Using Random Forest Classification and Nationally Available Geospatial Data to Screen for Wetlands over Large Geographic Regions
| Autor: | Mary-Michael Robertson, Gina L. O'Neil, Benjamin R. Felton, Jonathan L. Goodall, G. Michael Fitch |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Geospatial analysis
lcsh:Hydraulic engineering 010504 meteorology & atmospheric sciences Process (engineering) Geography Planning and Development 0211 other engineering and technologies Wetland 02 engineering and technology Aquatic Science computer.software_genre water resources 01 natural sciences Biochemistry wetlands lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 021101 geological & geomatics engineering 0105 earth and related environmental sciences Water Science and Technology geography lcsh:TD201-500 geography.geographical_feature_category Impact assessment business.industry Environmental resource management environmental planning GIS Random forest Water resources Statistical classification Workflow Environmental science business computer random forest |
| Zdroj: | Water, Vol 11, Iss 6, p 1158 (2019) Water Volume 11 Issue 6 |
| ISSN: | 2073-4441 |
| Popis: | Wetland impact assessments are an integral part of infrastructure projects aimed at protecting the important services wetlands provide for water resources and ecosystems. However, wetland surveys with the level of accuracy required by federal regulators can be time-consuming and costly. Streamlining this process by using already available geospatial data and classification algorithms to target more detailed wetland mapping efforts may support environmental planning efforts. The objective of this study was to create and test a methodology that could be applied nationally, leveraging existing data to quickly and inexpensively screen for potential wetlands over large geographic regions. An automated workflow implementing the methodology for a case study region in the coastal plain of Virginia is presented. When compared to verified wetlands mapped by experts, the methodology resulted in a much lower false negative rate of 22.6% compared to the National Wetland Inventory (NWI) false negative rate of 69.3%. However, because the methodology was designed as a screening approach, it did result in a slight decrease in overall classification accuracy compared to the NWI from 80.5% to 76.1%. Given the considerable decrease in wetland omission while maintaining comparable overall accuracy, the methodology shows potential as a wetland screening tool for targeting more detailed and costly wetland mapping efforts. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|