gfw partner meeting 2017 -parallel discussions 3: the next frontier of forest monitoring

GFW PARTNERSHIP MEETINGWASHINGTON, DC | FEBRUARY 8TH & 9TH

PHOTO: CIFOR #GFWPartners17

#GFWPartners17Presenters: Mikaela Weisse, Doug Muchoney, Sasha Tyukavina, Joe Mascaro and Johannes Reiche

PARALLEL DISCUSSIONS 3:THE NEXT FRONTIER OF FOREST MONITORING

The Next Frontier ofForest Monitoring

GFW Partnership MeetingFebruary 9, 2017

Mikaela Weisse, Research Analyst

GFWs Vision

Global Forest Watch uses cutting edge technology and science to provide the timeliest and most precise information about the status of forest landscapes worldwide. and most

useful!

Forest Monitoring Systems

PRODES DETER

Terra-I

Global Forest Change/ Hansen

FORMA

GLAD alerts

Update frequency

Cove

rage

Coun

try-

leve

lG

loba

l

Annual Weekly

?

Limitations

Too coarse to find on the ground Cloud cover limits frequency Accuracy is unknown in my area of interest Doesnt define forest, only tree cover Treats all tree cover as equal Treats all tree cover loss as equal Cant detect forest degradation

Where do we go from here?

Tech providers: What is the cutting edge in

forest monitoring? What is feasible?

Users: What advances in forest

monitoring would be most beneficial in your work?

Agenda

Lightning updates from partners: new frontiers of forest monitoring Defining forest vs tree cover Doug Muchoney, FAO Differentiating types of forest and forest disturbance - Sasha Tyukavina, UMD Increasing spatial resolution Joe Mascaro, Planet Increasing temporal frequency - Johannes Reiche, Wageningen University

User priorities: sticky note exercise Karimah Hudda, Mondelez Morgan Erickson-Davis, Mongabay

Discussion Wrap-up

Defining forest vs tree cover Global Forest

Resources Assessment (FRA)

Douglas Muchoney Chief, FAO, Forest Policy and Resources Division

Global Forest Resources Assessment (FRA)

Definitions

FAO-FRA: Forest: Land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use.

Source: FRA 2015 Terms and Definitions

University of Maryland (UMD)/World Resources Institute (WRI):Tree cover: Trees are defined as woody vegetation taller than 5m in height and are expressed as a percentage per output grid cell as 2000 Percent Tree Cover. Tree Cover Loss is defined as a stand-replacement disturbance, or a change from a treed to non-treed state, during the period 20002014. Tree Cover Gain is defined as the inverse of loss, or a non-treed to treed change entirely within the period 20002012.

Source: University of Maryland

Forest land vs tree cover (1/2)

http://www.fao.org/docrep/017/ap862e/ap862e00.pdfhttp://earthenginepartners.appspot.com/science-2013-global-forest


Forest land vs tree cover (2/2)

The FRA reports forest land use area, and the data reported are official statistics submitted to FAO by the countries. Data collection is based on a combination of methods including national forest inventories, remote sensing, aggregated local-level reporting and expert opinion. The last FRA reports data from 1990 to 2015 and allows calculation of net forest area change between different reporting years.

UMD/WRI data report remote sensing-based estimates on annual tree cover loss, during the period 20002014, which can be disaggregated according to tree cover density classes. It also reports tree cover gain within the period 20002012, but does not recommend aggregating loss and gain to calculate net change.


Forest land use approach: each of these stages is considered forest

Tree cover approach: only young secondary or older is considered as tree cover

Forest use vs tree cover


The forest or the trees?IS THIS FOREST (YES =) ?

Tree cover... FRAOther sources,

Remote Sensing only

...in agricultural production systems (oil palm plantations, coffee plantations, etc.) X ...on land that is predominantly under agricultural or urban land use X ...temporarily removed as part of a forest management scheme X...temporarily lost through natural disturbances

XNewly established forest X


The Global Forest Resources Assessment (FRA)has reported on status and trends on global forest resources for national and international decision-makers and the public at large since 1948.

