remote sensing of canada’s forest properties · landsat-based 30-m disturbance mapping 1984-2015...

© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017

Remote Sensing of Canada’s Forest Properties:

An Invaluable Toolbox to Answer Key Forest Science Questions

CIF E-Lecture, February 20th 2019

André Beaudoin, NRCan/CFS/LFC


Outline

• Introduction

• National forest attribute maps at 250 m resolution:

– Initial & Improved maps

– Applications

• Regional/national attribute maps at 30 m resolution:– Forest Inventory in Northwestern Canada

– Multi-source National Biomass Map of Boreal Forests

• Conclusion & Perspective


Acknowledgement of Many Contributors

• CFS/LFC: P. Bernier, L. Guindon, P. Villemaire, R. St-Amant, K. Powell, M. Marchand, J. Guo, C. Gignac; S. Gauthier, Y. Boulanger, D. Boucher, D. Paré, N. Mansuy, K. Sambaraju …

• CFS/NoFC: G. Castilla, R. Hall, R. Skakun, M. Filiatrault, M. Gartrell; D. Thompson, B. Simpson…

• U. Lethbridge: C. Hopkinson, C. Mahoney • Gov’t of NWT: L. Smith, K. Groenewegen• CFS/PFC/National Forest Inventory: G. Stinson, S. Magnussen,

A. Dyk


Programs and Support

• CFS: – Sustainable Forest Management program

– CFS Forest Change Initiative

• Government of NWT, MFFPQ

• Space agencies:

– Canadian Space Agency (GRIP program)

– Japanese Space Agency

• Global Forest Observation Initiative


Introduction: Context

• Key knowledge of Canada’s forests through National Forest Inventory (NFI)

– 2 x 2 km NFI photo-plots: 1% sample of Canada– Compilation of provincial inventories– Statistical inference of key attributes by ecozone

• Increased need for consistent and frequent wall-to-wall map products of forest properties across Canada for reporting and modelling purposes

• Multi-source/scale/temporal remote sensing (RS) data are needed to cover the remaining 99%

• Towards dynamic national forest mapping: Landcover/Disturbances/Forest attributes

5


Introduction: Spaceborne Remote Sensing

• Spaceborne remote sensing sources: increasingly available! • Criteria: spaceborne, free, archived & highly processed, multi-yeartime series,

covers all of Canada, moderate resolution (25 - 250 m) – Passive, Optical:

• MODIS, multi-spectral, 250 m, yearly summer and winter composite, 2001 - 2015 (NASA/CCMEO)

• Landsat 5/7/8, multi-spectral, 30 m, dense time series, 1986 - current (NASA/USGS/CFS)

– Active, Radar: • L-band PALSAR, dual-polarized backscatter (HH, HV), 25 m,

2007-2010; 2014-current (JAXA/CFS)

– Active, LiDAR: • Airborne LiDAR survey• Spaceborne ICESAT-GLAS (NASA)


Introduction: Active vs Passive RS

MODISLandsat

PALSAR ICESAT-GLAS


Introduction: RS Sources and Reference Sets

…

…

Selected reference sets over boreal forests


Introduction: Modelling Attributes from

RS and Ancillary Variables

• Simple to implement, yet robust and flexible

• Proven efficient in other RS-based large area/national forestinventories (Finland, Sweden, USA…)

• Non-parametric machine learning algorithms:

– k Nearest Neighbours (k-NN)

– Random Forests (RF)


Introduction: Method Workflow

Modelling:optimization/

validation

Reference sampling set

(cal/val)

RS features

Ancillaryfeatures

(topography, climate…)

Independent validation set

Attributemaps

Mapping

Validation


NFI photo-plot 1% sample

Maps of 123 NFI forest attributes: - 4 land cover classes (treed, non-treed…) - 11 structure (height, volume, biomass…) - 108 species compositions

2001 MODIS Imagery 250 m(CCMEO)

TopographyClimate

k-NNalgorithm

National 250 m maps: Initial 2001 V0 maps

2014: First national 2001 maps of forest attributes based on 250 m MODIS imagery and NFI photo-plots using k-NN interpolation

Beaudoin et al., 2014, CJFR


National 250 m maps: Initial V0 maps

• Limitations: – Resolution: moderate, still relevant!

