James D. Hurd

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.

A competitive pixel-object approach for land cover classification

This paper describes a novel remote sensing land cover classification approach named competitive pixel-object classification, based on Bayesian neural networks and image segmentation. This approach makes use of both pixel spectral features and object features resulting from image segmentation through a competitive mechanism to resolve the problem of spectral confusion caused by reflectance similarity of some land cover types that traditional pixel-based classification cannot resolve. The competitive pixel-object method reduces the unreliability of object feature information produced by over- or under-segmentation of the image through a competitive mechanism. The experiment shows that the competitive pixel-object approach produces higher classification accuracy than either pixel-based classification or object-oriented classification.

Socioeconomics drive woody invasive plant richness in New England, USA through forest fragmentation

Woody invasive plants are an increasing component of the New England flora. Their success and geographic spread are mediated in part by landscape characteristics. We tested whether woody invasive plant richness was higher in landscapes with many forest edges relative to other forest types and explained land use/land cover and forest fragmentation patterns using socioeconomic and physical variables. Our models demonstrated that woody invasive plant richness was higher in landscapes with more edge forest relative to patch, perforated, and especially core forest types. Using spatially-explicit, hierarchical Bayesian, compositional data models we showed that infrastructure and physical factors, including road length and elevation range, and time-lagged socioeconomic factors, primarily population, help to explain development and forest fragmentation patterns. Our social–ecological approach identified landscape patterns driven by human development and linked them to increased woody plant invasions. Identifying these landscape patterns will aid ongoing efforts to use current distribution patterns to better predict where invasive species may occur in unsampled regions under current and future conditions.

A Comparison of Approaches to Impervious Surface Characterization

Impervious surface estimates derived from different modeling approaches and data types were compared to highly accurate and precise planimetric calibration and validation data. A linear regression model using population density and percent land cover class per unit area produced the most accurate results, with an overall RMSE of 3.2% for the 82 census tracts studied. Two methods using just land cover-based impervious coefficients yielded results with an RMSE of 4.8% and 5.4%, while the subpixel estimates had an RMSE of 5.8% and 7.5%.

Characterization of Coastal Wetland Systems using Multiple Remote Sensing Data Types and Analytical Techniques

A multistage process for classifying coastal marshes using a rule-based object-oriented procedure applied to Landsat ETM data is discussed. The method, which uses both spectral and spatial criteria, produces classifications more useful than those based on per-pixel, spectral data-alone techniques. Analysis of co- registered high resolution LIDAR-derived elevation and ADS40 CIR imagery has demonstrated it is possible to discriminate among areas dominated by Phragmites australis, Typha spp. and Spartina patens.

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.