First report: 1948 Responsible of the FRA Programme: FAO Forestry Department Frequency of the most recent reports: Every 5 years Website: http://www.fao.org/forest-resources-assessment/

http://www.fao.org/forest-resources-assessment/


FRA goes beyond forest area estimates

7 Thematic Elements of SustainableForest Management of FRA

1) Extent of forest resources

2) Biological diversity

3) Forest health and vitality

4) Protective functions of forest resources

5) Productive functions of forest resources

6) Socio-economic functions

7) Institutional and legal framework

110+ variables 234 countries FRA 2015 time series

covers the last 25 years


Data sources and partners

Country Reports

- Governments through National Correspondents- International and regional organizations and processes through the

Collaborative Forest Resources Questionnaire (CFRQ) initiative:- Central African Forest, Commission (COMIFAC/OFAC)- FAO Forestry (FRA)- FOREST EUROPE- International Tropical Timber Organization (ITTO)- Montral Process- United Nations Economic Commission for Europe (UNECE)

Remote Sensing

FAO with:- Joint Research Centre of European Commission- Regional partners- National focal points and specialists from the countries

Global Forest Resources Assessment (FRA) 18

Towards FRA 2020 - tools


Towards FRA 2020 - tools


SEPAL Towards FRA 2020 - tools


Towards FRA 2020 reporting, review, analysis and dissemination

INTERACTIVE DATA ENTRY

ON-THE-FLY LOGICAL CHECKS

NAT

ION

AL D

ATA

REVIEW AND COMMUNICATION

REPORTING

HARDCOPY

ONLINE

MAPS

DB LINKS

REPORTS ON

REQUESTNATIONAL DATA

REPOSITORY


NATIONAL DATA

RS FIRES

RS TREE COVER

PROTECTED AREAS

ECOZONES

ADMINNFI MAPS

FREE TOOLS

ANALYSISACCESS

UNFCCC

SDG

REGIONAL REPORTING PROCESSES

FAOSTATINCREASED CONSISTENCYREDUCED REPORTING BURDEN

IMPROVED QUALITYRELEVANCE

Towards FRA 2020 reporting, review, analysis and dissemination


Thank You

Alexandra (Sasha) Tyukavina, Matthew Hansen, Peter Potapov, Svetlana Turubanova, Alexander Krylov, Marc Steininger, Belinda Margono

The 4th annual Global Forest Watch Partnership MeetingWashington D.C., February 8-9 2017

Differentiating types of forest and forest disturbance

Background

Tree cover and loss products dont differentiate between the types of forest and forest disturbance;

Value-added analysis is needed to adequately interpret tree cover loss products for carbon accounting and other purposes;

There are no standard classifications of forest and disturbance types detectable via remote sensing.

Forests: by management type

Natural (unmanaged) Human-managed

Natural forest loss in Mato Grosso, B il

Plantation clearing, Parana, Brazil

Primary forests Mature secondary forests Natural woodlands

Forest plantations Agroforestry systems Secondary regrowth in shifting agriculture

Why this matters: 45% of gross 2000-2012 tropical forest loss and 42% of gross aboveground carbon (AGC) loss comes from human-managed forests (Tyukavina et al. 2015)

Forests: by management type

Primary intact Primary degradedUndisturbed and unfragmented mature natural

forests retaining natural structure and composition

Fragmented or subjected to forest utilization (e.g. selective logging) that have led to partial canopy

loss and altered forest structure and composition Intact Forest Landscapes (Potapov et al. 2017), Hinterland forests (Tyukavina et al. 2016)

Selective logging in primary forest, Brazil

2006

Forests: by degree of degradation

Why this matters: Fragmentation edge effects and selective logging increase humid tropical forest susceptibility to fire (Cochrane 2003; Cochrane & Laurance 2008)


Fire-degraded primary forest fragment in So Flix do Araguaia municipality in the state of Mato Grosso, Brazil

Photo from INPE Fototecahttp://www.obt.inpe.br/fototeca/fototeca.html

Why this matters: 98% of primary forest loss in Indonesia occurs within primary degraded forests (Margono et al. 2015) -> logging often precedes conversion


Stand-replacement(forest clearing)

Non stand-replacement(forest degradation)

Forest fire, Brazil

Forest disturbance types

Why this matters: In 2013 in Brazilian Legal Amazon clearing of primary forests accounted only for 47% of forest disturbance area (Tyukavina et al., in review), compared with 70% in 2003.

