Remote Sensing of Wetlands: Strategies and Methods · Remote Sensing of Wetlands •Wetland mapping challenges • Size and extent: • anada’s boreal forest covers 570 million

Michael A. Merchant

Ducks Unlimited Canada, Boreal Program

February 21st, 2019

Edmonton, AB

[email protected]

Remote Sensing of Wetlands: Strategies and Methods

Presentation for the Canadian Institute of Forestry

About Me• Background

• Lead Remote Sensing Specialist for Ducks Unlimited Canada, Boreal Program

• 3.5 years with DUC completing boreal wetland mapping, 5+ Years of geomatics work

• Background in Remote Sensing, Spatial/GIS Modelling, wetland and agricultural hydrology

• Recent positions: OMAFRA, City of Ottawa, University of Guelph

• B.A., and M.Sc. in Geography, University of Guelph

• RADAR Remote Sensing of Subarctic Peatlands

True Color False Color Soil Boundary

Surface Volume Double-Bounce

Merchant et al. 2017

RADAR Scattering

Contributions of C-Band SAR Data and PolarimetricDecompositions to Subarctic Boreal Peatland Mapping

Presentation Overview

• Introduction to Remote Sensing

• What is remote sensing

• Types of remotely sensed datasets

• Introduction to Wetland Remote Sensing

• Wetland characteristics

• Challenges of wetland mapping

• DUC Boreal Wetland Inventory

• Enhanced Wetland Classification (EWC)

• EWC methodology

• DUC Project Examples

What is Remote Sensing

• Definition from: Remote Sensing and Image Interpretation, Lillesand et al. (2004)

• “Remote Sensing is the science and art of obtaining info about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation.”

• Platforms deploy instruments/sensors that collect information.

• Information is relative to the energy (i.e. radiation) being measured.

• Platforms can include planes, helicopters, satellites, UAVs, etc.


• Passive Remote Sensing:

• When a sensor is detecting naturally occurring energy (e.g. from the sun)

• Active Remote Sensing:

• When a sensor creates its own energy.


• Electromagnetic spectrum

• It is the range of wavelengths which radiation extends to.

• Optical radiation and vegetation

• Chlorophyll in healthy vegetation absorbs red, blue wavelengths for photosynthesis

• If healthy, the spongy structure of the vegetation reflects green and IR wavelengths

Passive Remote Sensing: Optical


• Optical Example

• Natural Resources Canada (NRCan), Earth Observation for Sustainable Development (EOSD)

• Optical Landsat imagery to map forest cover across Canada, to 21 classes

https://www.nrcan.gc.ca/forests/measuring-reporting/remote-sensing/13433


• RADAR sensors

• Microwave energy is emitted from the sensor

• All weather, cloud-penetrating

• Radiation returned to sensor is “backscatter”

• Backscatter a function of physical geometry

• e.g. soil moisture

• e.g. biomass

• e.g. surface roughness

Active Remote Sensing: RADAR

Vertical

Horizontal


• LiDAR sensors

• Light detection and ranging (LiDAR)

• Infrared energy is emitted from the sensor

• Uses light in the form of a rapidly pulsed laser

• The sensor measures to time taken for the pulse to return

• Distance is calculated using the velocity of light

Active Remote Sensing: LiDAR

Vertical

Horizontal


• LiDAR Example

• Use of high resolution aerial photography and LiDAR to map wetlands

• Evergreen Center, Grand Prairie, AB

Classification

Upland/Other

Conifer Swamp

Emergent Marsh

Graminoid Rich Fen

Hardwood Swamp

Mixedwood Swamp

Open Water

Shrubby Rich Fen

Tamarack Swamp

Treed Rich Fen

LiDAR Vegetation Model

Ground

Short Vegetation / Shrub

Medium Vegetation / Shrub

Tall Vegetation / Shrub

Short Vegetation / Tree

Medium Vegetation / Tree

Tall Vegetation / Tree

¯Aerial Imagery

LiDAR: Bare Earth

LiDAR: Vegetation Model

Classification

Remote Sensing of Wetlands

• Definition from: Canadian Wetland Classification System, NWWG (1997)

• “A wetland is land where the water table is at, near, or above the surface or which is saturated for a long enough period to promote such features as wet- altered soils and water tolerant vegetation.”


• Wetland hydrology

• Seasonally or permanently waterlogged

• Water slightly below, at, or above the surface for at least some part of the year

• Wetland vegetation

• Vegetation adapted for life in the saturated/flooded soil conditions

• Species can be obligate or facultative

• Includes trees, shrubs, mosses, herbs, lichens or aquatics

Bla

ck Spru

ce

Bo

g B

irch

Rein

deer Lich

en


• Wetland mapping challenges

• Size and extent:

• Canada’s boreal forest covers 570 million ha (58% of Canada).

