# Presentation: 335646

## LANDSCAPE ASSESSMENTS ofEcological Condition

- 17-20 June 2002
- Daniel T. Heggem, Anne C. Neale, Donald W. Ebert, K. Bruce Jones, and Curtis M. Edmonds
- US EPA-Las Vegas, Nevada

**Critical Ecosystems Workshop**

## What is Landscape Ecology

**Landscape Pattern**

**Linked to...**

**An Ecological Process**

## Slide 3

## What Can the Landscape Analyses Tell Me?

- Monitoring - Change Detection
- Ecosystem Targeting
- Ecosystem Restoration
- Modeling/Predictions
- Habitat

## THE KEY LANDSCAPE ACTIVITIES  

*Data *
*Acquisition*

*Remote*
*Sensing *
*Research*

*Landscape*
*Indicator/*
*Model Dev.*

*Change *
*Detection*
*Research*

*Assessment*
*Methods*

*NATIONAL*
*ASSESSMENT*

## Goal:

- 	Establish quantitative relationships between landscape metrics and environmental endpoints
- Fact:
- 	This is ongoing research and the results are preliminary.  There is still a _lot_ we don’t know.

- Associations between Landscape Metrics and
- Environmental Endpoints

## General Process

- Define assessment endpoint
- Define assessment and     reporting unit
- Delineate assessment unit

- Develop and calculate the right landscape metrics
- Gather endpoint data
- Merge landscape data with endpoint data
- Employ multivariate analysis techniques to quantify relationships
- Apply validated relationships to reporting units

## Land Cover

- _Land Cover_
- Mid-Atlantic
- Region of the
- United States

## Slide 9

## Slide 10

## Primary Data Used to Generate Landscape Indicators

***Primary Data Used to Generate Landscape Indicators***

- Land Cover (MRLC)
- 30-m resolution
- 15 - 20 classes

- Elevation (DEM, NED)
- 10-, 30- m resolution

- Stream Networks (NHD, DLG, RF3)

- Road Networks (DLG)

- Census Bureau Block Data

- Soils (STATSGO, SURRGO
- Other Biophysical Data

## Landscape Metrics

**Mean Riparian agriculture**
**Riparian forest ****	**
**Forest fragmentation **
**Road density**
**Forest land cover ****	**
**Agricultural land cover ****	**
**Agricultural land cover **
**   ****on steep slopes**
**Nitrate deposition**
**Potential soil loss    ****	**
**Roads near streams ****	**
**Slope gradient  **
**Slope gradient range **
**Slope gradient variance**
**Urban land cover ****	**
**Wetland land cover****	**
**Barren land cover****	**

**Landscape Metrics**

## Analytical Tools Interface for Landscape Assessments (ATtILA)

- An Overview

## Flexible:

- selectable metrics

## R2 = .83

- R2 = .83
- Riparian Forest
- Nitrate Dep

- R2 = .86
- % Ag
- Nitrate Dep
- Roads x Streams
- % Urban
- Riparian  Ag

- R2 = .65
- Road Density
- Riparian Forest

## Applying the Total Nitrogen Model to a Surface

**Applying the Total Nitrogen Model to a Surface**

## Slide 17

## Slide 18

## ReVA has completed a 

**ReVA has completed a **
**landscape change assessment**
**across the entire region from **
**historical satellite imagery**

**It is possible to evaluate change**
**in many areas across the region**

## Slide 20

## Riparian

**Riparian**
**Habitat **

**ReVA has developed models that allow **
**Us to evaluate how future landscape**
**Change might improve stream conditions**

## Utilizing Landscape Indicators to Model Potential Pathogen Impaired Waters

- Jonathan H. Smith
- James D. Wickham
- Landscape Characterization Branch
- K. Bruce Jones
- Timothy G. Wade
- Landscape Ecology Branch
- U.S. Environmental Protection Agency
- Office of Research and Development
- National Exposure Research Laboratory

## Goal:

- Predict the non-attainment of TMDL limits by analyzing land cover information for the state of South Carolina.

