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High-resolution maps of forest-urban watersheds present an opportunity for ecologists and managers
- Stueve, Kirk M., Hollenhorst, Tom P., Kelly, John R., Johnson, Lucinda B., Host, George E.
- Landscape ecology 2015 v.30 no.2 pp. 313-323
- canopy, databases, decision making, ecologists, green infrastructure, image analysis, land cover, landscapes, managers, trees, water quality, watersheds, United States
- CONTEXT: Green infrastructure may improve water quality and mitigate flooding in forest-urban watersheds, but reliably quantifying all benefits is challenging because most land cover maps depend on moderate- to low-resolution data. Complex and spatially heterogeneous landscapes that typify forest-urban watersheds are not fully represented with these types of data. Hence important questions concerning how green infrastructure influences water quality and quantity at different spatial scales remain unanswered. OBJECTIVES: Demonstrate the feasibility of creating novel high-resolution land cover maps across entire watersheds and highlight deficiencies of standard land cover products. METHODS: We used object-based image analysis (OBIA) to create high-resolution (0.5 m) land cover maps and detect tree canopy overlapping impervious surfaces for a representative forest-urban watershed in Duluth, MN, USA. Unbiased estimates of accuracy and area were calculated and compared with similar metrics for the 30-m National Land Cover Database (NLCD). RESULTS: Mapping accuracies for the high-resolution land cover and canopy overlap maps were ~90 %. Error-adjusted estimates of area indicated that impervious surfaces comprised ~21 % of the watershed, tree canopy overlapped ~10 % of impervious surfaces, and that three high-resolution land cover classes differed significantly from similar NLCD classes. CONCLUSIONS: OBIA can efficiently generate high-resolution land cover products of entire watersheds that are appropriate for research and inclusion in the decision-making process of managers. Metrics derived from these products will likely differ from standard land cover maps and may produce new insights, especially when considering the unprecedented opportunity to evaluate fine-scale spatial heterogeneity across watersheds.