• Percentage of wetlands per unit area:

• Wetlands dominate the landscape throughout much of the boreal.

• Complexity

• Wetlands have a wide geographic distribution, complexity of growth forms, conditions, and gradations.

• Data availability

• i.e. imagery sources.

• Although, data is becoming more accessible over time.


• Wetland mapping challenges• Wide range of features within a single wetland class:

• Burnt, flooded, dry, dead vegetation, live vegetation, vegetation composition

• Some upland areas have the same spectral features as wetland areas

• e.g. spruce forests

• e.g. tall shrubs

• Delineation of wetland extent can be difficult

• Size: wetlands range from thousands of square kilometers to a puddle

• Gradation: wetlands transition between forms

Treed Fen

Shrubby Fen

Marsh


• Challenges

• What is a wetland?

• What types of wetlands are there?

• What features are used to distinguish wetland types?

• Can wetlands be mapped mutually exclusive within a region?

Near Infrared

Shortwave Infrared

Red

0 5 102.5 Kilometers

¯


• Challenges

• Water levels can change over time

• Classification of wetland type can be dependent on image date capture

• Ideally, wetland inventories should be refreshed over time

1988 1991 1992 19951993 1994


• Classification Systems

• Canadian Wetland Classification System

• 5 class data modelBog

Fen

MarshOpen Water

Swamp

http://www.gret-perg.ulaval.ca/fileadmin/fichiers/fichiersGRET/pdf/Doc_generale/Wetlands.pdf


• Classification Systems

• DUC Enhanced Wetland Classification

• 19 class data model• EWC

• Defines major and minor wetland classes for the entire ecozone

• Applicable for ground level surveys, but designed for helicopter-based orthogonal-view surveys

• Comprehensive description of each wetland

• Available online at www.borealforest.ca

Remote Sensing of Wetlands• Level 1 Inventory Detail

• Baseline wetland info, large scale, and very generalized

• Level 2 Inventory Detail

• Support policy, conservation and generalized understanding of wetland processes

• Level 3 Inventory Detail

• Improved support for Land use planning, conservation products, and support of BMPs


1 StagnantLowest Risk 2

Moving -

Seasonally FluctuatingMedium Risk

3Moving -

Slow Lateral FlowMedium Risk

4Inundated/

FloodedHighest Risk

Remote Sensing of Wetlands• BMP Development and Delivery

• Boreal forest conservation partnership

• MOU between RYAM (formerly Tembec) and DUC

• Advance stewardship of wetland/waterfowl resources

• BMP delivery (e.g. landscape flow, road crossings)

• High resolution airborne LiDAR to improve wetland maps

Hillmer Project Area

Roads

Gordon Cosens Forest MU

EWC Hydrodynamics Risk Assessment

¯

Remote Sensing of WetlandsHydrodynam

ic

Hydrodynam

ic

Regim

e

Regim

e

Mo

istu

re R

eg

ime

Mo

istu

re R

eg

ime

Nutrient RegimeNutrient Regime

Stagnant

Slow

Moving

Moving

Dynam

ic

Very

Dynam

ic

Very Poor Poor Medium Rich Very Rich

Very

Hydric

Hydric

Sub-

hydric

Hygric

Sub-

hygric

Excess

Mesic

BogsBogs

SwampsSwamps

FensFens

MarshesMarshes

Open WaterOpen Water

Hyd

rod

ynam

icsSo

il Mo

isture

Nu

trien

t regim

e

DUC’s Boreal Wetland Inventory

DUC Boreal Inventory

Project Status

EWC In-Progress

EWC Complete

EC In-Progress

EC Complete

CWCS In-Progress

CWCS Complete

Boreal Boundary

DUC’s Boreal Wetland Inventory• Methodology

• Imagery, ancillary data, and field data are used to develop wetland classifications

Satellite ImagerySpectral Features – Basis for Classification

Image Interpretation – complete view of project areaAutomation of classification

Ancillary DatasetsModel Spectral Confusion

Develop understanding of subsurface controls on wetlands

Variable Availability

Field Data Collection*Image analyst first person perspective*

High resolution training and accuracy datasetsIncorporation of ecological understanding of processes

