Remote Sensing of Wetlands: Strategies and Methods Presentation for the Canadian Institute of Forestry Michael A. Merchant Ducks Unlimited Canada, Boreal Program February 21 st, 2019 Edmonton, AB m_merchant@ducks.ca
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
Contributions of C-Band SAR Data and Polarimetric Decompositions to Subarctic Boreal Peatland Mapping RADAR Scattering Surface Volume Double-Bounce Merchant et al. 2017
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.
What is Remote Sensing 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.
What is Remote Sensing Passive Remote Sensing: Optical 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
What is Remote Sensing 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
What is Remote Sensing Active Remote Sensing: RADAR Vertical 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 Horizontal
What is Remote Sensing Active Remote Sensing: LiDAR Vertical 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 Horizontal
What is Remote Sensing LiDAR Example Use of high resolution aerial photography and LiDAR to map wetlands Evergreen Center, Grand Prairie, AB Aerial Imagery LiDAR: Bare Earth LiDAR: Vegetation Model Classification LiDAR Vegetation Model Ground Short Vegetation / Shrub Medium Vegetation / Shrub Tall Vegetation / Shrub Short Vegetation / Tree Medium Vegetation / Tree Tall Vegetation / Tree Classification Upland/Other Conifer Swamp Emergent Marsh Graminoid Rich Fen Hardwood Swamp Mixedwood Swamp Open Water Shrubby Rich Fen Tamarack Swamp Treed Rich Fen
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.
Remote Sensing of Wetlands 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 Reindeer Lichen Bog Birch Black Spruce
Remote Sensing of Wetlands 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.
Remote Sensing of Wetlands 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
Remote Sensing of Wetlands 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 0 2.5 5 10 Kilometers Shortwave Infrared Red
Remote Sensing of Wetlands 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 1993 1994 1995
Remote Sensing of Wetlands Classification Systems Canadian Wetland Classification System 5 class data model Bog Swamp Fen Open Water Marsh http://www.gret-perg.ulaval.ca/fileadmin/fichiers/fichiersgret/pdf/doc_generale/wetlands.pdf
Remote Sensing of Wetlands 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
Remote Sensing of Wetlands 1 Stagnant Lowest Risk 2 Moving - Seasonally Fluctuating Medium Risk 3 Moving - Slow Lateral Flow Medium Risk 4 Inundated/ Flooded Highest 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 EWC Hydrodynamics Risk Assessment Hillmer Project Area Roads Gordon Cosens Forest MU
Remote Sensing of Wetlands Moisture Regime Mesic Subhydric Subhygric Hygric Hydric Very Hydric Bogs Fens Open Water Stagnant Nutrient Regime Swamps Marshes Hydrodynamic Hydrodynamic Regime Regime Slow Moving Moving Very Poor Poor Medium Rich Very Rich Excess Dynamic Very Dynamic Hydrodynamics Soil Moisture Nutrient regime
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 Imagery Spectral Features Basis for Classification Image Interpretation complete view of project area Automation of classification Knowledge Base Ancillary Datasets Model Spectral Confusion Develop understanding of subsurface controls on wetlands Variable Availability Field Data Collection *Image analyst first person perspective* High resolution training and accuracy datasets Incorporation of ecological understanding of processes that control wetland type/distribution
DUC s Boreal Wetland Inventory Knowledge Base Imagery Interpretation Satellite Imagery Ancillary Data Field Dataset Wetland Classification Masking Techniques Supervised Classification Manual Classification Image Segmentation Calibration Data Validation Data Accuracy Assessment Wetland Classification
DUC s Boreal Wetland Inventory Segmentation The process of partitioning a satellite image into polygon objects Segmentation Guiding Principle: ALAP & ASAN As Large As Possible & As Small As Necessary Low homogeneity in wetlands High homogeneity in uplands and water 0 4 8 16 24 Kilometers Near Infrared Shortwave Infrared Red
DUC s Boreal Wetland Inventory 0 8 16 24 4 Kilometers 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 Cutblock Agriculture Anthropogenic Cloud Cloud Shadow Burn Ice/Snow Mountain
DUC s Boreal Wetland Inventory Project: Akaitcho 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 Inventory Project: 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
DUC s Boreal Wetland Inventory Project: Akaitcho 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 Near Infrared Shortwave Infrared Red Peace Athabasca Delta
DUC s Boreal Wetland Inventory Project: 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 L-Band SAR C-Band SAR DEM
NIR Narrow SWIR 1 Red Red SWIR 2 EVI 2 NDMI Elevation Red NDVI Red SAVI MNDWI Green TPI SRI Slope σ VH TWI σ HV VV/VH σ HH σ VV HH/HV Aspect Blue Normalized Variable Importance DUC s Boreal Wetland Inventory Project: Whitehorse Algorithm Performance 1 0.8 0.6 0.4 0.2 0 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 Inventory Project: 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?