2006 national monitoring conference, san jose, ca, may 2006
DESCRIPTION
The use of remotely sensed and GIS-derived variables to characterize urbanization in the National Water-Quality Assessment Program. James Falcone. [email protected]. 2006 National Monitoring Conference, San Jose, CA, May 2006. Presentation Objective. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/1.jpg)
U.S. Department of the InteriorU.S. Geological Survey
The use of remotely sensed and GIS-derived variables to characterize urbanization in the National Water-Quality Assessment Program
2006 National Monitoring Conference, San Jose, CA, May 2006
James [email protected]
![Page 2: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/2.jpg)
Presentation Objective
Overview of some data sources, tips, and techniques used in NAWQA urban studies
Perspective generally at the national/regional level
![Page 3: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/3.jpg)
Urbanization
Trend in last decades towards landscape fragmentation (‘sprawl’)
The “Mixing Bowl”, Washington, D.C. Beltway
Continually increasing stressor
Fragmentation argues for understanding spatial pattern as well as intensity
![Page 4: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/4.jpg)
Spatial pattern
A B
C
% impervious surface
low high
![Page 5: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/5.jpg)
How to measure ‘urbanization’?
We have tried to use traditional methods: Basin or riparian-scale statistics
And ‘value-added’ methods: Landscape pattern metrics Proximity-based metrics Data-source merges Multimetric indexes
![Page 6: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/6.jpg)
Major Sources of Urban Ancillary Data for Ecological Studies
Land cover/imperviousness Roads/transportation Census-derived EPA-regulated sites
e.g. NPDES
![Page 7: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/7.jpg)
Update on NLCD01
USGS National Land Cover Data (NLCD) for ’00-01 time frame Completed zones as of April 18, ‘06 (below) Includes sub-pixel imperviousness and forest canopy layers, in
addition to traditional categorical land cover data Urban classes differ from NLCD92 (“land cover”, not “land use”)
www.mrlc.gov
![Page 8: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/8.jpg)
NOAA 1-km Imperviousness
NOAA has mapped impervious surface for entire US at 1-km resolution
http://dmsp.ngdc.noaa.gov/html/download_isa2000_2001.html
![Page 9: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/9.jpg)
0.3-m orthoimagery – USGS 133 cities
http://seamless.usgs.gov
![Page 10: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/10.jpg)
Roads
CENSUS TIGER 2000 roads Entire US; free; consistent Positional accuracy not great But representational accuracy
reasonable Better accuracy roads available
for localized areas
Reston, VA
TIGER 1990 roads not easily available and inconsistent with 2000
![Page 11: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/11.jpg)
Census-derived data
Vast panoply of urban statistics from Census for various geographies (county, tract, block group).
Many Census statistics under-examined e.g. percent homes > 50 years old % homes with incomplete plumbing, etc.
![Page 12: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/12.jpg)
“Value-added” metrics/techniques: Example 1
merging residential classes from GIRAS
GIRAS
NLCD92-”enhanced”
NAWQA “enhanced” land cover – “improving” NLCD92 by merging with high-confidence classes from other data sources (Hitt, Nakagaki, Price)
![Page 13: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/13.jpg)
Example 2: Distance-weighting by proximity to sampling site or stream
Wolf Creek, Ohio 14% urban “standard” 43% urban “distance-weighted”
Lamington River, NJ14% urban “standard” 12% urban “distance-weighted”
![Page 14: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/14.jpg)
Urban Index (entire session on this method Tuesday PM)
Used by NAWQA Effects of Urbanization on Stream Ecosystems (EUSE) Program (and others)
Combining multiple urban variables in a single “urban intensity” metric
Most consistent correlation to population density across geographic settings: Road density % urban land cover Housing unit density
![Page 15: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/15.jpg)
Vexing Issues Often Overlooked or Under-considered
Scale – what is the appropriate scale of ancillary data for your project? Is a 1-km pixel “too coarse” for a 5-km2 basin?
Accuracy – what is “good enough” accuracy? Should you use land cover classes that are only 60% accurate? How about 40%?
Currency – how old is “too old”? Loudoun County, VA exploded in population by 100% between 1990 and 2000 – should you use 1990 land cover to evaluate stream sampling done in 2004?
![Page 16: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/16.jpg)
Summary
Both “traditional” and “non-traditional” metrics may add explanatory information
Roads powerful driver in process and generally consistent over broad areas
Some data/metrics may be better representations of “process” vs “structure”
Important considerations of scale, accuracy, and currency should drive data collection.
Acquiring consistent geospatial time-series data a significant issue
![Page 17: 2006 National Monitoring Conference, San Jose, CA, May 2006](https://reader035.vdocument.in/reader035/viewer/2022062805/56814daf550346895dbb065f/html5/thumbnails/17.jpg)
Portland by night