Mapping land cover in urban residential landscapes using very high spatial resolution aerial photographs |
| |
| Authors: | Salman Al-Kofahi Caiti Steele Dawn VanLeeuwen Rolston St Hilaire |
| |
| Institution: | 1. ICREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain;2. ERAAUB, Department of History and Archaeology, Universitat de Barcelona, c/Montalegre, 6-8, 08001 Barcelona, Spain;3. Evolutionary Studies Institute and School of Geosciences, University of the Witwatersrand, P Bag 3, WITS 2050, South Africa;4. Department of Earth, Ocean and Ecological Sciences, University of Liverpool, Brownlow Street, PO Box 147, Liverpool, L69 3GP, UK;5. The Stone Age Institute, Bloomington, IN 47407-5097, USA;6. GeoZentrum Nordbayern, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Schloßgarten 5, 91054 Erlangen, Germany;7. UNESIS, Department of Biology, Pontificia Universidad Javeriana, Cra. 7 40-62, 110231 Bogotá, Colombia;8. Department of Earth and Atmospheric Sciences, Indiana University, 1001 East 10th Street, Bloomington, IN 47405-1405, USA;9. Paleontological Scientific Trust (PAST), South Africa |
| |
| Abstract: | Accurate information on existing residential landscapes is essential for framing ordinances and monitoring residential water use in the Urban Greenspace Ecosystem. We classified residential landscapes of New Mexico's largest city, Albuquerque, to explore the spatial distribution of residential greenspace and its composition among zip codes and median incomes. Geographic Information System (GIS) vector files including parcels, city limits, zip codes and land-use maps, were integrated with ownership information. The database was stratified by Albuquerque's 16 zip codes. Four hundred eighty residential landscapes were selected randomly for study. Very high spatial resolution (0.15 m) 2008 true color aerial photographs and the object-oriented supervised classification module in ENVI EX were used to identify residential features. Spatial and textural variables, created by image segmentation, were classified using the K-Nearest Neighbor (K-NN) algorithm embedded in ENVI EX. Classification accuracy was 89%. Larger greenspace, tree, shrub, and grass areas were in larger parcels. Landscapes in lower income groups and older zip codes include larger greenspace and tree cover because of mature tree sizes, while grass dominated landscapes of higher income groups and newer zip codes. This knowledge of residential vegetation distribution could serve as a basis for policy makers, planners, and water conservation officers wishing to enact ordinances and regulations that govern the urban residential landscape. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
