REGIONAL TO GLOBAL SCALE MAPPING OF FOREST HEIGHT, BIOMASS AND

CARBON FROM MULTI-SOURCE SATELLITE AND FIELD DATA

Josef Kellndorfer

Alessandro Baccini, Oliver Cartus, Scott Goetz, Nadine Laporte, Richard

Houghton, Wayne Walker

The Woods Hole Research Center

Outline

The National Biomass and Carbon Dataset 2000

Fusion of SRTM InSAR, optical EO products and optical

data

Biomass mapping with ALOS PALSAR dual-polarization

data

Another kind of SAR/optical data synergy

Principal Investigator: Josef KellndorferWoods Hole Research Center

Research Team:Wayne Walker, Katie Kirsch, Greg FiskeWoods Hole Research Center

Elizabeth LaPoint, Mike Hoppus, Jim WestfallUSDA Forest Service FIA Program:

Collaboration:Dean Gesch, National Elevation Dataset, USGSCollin Homer, National Land Cover Database

2001 / MRLC, USGSZhi-Liang Zhu, LANDFIRE, USGS

Funding and Support:NASA Terrestrial Ecology ProgramLANDFIREPCI GeomaticsDefiniens Imaging/eCognition

J.Kellndorfer, National Biomass and Carbon Dataset 2000

Four year project to produce

-Forest vegetation height

-Biomass and

-Carbon Estimates

-Conterminous U.S.

-First attempt at 30 m

resolution ever

Kellndorfer et al., 2011

The Opportunity …

A “millennium” opportunity exists to combine SRTM and several national data sets: National Land Cover Database 2001

Provides Landcover, Treecover, Imperviousness

MRLC Landsat ETM+ Datasets 1999-2002

National Elevation Dataset Compiled from Topographic Survey data

Cohesive processing for the first time around 2000

USDA Forest Inventory and Analysis Data Ca. 300,000 surveyed plots with forest attributes

(including height, biomass)

Shuttle Radar Topography Mission:

Global Coverage in 11 Days

Source: USGS

SRTM Vegetation Response

Mean Canopy

Height

Mean Scattering

Phase Center

Height

SRTM Resolution Cell

Surface

Mean Radar

Measured

Height

Mean Canopy

Height

Ground

Elevation

Kellndorfer et al., 2011

SRTM Vegetation Signal Extraction

Per pixel measurements have typical SAR noise characteristics -> Need to develop noise reduction approach which optimizes the retrieval of vegetation height

Kellndorfer et al., 2011

C-band Difference Image

X-band Difference Image

C-band Difference Image

X-band Difference Image

Before Object-based Averaging After Object-based Averaging

Example: Michigan Woodlots

Improving Radar Radiometry

Validation

Response

Variables

Reference

Data

DBH/Height

-> Biomass

Biomass

Predictor

Layers

Height

Predictor

Layers

Modeling:

RandomForest

For 66 ecoregions

Predicted

Height

Predicted

Biomass

SAR

Backscatter

InSAR

Height

Optical

Reflectance

Elevation

Slope

Landsat - National Land

Cover Data Base 2001

Statistical Fusion of Field and Satellite Data

Reference Data:

US Forest Inventory

and Analysis Plot

Network

300,000 Plots at Full

Implementation

NBCD 2000 - Basal-Area Weighted Height

11

PUBLIC DATA

RELEASED April 20th AT

http://whrc.org/nbcd

Kellndorfer et al., 2011

Model Variable Importance Analysis with randomForest

SRTM phase

scattering center

and derived

height

NBCD 2000 Height and Biomass Estimates Compared with USDA Forest Inventory (FIA) at

Plot Level via Bootstrap ValidationNBCD Predicted Height vs. FIA Height NBCD Predicted ALD Biomass vs. FIA ALD Biomass

n Height r Height RMSE [m] Biomass r B. RMSE (Mg/ha]

National 43038 0.83 3.8 0.75 54.6

Pacific 5352 0.73 6.4 0.75 94.6

Interior West 8347 0.88 3.6 0.77 42.1

South 12203 0.79 3.6 0.67 51.9

Northcentral 10021 0.76 2.7 0.62 37.7

Northeast 7115 0.75 3.0 0.58 50.7

Multi-Scale NBCD Biomass Estimates Comparison with FIAEstimates

Hexagon Scale[ Hex Size = ~650 km2

= ~ 160,000 ac, i.e.

In ideal case: ~ 25 FIA plots ]

N = 8139

NBCD

19 Mg/ha / 0.92(RMSD/Corr.Coef.)

County ScaleN = 2635

NBCD

14 Mg/ha / 0.95

NBCD

NBCD represents a unique product

because several 30 m remote sensing

products were available for the same

time frame

Update of NBCD with ALOS PALSAR?

USDA project: Towards Spatially Explicit Quantification of Carbon Flux (2000-2007) in Northeastern U.S. Forests Linking Remote Sensing with Forest Inventory Data

Investigators: Kellndorfer, J., Cartus, O., Houghton, R. A., Walker, W. S.

Collaboration: Maurizio Santoro, GAMMA RS

655 PALSAR FBD

images for 2007/08

Multi-temporal coverage:

1-5

Method

Automated Model training and inversion with the aid of the NLCD canopy density

or the Vegetation Continuous Field maps

Identification of open and dense forest areas in the SAR imagery to calibrate

the model with respect to changes in the backscatter signatures of forests due

to, e.g., different weather conditions

Multi-temporal combination of single image biomass estimates

Similar to what was developed for ENVISAT ASAR C-band data (Santoro et al.

2011, RSE) and for ERS-1/2 tandem coherence (Cartus et al., 2011, RSE)

ALOS biomass

map for 2007

Compared to NBCD

When aggregating to county scale …

Vs. FIA county carbon statistics Vs. NBCD 2000

Conclusions:Two different types of SAR/InSAR/optical

data synergy have beeninvestigated

Availability of very different data types was the key for the successful mapping of forest biophysical parameters over large areas

NBCD represents a unique product

At scales of >500 m, however, ALOS PALSAR Dual polarization appeared to allow reliable biomass estimates up to ~200 t/ha