kellndorfer_we3.t05.4.pptx
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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