Irrigated Vegetation Assessment for Urban Environments
Assuring the availability of water in the southwestern United States is a major resource management problem. Irrigation of landscape vegetation within urban environments represents a large portion of the total urban water consumption for this region. Current estimates suggest that
up to 50 percent of residential water is used for landscape irrigation. This paper examines the utility of Ikonos multispectral satellite imagery and expert classifier approaches for quantifying the amount and distribution of urban irrigated landscape vegetation.
A decision tree, expert classifier model applied to Ikonos image and land-use GIS layer inputs was tested against conven tional image classification approaches. With all branches of the decision tree activated, percentage estimates of urban irrigated vegetation versus impervious cover differed from airborne image-derived reference data by less than 8 percent. Highest agreement was achieved using all model branches except a spatial structure rule, which utilized a texture metric derived from Ikonos 1-m panchromatic data. For this same product, proportion estimates of two growth form types (Tree/Shrub and Grass) and impervious cover differed from reference data by less than 3 percent, and root-mean-square error (RMSE) values for all neighborhood-size sampling units were within 5 percent for all cover types. This “optimal” expert classifier product yielded areal proportion estimates and RMSE values that were approximately 2 percent closer to those of the reference data, compared to standard unsupervised classification applied to Ikonos multispectral data.
A decision tree, expert classifier model applied to Ikonos image and land-use GIS layer inputs was tested against conven tional image classification approaches. With all branches of the decision tree activated, percentage estimates of urban irrigated vegetation versus impervious cover differed from airborne image-derived reference data by less than 8 percent. Highest agreement was achieved using all model branches except a spatial structure rule, which utilized a texture metric derived from Ikonos 1-m panchromatic data. For this same product, proportion estimates of two growth form types (Tree/Shrub and Grass) and impervious cover differed from reference data by less than 3 percent, and root-mean-square error (RMSE) values for all neighborhood-size sampling units were within 5 percent for all cover types. This “optimal” expert classifier product yielded areal proportion estimates and RMSE values that were approximately 2 percent closer to those of the reference data, compared to standard unsupervised classification applied to Ikonos multispectral data.
Document Type: Research Article
Publication date: 01 April 2003
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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