Measuring exurban change in the American West: A case study in Gallatin County, Montana, 1973–2004

Compas, Eric

doi:10.1016/j.landurbplan.2007.01.016

Cited by 39 publications

(18 citation statements)

References 31 publications

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“…The primarily strength of the Digitized Building data set is that it is more complete for periods prior to 1954. The Parcel Buildings data set Macon County, 1900-2007 is a more continuous temporal record, but contains no records of removed, replaced, or abandoned buildings, which are notably evident for dates prior to approximately 1965 (Fig. 2).…”

Section: Database Developmentmentioning

confidence: 99%

“…There are a variety of methodologies for analyzing development patterns at landscape to regional scales that emphasize exurban development and the ability to differentiate development types. These methods include using aggregated and disaggregated census data to map development trends (Berube et al, 2006;Clark et al, 2009;Hammer, Stewart, Winkler, Radeloff, & Voss, 2004;Radeloff, Hagen, Voss, Field, & Mladenoff, 2000;Theobald, 2005); evaluating spatial characteristics using satelliteimage derived classifications and spatial indices (Compas, 2007;Irwin, Cho, & Bockstael, 2007); interpreting current and historic aerial photography to map trends of building locations (Kline, Azuma, & Moses, 2003;Leinwand et al, 2010;Martinuzzi, Gould, Ramos, & Ramos Gonzalez, 2007;Wear & Bolstad, 1998); extracting data from government sources such as county parcel records or permitting programs (Aspinall, 2004;Carlson & Dierwchter, 2007); or analyzing the socio-economic and demographic characteristics of the inhabitants (Crump, 2003;Fernandez, Brown, Marans, & Nassauer, 2005). These differing approaches are often defined by the data availability and geographic scale of analysis.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal trend analysis of long-term development patterns (1900–2030) in a Southern Appalachian County

Kirk

Bolstad²,

Manson

2011

Landscape and Urban Planning

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Section: Database Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio-temporal trend analysis of long-term development patterns (1900–2030) in a Southern Appalachian County

Kirk

Bolstad²,

Manson

2011

Landscape and Urban Planning

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“…Incluye técnicas como diferencia entre imágenes, regresión entre imágenes, proporción de imágenes, diferencias de índices de vegetación, análisis de vectores de cambio y sustracción de fondo. Estos algoritmos detectan los cambios mayores de ciertos umbrales identificados y proporcionan la información del cambio cuantitativamente en términos de reflectancia (USGS, 1999;Romero y López, 2000;Yagüe, 2002;Azocar, Sahueza y Henríquez, 2003;Catalán, et al, 2007;Compas, 2007;Zhang, et al, 2007;Chuvieco, 2008).…”

Section: Methods Of Analyzing Land-use Changeunclassified

“…It includes techniques such as image differencing, regression differencing, image ratioing, vegetation index differencing, change vector analysis and background subtraction. These algorithms detect changes over certain identified thresholds and provide change information quantitatively in terms of reflectance (USGS, 1999;Romero and López, 2000;Yagüe, 2002;Azocar, Sahueza and Henríquez, 2003;Catalán, et al, 2007;Compas, 2007;Zhang et al, 2007;Chuvieco, 2008).…”

Section: Methods Of Analyzing Land-use Changementioning

confidence: 99%

Comparación De Métodos Espaciales Para Detectar Cambios en El Uso Del Suelo Urbano

