# Presentation: 238691

## Notice: The views expressed here are those of the individual authors and may not necessarily reflect the views and policies of the United States Environmental Protection Agency (EPA).  Scientists in EPA have prepared the EPA sections, and those sections have been reviewed in accordance with EPA’s peer and administrative review policies and approved for presentation and publication.  The EPA contributed funding to the construction of this website but is not responsible for it's contents. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

**Notice: The views expressed here are those of the individual authors and may not necessarily reflect the views and policies of the United States Environmental Protection Agency (EPA).  Scientists in EPA have prepared the EPA sections, and those sections have been reviewed in accordance with EPA’s peer and administrative review policies and approved for presentation and publication.  The EPA contributed funding to the construction of this website but is not responsible for it's contents. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.**

## Assessing the Vulnerability of Resource Lands in the Chesapeake Bay Watershed

**Assessing the Vulnerability of Resource Lands in the Chesapeake Bay Watershed**

**ReVA-MAIA Conference**
**King of Prussia, PA**
**May 13-15, 2003**

_**Peter Claggett**_
**U.S. Geological Survey**
**Chesapeake Bay Program**

_**Claire Jantz**_
**University of Maryland**
**Department of Geography**

## The Chesapeake Bay Watershed

**Notes:**
We don’t have to keep this one in; I just pulled it from a previous presentation. It’s good to give context and to introduce some of the land use issues in the CBW. It may also be good to show a slide of resource lands in the CBW and discuss threats in the intro section.

## What does “vulnerability” mean?

- “Susceptible to physical or emotional injury” (Webster);
- Combination of _threat_ (exposure to stressors)  and _sensitivity_ (susceptibility to impairment)

## Relating vulnerability to land conservation:

- Threat

**Low**

**High**

**High**

- Sensitivity

**X**

**X**

- Conservation
- Priority!

**X**

## Vulnerability to what?

- “Habitat loss and degradation is a major problem throughout the Mid-Atlantic region. In the eastern half of the region, urban sprawl is contributing to this loss and degradation”
**	****~ “What is the State of the Environment in the Mid-Atlantic Region?”, ****	****EPA-MAIA, 2001 (EPA/903/F-01/003)**

- “If recent trends continue, the area of developed land in the (Bay) watershed will increase by more than 60% by 2030”
- 	**~ “Chesapeake Futures: Choices for the 21****st**** Century”, STAC 2003. **

## Integrated modeling approach

- _Theobald supply/demand/ allocation model_
- Supply: developable land
- Demand: housing unit projections
- Allocation and spread
- _SLEUTH pattern based cellular automaton_
- Calibrated on historic patterns of urban land cover change
- Forecasts patterns into the future

**Notes:**
Emphasize that each model captures different aspects of change; both are relatively “simple” and accessible.
What does Theobold tell us that SLEUTH doesn’t (considers population increases but not per capita land consumption)
What does SLEUTH tell us that Theobold doesn’t (development patterns—not a 1 to 1 link with population increase)

## Integrating the Models

- Overlay and aggregate modeling results to a grid of one square mile cells
- Categorical classification of modeling results
- Development potential = very high, high, moderate, or low
- Combine the results from each model (threat map)
- Overlay threat map on sensitive resource lands to prioritize conservation activities

## Study Area: Washington, DC-Baltimore Region

**Notes:**
Highlight why this makes a good study area (e.g. major urban center in the CBW, extremely rapid growth, multiple jurisdictions, etc.)
Describe how the census data were allocated to 1 mi grid.
SLEUTH was originally run at 45 meter resolution, results were aggregated to this grid.

## SLEUTH modeling of impervious surfaces

- <date/time>

**Mapped and modeled**
**impervious surface**

**1990 mapped**

**2000 mapped**

**2010 modeled**

**Notes:**
Another one I pulled from a previous presentation (and I think you’ve seen this before) so we can toss if you want. Just shows the dominance of the DC-Baltimore region in terms of urban areas in the CBW.

