From footprint to ferns: Alberta Biodiversity Monitoring ...€¦ · Biodiversity Monitoring...
Transcript of From footprint to ferns: Alberta Biodiversity Monitoring ...€¦ · Biodiversity Monitoring...
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It’s Our Nature to KnowAlberta Biodiversity Monitoring Institute
From footprint to ferns: Alberta Biodiversity Monitoring Institute
supporting forestry planning
It’s Our Nature to KnowAlberta Biodiversity Monitoring Institute
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It’s Our Nature to KnowAlberta Biodiversity Monitoring Institute
ABMI Goal
• Monitor status (distribution and abundance) and trend (change over time) of biodiversity throughout Alberta
• Species• Human footprints• Native vegetation
• Main focus on assessing cumulative effects on biodiversity
• Multi-species• Regional scale
• But also wish to understand stressor-response relationships
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It’s Our Nature to KnowAlberta Biodiversity Monitoring Institute
Taxa Surveyed
• Data collection
• Lab processing
• Data verification & storage
• Analyses
• Reporting
BirdsMammalsVascular Plants Mosses LichensMitesAquatic Vascular PlantsAquatic Invertebrates
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• 1,039 systematic sites
• 4,370 targeted sites- 251 all taxa- 250 mammals (camera)- 203 mammal (snow tracking)- 3002 bird (ARU)- 610 plant- 54 other
Species Data 2007-2017
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Data collation partnerships
BBSn=7538
BAMn = 18312
ABMIn=9367 EVERYBODY!
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Land Surface Monitoring and Outcomes
Two key data types are created to detect, assess and report on relationships between land-use patterns and biodiversity in AB
Human Footprint Inventory
Land Cover Inventory • Province-wide (Wall to wall )• Sample-based (3x7 km sites)
– 3x7 km sampling site– 1,656 sites– 20 km grid– 5.25% of Alberta
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Land Surface Monitoring and Outcomes
Provincial scale Land Cover inventory:
• Complete seamlessly created
representation of provincial scale Land
Cover inventory for AB
Castilla, G., Hird, J., Hall, R., Schieck, J. and McDermid, G. 2015. “Completion and updating of a Landsat-based land cover polygon layer for Alberta, Canada.” Canadian Journal of Remote Sensing, 40:2, pp. 92-109.
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Human Footprint: key driver of biodiversity change
Land Surface Monitoring and Outcomes
ABMI produces AB-wide Human Footprint Inventory (HFI) by mapping:
• 21 layers of HF types
• More than 115 feature types
• More than 4 million features
• Available 2007, 2010, 2012, 2014; 2016 in progress
Area of Human Footprint per Township [%]
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AHFMP: Well sites enhancements
Land Surface Monitoring and Outcomes
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Human Footprintfor Biodiversity MonitoringFinal AB-wide Human Footprint Inventory (HFI) based on 21 layers of HF types
– Available 2007, 2010, 2012, 2014 versions; 2016 in progress
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Species Models >900 species(cumulative effects)
VegetationHuman Footprint Change
From Reference(Intactness /
Cumulative Effects)
Reference Habitat Suitability
Current Habitat Suitability
Habitat Suitability(forested regions)
American Redstart
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Species Models >900 species
VegetationHuman Footprint
Perennial Sow Thistle(not native to Alberta)
ChangeFrom Reference
(Intactness / Cumulative Effects)
Predicted Current
ConditionsReference Condition
Was Not Present
Historically
Detections
White Zone
Green Zone
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Integration Among Species
Sow Thistle
Moose
American Redstart
Convert each pixel to a common scale100 = abundance equal to reference
…….0 = abundance very different than reference
(either decrease or increase)
then Average across species
Biodiversity Intactness
(birds, mammals, plants, mosses, lichens, mites)
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Response to Disturbance(Green Zone)
Brown Creeper
23 Species ↑ >100%Shrubby, urban & open-country birds
eg. Orange-crowned Warbler Alder Flycatcher
% P
opul
atio
n Ch
ange
All Birds
% P
opul
atio
n Ch
ange
25 Species ↓ >50%Top 10• Black-throated Green Warbler• Bay-breasted Warbler• Cap-may Warbler• Golden-crowned Kinglet• Western Tanager• Winter Wren• Brown Creeper• Canada Warbler• Boreal Chickadee• Red-breasted Nuthatch• Ovenbird
All Plants
% P
opul
atio
n Ch
ange