– Accuracy: • Poor to good; under/over-estimation

• Poorest: – mountainous areas; taiga ecozones

– rare species; age

– 2001: outdated

– Static vs dynamic for tracking changes


Improved ref set from NFI photo-plot 1% sample

Maps of 93 NFI forest attributes: - 4 land cover classes (treed, non-treed…) - 11 structure (height, volume, biomass…) - 78 species compositions

2001-2011 MODIS 250 m(CCMEO)

TopographyClimate

Stratified/optimized

k-NNalgorithm

National 250 m maps: Improved V1 maps

2017: improved 2001 maps updated to 2011

Beaudoin et al., 2018, CJFR

2001

2011


• Inclusion of various ancillary datasets : – Tree cover*, 2000 (TC, %)

->updated to 2001 and 2011

– Four change classes, 2001-2011 & Fractional Change (FC, %):

• No-change*, **

• Cover loss**: Fire, Harvest

• Cover gain*: regrowth for disturbances prior to 2001

(** Guindon et al. 2014) (*Hansen et al. 2013)

National 250 m maps: Improved V1 maps


MODIS-based 250-m disturbance mapping 2000-2011

Fires

Forest harvesting

Flooding

Fires

Forest harvesting

Flooding

Guindon et al., 2014, CJFR


Landsat-based 30-m disturbance mapping 1984-2015 (CanLAD)

Guindon et al., 2018, Ecosphere


Landsat-based 30-m disturbance mapping 1984-2015 (CanLAD): exemples of cumulative map of fire and harvesting

Validation• Fires : NBAC 14.4 Mha = 85%• Harvesting: Provincial Inventory

(2.2 Mha)= 85% • Our Validation set = 91%


• Widely distributed through NFIS portal (V0) then Open Canada (V1);

One of key national geospatial products available for many applications

Guindon et al.

(2014) CJFR Beaudoin et al.

(2014) CJFR

Boulanger et al.

(2014) CJFR

NFI program

Annual disturbance

mapping

Forest

attributes

Regional fire risks

Led by PFC

National V0/V1 maps: Applications

Beaudoin et al.

(2017) CJFR

Guindon et al.

(2018)

Ecosphere


Reporting:

State of Canada’s Forests Annual Report

https://www.nrcan.gc.ca/forests/report/16496http://cfs.nrcan.gc.ca/publications?id=39333

20182016

https://nfi.nfis.org/img/knnDownloadImages/kNN_Genus_Dominant_2km_en.png

https://www.nrcan.gc.ca/forests/report/16496

http://cfs.nrcan.gc.ca/publications?id=39333

https://nfi.nfis.org/img/knnDownloadImages/kNN_Genus_Dominant_2km_en.png


Value-added products: FBP Fuel Grid

• FBP Fuel Grid: The Fire Behaviour Prediction (FBP) component of the Canadian Forest Fire Danger Rating System (CFFDRS) uses 16 benchmark fuel types to predict fire behaviour

http://cwfis.cfs.nrcan.gc.ca/downloads/fuels/development/National_Risk_Analysis_Fuels_Map/

ContactsBrian Simpson Canadian Forest Service / Northern Forestry Centre E-mail: [email protected]

Tom SwystunCanadian Forest Service / Great Lake Forestry CentreE-mail: [email protected]

http://cwfis.cfs.nrcan.gc.ca/downloads/fuels/development/National_Risk_Analysis_Fuels_Map/


Value-added products: Peatland Map

Contact

Daniel Thompson

Natural Resources Canada

Canadian Forest Service / Northern Forestry

Centre

[email protected]

Thompson et al., 2016, For. Ecol. Man.

Probability of presence of peatland (%)


Mountain pine beetle: Lodgepole pine Ash borer: Ash species

Value-added products: Host Species Maps of Key Pests

USFS maps: https://www.nrs.fs.fed.us/people/Wilson


Value-added products: North American Biomass Map


Proportion of productivity taken by fire + harvesting:

2011-2040

What is the vulnerability of timber supply under current

harvesting rate and current/future fire regime?

Gauthier et al., 2015, CJFR

harvest > Productivity - fire


What is the fire selectivity relative to cover types?

Bernier et al., 2016, Forests

Broadleaved

Mixed-Broadleaved

Mixed-Needleleaved

Needleleaved

Mature

Old

Young

Random

Preferred

Avoided

25


What is the fire risk according to

vegetation type?

Short

Medium

Long

Return interval

Bernier et al., 2016, Forests


Climate + vegetation

Climate

Boulanger et al., 2017, CJFR

What is the impact of vegetation reaction to fire on

projected fire behavior?

27

© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2017Mansuy et al. 2017

Biomass from

fires

Biomass from

harvest residues

Where do fires and harvesting provide

the most biomass for bioenergy?

28


What are the current and projected cumulative effects of disturbances (fire, harvest, drought, insects) on wood volume?

