Monitoring forest biomass: an overview

Valerio Avitabile Laboratory of Geo-Information Science and Remote Sensing

Wageningen University

valerio.avitabile@wur.nl

Overview

Introduction

● What is Biomass

● Why is important

● How to quantify biomass

Monitoring systems

● Concepts

● Examples

Biomass mapping

● Current status

● A data integration approach

● Future directions

Introduction

3

• What is Biomass • Why is important • How to quantify biomass

What is (forest) biomass

Biomass: mass of organic material of vegetation measured as dry weight

Biomass density: tons/ha

Biomass pools

Biomass... Why Biomass?

Carbon = ~50% Biomass

Carbon cycle component - Global

Climate change (REDD+) - National

Natural resource - National

Ecosystem service – Sub-national

How to quantify biomass

In-situ destructive measurements

In-situ estimations (non-destructive)

Estimation from proxy (e.g., satellite images)

Models (e.g., forest growth)

6

In-situ destructive measurements

Measuring biomass:

● Cut

● Dry

● Weigh

In-situ estimation

Estimate biomass from tree parameters using allometric equations

● Biomass = f (Diameter, wood density, height)

● Biomass = Volume · wood density

FAO (1997), Forestry paper 134

Approaches to quantify biomass

1. Field Plots only

-> Statistics (non-spatial)

E.g. Forest 1: 200 T/ha,

Forest 2: 130 T/ha, etc.

2. Field Plots + Landscape map

-> spatial distribution (by class)

3. Field plots + Satellite images

-> spatial distribution (continuous)

Limitations

1. Thematic:

Few classes =High Variability

2. Spatial:

Mixed unit (MMU)

Landscape map approach

Mean Biomass x LC Class

0,00

50,00

100,00

150,00

200,00

250,00

3 4 5 6 7 9

CVs ≥ 100%

Satellite data

+

Land Cover

+

Ancillary info

High resolution

Quantitative input (DNs)

Regression model

Continuous estimates

Remote sensing approach

Fully exploit the spectral information

Landscape map approach Remote sensing approach

80 T/ha

200 T/ha

40 T/ha

Remote sensing of biomass

Existing remote sensing sensor do not measure biomass

Estimate biomass from RS signal using empirical models calibrated with ground data

Sensors:

● Optical (canopy properties)

● Lidar (vertical structure)

● Radar (canopy and structure) National

Local

Scale of monitoring

Method vs. uncertainty

Monitoring systems

● Concepts & examples

Monitoring systems

Biomass-related examples on:

Data: Acquisition strategy

Model: Estimate parameter

Validation: Assess accuracy

Data acquisition strategy (Biomass example)

Field campaign ($$$$)

Make map ($$)

Acquire ($)

Yes No

National data

available?

Acquire ($)

Yes No

Use regional

map / values

Research

plots Sub-Nat.

maps

Forest

inventory

National

maps

Locally adjusted

map

Local data

available?

Acquire ($)

Yes No

Sub-National

data available?

Local biomass

map

Use National

datasets Yes

Coverage? Accurate?

No

Data may

exist but

may not

be

available!

(e.g., NFI)

Integrated monitoring system (carbon emissions)

Local Ranger Data Remote sensing

time series Land cover change

Forest inventory

data

Biomass mapping

Measuring, Reporting & Verification Kafa UNESCO Biosphere reserve, Ethiopia

Credit: Ben DeVries & Arun Pratihast (WU)

C stocks and changes

Validation approach

18

A comprehensive approach for validation of biomass maps

Monitoring systems

● Examples

Sampling approach:

● Systematic

● Stratified random

Repeated every 5-10 years

Designed for national forest management

http://www.wageningenur.nl/nl/Expertises-Dienstverlening/Onderzoeksinstituten/Alterra/Projecten/Bosinventarisatie.htm

National Forest Inventory

Dutch Forest Inventory

Forest monitoring system: Brazil

PRODES – Amazon Deforestation

Monitoring Project (Annual)

DETER – Near real-time Deforestation

Detection with MODIS (Support for Law

Enforcement for Deforestation Control)

DEGRAD – Amazon Degradation

Monitoring Project

DETEX - Selective logging activities http://www.dpi.inpe.br/prodesdigital/prodes.php

Forest monitoring system: Global

http://www.globalforestwatch.org/

Monitoring tropical biomass

23

• Current status • A data integration approach • Future directions

• Biome average (Tier 1): IPCC Default Biomass values

high uncertainty = low carbon credits

• Pan-tropical biomass maps (Tier 1 – 2)

based on optical images (MODIS) and Lidar (GLAS) data

+ Include forest height and use consistent methodology

- Use continental allometric equations may introduce bias locally?

