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ASSESSING NON-TIMBER VALUES USING LIDAR AND ADVANCED REMOTE SENSING DATA
• Richard Fournier, Université de Sherbrooke (UdeS)
• Catherine Frizzle (UdeS), A framework to map ecosystem services with airborne
lidar
• Sam Herniman (UBC), Avian habitat suitability in Newfoundland
• Chris Mulverhill (UBC), Forest structure : stem size distribution
• Kaysandra Waldron, Alexis Achim (Ulaval), predict stand structure and wood quality
in boreal forests with a long fire cycle
• Chris Bater (Gov. of Alberta)
eBee (leaf on)
Riegl Vux-1 LR (leaf on)
Velodyne HD 32 E (leaf off)
Slice of 10 x 100 m 22
© Bastien Vandendaele
Potential impacts on ecosystem services
• Forest loss cover •Water cycle disturbance
• Soil disturbance • Biodiversity loss
• Landscape fragmentation
➢ Forest industry requires significant wood resources
➢ Resulting in forest activities
Need to map ecosystem servicesIn relation with the evolution of the forest ecosystems
InfrastructureLoggingHarvest
Context Objectives Method Preliminary results Future work
Ecosystem ServicesDefinition
“ Benefits that humans
obtain from ecosystems
directly or indirectly ”
(Millennium Ecosystem
Assessment report, 2005)
© metrovancouver,org
Services = Benefits
Ecosystem Services (ES)Mapping
Causal
relationshipModelling
(regression or physical
model)
Expert knowledge
Increase needs of data,
human resources and time
Increase the
quality of ES
mapping
❑ Main approaches to quantify and map ES
CONTRIBUTION OF LIDAR DERIVED INDICATORS FOR HYDROLOGICAL ECOSYSTEM SERVICES MAPPING TO SUPPORT SUSTAINABLE FOREST MANAGEMENT
Main objective :
Develop ecological function indicators (FI) from LiDAR to support
sustainable forest management
Secondary objectives :
1) Derive function indicators to map erosion regulation ecosystem
serivces (ES) and water regulation ES from LiDAR
2) Determine if trade-off between wood supply ES, erosion regulation ES
and water regulation ES can be analyzed with LiDAR derived FI
Context : Actual certification principles require adherence to
practices that protect water quality and encourage compliance
with local water regulations.
Catherine Frizzle, Richard Fournier, Mélanie Trudel, Aurélie Schmidt
Université de Sherbrooke
7
Study area:Harry’s river watershed640 km2
Schmidt et al., 2019 (submitted)
HYDROLOGICAL PROCESSES AND ECOSYSTEM SERVICES
8(Brauman et al, 2007, De Groot et al. 2010)
Ecosystems
Biophysical
structure or
processus
e.g. Forest
cover help
reduce runoff
Function
e.g. catch
sediments
Service
e.g. Erosion
regulation
Selected ES :
Erosion regulation ES
Water regulation ES
HYDROLOGICAL MODELLING
9
No Harvest
Harvest
Mapping units :
31subwatershed
(between 10 and 50 km2)
HYDROLOGICAL MODELLING
10
With 10% and 20% harvest scenariosWith past harvest scenario
Variables affecting sediment yield (ongoing tests)
- Harvesting
- Precipications
- Slopes
- Filter strips
Class subwatersheds based on their capacity
to supply the ecosystem service
Very good = very good capacity
to supply the ES (Low sediment yield)
LIDAR FUNCTION INDICATORS
11
P95 of height (m)
Use of SWAT to define the
Function indicator :
- Adjust % of harvest
- Find the height threshold for
forest metrics
Capacity to supply ES
SEDIMENT CONTROL SERVICE SUPPLY INDEX
Schmidt et al., 2019 (submitted)
CONTRIBUTION OF LIDAR FUNCTION INDICATORS
13
LiDAR derived Function
indicators related to SWAT
hydrological modeling :
Forest Cover
Filter strips
Slopes
Wetlands
Contribution of LiDAR for
Function indicators that
cannot be modeled in SWAT:
Wood production
Roads/river crossing
Trafficability
Potentiel LiDAR Function
indicators for other Ecosystem services:
Wildlife habitat
Carbon accounting
Aesthetic values
RESEARCH OUTCOMES
Considerations for sustainable forest management
1) This methodology allows forest companies to limit their impact on
hydrological resources and ecological functions from knowledge
acquisition
2) This will lead to an enhanced capacity to take appropriate management
decisions based on trade-off analysis
3) Finally, it will allow companies to better communicate their environmental
reporting both to general public and certification auditors.
14
Avian habitat suitability in Newfoundland
23 October 2019
Sam Herniman
Gray Jay by Lissa Ann Photography CC BY-NC-SA 2.0
Question:
Which structural characteristics of Newfoundland forests have the greatest influence on the presence of birds?
