Sheng-Guo Wang1*, Morgan Weatherford2†, LeiLani Paugh2†, Neil Mastin2#, John Kirby2#

1 University of North Carolina – Charlotte, 2 North Carolina Department of Transportation

*PI, swang@uncc.edu, †Str. Com. Chair/Expert, #Manager – 2016 TRB Annual Meeting 01-11-2016

Improvements to NCDOT’s Wetland Prediction Model

2015 AASHTO RAC Awarded “Sweet 16” High Value Research Project NCDOT RP 2013-13

Background Study Area Model Comparison for FW, RCP & SFLT Projects

Wetland is important and worth protecting (NEPA 1970)

– Need to improve reliability and performance of LiDAR based wetland model

● Part of NEPA Streamlining effort

● Model utilizes digital elevation model and terrain derivatives from bare-

earth LiDAR data

● Layers created and analyzed in ArcGIS

– Needs for more accurate and efficient method to identify wetlands

– Avoid and minimize wetland impacts during project development

Variables Table

13 different wetland delineation projects in

North Carolina, USA, conducted by NCDOT

within three ecoregion groups.

Group A: 5 projects in Flatwoods (FW)

Group B: 4 projects in Rolling Coastal Plain

(RCP)

Group C: 4 projects in Southeastern

Floodplains and Low Terraces (SFLT)

Result

Logistic Regression Random Forest (RF)

Model Comparison

Conclusions

4. Full Automation process* dramatically facilitates the whole

wetland prediction process, including automation of

(i) Generating wetland variables (ii) Modeling

(iii) Prediction (iv) Map display

(v) Analysis

5. Beyond cost efficiencies, automated tools provide early

awareness of potential wetland impact areas during the project

planning.

* US Pending Patent “Wetland Modeling and Prediction”,

US 14/724,787 (05-28-2015)

DISCLAMIERThe contents of this poster reflect the views of the authors and do not

necessarily reflect the official views or policies of the North Carolina

Department of Transportation or the Federal Highway Administration at the time

of publication.

Acknowledgment

Especially thanks to Sandy Smith and Scott Davis at Axiom for their expert field

work, and UNCC WAM Research Team for their team work with the PI.

Overall Scheme for Automation Random Forest Model and Automation

Random Forest (RF) method based on decision trees is applied to

identify wetland, which is built by a set of rules with random and

optimization.

Introduction

Methodology

true - predict: 1-1 green (wetland), 0-0 grey, 1-0 error red, 0-1 error yellow Field verification

Full Automation Process – Wetland PredictionModules – generate variables, modeling, prediction, post-treatment, display, analysis

Generate DEM Derivatives

– FHWA: 2011 Environmental Excellence Awards

(EEA) to NCDOT and NCDENR for Excellence in

Environmental Research:

“GIS-based Wetland and Stream Predictive Models”

Fig. Comparison of Error rate on all data: red – RF, blue – Logit

1. Raw modeling

data

2. Generating Wetland

Variables & Table

3. Training Data

Set4. Modeling

5. ModelsModeling

Prediction

6. Predict area data 7. Prediction Models 8. Wetland Prediction

Post-

Treatment

9. Post-treatment

data10. Post-treatment

11. Post-treated Wetland

Prediction

Analysis &

Verification13. Verification12. Verification areas

14. Analysis

Results

Data

Y N

web photo our photo

1. Leads to more informed decisions and high model confidence

• Improve protection of state’s natural resources

• Additional research project now in place to examine

automated defining of wetland types

• Significant cost and time saving

• Potential saving of $350,000 per project (depending on

project size)

2. RF method is proposed for wetland modeling and prediction,

and it improves prediction accuracy and outperforms Logit

regression method

3. New technology is applied to the wetland identification

SC Region 2 Logit Method SC Region 2 RF MethodTraining areas Prediction Training areas Prediction

80% data for model training, 20% data for test checking, 100% check

1 — 0 error = missing, 0 —1 error = over-estimate

U3826 (SFLT) Logit U3826 (SFLT) RF

Rowan County, NC U3826 (SFLT) Logit RF update RF

Elv

Download LiDAR

data from NCFMhttp://www.ncfloodma

ps.com/

Mosaic

Filter

Breach

All

Slope

Curvature

Slp

Cv

Prcv

Plcv

Asp

Curv5

depan

Download land cover data

From GAP (USGS)

Download soil data from

NRCS (USDA)

