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Bridging the Temporal Mismatch between Remotely Sensed Land Use Changes and Field-based Water Quality/Quantity Observations
Start Date 08/01/2008
End Date 07/31/2009
Primary Partner: SUNY College of Environmental Science & Forestry
Primary Contact: Mountrakis, Giorgos – Assistant Professor
Project Type: CARTI III
Technical Description:
The overarching objective is to assess water quality impacts due to expanding impervious surface cover. We target impervious surfaces because of their significant impact on hydrology, water quality and micro-climate. Our study site is the southwestern portion of Onondaga County.
Expected Outcomes:
Our approach targets the development of:
1) Enhanced detection and classification algorithms of impervious surfaces using remotely-sensed imagery; and
2) Watershed impact models designed to take advantage of up-to-date maps of imperviousness.
Accomplishments:
We developed the necessary image processing algorithms to automatically detect impervious surfaces from satellite images. Our methodology is designed to integrate multiple algorithms as opposed to existing methods that are limited into a single algorithm. In addition to a more accurate representation, the method produces spatially explicit accuracy metrics that link every impervious pixel to a specific accuracy metric. This allows subsequent hydrological modelers to use remote sensing products in a more efficient and educated manner without necessarily requiring image analysis expertise.
Suitability maps showing the likely areas of new impervious surface development were generated from the impervious surface area (ISA) maps.
Using the suitability map as input, we generated projections of new ISA to the year of 2020. The projected land use change was used as input to the hydrology and nutrient loading model ReNuMa to predict the consequent changes in streamflow and nitrate export.
We tested if improved ISA maps translated into better hydrological modeling by running the simulation with and without the ISA map (NLCD ISA maps or NLCD only). Similar results were obtained, indicating that ReNuMa, which is not spatially explicit and considers only the total IS in the catchment, has limitations in dealing with variability in spatial configuration of impervious cells. We are developing a truly spatially explicit model that considers the connectivity of impervious cells, which may be more appropriate to match the high detail of land use model outputs.
We also took a closer look at nutrients within the Onondaga Creek watershed. Synoptic surveys of water chemistry revealed complex patterns of nutrient availability and uptake, with spatial and temporal complexity.
Benefits:
These new techniques provide an enhanced method to study, track and predict water quality impacts due to expanding impervious surfaces.
For more information: http://www.aboutgis.com
Publications and Presentations:
Publications:
L. Luo, G. Mountrakis (in press). Integrating intermediate inputs from partially classified images within a hybrid classification framework: An impervious surface estimation example. Remote Sensing of Environment.
G. Mountrakis R., Watts L., Luo J., Wang (2009). Developing Collaborative Classifiers Using an Expert Based Model. Photogrammetric Engineering and Remote Sensing 75(7):831-844.
G. Mountrakis A., Stefanidis (2008). Foreword for Special Issue: Artificial Intelligence in Remote Sensing. Photogrammetric Engineering and Remote Sensing, 74(l0):1199.
G. Mountrakis (2008). Next generation classifiers: Focusing on Integration Frameworks. Highlight Article for Photogrammetric Engineering and Remote Sensing 74(10):l178-l180.
Presentations:
Li Luo, Giorgos Mountrakis, Impervious surface area detection in the Onondaga Creek watershed using satellite imagery, 8th Annual CNY ASPRS New York State Remote Sensing Symposium, Syracuse, NY.
Kacie Gehl, Karin Limburg, John Stella, Three Watershed Approaches for Evaluating Nutrient Loads, Onondaga Creek NY, 8th Annual Symposium on Environmental & Energy Systems, Syracuse, NY.
Li Luo, Giorgos Mountrakis, Impervious surface area detection in the Onondaga Creek watershd using satellite imagery, 8th Annual Symposium on Environmental & Energy Systems, Syracuse, NY.
Li Luo, Giorgosv Mountrakis, Satellite-derived impervious surface detection with spatially-explicit uncertainty metrics, NYS Geographic Information System Conference, Syracuse, NY.
Myrna H. Hall, Karin E. Limburg, Peter M. Groffman, Bongghi Hong, Karla Hyde, Lori L. Luo, and Giorgos E. Mountrakis, and Seth J. Myers, An integrated monitoring/modeling frame work for assessing human-nature interactions in urbanizing watersheds: Onondaga and Wappinger Creeks, New York, Ecological Society of America, Albuquerque, NM.
Impervious surface representation of Central NY
Image Credit: Giorgos Mountrakis, SUNY ESF
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