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Enjie Li

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ABSTRACT:

This dataset documents the important categorical land use changes from 2001 to 2011 for the basins in the Wasatch Range Metropolitan Area (WRMA). Four cross-tabulation matrixes are provided, to summarize the changes of categorical land uses at Bear River Basin, Weber River Basin, Utah Lake Basin and Jordan River Basin.

These matrixes are useful to compare two maps of land use from different times, and to understand the categorical land use transitions over the time. By breaking down the whole WRMA into four sub-regions, these matrixes also help to compare the different patterns and processes of land use change among the four river basins.

We used year 2001 and 2011 National Land Use and Land Cover Dataset (NLCD) to produce these matrixes. Land use categories used in this dataset include: Water, Development/Open Space, Development/Low Density, Development/ Medium Density, Development /High Density, Barren, Forest, Scrubland, Grassland, Pasture, Cultivated Crops, and Wetland.

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ABSTRACT:

The most traditional way to examine land use change is to use a cross-tabulation matrix to identify the most important categorical land use transition from time 1 to time 2. However, such method does not necessarily capture or indicate the real changes on the landscape. For example, assuming that from 1986 to 2015, Utah’s total agricultural land loss (aka, net change) is 200 square miles, but this does not mean that only 200 square miles of agricultural land have experienced land use change in the last 30 years. It is highly possible that a given quantity of agricultural land loss at one location can be accompanied by another quantity of agricultural land gain at another location (aka, swapping). Thus, by purely using net change, we might fail to capture the swapping component of change, and fail to capture the intricate transitions of landscape. This dataset analyzed important categorical land use change while account for persistence and swaps. It provides additional information concerning what happened on the landscape.

This dataset includes a statistical table and a GIS raster file. The table summarizes the persistence and swaps, as well as gross gain and gross loss in the Wasatch Range Metropolitan Area (WRMA). The GIS file is the compiled spatial layer that represents the gain, loss, persistence, and swaps on the landscape. We used Water Related Land Use data of Year 1986 to Year 2015 for this analysis. Land use categories used in this dataset include urban (URB), irrigated agricultural land (IR), and non-irrigated agricultural land (NI), sub-irrigated agricultural land (SubIR), riparian (RIP), water, (WATER), and other (OTHER). We then examined the categorical land use changes with a transition matrix.

A categorical land use gain is determined as the conversion from other sources to this particular categorical land use, and a categorical land use loss is defined as conversion from this particular categorical land use to other uses. For example, the gain of irrigated agricultural (IR) land use will be the sum of areas of urban to IR, non-irrigated agricultural land to IR, sub-irrigated agricultural land to IR, riparian to IR, water to IR, and other to IR. The total change is calculated as the sum of gain and loss. The net change equals to |Gain|-|Loss|. The Swap =2* MIN(Gain,Loss).

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ABSTRACT:

This dataset contains the urban growth simulation results of future land use in 2040 of the Wasatch Range Metropolitan Area (WRMA) .In this study, we defined the WRMA as a broad, ten-county region that surrounds the Wasatch Mountain Range east of the Great Salt Lake and Salt Lake City in Utah. This region encompasses four Wasatch Front counties west of the mountain range (Weber County, Davis County, Salt Lake County, and Utah County), three Wasatch Back counties east of the mountain range (Morgan County, Summit County, and Wasatch County), and three counties neighboring the Wasatch Front (Cache County, Box Elder County, and Tooele County).

SLEUTH-3r urban growth simulation model is used to generate this dataset. Detailed SLEUTH model protocol can be found at: http://www.ncgia.ucsb.edu/projects/gig/index.html. The data used to run the SLEUTH-3r model include National Land Cover Database 2001, 2006, and 2011, US Census TIGER/Line shapefile for 2000 and 2011, United States Geological Survey 7.5 min elevation model, and Utah Landownership map from Utah Automated Geographic Reference Center.