98 Species ↓ >50%Top 10• Twayblade• Peppergrass• Speedwell• Smartweed• Buttercup• White Gernaium• Anemone• Crowberry• Hedge Nettle• Fruited Sedge• Bog Orchid
10 Species ↑ >200%Mainly grasses & sedges
including non-native species
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The Ultimate Goal
Challenges
• Waiting for significant trend in a population risks changes we do not want to see happen
• So we increasingly use models to predict the status of wildlife populations and link to survey data to see if things are as we expect them to be
• BUT waiting for newest habitat and footprint layers means there is a delay in predictions about state of biodiversity
• Working towards real time monitoring of state of environment and biodiversity via deep learning & artificial intelligence
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Predictive Land Cover Monitoring System
• Near-real time monitoring
• Predictive mapping and modeling
• Dynamic system to account for natural variability vs. human-driven landscape changes
Geospatial Product Innovation
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Sample-based (3x7 km) scale: Vegetation/Land Cover mapping (1:500/1,500)
ABMI Geospatial DataLand Cover Data and Product
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• Internal QA/QC process
• External audit process• Used for validation of other Land Cover
products, e.g., newly developed Probability of Wetland dataset
• Very detailed & attribute rich data:• 9 moisture classes• 21 non-vegetation classes• 8 management classes• 28 modified wetland classes• 22 tree species
Sample-based (3x7 km) scale: land cover mapping at 1:500/1,500 scale
ABMI Geospatial DataLand Cover Data and Product
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Geospatial Product Innovation
Landcover monitoring: continuous, integrated system for predictive mapping of landcover and vegetation in Alberta
• Topographic informationo AB-wide DEMo Catchment boundaries
• Hydrographic informationo Current surface watero Hydro-temporal variarion (hydroperiod)
• Landcover informationo Probability of wet/dry o Probability of Fen cover
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Predictive Landcover system for Biodiversity Monitoring
AB-wide DEM from ALOS (Japan Space Agency) circa 2011
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TandemX DEM data (German Space Agency collaboration)
LiDAR-based DEM (GOA collaboration)
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Predictive Landcover system for Biodiversity MonitoringCatchment delineation for ABMI’s wetlands
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Pre-processing: filling the sinks/depressions and breaching
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Predictive Landcover system for Biodiversity MonitoringCurrent surface water in LAR
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Predictive Landcover system for Biodiversity MonitoringHydro-temporal variation (hydroperiod): % of time each pixel is water
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• Tracks if a lake/waterbody is permanent or recurring• Uses pixel stack – 104 billion pixels across time/space• DeLancey E.R., Kariyeva J., Cranston J., Brisco B. 2017. “Monitoring hydro temporal variability in Alberta,
Canada with multi-temporal Sentinel-1 SAR data. Canadian Journal of Remote Sensing. In review.
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Geospatial Product Innovation
Probability of Wet Area in LAR Probability of Fen in LAR
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Probability of Wet/Dry in LAR
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Geospatial Product Innovation
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Geospatial Product Innovation
Probability of Wet Area
Probability of Fen
Predictive Land Cover in LAR
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Future species models
Why try and guess what species like?
• A lot of time and effort goes into developing layers that describe whether something is a fen, a bog, etc. We then put these into wildlife models and use a lot of arbitrary human decision to see what is predictive.
• An alternative approach we are currently working on is image processing using neural networks. Here we provide raw images of the landscape and let the artificial intelligence know where the species were and were not found. Results on a few species show better predictive accuracy than traditional modelling approaches
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AI and autonomous monitoring
www.wildtrax.ca
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Thanks for listening