsh vegetation

This study addresses the use of multitemporal field spectral data, satellite imagery, and LiDAR top of canopy data to classify and map common salt marsh plant communities. Visible to near-infrared (VNIR) reflectance spectra were measured in the field to assess the phenological variability of the dominant species - Spartina patens, Phragmites australis and Typha spp. The field spectra and single date LiDAR canopy height data were used to define an objectoriented classification methodology for the plant communities in multitemporal QuickBird imagery. The classification was validated using an extensive field inventory of marsh species. Overall classification accuracies were 97% for Phragmites, 63% for Typha spp. and 80% for S. patens meadows. Using a fuzzy assessment analysis, these accuracies were 97%, 76%, and 92%, respectively, for the three major species.