Three alternative scenarios were developed to explore how conserving Utah’s agriculturale land and maintaining healthy watersheds would affect the patterns and trajectories of urban development:
1) The first scenario is a “Business as Usual” scenario. In this scenario, federal, state, and local parks, conservation easement areas, and surface water bodies, were completely excluded (value = 100) from development, and all the remaining lands are were naively assumed as developable (value = 0). This is the same excluded layer that was also used during model calibration. Under this scenario, we hypothesized that future urban grow will occur following the historical growth behaviors and trajectories and no changes in land designation or policies to restrict future growth will be implemented.
2) The second scenario is an “Agricultural Conservation” scenario. Within the developable areas that we identified earlier, we then identified places that are classified by the United States Department of Agriculture (USDA) as prime farmland, unique farmland, farmland of statewide importance, farmland of local importance, prime farmland if irrigated, and prime farmland if irrigated and drained. Each of these classes were assigned with an exclusion value from urban development of 100, 80, 70, 60, 50, and 40 respectively. These exclusion values reflect the relative importance of each farmland classification and preservation priorities. By doing so, the model discourages but does not totally eliminate growth from occurring on agricultural lands, which reflects a general policy position to conserve agricultural landscapes while respecting landowners’ rights to sell private property.
3) A “Healthy Watershed” scenario aims to direct urban growth away from areas prone to flooding and areas critical for maintaining healthy watersheds. First, we made a 200-meter buffer around existing surface water bodies and wetlands and assigned these areas an exclusion value of 100 to keep growth from occurring there. In addition, we assigned areas that have frequent, occasional, rare and no-recorded flooding events with exclusion values of 100, 70, 40 and 0 accordingly. We also incorporated the critical watershed restoration areas identified by the Watershed Restoration Initiative of Utah Division of Wildlife Resources (https://wri.utah.gov/wri/) into this scenario. These watershed restoration areas are priority places for improving water quality and yield, reducing catastrophic wildfires, restoring the structure and function of watersheds following wildfire, and increasing habitat for wildlife populations and forage for sustainable agriculture. However, there are not yet legal provisions for protecting them from urbanization, so we assigned these areas a value of 70 to explore the potential urban expansion outcomes if growth were encouraged elsewhere.

Future land use projections of 2040 are in GIF format, which can be reprojected and georeferenced in ArcGIS or QGIS, or be read directly as a picture.

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ABSTRACT:

This dataset documents the historical agricultural land use changes in WRMA from 1986 to 2014. We extracted agricultural land uses from the Water Related Land Use Dataset. Agricultural land uses include non-irrigated agricultural land (NI), irrigated agricultural land (IR), and sub-irrigated agricultural land (SubIR).

This dataset includes five years' GIS layers of agricultural land use in WRMA, namely, 1986, 1992, 2003, 2009, and 2014. Also, a statistical summary of the historical agricultural land use is provided.

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Generic Generic
Changes of Agricultural Land Use in WRMA
Created: July 22, 2016, 10:25 p.m.
Authors: Enjie Li

ABSTRACT:

This dataset documents the historical agricultural land use changes in WRMA from 1986 to 2014. We extracted agricultural land uses from the Water Related Land Use Dataset. Agricultural land uses include non-irrigated agricultural land (NI), irrigated agricultural land (IR), and sub-irrigated agricultural land (SubIR).

This dataset includes five years' GIS layers of agricultural land use in WRMA, namely, 1986, 1992, 2003, 2009, and 2014. Also, a statistical summary of the historical agricultural land use is provided.

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Generic Generic
Future WRMA's Land Use Dataset
Created: July 22, 2016, 10:41 p.m.
Authors: Enjie Li

ABSTRACT:

This dataset contains the urban growth simulation results of future land use in 2040 of the Wasatch Range Metropolitan Area (WRMA) .In this study, we defined the WRMA as a broad, ten-county region that surrounds the Wasatch Mountain Range east of the Great Salt Lake and Salt Lake City in Utah. This region encompasses four Wasatch Front counties west of the mountain range (Weber County, Davis County, Salt Lake County, and Utah County), three Wasatch Back counties east of the mountain range (Morgan County, Summit County, and Wasatch County), and three counties neighboring the Wasatch Front (Cache County, Box Elder County, and Tooele County).

SLEUTH-3r urban growth simulation model is used to generate this dataset. Detailed SLEUTH model protocol can be found at: http://www.ncgia.ucsb.edu/projects/gig/index.html. The data used to run the SLEUTH-3r model include National Land Cover Database 2001, 2006, and 2011, US Census TIGER/Line shapefile for 2000 and 2011, United States Geological Survey 7.5 min elevation model, and Utah Landownership map from Utah Automated Geographic Reference Center.

