You are here:
IDENTIFYING RECENT SURFACE MINING ACTIVITIES USING A NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) CHANGE DETECTION METHOD
Yankee, D., R. D. Tankersley, AND F W. Kutz. IDENTIFYING RECENT SURFACE MINING ACTIVITIES USING A NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) CHANGE DETECTION METHOD. Presented at 14th Annual Geographic Information Science Conference, Baltimore, MD, May 7-8, 2001.
The primary objectives of this research are to:
Develop methodologies so that landscape indicator values generated from different sensors on different dates (but in the same areas) are comparable; differences in metric values result from landscape changes and not differences in the sensors;
Quantify relationships between landscape metrics generated from wall-to-wall spatial data and (1) specific parameters related to water resource conditions in different environmental settings across the US, including but not limited to nutrients, sediment, and benthic communities, and (2) multi-species habitat suitability;
Develop and validate multivariate models based on quantification studies;
Develop GIS/model assessment protocols and tools to characterize risk of nutrient and sediment TMDL exceedence;
Complete an initial draft (potentially web based) of a national landscape condition assessment.
This research directly supports long-term goals established in ORDs multiyear plans related to GPRA Goal 2 (Water) and GPRA Goal 4 (Healthy Communities and Ecosystems), although funding for this task comes from Goal 4. Relative to the GRPA Goal 2 multiyear plan, this research is intended to "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors." Relative to the Goal 4 Multiyear Plan this research is intended to (1) provide states and tribes with an ability to assess the condition of waterbodies in a scientifically defensible and representative way, while allowing for aggregation and assessment of trends at multiple scales, (2) assist Federal, State and Local managers in diagnosing the probable cause and forecasting future conditions in a scientifically defensible manner to protect and restore ecosystems, and (3) provide Federal, State and Local managers with a scientifically defensible way to assess current and future ecological conditions, and probable causes of impairments, and a way to evaluate alternative future management scenarios.
Coal mining is a major resource extraction activity on the Appalachian Mountains. The increased size and frequency of a specific type of surface mining, known as mountain top removal-valley fill, has in recent years raised various environmental concerns. During mountaintop removals, huge shovels and dozers shave off entire tops of mountains to reach the valuable coal seams underneath. The rock and earth are placed in the valleys below, burying existing streams. These activities encompass relatively large areas, making identification via satellite imagery feasible.
The purpose of this study is to rapidly identify recent surface mining activity using remotely-sensed data. Using early 1990's raw and classified satellite imagery from the MRLC as our baseline and in conjunction with raw imagery from 1999, we are using a change detection procedure to identify areas that have been mined since the baseline. By looking for drastic changes in the Normalized Difference Vegetation Index (NDV4, corresponding to loss of greenness or vegetation, we were able to avoid many of the common problems associated with multi-temporal or change detection studies. The results indicate this is a viable method for rapid determination of recent mining activity. With this information we will estimate the rate at which mountaintop removal is occurring in the Appalachians.