depressional wetland mapping in the prairie pothole …application example ... Øsaga gis, whitebox...

Depressional Wetland Mapping in the Prairie Pothole Region

Esri User Conference, San Diego, CA

Qiusheng Wu a,

a CSS-Dynamac c/o U.S. EPA, Cincinnati, Ohio

Charles Lane b,

b U.S. Environmental Protection Agency, Cincinnati, Ohio

Office of Research and DevelopmentNational Exposure Research Laboratory July 22, 2015

The views expressed in this presentation are those of the author[s] and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

Heather Golden b,

Grey Evenson b, Ellen D’Amico a

Outline• Research Background• Numerical Representation of Surface Depressions • Graph Theory-based Contour Tree Method• Application Example• Conclusions

2

Outline• Research Background• Numerical Representation of Surface Depressions • Graph Theory-based Contour Tree Method• Application Example• Conclusions

3

Surface Depressions and Wetland Landscape§Natural processes§Anthropogenic processes

Detention basins Reservoirs

Mining Quarrying

Volcanic craters Karst sinkholes

Vernal pools Prairie potholes

4

Surface Depressions in Hydrologic Modeling

Flow

Time

Time Series

Hydrograph

The Water CycleFloodplain mapping

GIS data layers

Source: U.S. EPA Office of Water

5

Surface Depressions as Analyses Problems

Depressions in digital elevation model (DEM)

Depressionless DEM

Discontinuous streams

Continuous streams6

Methods for Surface Depression Filling• Early method by O'Callaghan and Mark (1984)• The most widely-used algorithm, by Jenson and Domingue (1988)ØArcGIS, GRASS, TOPOZ,

River Tools …• The priority-flood algorithm by Wang and Liu (2006)ØSAGA GIS,

Whitebox GAT …

Ele

vatio

n

Spill Elevation

c0ci-1cici+1cn

Longitudinal profile7

Disadvantages of Traditional Methods• Do not distinguish between real and artifact depressions• Do not fully exploit high-resolution topographic data (LiDAR,

IFSAR, etc.)• Do not consider dynamic fill-and-spill hydrological processes• Do not derive quantitative information about nested

hierarchical structure

1-m resolution LiDAR DEM LiDAR DEM shaded relief 1-m resolution aerial imagery

Vernal pools 1-m resolution aerial imagery

Prairie potholes

8

Outline• Research Background• Numerical Representation of Surface Depressions • Graph Theory-based Contour Tree Method• Application Example• Conclusions

9

Numerical Representation of Surface Depressions A simple surface depression A compound surface depression

Longitudinalprofile

Contourrepresentation

Longitudinalprofile

Contourrepresentation

10

Outline• Research Background• Numerical Representation of Surface Depressions • Graph Theory-based Contour Tree Method• Application Example• Conclusions

11

Graph Theory-based Contour Tree Method§NodeØContour lines

§LinkØAdjacency

(topology)§Node attributesØArea, shape, etc.

(geometry)

Plan view of contour representation

Contour tree graph

Root node

Leaf nodes

12

Contour Tree Representation of Depressions

A simple surface depression A composite surface depression

13

Key Concepts§Sink point§Pour point§Seed contour§Pour contourØQuasi-pour

contourØTrue-pour contour

14

Simplification of Contour Trees §Single-branch contour

treeØOnly root node left

§Multi-branch contour treeØSmaller compact tree

§Simplified contour tree ØDepression tree

Single-branchcontour tree

Multi-branchcontour tree

Simplifiedcontour tree

15

Computation Procedures and Pseudo Codes

Pseudo codesArcToolbox

• Implemented using C++, Python, and ArcGIS• Fully automated

16

Outline• Research Background• Numerical Representation of Surface Depressions • Graph Theory-based Contour Tree Method• Application Example• Conclusions

17

Application ExampleEstimation of depression

storage capabilityPipestem Watershed,

North Dakota

18

Hydrologic Connectivity of Prairie Potholes

LiDAR DEM shaded relief

LiDAR intensity imagery

Aerial photographs

National Wetlands Inventory

2003 2005 2006 2009 2012 2014

Nov. 2011 Nov. 2011 Jul. 2012 1980s

19

Identification of Surface DepressionsDetected depression polygons vs.

National Wetlands Inventory polygonsContour tree method

20

Depression Storage Capability§Below-water volume

§Above-water volume

4742.125.0 AVbw =

2)( RSCZVaw ´-´=

Empirical function fitting

21

Outline• Research Background• Numerical Representation of Surface Depressions • Graph Theory-based Contour Tree Method• Application Example• Conclusions

22

Conclusions• Detection, delineation, and characterization of surface depressions across scales

• Derivation of geometric and topological properties• Simulation of filling-merging-spilling hydrological processes

• Functionally effective and computationally efficient

23

Charles LaneOffice of Research and Development

U.S. Environmental Protection Agencylane.charles@epa.gov

Qiusheng WuCSS-Dynamac c/o U.S. EPA

wqs@binghamton.edu24