Remote Sensing

A Hybrid Approach to Detecting Impervious Surface at Multiple Scales

Marina Alberti (PI), Stefan Coe, Yan Jang, Robin Weeks

Detecting impervious surface in urban areas is critical to understanding the effects of urbanization on ecological processes. However, it presents unique challenges due to the spatial and spectral heterogeneity of the urban surfaces and the rapid changes in land cover that occur over short time periods. In this project, we develop a hybrid approach that combines an object-oriented and a pixel-based classification approach. Our approach integrates remotely sensed data – Landsat, Ikonos, and Lidar – and parcel data to develop a spatial database featuring urban object information at multiple spatial scales and class resolutions. Towards these objectives, eCognition™ software is used to perform image segmentation, nearest neighborhood classification, and the development of semantic rules incorporating object attribute information.

Funded by: National Oceanic and Atmospheric Administration

Operational Remote Sensing Solutions for Estimating Total Impervious Surface Areas

Marina Alberti (PI), Stefan Coe, Yan Jang

The Washington State Department of Transportation (WSDOT) commissioned this research, conducted by the Urban Ecology Research Laboratory (UERL) at the University of Washington, to assist in effectively designing and managing operational, maintenance and improvement activities within the context of the many growth management and clean water regulations and ordinances in Washington State. The goals of this study were to 1) implement a classification scheme for mapping the percentage of total impervious surfaces due to different types of transportation infrastructure based on satellite imagery, 2) develop and assess a remote sensing methodology for detection of road impervious surface area (RISA) and the fraction of RISA compared to the total impervious surface area (TISA) and 3) make recommendations on the imagery best suited for identifying impervious surfaces related to transportation infrastructure. Specifically, the objectives of this project were as follows:

1. Develop a typology of transportation infrastructure-related impervious surfaces based on a feasibility assessment of identifying transportation mode features from remote sensing data.

2. Develop and implement rules for detection and classifying percent impervious surfaces specifically attributable to mode of transportation.

3. Conduct classification accuracy assessment and sensitivity analysis of transportation infrastructure impervious surface classification.

4. Provide WSDOT an initial dataset describing the contribution of transportation infrastructure to statewide total impervious surface area.

To meet the study objectives, the UERL developed a predictive model to determine the contribution of RISA for a given area using readily available data such as total impervious area and total road length. The model will enable WSDOT to estimate the amount of RISA impervious surface for a given area without having to digitize or classify road footprints. Additionally, the UERL devised a strategy for comparison of optimal data sources for extracting the impervious surface contribution to road infrastructure.

Funded by: Washington State Department of Transportation