## Data Sources:

- National Land Cover Data (NLCD)                                     formerly known as the MRLC data set
- Watershed boundaries
- Water test results
- Stream network
- Digital Elevation Model (DEM)

## Logistic Regression

- Analyzes the relationship between a binary response variable (exceeding standards, not exceeding standards) and a number of explanatory variables
- Calculates  the probability of a specific response occurring
- Produces a number of diagnostic measures:
- -2 Log Likelihood (-2LL)
- Score
- Concordance

## Model Accuracy:

- 	-2 Log L			86.11 (p < 0.0001)
- 	Score			96.52 (p < 0.0001)
- 		Concordant		71.8%
- 		Discordant			27.5%
- 		Tied				0.7%
- (153,846 pairs)

## Fecal Coliforms

**Fecal Coliforms**

## Integration of Surface Water and Landscape Data from EMAP West - Prediction of Total Phosphorus in Oregon

- Integration of Surface Water and Landscape Data from EMAP West - Prediction of Total Phosphorus in Oregon

**John L. Stoddard**
-  U.S. EPA, Corvallis
**Daniel T. Heggem**
**Anne C. Neale**
- U.S. EPA, Las Vegas

## Slide 29

## Example 1 - Relating landscape metrics to nutrient content of streams in EMAP-W Oregon Pilot Study area 

- Water quality data acquired
- 83 watersheds delineated
- Landscape indicators calculated

- Example 1 - Relating landscape metrics to nutrient content of streams in EMAP-W Oregon Pilot Study area

## Example 1 - Relating landscape metrics to phosphorus content of streams in EMAP-W Oregon Pilot Study area 

- Example 1 - Relating landscape metrics to phosphorus content of streams in EMAP-W Oregon Pilot Study area

## Results from stepwise multiple regression:

- Percent forest cover
- Percent urban cover
- Percent natural cover
- Percent human use
- Percent agricultural cover
- Percent riparian agriculture
- Percent riparian forest
- Percent agriculture on steep slopes

- Landscape metrics included in this analysis:

- Predicted Total P = (0.5178 - 0.0034 (natural cover index))2

- R-Square = 0.72

- Example 1 - Relating landscape metrics to phosphorus content of
- streams in EMAP-W Oregon Pilot Study area

## Slide 33

## Oregon Landscape Data 

## Oregon EMAP Data Sources

- ca. 300 stream and river watersheds

## Oregon Total Phosphorus Distribution

## Relationship of Phosphorus to Land Use

## PredictedPhosphorus in OregonStreamsand Rivers

## Draft Assessment

- Integrated risk assessment for phosphorus in Oregon streams and rivers:
- (a) relationship between natural land use/land cover and measured phosphorus concentrations in streams
- (b) mapped phosphorus concentrations predicted from this relationship: Predicted Phosphorus = (0.518 – (.0034 * %natural land cover))2
- (c) cumulative distribution of phosphorus concentrations in population of flowing waters in the state of Oregon (from a probability survey)
- Ten percent of the stream length in Oregon has phosphorus concentrations exceeding the EPA criterion for flowing waters (0.1 mg/L); these streams are most likely to occur in the areas shown as yellow through red on the map in (b).

- _Draft Assessment_
- Phosphorus in OR
- Surface Waters

- N-Index

- Water Data

## Nutrient Ecoregions

## Ecoregion-Specific Phosphorus Models

**Western Mountains:**
- Total Phosphorus 
-  riparian shrublands
-  riparian urban landuse
-  riparian cropland
- (n = 103, r2 = 0.57)

**Willamette Valley:**
- Total Phosphorus 
-  riparian cropland
-  riparian urban landuse
- (n = 25, r2 = 0.86)

**Xeric West:**
- Total Phosphorus 
-  % forest in watershed
-  riparian urban landuse
-  riparian shrublands
- (n = 39, r2 = 0.47)

## Probabilityof Exceeding100 μg/LPhosphorusin OregonStreams and Rivers

## Conceptual Design of AGWA

*runoff, sediment hydrograph*

- STATSGO

- NALC, MRLC

- USGS 7.5' DEM

- Build Model Input Files

- Derive Secondary Parameters
*look-up tables*

- Characterize Model Elements
*f (landcover, topography, soils)*

- Discretize Watershed
*f (topography)*

- View Model Results
*link model to GIS*

- Build GIS Database

_*Components*_

- Contributing
- Source Area

- _Gravelly loam Soil_
-   Ks = 9.8 mm/hr
-   G = 127 mm
-   Por. = 0.453

- intensity

- time

*10-year, 30-minute event*

## The End