United States Department of Transportation
i
Improvements to NCDOT’s Wetland Prediction Model (Phase II)
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2020-03-03
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[PDF-2.05 MB]
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Edition:Final Report March 16, 2015 – Aug. 15, 2019
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Abstract:This Final Report is to summarize several main achievements of this project as follows: (i) Method Development for Wetland Type Identification and Prediction; (ii) Wetland Type (Prediction) Automation Tool (WAMTAT) using LiDAR data for non-coastal areas; (iii) Systematic Method Development of Wetland Functional E-Assessment for 16 NCWAM Metrics and function combination; (iv) Initial Wetland Functional E-Assessment Tools (WAMFEAT) as extra; and (v) User Friendly deliverables of methods, models and documentations. These achievements fit the NCDOT research needs as: “while NCDOT has made significant advances with the concept, the process and tools of predicting wetlands using LiDAR is under-developed.” That also completes the goal of the project to provide an advanced wetland type prediction method and automation tool based on ArcGIS, and to develop wetland functional e-assessment method. The UNC Charlotte WAM Research Team with Axiom Research Team has successfully completed a number of valuable research topics related to wetland type prediction process, such as process automation, variables exploration, data mining, and statistical analysis, and samples selection; and wetland functional e-assessment methodology and its tools. The acclaimed results include the deliverable WAMTAT and WAMFEAT tools and the User Guides to the tools, wetland type prediction method, and the wetland e-functional assessment method.
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