The Consequences of Landscape Change on Ecological Resources: An Assessment of the United States Mid‐Atlantic Region, 1973‐1993

Jones, K. Bruce; Neale, Anne C.; Wade, Timothy G.; Wickham, James D.; Cross, Chad L.; Edmonds, Curtis M.; Loveland, Thomas R.; Nash, Maliha S.; Riitters, Kurt H.; Smith, Elizabeth

doi:10.1046/j.1526-0992.2001.01047.x

Cited by 41 publications

(26 citation statements)

References 33 publications

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“…From purely rural or sometimes uninhabited natural lands, many have transformed to complex urban landscapes. Because of these changes, several studies (e.g., [2][3][4][5][6][7][8][9][10][11]) have examined the changing urban landscapes of several locations globally. For instance, within the United States, researchers have sought to understand the dynamics of urban landscape changes to account for their causes, patterns, responses and implications [12,13].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Zubair

Weilert

2017

Sustainability

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Urban wetlands play important roles in providing several ecosystem services that support the urban environment. As such, scientists have studied them to understand the urban processes that lead to their continued decline. However, little attention has been given to the drivers of land-use change that may affect this fragile ecosystem in the future. Understanding this could serve as a critical step towards urban wetland management and sustainability. In this study, we utilized an integrated approach that combined Similarity Weighted Instance-based Machine Learning and Markov chain, both embedded in the IDRISI Land Change Modeler to simulate change in the landscape of three watersheds in the Kansas City Metropolitan area. The purpose was to assess the possible future impacts of urban expansion-induced landscape change on wetlands within the study area, using a retrospective approach. To achieve this, classified SPOT satellite data covering the three watersheds were used to generate historical land cover maps of the study area between 1992 and 2010 to analyze changes to the landscape. In addition, the study identified several drivers of land change associated with the historical change process in the study area, and accounted for their role in the modeling process. On this basis, the study made the prediction of urban landscape transformation to the end date of 2014. The prediction result was verified with a more accurate map that was derived from independently classifying a 2014 SPOT image of the study area. Results from this study show that impervious surfaces, which were used as an index of urban expansion, may increase by approximately the same magnitude experienced historically, which may result in a small but significant loss of wetlands and other land cover classes within the study area.

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Section: Introductionmentioning

confidence: 99%

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Zubair

Weilert

2017

Sustainability

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“…Many landscape-based models incorporate remote sensing-derived LC data as primary data sources [29]. For example, the National Land Cover Dataset (NLCD) has been used to assess landscape change [30], delineate forest fragmentation [31], model pathogen-impaired waters [32], and evaluate increases in nutrient export owing to future urbanization [33]. Understanding inaccuracies associated with LC products is essential for determining the uncertainties in modeling outcomes.…”

Section: Measurement Uncertainty: Land Cover Classification Variabilitymentioning

confidence: 99%

Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products

et al. 2015

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Abstract:The validation process for a moderate resolution leaf area index (LAI) product (i.e., MODIS) involves the creation of a high spatial resolution LAI reference map (Lai-RM), which when scaled to the moderate LAI resolution (i.e., > 1 km) allows for comparison and analysis with this LAI product. This research addresses two major sources of uncertainty in the creation of the LAI-RM: (1) the uncertainty associated with the indirect in situ optical measurements of southeastern United States needle-leaf LAI and (2) the uncertainty in the process of classifying land cover (LC). Uncertainty within the loblolly pine (Pinus taeda) in situ data collection was highest for the assessment of the plant area index (PAI), Le (27.2%), and the woody-to-total ratio, α, (30.6%). The needle-to-shoot ratio, λE, and the element clumping index, ΩE, contributed 14.9% and 9.3%, respectively, to the uncertainty in the calculation of LAI. Combining LC differences (3.4%) with the uncertainty within the loblolly pine component resulted in doubling the LAI-RM variability (σ = 0.50 to σ = 0.97) at the 1 km 2 validation site located in Appomattox, Virginia, USA.

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“…The strength of this approach is that it uses publically available geospatial and tabular data, and commonly available GIS software and the freely available Fragstats analysis software. The approach demonstrated in this study brings indicators to the per capita landscape usage level for imperviousness, which is an important ecological quality indicator [75,76]. Building on Crawford"s trajectories [77] of residential development, a temporal component was initially discussed; however, since there has not yet been a public accuracy assessment for the 2006 National Land Cover Data, there could be a mismatch between the most recent National Land Cover Data and the 2010 U.S. Census data.…”

Section: Lessons Learnedmentioning

confidence: 99%

Measuring Sprawl across the Urban Rural Continuum Using an Amalgamated Sprawl Index

Kew

Lee

2013

Sustainability

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Urban sprawl is rapidly transforming the landscape of Kentucky"s prime farmland from a dominant agricultural land use pattern to a patchwork of dispersed and loosely defined parcels. This state, located in the east central portion of the U.S., is not unlike many states considered rural, nor is it unlike many rural regions found throughout the world where urban sprawl is concentrated in metropolitan areas that are often encroaching into these rural areas. Authors have argued for and against urbanization patterns generally understood to be sprawl on the basis of social, economic, and biophysical opportunities and constraints. Finding consensus in the literature about defining and measuring urban sprawl is difficult. This paper demonstrates a method for cost effectively measuring urban development using National Land Cover Data, Census data, and ancillary data across 34 counties. Based on seven indicators framed around the amount, configuration, and per capita land usage, an amalgamated sprawl index (ASI) is demonstrated through an example in north central Kentucky, USA. While the public believes this growth area of Kentucky is rapidly sprawling, this study indicates the pattern of sprawl is spreading faster in areas not obvious to this same public.

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The Consequences of Landscape Change on Ecological Resources: An Assessment of the United States Mid‐Atlantic Region, 1973‐1993

Cited by 41 publications

References 33 publications

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products

Measuring Sprawl across the Urban Rural Continuum Using an Amalgamated Sprawl Index

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