Land Use classification using satellite data for stormwater management
Lourdes V. Abellera 1 and Michael K. Stenstrom 2
1 Graduate Student and 2 Professor,
Department of Civil and Environmental Engineering
University of California, Los Angeles, California 90095-1593, USA
Tel: 1-310-825 1346; Fax: 1-310-206 2222
Email: labeller@ucla.edu
ABSTRACT
Land use is an important input parameter for stormwater models. It is used to calculate surface
imperviousness, which determines runoff rates and volumes. The type of land use is also
associated with the kind and amount of pollutants generated in a parcel of land. Many projects
still utilize the traditional ways of delineating land uses such as photo-interpretation and field
surveys. By using satellite data, scientists have attempted to increase the efficiency and accuracy
of the land use classification process. Statistical classifiers, such as the parallelepiped, minimum
distance to means, maximum likelihood, and clustering algorithms were the first classifiers to be
developed. Recently, scientists have incorporated ancillary data in the classification, usually
employing geographic information systems (GIS). They have also developed contextual and
fuzzy classifiers. Furthermore, they have applied artificial intelligence techniques such as neural
networks and knowledge-based systems. However, only a few studies are related to stormwater
management. As the conventional methods of delineating land uses are time-consuming and
labor-intensive, more engineers and planners should consider utilizing satellite data to provide
inputs to their stormwater models. In this endeavor, some of the factors to consider are the level
of classification detail, relevant land use categories, and methods to assess accuracy.
