Grantee Research Project Results
1999 Progress Report: Assessment and Analysis of Ecosystem Stressors Across Scales Using Remotely Sensed Imagery Reducing Uncertainty in Managing the Colorado Plateau Ecosystem
EPA Grant Number: R825152Title: Assessment and Analysis of Ecosystem Stressors Across Scales Using Remotely Sensed Imagery Reducing Uncertainty in Managing the Colorado Plateau Ecosystem
Investigators: Weigel, Stephanie J.
Institution: Colorado State University
Current Institution: Colorado State University , University of Wisconsin - Madison
EPA Project Officer: Packard, Benjamin H
Project Period: October 1, 1996 through September 30, 1999
Project Period Covered by this Report: October 1, 1998 through September 30, 1999
Project Amount: $251,237
RFA: Ecological Assessment (1996) RFA Text | Recipients Lists
Research Category: Aquatic Ecosystems , Ecological Indicators/Assessment/Restoration
Objective:
The project investigates issues of scale in reducing uncertainty in ecosystem management for the Colorado Plateau ecosystem, by examining potential characteristic scales at which environmental stressors and their effects may be manifested on ecosystem landscapes, as detected by remotely sensed imagery. The project is developing an analytical algorithm for using multiscale, remotely sensed data in the characterization and analysis of landscapes at the ecosystem level. This will result in the characterization of environmental stressors across both temporal (1970s?1990s) and spatial (60 m?1 km pixel resolution) scales. Knowledge of characteristic scales provides managers and researchers with guidelines for selecting scales at which to capture or aggregate data, as well as information on the scales' processes and factors that have the potential to threaten ecosystem integrity.Progress Summary:
Year 1 of the project focused on development of the tools for the scale analysis algorithms: fractal analysis, multiscale variance, variogram analysis, and local variance analysis. These were developed using both the ICAMS software (Qui, et al., 1999) and the modeling capabilities of the ERDAS Imagine image processing software. Also included in the Year 1 research was initial evaluation and development of the image resampling algorithm for rescaling. In Years 1 and 2, a rescaling algorithm was chosen and developed based on a dampened sine wave interpolation function. Dr. Kenneth McGwire was a project consultant for the development and implementation of the code for the algorithm. Years 2 and 3 have focused on image mosaic creation, scale analysis of mosaics and subsets, and change detection analyses. We created mosaics for the 1970s, 1980s, and 1990s from the Landsat MSS NALC datasets. Several issues arose with the 1970s dataset, involving missing data resulting in a noncontinuous surface. Efforts to amend or supplement these data through the NALC program were unsuccessful; for that reason, the subsequent ecosystem-wide (mosaic) analyses were performed only on the 1980s and 1990s datasets. A series of image subsets were developed to represent some of the variety of landscapes in the ecosystem (e.g., agricultural, sparsely vegetated desert, vegetated riparian), and analyses were performed on all available years of these data sets. Scale analysis using the four scale analysis algorithms was performed on the mosaic images (1980s and 1990s) and subset images (1970s where available, 1980s, and 1990s). Currently in progress are the change detection analyses on the subsets, which also will be performed on the mosaic images.
Methodologies for change detection were chosen from methods in the published literature with potential for accurate description of change and appropriate to the landscape. The methods being used are: NDVI image differencing (discussed in Lyon, et al., 1998); single band image (MSS Band 2) differencing based on At Satellite Planetary Reflectance (ASPR) normalization (Chavez and MacKinnon, 1994); and selective Principal Components Analysis (PCA) using a single band from each image (Chavez and MacKinnon, 1994; Mas, 1999). These results will be summarized in the final report and submitted in journal format to a remote sensing journal. The efforts of graduate research assistant Dietrich Erdmann on this project are recognized and appreciated.
Future Activities:
Future activities include the completion of the change detection and completion of the manuscript for submission to Photogrammetric Engineering and Remote Sensing.References:
Chavez PS, MacKinnon DJ. Automatic detection of vegetation changes in the southwestern United States using remotely sensed images. Photogrammetric Engineering and Remote Sensing 1994;60(5):571-583.
Lyon JG, Yuan D, Lunetta R, Elvidge C. A change detection experiment using vegetation indices. Photogrammetric Engineering and Remote Sensing 1998;64(2):143-150.
Mas JF. Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing 1999;20(1):139-152.
Qui H-L, Lam NS-N, Quattrochi D, Gamon JA. Fractal characterization of hyperspectral imagery. Photogrammetric Engineering and Remote Sensing 1999;65(1):63-71.
Journal Articles:
No journal articles submitted with this report: View all 4 publications for this projectSupplemental Keywords:
ecosystem, scaling, change detection, integrated assessment, remote sensing, scale analysis methodologies, Landsat, NALC., RFA, Ecosystem Protection/Environmental Exposure & Risk, Scientific Discipline, Geographic Area, Ecological Indicators, Ecological Risk Assessment, State, Ecosystem Protection, Ecology, Ecosystem/Assessment/Indicators, exploratory research environmental biology, Ecological Effects - Environmental Exposure & Risk, Ecological Effects - Human Health, Chemical Mixtures - Environmental Exposure & Risk, Ecology and Ecosystems, Environmental Monitoring, landscape characterization, multi-scale biophysical models, multiple stressors, assessment methods, Colorado Plateau ecosystem, ecosystem assessment, ecological assessment, variance analysis, environmental stress, remote sensing, scaling, ecological exposure, analytical algorithm, environmental stressor, Colorado (CO), ecological impacts, fractal analysisRelevant Websites:
The project is included in the Environmental Health Advanced Systems Laboratory (EHASL) Web page at http://ehasl.cvmbs.colostate.edu
. The page is currently under
revision, but the link to the project can be found at http://ehasl.cvmbs.colostate.edu/remote
. The PI no longer works at EHASL,
so future updates to the project page would occur at another site.
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.