Boucher et al., 2018, Ecological Applications.


Can we track decadal attribute changes using 2001

and 2011 national maps?

unknown

• Integration of:

- Improved 2001 & 2011 kNN 250 m maps: AGB, TREED & NLS,

2001 to 2011, 30 m CanLAD change and Hansen cover loss maps:

% fire, harvest & unknown


Global change in needle-leaf proportion (NLS, %) Global change in treed proportion (TREED, %)

• Decadal change estimates summarized over 20 x 20 km units centered of NFI photo-plots

Global change in biomass (AGB, t/ha)

Can we track decadal attribute changes using 2001

and 2011 national maps?


∆𝐴𝐺𝐵 = 𝐺𝐴𝐺𝐵 − (𝐿𝐴𝐺𝐵𝑓𝑖𝑟𝑒+ 𝐿𝐴𝐺𝐵ℎ𝑎𝑟𝑣𝑒𝑠𝑡 + 𝐿𝐴𝐺𝐵𝑢𝑛𝑘𝑛𝑜𝑤𝑛) + 𝑒𝐴𝐺𝐵

• AGB global change with five modelled change components:

Global change in AGB DAGB (t/ha) Change driver: dominant change component

1.2 2001-2011 drivers of attribute changes across Canada

Beaudoin et al., 2019, in prep

What are the key drivers of

attribute changes across Canada ?


• Access to national map products along with peer-reviewed articles:

• Maps of Canada’s forest attributes: • Initial 2001 V0 maps:

https://nfi.nfis.org/en/maps• Beaudoin, A., Bernier, P.Y., Guindon, L., Villemaire, P., Guo, X.J., Stinson, G., Bergeron, T., Magnussen, S., and Hall,

R.J. 2014. Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery. Canadian Journal of Forest Research, 44(5): 521–532. doi:10.1139/cjfr-2013-0401.

• Improved 2001 and 2011 V1 maps:https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990.

• Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., and Guo, X. 2018. Tracking forest attributes across Canada between 2001 and 2011 using a k nearest neighbours mapping approach applied to MODIS imagery. Canadian Journal of Forest Research, 48(1): 85-93. doi:10.1139/cjfr-2017-0184.

• Disturbance maps (CanLAD): • 30-m resolution change detection and attribution (fire and harvest) product from 1984 onwards (CanLaD):

https://open.canada.ca/data/en/dataset/add1346b-f632-4eb9-a83d-a662b38655ad.• Guindon, L., Villemaire, P., St-Amant, R., Bernier, P.Y., Beaudoin, A., Caron, F., Bonucelli, M., and Dorion, H., 2017.

Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30 m resolution product of fire and harvest detection and attribution since 1984. doi:10.23687/add1346b-f632-4eb9-a83d-a662b38655ad.

https://nfi.nfis.org/en/maps

doi:10.1139/cjfr-2013-0401

https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990

doi:10.1139/cjfr-2017-0184

https://open.canada.ca/data/en/dataset/add1346b-f632-4eb9-a83d-a662b38655ad

doi:10.23687/add1346b-f632-4eb9-a83d-a662b38655ad


• Some of the peer-reviewed articles based on the use of national RS-based attribute maps and disturbance maps

• Boucher et al. 2018. Current and projected impacts of fire, drought and insects on timber volume across Canada. Ecological Applications. doi.org/10.1002/eap.1724.

• Boulanger et al. 2017. Changes in mean forest age in Canada’s forests could limit future increases in area burned but compromise potential harvestable conifer volumes. Canadian Journal of Forest Research 47(6): 755-764. doi:10.1139/cjfr-2016-0445.

• Mansuy et al. 2017. Estimating the spatial distribution and locating hotspots of forest biomass fromharvest residues and fire-damaged stands in Canad’s managed forests. Biomass and Bioenergy97:90:99. doi.org/10.1016/j.biombioe.2016.12.014.

• Boulanger, et al. 2016. Climate change impacts on forest landscapes along the Canadian southern boreal forest transition zone. Landscape Ecology, 32(7):1415-1431. doi:10.1007/s10980-016-0421-7.

• Bernier et al. 2016. Mapping local effects of forest properties on fire risk across Canada. Forests 7(8), 157. doi:10.3390/f7080157.

• Thompson et al. 2016. Using forest structure to predict the distribution of treed boreal peatlands in Canada. Forest Ecology and Management 372:19-27. doi:10.1016/j.foreco.2016.03.056.

