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Smart solutions for smart cities: Urban wetland mapping using very-high resolution satellite imagery and airborne LiDAR data in the City of St. John's, NL, Canada.

Authors :
Mahdianpari, Masoud
Granger, Jean Elizabeth
Mohammadimanesh, Fariba
Warren, Sherry
Puestow, Thomas
Salehi, Bahram
Brisco, Brian
Source :
Journal of Environmental Management. Feb2021, Vol. 280, pN.PAG-N.PAG. 1p.
Publication Year :
2021

Abstract

Thanks to increasing urban development, it has become important for municipalities to understand how ecological processes function. In particular, urban wetlands are vital habitats for the people and the animals living amongst them. This is because wetlands provide great services, including water filtration, flood and drought mitigation, and recreational spaces. As such, several recent urban development plans are currently needed to monitor these invaluable ecosystems using time- and cost-efficient approaches. Accordingly, this study is designed to provide an initial response to the need of wetland mapping in the City of St. John's, Newfoundland and Labrador (NL), Canada. Specifically, we produce the first high-resolution wetland map of the City of St. John's using advanced machine learning algorithms, very high-resolution satellite imagery, and airborne LiDAR. An object-based random forest algorithm is applied to features extracted from WorldView-4, GeoEye-1, and LiDAR data to characterize five wetland classes, namely bog, fen, marsh, swamp, and open water, within an urban area. An overall accuracy of 91.12% is obtained for discriminating different wetland types and wetland surface water flow connectivity is also produced using LiDAR data. The resulting wetland classification map and the water surface flow map can help elucidate a greater understanding of the way in which wetlands are connected to the city's landscape and ultimately aid to improve wetland-related conservation and management decisions within the City of St. John's. • Producing the first high-resolution wetland map of the City of St. John's using remote sensing data and tools. • Producing the first wetland connectivity map of the City of St. John's. • Applying an advanced machine learning algorithm in an object based framework. • Using very high resolution multi-spectral and airborne LiDAR data in order to produce wetland map at 1 m resolution. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03014797
Volume :
280
Database :
Academic Search Index
Journal :
Journal of Environmental Management
Publication Type :
Academic Journal
Accession number :
147945424
Full Text :
https://doi.org/10.1016/j.jenvman.2020.111676