• Improvements to NCDOT’s Wetland Prediction Model

    NCDOT Research Project Number: 2016-16

Executive Summary

  • 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 along with Axiom Research Team has successfully completed a number of valuable research projects 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.

  
Sheng-Guo Wang
Researchers
  
Sheng-Guo Wang; Sandy Smith; Scott Davis
  
Morgan Weatherford
  
John W. Kirby

Report Period

  • March 16, 2015 – August 15, 2019

Status

  • Complete

Category

  • Environment and Hydraulics

Sub Category

  • Wetlands Mitigation

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