• Gauthier et al. 2015. Vulnerability of timber supply to projected changes in fire regime in Canada’s managed forests. Canadian Journal of Forest Research 45(11):1439-1447. doi.org/10.1139/cjfr-2015-0079

https://doi.org/10.1002/eap.1724

doi:10.1139/cjfr-2016-0445

https://doi.org/10.1016/j.biombioe.2016.12.014

doi:10.1007/s10980-016-0421-7

doi:10.3390/f7080157

doi:10.1016/j.foreco.2016.03.056

https://doi.org/10.1139/cjfr-2015-0079


Satellite-based Forest Inventory in Northwestern Canada

* Excerpt from a conference presented at ForestSAT 2018, College Park MD, Oct 9

G Castilla1, R Hall1, A Beaudoin2, R Skakun1, M Filiatrault1, M Gartrell1, C Mahoney3, C Hopkinson3, P Villemaire2, L Guindon2, L Smith4, K Groenewegen4

1 Canadian Forest Service, Northern Forestry Centre2 Canadian Forest Service, Laurentian Forestry Centre3 University of Lethbridge, Department of Geography4 Government of Northwest Territories, Forest Management Division

Satellite-based Forest Inventory in Northwestern Canada*


IntroductionContext:

• Canada-wide Earth Observation for the Sustainable Development of forests (EOSD) land cover map c. 2000 was the only source of information for most Northwest Territories (NWT) forests.

• GNWT and CFS produced c.2007 EOSD in southern NWT, and wondered:Could an approach based on scaling field, multi-sensor remote sensing data and models be used to estimate forest structure as value-added products to the new EOSD map that could complement GNWT forest inventory (<10% of NWT forests)?

Objective:

• Estimate stand height, crown closure, stand volume, total volume, aboveground biomass (AGB), and age in 30 m cells deemed forested by the land cover map.

Approach:

• Integrate field, airborne laser scanning (ALS), ICESat’s Geoscience Laser Altimeter System (GLAS) data, optical (Landsat) and SAR (PALSAR) imagery, and models to estimate and map forest attributes.


Study Area


Amplitude (volts)

Ca

no

py

He

igh

t

Top of Canopy

Ground

Heig

ht (m

)

Total Biomass

Total Volume

Stand Volume

Crown Closure

Stand Age

Stand Height

Field data

EOSD

Modelling

ALS

GLAS

Mapping

GLAS

K-NNpredictors

IndependentALS

K-NN

Assessment

Conifer

Deciduous

Mixedwood

Wetland treed

Methods


Field models of volume and biomass

Use of field measured stand/Lorey height to estimate stand attributes follows approach of Saatchi et al. (2011) PNAS 108: 9899-9904.


Modeling approach:1. Airborne laser scanning data processed with Fusion

2. Fusion metrics related to stand attributes in 38 field plots

3. Attributes: Stand height, Lorey height, crown closure

NB: Stand height and Lorey height used for GLAS model calibration

ALS Stand Attribute Models

Variable Model Adj R2 RMSEStand ht (m) 0.53 + 0.96 P95 0.89 1.4 m

Lorey ht (m) 0.64 + 0.84 P95 0.89 1.2 m

Crown closure (%) 77.06 x Lz0.25 0.63 5%


ICESat - GLAS

Objective: to create a large set of surrogate forest inventory plots for k-NN FI mapping

GLAS: Geoscience Laser Altimeter System


GLAS Models using ALS

Model Adjusted R2 RMSE

Lorey ht (m) = 2.46 + 0.91 p85 0.89 1.1 m

Stand ht (m) = 2.05 + 1.12 p85 0.89 1.4 m

Crown closure (%) = 78.74 Lz0.26 0.55 6.4 %

N=43


4.4 k-NN mapping & 4.3 accuracy assessment

• K-NN Mapping C++ code (CFS/LFC) used to map the five attributes across C,D,M and AGB over treed wetland land cover classes

Crown closure (%) Stand height (m)

Stand volume (m3/ha)

Total volume (m3/ha)

AGB (t/ha)

k-NN mapping


1 35 1 100 1 500

Stand Height (m) Crown Closure (%) Total Volume (m3/ha)

Phase 1

Phase 2

Results


k-NN ValidationGoals• To provide confidence intervals for the mean pixel-wise bias and absolute error for each forest attribute:

• To assess the spatial distribution of errors along the Boreal Transect ALS

Materials• Available NFI ground plots (~80, measured in 2002-2006)

• Boreal Transect ALS metrics in forested 25 m cells

• Collocated (to all of the above) k-NN pixels

Methods• NFI forest attributes computed from raw ground data using same methods as MVI ground plots and plot coordinates used to sample the k-NN rasters

