ASSESSING NON-TIMBER VALUES USING LIDAR AND ADVANCED REMOTE SENSING … · 2019-10-23 · ASSESSING...

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

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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.

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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

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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