from footprint to ferns: alberta biodiversity monitoring ...€¦ · biodiversity monitoring...

It’s Our Nature to KnowAlberta Biodiversity Monitoring Institute

From footprint to ferns: Alberta Biodiversity Monitoring Institute

supporting forestry planning



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


Taxa Surveyed

• Data collection

• Lab processing

• Data verification & storage

• Analyses

• Reporting

BirdsMammalsVascular Plants Mosses LichensMitesAquatic Vascular PlantsAquatic Invertebrates

http://www.google.ca/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRw&url=http://pinkka.helsinki.fi/virtuaalikasvio/plant.php?id%3D15789%26haku%3D%26page%3D1%26global%3D%26bundle%3D32275%26pcat%3D2&ei=WE60VI7-L5TkoATg0IDgAg&bvm=bv.83339334,d.cGU&psig=AFQjCNF28JXlragZtpZZgQ1XZ3xKp0eMlg&ust=1421189055353021

It’s Our Nature to KnowAlberta Biodiversity Monitoring Institute 4

• 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


Data collation partnerships

BBSn=7538

BAMn = 18312

ABMIn=9367 EVERYBODY!


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



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.


Human Footprint: key driver of biodiversity change


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 [%]


AHFMP: Well sites enhancements



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


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


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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http://www.google.ca/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwiFwO_LzdLWAhVD1mMKHTM_DYMQjRwIBw&url=http://www.arizonacrop.org/weeds/P.sowthistle.html&psig=AFQjCNH5i5Gukhs0TOQAM5jCsfkr1E6r-g&ust=1507056567301816


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)


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


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


Sample-based (3x7 km) scale: Vegetation/Land Cover mapping (1:500/1,500)

ABMI Geospatial DataLand Cover Data and Product


• 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



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


Predictive Landcover system for Biodiversity Monitoring

AB-wide DEM from ALOS (Japan Space Agency) circa 2011

20

TandemX DEM data (German Space Agency collaboration)

LiDAR-based DEM (GOA collaboration)


Predictive Landcover system for Biodiversity MonitoringCatchment delineation for ABMI’s wetlands

21

Pre-processing: filling the sinks/depressions and breaching


Predictive Landcover system for Biodiversity MonitoringCurrent surface water in LAR

22


Predictive Landcover system for Biodiversity MonitoringHydro-temporal variation (hydroperiod): % of time each pixel is water

23

• 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.



Probability of Wet Area in LAR Probability of Fen in LAR


Probability of Wet/Dry in LAR

25




Probability of Wet Area

Probability of Fen

Predictive Land Cover in LAR


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


AI and autonomous monitoring

www.wildtrax.ca


Thanks for listening

from footprint to ferns: alberta biodiversity monitoring ...€¦ · biodiversity monitoring...

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