Jiménez-Moreno¹,

González-Guillén²,

Escalona-Maurice³

et al. 2011

RCHSCFA

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RESUMENEsta investigación describe y analiza algunos métodos de detección de cambios en el uso del suelo originado por el crecimiento urbano con la finalidad de mostrar sus ventajas y desventajas; también expone aquellos métodos que proporcionan resultados favorables fundamentados en la información geográfica y que permiten una correcta toma de decisiones en la planificación del uso del suelo urbano. ABSTRACTThis research describes and analyzes some methods of detecting land-use changes caused by urban growth in order to show their advantages and disadvantages. It also outlines those methods that provide favorable results based on geographical information and enable proper decision-making in urban land-use planning. PALABRAS CLAVE:Uso del suelo urbano, crecimiento urbano, planificación territorial, toma de decisiones, SIG. KEY WORDS:Urban land use, urban growth, land-use planning, decision-making, GIS. INTRODUCCIÓNEl desarrollo poblacional demanda una gran cantidad de servicios y recursos, lo cual puede llegar a impactar negativamente al ambiente y deteriorar la calidad de vida de sus habitantes cuando no se realiza de manera planificada (USGS, 1999). Por tanto, es importante que las autoridades encargadas de la planificación territorial conozcan e implementen metodologías de planificación espacial para detectar y establecer las posibles modificaciones del crecimiento urbano para reorientar y minimizar los impactos bajo un contexto de sustentabilidad.En este proceso de planificación territorial es fundamental el uso de las diferentes disciplinas de la Geomática para caracterizar espacial y temporalmente la dinámica del crecimiento urbano con cierta precisión y detalle, con la finalidad de generar conocimiento útil en la planificación y ordenamiento del te-

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“…Landscape shape index, PLADJ (class-specific contagion), and AI (internal like-adjacencies) all measure slightly different aspects of class aggregation [56]. These simple metrics have been used in many other studies including studies of urban landscape pattern and change over time [23,28,[57][58][59][60][61]. These metrics, and other similar metrics, have also been used by many ecological studies to document how ecosystems [62] or species respond to landscape pattern [14,63].…”

Section: Land Cover Composition and Configuration Over Timementioning

confidence: 99%

Using Urban Landscape Trajectories to Develop a Multi-Temporal Land Cover Database to Support Ecological Modeling

2009

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Urbanization and the resulting changes in land cover have myriad impacts on ecological systems. Monitoring these changes across large spatial extents and long time spans requires synoptic remotely sensed data with an appropriate temporal sequence. We developed a multi-temporal land cover dataset for a six-county area surrounding the Seattle, Washington State, USA, metropolitan region. Land cover maps for 1986, 1991, 1995, 1999, and 2002 were developed from Landsat TM images through a combination of spectral unmixing, image segmentation, multi-season imagery, and supervised classification approaches to differentiate an initial nine land cover classes. We then used ancillary GIS layers and temporal information to define trajectories of land cover change through multiple updating and backdating rules and refined our land cover classification for each date into 14 classes. We compared the accuracy of the initial approach with the landscape trajectory modifications and determined that the use of landscape trajectory rules increased our ability to differentiate several classes including bare soil (separated into cleared for development, agriculture, and clearcut forest) and three intensities of urban. Using the temporal dataset, we found that between 1986 and 2002, urban land cover increased from 8 to 18% of our study area, while lowland deciduous and mixed forests decreased from 21 to 14%, and grass and agriculture decreased from 11 to 8%. systems that are present in this region were likely significantly altered by these changes in land cover. Our results suggest that multi-temporal (i.e., multiple years and multiple seasons within years) Landsat data are an economical means to quantify land cover and land cover change across large and highly heterogeneous urbanizing landscapes. Our data, and similar temporal land cover change products, have been used in ecological modeling of past, present, and likely future changes in ecological systems (e.g., avian biodiversity, water quality). Such data are important inputs for ecological modelers, policy makers, and urban planners to manage and plan for future landscape change.

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Measuring exurban change in the American West: A case study in Gallatin County, Montana, 1973–2004

Cited by 39 publications

References 31 publications

Spatio-temporal trend analysis of long-term development patterns (1900–2030) in a Southern Appalachian County

Spatio-temporal trend analysis of long-term development patterns (1900–2030) in a Southern Appalachian County

Comparación De Métodos Espaciales Para Detectar Cambios en El Uso Del Suelo Urbano

Using Urban Landscape Trajectories to Develop a Multi-Temporal Land Cover Database to Support Ecological Modeling

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