Aspect

Extract Multi

Values to PointsWetness

Elevation

Index

Flow

Direction

Raw

DEM WeiRe

Wei

Stochastic

depression

analysisrawda

Other Image

process

flowdr

batwi

Maximum

downslope

elevation

changeDEM2

Tools using ArcGIS

Developed Tools

Other Processes

Intermediate

Variable

Final

Variable

Soils

GAP

Mdec

Reclassify

Slope Area

Ratio

Flow

Accumulation

CA

(contributing

area)

Histogram

Projection

Interpolate

Feature to Raster

Reclassify

Final Data Table for Modeling

Clip

Clip

Points for both riparian and non-

riparian area

Raster to

Other Format

Block

Statistics

Random Forest

Logistic

Regression Comparison

Type Project Data Total

Records

0 – 1

Error

Rate

1 – 0

Error

Rate

Total

Error

Rate

Total

Error

Rate

Total

Improve-

ment

FW

B4168

80% 812 0.00% 0.00% 0.00% 3.69% 100.00%

20% 203 3.67% 2.13% 2.96% 2.46% -20.00%

100% 1015 0.77% 0.40% 0.59% 3.45% 82.86%

R2514

80% 32827 0.00% 0.00% 0.00% 32.36% 100.00%

20% 8207 22.00% 8.47% 14.09% 32.75% 56.99%

100% 41034 4.43% 1.69% 2.82% 32.44% 91.32%

Group from 5 projects 184,262 1.06% 4.75% 2.17% 20.67% 89.51%

RCP

R2554

80% 31469 0.00% 0.00% 0.00% 12.29% 100.00%

20% 7868 2.33% 6.41% 3.69% 11.64% 68.34%

100% 39337 0.47% 1.29% 0.74% 12.16% 93.91%

B3654

80% 626 0.00% 0.00% 0.00% 7.19% 100.00%

20% 157 17.39% 1.49% 3.82% 3.82% 0.00%

100% 783 2.56% 0.32% 0.77% 6.51% 88.24%

Group from 4 projects 104,731 0.72% 0.68% 0.70% 10.34% 93.21%

SFLT

B4135

80% 1769 0.00% 0.00% 0.00% 1.87% 100.00%

20% 443 1.89% 2.11% 2.03% 2.26% 10.00%

100% 2212 0.37% 0.43% 0.41% 1.94% 79.07%

U3826

80% 20172 0.00% 0.00% 0.00% 11.45% 100.00%

20% 5044 3.51% 5.69% 4.48% 11.30% 60.35%

100% 25216 0.71% 1.13% 0.90% 11.42% 92.12%

Group from 4 projects 30,588 0.74% 0.97% 0.85% 10.17% 91.61%

Variable Full Name Formula and Illustrations

elev Elevation Elevation of each cell: z(x, y)

asp Aspect asp = 57.29578 * atan2 ([dz/dy], -[dz/dx])

slp Slope slp(x, y) = 57.29578 × atan( 𝑑𝑧/𝑑𝑥 2 + 𝑑𝑧/𝑑𝑦 2 )

cv Curvature Slope of the slope:

cv = 57.29578 × atan( 𝑑 𝑠𝑙𝑝/𝑑𝑥 2 + 𝑑 𝑠𝑙𝑝/𝑑𝑦 2 )

prcv Profile Curvature Curvature on vertical (y) direction

plcv Plan Curvature Curvature on horizontal (x) direction

batwi Ratio of Slope by

Drainage Area

batwi = slp / drainage contributing area

(calculated with breached DEM)

wei Wetness Elevation

Index

Series of increasingly larger neighborhoods used to determine the

relative landscape position of each cell.

weiRe Reclassification of

wei

Wei value of each cell will be reclassified as 0 if original value is

bigger than a predefined threshold, else is reclassified as 1.

mdec Maximum

Downslope

Elevation Change

Maximum difference of z(x,y) between the cell and its neighbor cells.

rawda Stochastic

Depression Analysis

Stochastic depression analysis based on raw DEM.

curv5 Smooth Curvature Each cell gets mean value of curvature from its 5*5 neighbors.

𝐶𝑢𝑟5 = 𝑖=𝑖1𝑖25 𝑐𝑣(𝑖)/25

depan Stochastic

Depression Analysis

Stochastic depression analysis based on breach-all DEM.

gap Land Cover Data Categorized land use types.

soil Soil Data Reclassified as 1 or 0 to indicate hydric or non-hydric soil type.