## SLEUTH’s Accuracy: Area of Developed Land 

**Notes:**
SLEUTH was calibrated using a 1986-2000 time series of urban land use change.
The model was initialized in 1986 and 2000 was predicted.
Both mapped and modeled areas of development were aggregated to the sqmi grid and compared.
SLEUTH does a pretty good job at capturing what it’s supposed to capture.
NOTE: pink solid line is the predicted Y (line of best fit); orange dashed line is the 1:1 line.

## Mapped Change in Impervious Surface (1990 - 2000)

- Modeled Change in Impervious Surface (2000 - 2010)

**Notes:**
Again, let’s just do one time step, either 2000 to 2010 or 2000 to 2020.

## Theobald’s Accuracy: Housing Units 

**Correlation coefficient: 0.85**

**R****2**** = 0.73**
**Y = 0.88x + 253.89**
**P < 0.001**

- Theobald’s Accuracy: Housing Units

**Notes:**
Model was initialized in 1980 and 2000 was predicted based on 1970-1980 rates of change (??is this right??).
Theobold does a pretty good job capturing what it’s supposed to capture

## Measured change in housing units (1990 – 2000)

## Determining mean lot size based on percent development

## Land consumption: # of housing units * lot size

- (1990 - 2000)

-  (2000 - 2010)

**Notes:**
This should be change from 2000 to 2010 or change from 2000 to 2020. Also, I think we should decide on presenting either with spread or without spread, but not both (but maybe tack the other on at the end in case we get questions).

## Determination of threat level

- “Very high” 		= top 2 quintiles from both models
- “High” 		= top 2 quintiles from either model
- “Low” 			= bottom quintile from both models
- “Moderate”		= all other combinations

## Relative threat level

## Resource

- Lands

- Sensitive
- Lands

## Conclusions

- Combination of SLEUTH and Theobald models provide an adequate forecast of short-term, relative development potential (10-year timeframe)
- Long-term forecasts (20+ years) require the incorporation of economic data and a better representation of growth rates and patterns
- Incorporating lot size as a factor enables the exploration of low density growth and alternative future scenarios
- Aggregating model output within a large overlay grid enables the combination of multi-temporal and multi-spatial scale data

## Future Research

- Validate analysis results
- Include more data in assessment of mean lot sizes
- Forecast long-term (20 – 30 year) urban, urbanizing, and rural growth rates and patterns, incorporating economic data and forecasts

## Acknowledgements

- Resource Lands Assessment Technical Team, Chesapeake Bay Program
- Debbie Weller, Lynda Eisenberg, and Richard Hall,    Maryland Office of Planning
- John  Schlee and Katherine Nelson, Maryland National Capital Park and Planning Commission
- Portions of this research were funded by NASA grants NAG513397 and NAG1302010, principle investigator Dr. Scott Goetz, Woods Hole Research Center/University of Maryland

## For more information about David Theobald’s supply/demand/allocation model
Western Futures Mapping: http://ndis.nrel.colostate.edu/davet/dev_patterns.htm

For more information about SLEUTH
USGS Gigalopolis website: http://www.ncgia.ucsb.edu/projects/gig/

UMD RESAC implementation of SLEUTH in the Baltimore-Washington region: http://www.geog.umd.edu/resac/urban-modeling.htm

- Western Futures Mapping: _[http://ndis.nrel.colostate.edu/davet/dev_patterns.htm](http://ndis.nrel.colostate.edu/davet/dev_patterns.htm)_
- For more information about SLEUTH
- USGS Gigalopolis website: _[http://www.ncgia.ucsb.edu/projects/gig/](http://www.ncgia.ucsb.edu/projects/gig/)_
- UMD RESAC implementation of SLEUTH in the Baltimore-Washington region: _[http://www.geog.umd.edu/resac/urban-modeling.htm](http://www.geog.umd.edu/resac/urban-modeling.htm)_