Olive sided flycatcher by Armado Demesa – Public Domain
Study area
Study area
1. We chose a few uncorrelated variables from different methods land managers use to measure forests:
Field plots, ALS, spectral, terrain, climatic
Methods
Group Metric
Ground plot
Basal area
Elevation
Percent hardwood
Canopy cover
Mean annual precipitation (MAP)
Airborne laser scanning (ALS)
Kurtosis of the height distribution
Skewness of the height distribution
Standard deviation of the height distribution
ALS derived understory
ALS derived canopy cover
90th percentile
20th percentile
Maximum height
Spectral
Tasseled cap transformation for water
Normalized difference vegetation index (NDVI)
Normalized burn ratio (NBR)
Spectral + topographic, landscape, and climatic
Variables from spectral plus:
Compound topographic index
Distance to forest edge
Solar-radiation aspect index (TRASP)
Combined spatial layers All spatial layers
Methods
2. Best subset regression on presence-absence data with a logit link
3. Predict presence on all plots and remove unsuitable plots
4. Best subset regression on abundance data with an identity link
Methods
Outcomes
Outcomes
Measurements from in-situ field plots often outperform remote sensing metrics from a single sensor
however, multi-sensor models (ALS + multispectral) consistently outperform field plots.
Measurements from in-situ field plots often outperform remote sensing metrics from a single sensor
however, multi-sensor models (ALS + multispectral) consistently outperform field plots.
Outcomes
Outcomes
Black-throated green warblerPresence-absence
Black-throated Green Warbler By Dan Pancamo CC BY-SA 2.0 BTNW ~ elevation + normalized burn ratio
Outcomes
AbundancePresence-absence
BTNW ~ elevation + normalized burn ratio BTNW ~ distance to forest edge + kurtosis
Forest Structure
• “The physical and temporal distribution of trees and other plants in a stand” (Oliver and Larson, 1996)
• Importance:• Habitat
• Resilience
• Productivity
Chris Mulverhill (UBC)
Measuring Structure
How is it measured?
• Indices• Variability• Gini coefficient
• Stem Size Distributions (SSD)• DBH• Height• Volume
Study Area
• Boreal mixedwood forest in northern Alberta, Canada
• High rates of disturbance – in 50 years…• 21% of area had stand-replacing fires• 3% had clearcut harvesting
• Dominant species:• Trembling aspen (Populus
tremuloides)• White spruce (Picea glauca)• Lodgepole pine (Pinus contorta
latifolia)• Black spruce (Picea glauca)
Predicting Stem Size Distributions
Using an area-based approach (Mulverhill et al. 2018):
1. Determine parameters for SSD on sample plots
2. Predict parameters with Airborne Laser Scanning (ALS) metrics
3. Apply across study area
Results
Structural Recovery Following Disturbance
If we know the SSD of a previously disturbed area, we can evaluate that area’s ability to regenerate following a disturbance
Structural Recovery Following Disturbance
We used over 7000 stand-replacing disturbances representing over 50 years of disturbance (Mulverhill et al. 2019).
Conclusions
• Structure is an important and robust measure of forest values
• Penetrative ability of ALS allows it to provide below-canopy variability
• With this, we can predict SSD across any area of interest
• Applying the predictions can tell us about, for example, the area’s ability to recover following a stand-replacing disturbance
References
Mulverhill, C., Coops, N., White, J., Tompalski, P., Marshall, P. and Bailey, T., 2018. Enhancing the estimation of stem-size distributions for unimodal and bimodal stands in a boreal mixedwood forest with airborne laser scanning data. Forests, 9(2), p.95.
Mulverhill, C., Coops, N.C., White, J.C., Tompalski, P. and Marshall, P.L., 2019. Structural development following stand-replacing disturbance in a boreal mixedwood forest. Forest Ecology and Management, 453, p.117586.
Oliver, Chadwick Dearing, Larson, Bruce C, 1996. Forest Stand Dynamics: Updated Edition. John Wiley and sons.
Using LiDAR metrics to predict stand structure and wood quality in boreal forests with a long fire cycle Kaysandra Waldron, Jean-Romain Roussel, Doug Bolton and Alexis Achim
Research question:
What is the potential of LiDAR metrics to assess stand structural characteristics and time since the last fire of black spruce stands in the eastern boreal forest of Canada?
LiDAR point clouds: a section of the trans-Canadian transect provided by Canadian Forest Service (Mike Wulder). Crossing over 20 fire polygons
Fires from 1790 to 1980 with sizes ranging from 500 to around 2000 ha
SD of tree height Max height – Mean height
86
130
178
190
220
TSLF (yrs)
30
55
220
TSLF
TSLF
Summary
▪ LiDAR metrics have the potential to predict stand evolution after fire in the eastern boreal forest before they reach the old-growth stage
▪We did not find yet how to distinguish all the succession stages
▪Metric values decreased at year 125 after fire -> before that, regular stand structure
41
Needs and pressures
Risk management
Limited human resources
Transparency and accountability
Clean water
Biodiversity
Wildlife habitat
Auditing
Certification
Inventory
Growth and yield
Forest health
Reforestation
Forest fuels…….
Operational tools for decision support
Wet areas mapping
Netmap and erosion modelling
Mountain pine beetle mortality classification
Enhanced forest inventory
Synoptic snow cover mapping
Land cover and fuel classification
Derived ecosite phase
Research and development
Reforestation success
Improved mountain pine beetle stand susceptibility models
Monitoring disturbance withsatellite imagery
Spring burn window prediction
Multispectral lidar for Fire Smart assessment
Habitat complexity and biodiversity
Site index from lidar-derived metrics
Adopting lidar – consider the four P’s
Platform
Provider
Protocols
Processing
Questions ?
© Bastien Vandendaele