Forest disturbance types

Post-deforestation land-use

Forest

Palm estates

Tree plantations

Small-holder land use

Mining

Road

Settlement

Other

Forest land

Grassland

Cropland

Wetlands

Settlements

Other

IPCC Land Use classes Custom classifications(national- or regional-scale)

Example: Indonesia

Yucatan, Mexico

Chaco, Argentina

Post-deforestation land-use

Conclusions

Differentiating types of forest and forest disturbance allows to place tree cover loss stats into a more specific context;

Non-primary forest loss is an increasingly large proportion of the tree cover and carbon dynamics;

There is an ongoing effort to map forest types (e.g. primary vs. non-primary forests) and forest disturbance types (e.g. fire vs. non-fire forest loss);

Sampling can be used to supplement wall-to-wall tree cover and loss mapping to identify types of forest, forest disturbance and post-deforestation land-uses, as well as to assess accuracy of forest and disturbance type maps.

Thank you for your attention!

If you could choose one improvement to forest monitoring, what would it be?

Uluru, Australia, DEC 2, 2015

Joe MascaroFebruary 9, 2017

Daily Monitoring of the Land Surface of the Earth

Planet Labs Proprietary & Confidential39

The Planet ExplorerNoor solar facility in Morocco:

Planet imagery is tracking its construction nearly each day, with recent looks Dec 15, 14, 13, 10, 9, 8

InfrastructureNoor solar facility in Morocco

DeforestationSugarcane

expansion into primary forest

over just 40 days

Speciesdiversity and demography

Flowering trees, Colombian

Amazon

Pre- and Post-disaster data for

emergency response

HumanitarianEmergencies

https://www.planet.com/disasterdata/

NASALANDSAT8

PLANETDOVE

https://www.planet.com/markets/ambassador-signup/

Planets Ambassadors Program

Some of Our Ambassadors:Andreas Kb and Bas Altena University of Oslo

Greg Asner Carnegie Airborne Observatory

Ulyana Horodyskyj Science in the Wild

Matt McCabe King Abdullah University of Saudi Arabia

Doug Edmonds and Sam Roy Indiana University

Matt Finer Amazon Conservation Associtaion

Jan 2016

Jul 2016

Sep 2016

Landsat Thermal Enhancement for High-res Soil Temperature

Matt McCabe

Forest MonitoringGlobal

AccurateTimely

Dimensions of Accuracy

1. Amount of forest cover change

2. Amount of forest carbon change

3. Type of change (forest loss, deforestation, degradation, disturbance)

4. Cause of change (mining, logging, selective logging, agriculture, etc.)

Deforestation

Disturbance

Degradation

Forest loss

Degradation

GLAD Alerts (UMD)

Diagnosis with Planet

Explorer

Forest MonitoringGlobal

AccurateTimely

Illegal gold mining in Peru: Planet imagery resulted in news coverage and later intervention to destroy illegal mining equipment.

In the Peruvian Amazon rainforest, an illegal gold mine encroaches into the TambopataNational Reserve.

Matt Finer

Johannes Reiche

Wageningen University & Research, The Netherlands

Credits to: Martin Herold, Jan Verbesselt, Eliakim Hamunyela, Dirk Hoekman

Next Frontier of Forest MonitoringIncreasing temporal frequency

Temporal frequencyAnnual Monthly

Why increasing temporal frequency?

30 m

Landsat (GLAD)

Weekly

Near real-time capacity

dense cloud cover sparse

Landsat observations for Peru, 2014 (Hansen et al, 2016)

Potential observations Cloud free observations


How to increase the temporal frequency?

30 m

Landsat (GLAD)

Weekly



3

Optical and RADAR satellites

BOLD Free data access (G) Global acquisition strategy

Landsat

+ 40 years of data & open archive

+ Ready-to-use data

Limited RADAR capacity in the past

- Commercial data distribution- Fragmented archives


Multi-sensor optical approaches?