Three alternative scenarios were developed to explore how conserving Utah’s agriculturale land and maintaining healthy watersheds would affect the patterns and trajectories of urban development:
1) The first scenario is a “Business as Usual” scenario. In this scenario, federal, state, and local parks, conservation easement areas, and surface water bodies, were completely excluded (value = 100) from development, and all the remaining lands are were naively assumed as developable (value = 0). This is the same excluded layer that was also used during model calibration. Under this scenario, we hypothesized that future urban grow will occur following the historical growth behaviors and trajectories and no changes in land designation or policies to restrict future growth will be implemented.
2) The second scenario is an “Agricultural Conservation” scenario. Within the developable areas that we identified earlier, we then identified places that are classified by the United States Department of Agriculture (USDA) as prime farmland, unique farmland, farmland of statewide importance, farmland of local importance, prime farmland if irrigated, and prime farmland if irrigated and drained. Each of these classes were assigned with an exclusion value from urban development of 100, 80, 70, 60, 50, and 40 respectively. These exclusion values reflect the relative importance of each farmland classification and preservation priorities. By doing so, the model discourages but does not totally eliminate growth from occurring on agricultural lands, which reflects a general policy position to conserve agricultural landscapes while respecting landowners’ rights to sell private property.
3) A “Healthy Watershed” scenario aims to direct urban growth away from areas prone to flooding and areas critical for maintaining healthy watersheds. First, we made a 200-meter buffer around existing surface water bodies and wetlands and assigned these areas an exclusion value of 100 to keep growth from occurring there. In addition, we assigned areas that have frequent, occasional, rare and no-recorded flooding events with exclusion values of 100, 70, 40 and 0 accordingly. We also incorporated the critical watershed restoration areas identified by the Watershed Restoration Initiative of Utah Division of Wildlife Resources (https://wri.utah.gov/wri/) into this scenario. These watershed restoration areas are priority places for improving water quality and yield, reducing catastrophic wildfires, restoring the structure and function of watersheds following wildfire, and increasing habitat for wildlife populations and forage for sustainable agriculture. However, there are not yet legal provisions for protecting them from urbanization, so we assigned these areas a value of 70 to explore the potential urban expansion outcomes if growth were encouraged elsewhere.

Future land use projections of 2040 are in GIF format, which can be reprojected and georeferenced in ArcGIS or QGIS, or be read directly as a picture.

Show More
Generic Generic
Swaps and Persistence of WRMA's 30 years' Land Use Changes
Created: July 23, 2016, 2:36 p.m.
Authors: Enjie Li

ABSTRACT:

The most traditional way to examine land use change is to use a cross-tabulation matrix to identify the most important categorical land use transition from time 1 to time 2. However, such method does not necessarily capture or indicate the real changes on the landscape. For example, assuming that from 1986 to 2015, Utah’s total agricultural land loss (aka, net change) is 200 square miles, but this does not mean that only 200 square miles of agricultural land have experienced land use change in the last 30 years. It is highly possible that a given quantity of agricultural land loss at one location can be accompanied by another quantity of agricultural land gain at another location (aka, swapping). Thus, by purely using net change, we might fail to capture the swapping component of change, and fail to capture the intricate transitions of landscape. This dataset analyzed important categorical land use change while account for persistence and swaps. It provides additional information concerning what happened on the landscape.

This dataset includes a statistical table and a GIS raster file. The table summarizes the persistence and swaps, as well as gross gain and gross loss in the Wasatch Range Metropolitan Area (WRMA). The GIS file is the compiled spatial layer that represents the gain, loss, persistence, and swaps on the landscape. We used Water Related Land Use data of Year 1986 to Year 2015 for this analysis. Land use categories used in this dataset include urban (URB), irrigated agricultural land (IR), and non-irrigated agricultural land (NI), sub-irrigated agricultural land (SubIR), riparian (RIP), water, (WATER), and other (OTHER). We then examined the categorical land use changes with a transition matrix.

A categorical land use gain is determined as the conversion from other sources to this particular categorical land use, and a categorical land use loss is defined as conversion from this particular categorical land use to other uses. For example, the gain of irrigated agricultural (IR) land use will be the sum of areas of urban to IR, non-irrigated agricultural land to IR, sub-irrigated agricultural land to IR, riparian to IR, water to IR, and other to IR. The total change is calculated as the sum of gain and loss. The net change equals to |Gain|-|Loss|. The Swap =2* MIN(Gain,Loss).

Show More
Generic Generic
Important categorical land use changes from 2001-2011
Created: July 25, 2016, 5:01 p.m.
Authors: Enjie Li

ABSTRACT:

This dataset documents the important categorical land use changes from 2001 to 2011 for the basins in the Wasatch Range Metropolitan Area (WRMA). Four cross-tabulation matrixes are provided, to summarize the changes of categorical land uses at Bear River Basin, Weber River Basin, Utah Lake Basin and Jordan River Basin.

These matrixes are useful to compare two maps of land use from different times, and to understand the categorical land use transitions over the time. By breaking down the whole WRMA into four sub-regions, these matrixes also help to compare the different patterns and processes of land use change among the four river basins.

We used year 2001 and 2011 National Land Use and Land Cover Dataset (NLCD) to produce these matrixes. Land use categories used in this dataset include: Water, Development/Open Space, Development/Low Density, Development/ Medium Density, Development /High Density, Barren, Forest, Scrubland, Grassland, Pasture, Cultivated Crops, and Wetland.

Show More