Status of forest biomass mapping

Availability & disagreement

in pan-tropical biomass maps

Mitchard et al. CBM 2013 8:10

Further disagreement: Maps vs. Plots!

The reply:

“Seeing the forest beyond the trees” (Saatchi et al., 2014):

- The fallacy of Ground Truth

- Are the primary data from Mitchard et al. (2014) superior because they are closer

to the trees? No

Baccini Saatchi From ground data

Mitchard et al., 2014

A new reference dataset

Reference data: field plots & high-res. biomass maps

Quality criteria:

1. Reliability: metadata complete, good accuracy & methodology

2. C pool: Aboveground biomass, DBH > 10 cm

3. Acquisition: after year 2000

4. Min. Resolution/Area: Maps: ≤ 100 m ; Plots: > 0.1 ha

Reference plots

X √

1. Pre-Screening Metadata analysis

2. Upscaling Visual analysis with Google Earth

3. Aggregation Average Biomass @ 1Km

Available Selected Aggregated

Plots Plots Pixels

6525 4213 1204

-35% -71%

A data integration approach

Concept:

Use independent reference data to combine regional maps

Approach:

Bias removal and Weighted average

by strata

● Weights from the error covariance of input maps

● Strata representing error patterns of input maps

Aim:

reduced bias at regional scale

A new pantropical biomass map

30

An integrated pan-tropical biomass maps using multiple reference datasets Global Change Biology (2016)

Avitabile V., Herold M., Heuvelink G.B.M., Lewis S.L., Phillips O.L., Asner G.P., Armston J., Asthon P., Banin L.F., Bayol N., Berry N., Boeckx P., de Jong B., DeVries B., Girardin C., Kearsley E., Lindsell J.A., Lopez-Gonzalez G., Lucas R., Malhi Y., Morel A., Mitchard E., Nagy L., Qie L, Quinones M., Ryan

C.M., Slik F., Sunderland, T., Vaglio Laurin G., Valentini R., Verbeeck H., Wijaya A., Willcock S.

Map and reference data online at: www.wageningenur.nl/grsbiomass

Results for undisturbed forest and Savannah

31

Undisturbed forest (Congo basin)

Woodland and Savannah (East Africa)

Saatchi, Baccini

Fused map

Saatchi, Baccini

Fused map

Monitoring tropical biomass

32

• Current and future directions

Towards a global biomass reference dataset

~ 15,000 plots in the tropics

~ 100,000 plots in Europe

14 reference biomass maps

33

Strategy for biomass reference data

1. Best use of available biomass data

2. Acquire new data

● Acquire data in under-represented areas

● Sampling design tailored to upscaling to coarser res.

3. Improve quality of reference data

● Improved allometry

● Potential of Terrestrial Laser Scanning (TLS)

34

New datasets - Global

Baccini et al. (2016): pantropical, 30m (Landsat + GLAS)

Saatchi et al. (in review): global, 100m (Landsat + GLAS +

wood density + ...?)

New datasets - National

“Greg Asner approach”:

• Nationally-calibrated maps

• 1 ha resolution (100 m)

• Landsat + Airborne Lidar

+ ground data

• National: Peru, Panama

• Sub-national: Colombia, Madagascar

Future directions

NASA GEDI

● GEDI: Global Ecosystem Dynamics Investigation

● Sampling Lidar on the ISS, @25 footprint

● Measure canopy height and vertical structure

● Estimate biomass and biomass change (+Landsat)

● Launch: 2018

ESA BIOMASS mission

● P-band SAR

● Biomass and height @200m

● “Globally” (ok, not really..)

● 3D by tomography

● Launch: 2020

37

Rationale of the BIOMASS monitoring system

1.Why & where: The crucial information need is in the tropics

● deforestation (~95% of the Land Use Change flux)

● regrowth (~50% of the global biomass sink)

2.Scale: Biomass measurements are needed where the changes occur

and at the effective scale of change: 4 hectares

3.Accuracy: A biomass accuracy of 20% at 4 hectares, comparable to

ground-based observations

4.Complementary information:

● Measure forest height to further constraint biomass estimates

● Detect deforestation at 0.25 ha (Biomass change)

5.Monitoring strategy: Repeated measurements over multiple years to

identify deforestation and growth