• k-NN crown closure was classified into NFI density classes and accuracy assessed using a confusion matrix

• FS ALS models applied to BT ALS and 25m cells centroids were used to sample k-NN raster

(Wulder et al. 2012. CJRS 38:600-618)


Bias and MAE (NFI-based) Results

Stand height (m)Total Volume

(m3/ha)Stand volume

(m3/ha)Total biomass

(t/ha)

∆ |∆| ∆ |∆| ∆ |∆| ∆ |∆|

N 40 40 40 40 40 40 46 46

Mean -2.18 4.14 7.75 69.77 4.37 44.09 11.25 35.36

Median -2.57 3.27 24.27 54.70 10.61 30.95 15.27 20.99

StDev 4.90 3.37 99.37 70.31 65.53 48.16 49.39 35.92

95%CI [-4, -0.7] [3, 5][-23, 39]

[48, 92]

[-16, 25]

[29, 59]

[-3, 26][25, 46]

95% CI for the estimate of CC Overall Accuracy:[43, 74]


Bias and MAE (BT-based) Results

All

BT K-NN ∆ |∆|

N108949

0108949

0108949

0108949

0

Min 2.51 4.00 -27.75 0.00

Max 34.96 32.00 24.80 27.75

Mean 11.54 11.50 -0.04 3.10

Median 9.46 10.00 0.01 2.13

Stand height


Stand height

BT ALS vs k-NN: Conifer

Crown closure

Land cover (CDMW)

< -5-5 - -3-3 - 00 - 33 - 5> 5

∆ Stand height (m)

< -10-10 - -5-5 - 00 - 55 - 10> 10

∆ Crown closure (%)

0 1 2 km


Stand height

Land cover(CDMW)

< -5-5 - -3-3 - 00 - 33 - 5> 5

∆ Stand height (m)

< -10-10 - -5-5 - 00 - 55 - 10> 10

∆ Crown closure (%)

0 1 2 km

Crown closure

BT ALS vs k-NN: Broadleaf


Conclusions

• Using a multi-scale, multi-source framework involving a limited number of field plots and a much larger number of GLAS surrogate plots, it was possible to estimate key forest attributes in each 30 m forested cell in an area the size of California.

• Despite the inherent uncertainties in scaling up from field and airborne to satellite LiDAR, the results attained demonstrate the viability of an inventory mapping approach over a northern boreal region where field plots are scant.

Mahoney et al. (2018) A Forest Attribute Mapping Framework: A Pilot Study in a Northern Boreal Forest, Northwest Territories, Canada. Remote Sensing 10(9), 1338.


Multi-Source National Biomass Map of Boreal Forests

• based on lessons learned in NWT:

– Improve national biomass maps:

• 30 m resolution

• For boreal forests (non-inventoried taiga ecozones)

• From optical-radar synergy

• From two candidate training sets (NFI, GLAS)


RS Imagery and Reference Sets


1- Developed approach for yearly mosaic (LFC)

• Yearly mosaic and

cloud shadow detection 1984-2017

2- Developed approach for Multiyear Mosaic for

imputation (LFC) (Seamless, Original scenes)

Landsat mosaic development

1984 – 2017


2009 2010

20082007

PALSAR Multi-Year Compositing

Beaudoin et al., in prep.


Method Workflow

RF modelling:optimization/

validation

Sampling set GLAS, NFI, QC

plots

RS features

Ancillaryfeatures

(topo, climate…)

Independent validation set:

QC plots

Attributemaps

Mapping

Validation

Canada 2010 PALSAR mosaic

Canada 2010 Landsat mosaic


Results

GLAS-based, Landsat + PALSAR NFI-based, Landsat + PALSAR


Map Comparison/Validation in QuebecGLAS-based NFI-based Plot-based

QC inventory plots: courtesy MFFPQ

Beaudoin et al., in prep.


Conclusion and Perspectives

• Improved national baseline forest information is needed in Canada(science, reporting, policy and management)

• Multi-source EO data offers the only viable means for such purposes (dynamic, transparent, systematic, repeatable, and spatially exhaustive)

• New and awaited CFS baseline map products have proven their utility for a variety of strategic uses

• NFI is key and enables value-added map products (amplification effect)

• Further improvements will provide attribute maps with higher spatial/temporal/estimation accuracy, moving towards better monitoring of Canada’s forests

remote sensing of canada’s forest properties · landsat-based 30-m disturbance mapping 1984-2015...

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