30 mLandsat + S2

Landsat (GLAD)

Weekly



+

The game changer Sentinel-1


Sentinel-1

+ First time dense RADAR time series for the tropics

+ Open data access

Sentinel-1 global acquisition strategy (from 10/2016)

https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario

2015-09-07 0 10 km

Sentinel-1 data for Santa Cruz, Bolivia

2014-10-18

0 5 km

2015-09-07

0 5 km

Logging roads

Logging patterns

2015-09-07

0 5 km

77

S1

VV [

dB]

VV [

dB]

Sentinel-1 time series example


Sentinel-1 approaches

30 mLandsat + S2

Landsat (GLAD)

Weekly


S1


+

24 revisit 12 (days)


Multi-sensor approaches

30 mLandsat + S2

Landsat (GLAD)

Weekly


+

S1

Level of operationalisation


+


Landsat + S1

Santa Cruz, Bolivia

Tropical dry forests (10.000 km)

Industrial logging

Data

Landsat

Sentinel-1

ALOS-2 PALSAR-2

Methods

Probabilistic approach for time series combination and NRT change detection (Reiche et al., 2015)

Multi-sensor near real-time deforestation monitoring(Reiche et al., under review)

Time series example

Landsat Sentinel-1 PALSAR-2

Temporal accuracy (Mean time lag of detected changes)

Landsat = 60 days

Sentinel-1 = 27 days

Multi-sensor = 19 days

Detected deforestation (10/2015 - 09/2016)


Opportunities and key challenges

30 mLandsat + S2

Landsat (GLAD)

Weekly


Landsat + S1

+

S1

Level of operationalisation



+ Near real-time (24 | 12 days guaranteed obs. )

+ Deforestation, Degradation (?)

- RADAR pre-processing- Image co-registration

Increasing free RADAR data stream


More observations make everything better!

Curtis Woodcock (Boston University), 2016

GFW Partnership MeetingWashington, DC | February 8th & 9thParallel discussions 3:the next frontier of forest monitoringThe Next Frontier ofForest MonitoringGFWs VisionForest Monitoring SystemsSlide Number 6LimitationsWhere do we go from here?AgendaSlide Number 10Slide Number 11Forest land vs tree cover (2/2)Slide Number 13The forest or the trees?Slide Number 15FRA goes beyond forest area estimates Data sources and partnersTowards FRA 2020 - toolsSlide Number 19SEPAL Towards FRA 2020 reporting, review, analysis and dissemination Towards FRA 2020 reporting, review, analysis and dissemination Slide Number 23Slide Number 24Slide Number 25Slide Number 26Slide Number 27Slide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32Slide Number 33Slide Number 34Slide Number 35Slide Number 36If you could choose one improvement to forest monitoring, what would it be?Daily Monitoring of the Land Surface of the EarthSlide Number 39Slide Number 40Slide Number 41Slide Number 42Slide Number 43HumanitarianEmergenciesSlide Number 45Slide Number 46Slide Number 47Slide Number 48Planets Ambassadors ProgramSome of Our Ambassadors:Slide Number 51Slide Number 52Slide Number 53Landsat Thermal Enhancement for High-res Soil TemperatureLandsat Thermal Enhancement for High-res Soil TemperatureForest MonitoringSlide Number 57Forest MonitoringSlide Number 59Slide Number 60Slide Number 61Slide Number 62Slide Number 63Forest MonitoringSlide Number 65Slide Number 66Slide Number 67Slide Number 68Why increasing temporal frequency?Landsat observations for Peru, 2014 (Hansen et al, 2016)How to increase the temporal frequency?Slide Number 72Multi-sensor optical approaches?Slide Number 74Sentinel-1 global acquisition strategy (from 10/2016)Sentinel-1 data for Santa Cruz, BoliviaSlide Number 77Sentinel-1 approachesMulti-sensor approachesMulti-sensor near real-time deforestation monitoring(Reiche et al., under review)Time series exampleTime series exampleSlide Number 83Opportunities and key challengesSlide Number 85Slide Number 86

gfw partner meeting 2017 -parallel discussions 3: the next frontier of forest monitoring

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