Highway runoff stormwater management potential (HRSMP) site characterization using NASA public domain imagery.

The focus of this research project was the development of geospatial technology (GST) methodology to characterize and evaluate highway runoff stormwater management potential (HRSMP) sites in order to reduce their impact on properties, save lives and cut operational costs. Reduction of Total Maximum Daily Load (TMDL), an important initiative of the Maryland State Highway Administration (SHA), could undoubtedly be achieved through the development and use of GST (remote sensing, geographic information system (GIS), and differential global positioning system (DGPS)). Field activities and groundtruthing were conducted at selected Best Management Practices (BMP) sites to better understand their conditions and the land use/land cover (LULC) types currently present at these sites. Landsat images were assessed for quality-related issues including cloud cover and downloaded from the United State Geological Survey (USGS). Based on the outcome of the image assessment, 5 Landsat Thematic Mapper (TM) and 1 Landsat OLI_TIRS images which span from 1990 to 2015 were processed and analyzed using the Environment for Visualizing Images (ENVI) software. LULC and the normalized difference vegetation index (NDVI) images were created. Both LULC and NDVI values for the selected BMP sites, which were ranked by the SHA from I (Good) to IV (Failed), were extracted and analyzed to determine their relationship with the performances. The results from the LULC analyses suggested that vegetation was a major factor affecting the performance of the BMP facilities; poor and failed sites showed the excessive overgrowth of vegetation. Analysis of NDVI did not show definitive results, which might have been due to the relatively low spatial resolution of the TM images. Use of higher spatial resolution such as IKONOS multispectral images in the future could help resolve these inconsistencies.