Using Random Forest Classification and Nationally Available Geospatial Data to Screen for Wetlands over Large Geographic Regions

Abstract

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.

Classification Feature Optimization for Global Wetlands Mapping

“…However, some machine learning models, in particular deep neural networks (DNNs), cannot be very well understood (Räz and Beisbart, 2022;Sullivan, 2022); the performance will vary depending on the architectures and hyperparameter selection of the chosen algorithm (Shrestha and Mahmood, 2019;Yang and Shami, 2020); applying them globally is extremely hard (Shanthamallu and Spanias, 2021). Therefore, this study only selected the random forest (RF) algorithm for comparison on variable importance ranking because it is currently one of the most widely used algorithms for large-scale wetland remote sensing (Felton et al, 2019;Millard and Richardson, 2013;Tian et al, 2016). This work calculated JM distance, ED, SAD, and RF variable importance (VI) ranking models for importance evaluation to calculate separability.…”

Wetlands of North Africa During the Mid‐Holocene Were at Least Five Times the Area Today

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Content analytics based on random forest classification technique: An empirical evaluation using online news dataset

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