that control wetland type/distribution

KnowledgeBase


Knowledge Base

Field Dataset

Calibration Data

Validation Data

Imagery Interpretation

Ancillary Data WetlandClassification

Masking Techniques

Supervised Classification

Accuracy Assessment

Manual Classification

Satellite Imagery

Image Segmentation

WetlandClassification


¯0 8 16 244Kilometers

Segmentation

• Segmentation• The process of

partitioning a satellite image into polygon objects

Low homogeneity in wetlands

High homogeneity

in uplands and water

Guiding Principle: ALAP & ASAN

As Large As Possible & As Small As Necessary

Near Infrared

Shortwave Infrared

Red

DUC’s Boreal Wetland InventoryOpen Water

Aquatic Bed

Mudflats

Emergent Marsh

Meadow Marsh

Graminoid Rich Fen

Graminoid Poor Fen

Shrubby Rich Fen

Shrubby Poor Fen

Treed Rich Fen

Treed Poor Fen

Open Bog

Shrubby Bog

Treed Bog

Shrub Swamp

Hardwood Swamp

Mixedwood Swamp

Tamarack Swamp

Conifer Swamp

Upland Conifer

Upland Deciduous

Upland Mixedwood

Upland Other

Cutblock

Agriculture

Anthropogenic

Cloud

Cloud Shadow

Burn

Ice/Snow

Mountain

¯

0 8 16 244Kilometers

¯

• Wetland Inventory to Support Indigenous Land Use Planning (LUP)• MOU signed with the NWT

Treaty 8 Tribal Corporation

• 31 million hectares of habitat mapping

• Classification to various levels of detail, predominately CWCS

• Boreal Plains portion classified to EWC standards (see later slides)

• Data distributed in phases

DUC’s Boreal Wetland InventoryProject: Akaitcho


Open Water

Aquatic Bed

Mudflats

Emergent Marsh

Meadow Marsh

Graminoid Rich Fen

Graminoid Poor Fen

Shrubby Rich Fen

Shrubby Poor Fen

Treed Rich Fen

Treed Poor Fen

Open Bog

Shrubby Bog

Treed Bog

Shrub Swamp

Hardwood Swamp

Mixedwood Swamp

Tamarack Swamp

Conifer Swamp

Upland Conifer

Upland Deciduous

Upland Mixedwood

Upland Other

Anthropogenic

Cloud

Cloud Shadow

Burn

Upland Pine

¯


• WBNP Wetland Mapping

• 11.2 million acres of habitat mapping

• Classification to EWC using Sentinel-2

• Field data collected August 2018

• Project completion Fall 2019

Pe

ace A

thab

asca De

lta

Near Infrared

Shortwave Infrared

Red

DUC’s Boreal Wetland InventoryProject: Whitehorse

• Whitehorse wetland mapping

• Research driven project designed to assess multiple remotely sensed datasets for Yukon wetland mapping.

• Which datasets provide the most value for wetland classification in southern Yukon?

• Which algorithms perform the best?

• Datasets assessed:

• Optical Imagery

• L-Band RADAR Imagery

• C-Band RADAR Imagery

• Elevation Data

Optical C-Band SAR

L-Band SAR DEM

0

0.2

0.4

0.6

0.8

1

NIR

Narrow…

SW

IR 1

Red…

Red…

SW

IR 2

EV

I 2

ND

MI

Ele

vat

ion

Red

ND

VI

Red…

SA

VI

MN

DW

I

Gre

en

TP

I

SR

I

Slo

pe

σ°V

H

TW

I

σ°H

V

VV

/VH

σ°H

H

σ°V

V

HH

/HV

Asp

ect

Blu

e

Norm

aliz

ed V

aria

ble

Im

port

ance

DUC’s Boreal Wetland InventoryProject: Whitehorse• Algorithm Performance

• Random Forest (RF) produced the best results

• Variable Importance

• Optical variables were amongst the most important

• Variable Correlation

• Several variables highly correlated

DUC’s Boreal Wetland InventoryProject: Whitehorse

• Results

• Optical variables were amongst the most important

• Best classifications incorporated all optical, SAR, and DEM datasets

• RF most successful algorithm

• Support Vector Machine (SVM) second

• k-Nearest neighbour (KNN) third

• RF and correlation analysis allowed for variable reduction

• From 27 variables to 16, and achieved the same accuracy (%)

Conclusions

• Remote sensing techniques

• Numerous techniques and datasets exist for wetland mapping

• Optical

• RADAR

• LiDAR / DEMs

• Boreal Wetland Mapping

• Time and resource intensive

• Steps includes:

• image preprocessing and cataloguing,

• Field data collection, and QA/QC

• Image classification and data management

• Mapping Products

• Wetland maps contribute to several initiatives:

• BMPs for industry

• Support of land use planning

• Assessments of biodiversity and hydrology

Questions?

Documents

Remote Sensing of Wetlands: Strategies and Methods · Remote Sensing of Wetlands •Wetland mapping challenges • Size and extent: • anada’s boreal forest covers 570 million