Biomass retrieval algorithm based on P-band BioSAR experiments of boreal forest

Lars Ulander1,2, Gustaf Sandberg2, Maciej Soja2

1Swedish Defence Research Agency (FOI), Sweden2Chalmers University of Technology, Göteborg, Sweden

Outline

• Background

• Test sites and data collections

• Temporal stability of backscatter

• Backscatter vs biomass

• Regression modeling

• Validation

Background

• BIOMASS is a P-band polarimetric SAR mission

• Frequency band 432-438 MHz

• Repeat pass PolInSAR and - during initial orbit - tomography

• BIOMASS is in phase A for ESA:s 7th Earth Explorer

• Three candidate missions are competing

• The other candidates are CoreH20 and Premiere

• Final selection of mission with be in 2013

• This presentation reports on development & evaluation of biomass estimation algorithms for BIOMASS

P-band SAR experiments over boreal forest

• ESA has funded multiple SAR campaigns in support of satellite P-band SAR BIOMASS candidate mission

• BioSAR-1 (2007), Remningstorp

• 3 dates (Mar, Apr, May), 2 headings

• Stand-level biomass < 290 ton/ha

• Flat topography

• BioSAR-2 (2008), Krycklan

• 1 date (oct), 4 headings

• Stand-level biomass < 180 tons/ha

• Hilly topography

Sweden

Finland

Norway

Denmark

Remningstorp

30°E

20°E

20°E

10°E

10°E0°

70°N70°N

65°N65°N

60°N60°N

55°N 55°N

Remningstorp

Krycklan

BioSAR-1: Remningstorp, southern Sweden

E-SAR coverage Species

Spruce

Pine

Birch

Biomass range: 10-290 tons/haLow topography: 120–145 m asl

In situ and lidar data

BioSAR-2: Krycklan, northern Sweden

Biomass range: 20-180 tons/ha; Hilly topography: 135–350 m asl

SAR images (blue); forest stands (red) Species

Spruce

Pine

MIxed

BioSAR-1: Short-term backscatter stability

Each data point corresponds to a forest stand and is defined by o [dB] backscatter from 2 tracks on 1 day

Max diff is 0.6 dB, which is similar to the radiometric stability.

BioSAR-1: Long-term backscatter stability

Mar vs May

HH VV

Apr vs May

HV HH VV

HV HH VV

Mar vs May

Apr vs May

Mar vs May

Apr vs May

Observed backscatter reduction most likely due to decreasing moisture

BioSAR-1/-2: PHV backscatter vs Biomass Data from all stands, tracks, test sites

• Re = Remningstorp• Kr = Krycklan• In general,

backscatter spread is due to:– Moisture variations– Ground topography– Forest Structure

Site Date Heading

…and the same for HH and VV

Site Date Heading

General observations

• HV and HH shows good sensitivity to biomass across the entire range of biomass (10-290 tons/ha)

• HH gives largest dynamic range but also largest spread

• VV shows little dependency on biomass

• Krycklan results are, in general, a few dB lower than Remningstorp

• Observed backscatter variability implies that algorithm based only on single polarisation will perform poorly.

• Need for both multi-pol and topographic corrections

BioSAR-1/-2: Polarisation ratio

• Potential to correct for soil moisture effects, since dependent on dielectric constant for a slightly rough surface.

• Surface backscatter dominates for low biomass (VV > HH)

• Sensitivity to biomass but large spread for low biomass

Algorithm development and evaluation

• Six regression models developed and evaluated

• Algorithm test designed to be challenging, i.e.

• Training using data from Krycklan (single date)

• Validation using all data from Remningstorp (three dates)

• All algorithms use a logarithmic transform of the biomass (W = above-ground dry biomass) to stabilise the variance

Six regression models tested

HV only, one-parameter model

HV + polarization ratio, 3 parameters

Model from Saatchi et al (TGRS 2007). Multiple polarisations, 7 parameters

Model from Saatchi et al (TGRS 2007). Multiple polarisations, 14 parameters. Requires separate crown and stem biomass; includes slope corrections.

HV + polarization ratio, 6 parameters.Includes slope correction.

HV only, two-parameter model

NB. Backscatter in dB

Recently proposed models:

Algorithm development and evaluation

• Models trained on 29 in-situ stands from Krycklan October 2008

• Model validation using data from Remningstorp 2007

• The validation results have been separated into different dates (Apr-May) and different test site data in Remningstorp, i.e.

• plot-level biomass where all individual trees within 80 m x 80 m have been measured and biomass error is a few percent

• stand-level biomass from helicopter lidar and field measurement with an error of 25 tons/ha

Residuals after model training; Krycklan data

Test site data Date Heading

Alg 1 RMSE (t/ha)

Alg 2 RMSE (t/ha)

Alg 3 RMSE (t/ha)

Alg 4 RMSE (t/ha)

Alg 5 RMSE (t/ha)

Alg 6 RMSE (t/ha)

Stands Oct 2008 43° 36.3 36.5 21.9 30.0 30.2 23.6

Stands Oct 2008 134° 34.3 33.7 22.9 29.1 25.9 23.1

Stands Oct 2008 314° 34.8 34.0 25.4 27.4 30.7 27.4

Stands Oct 2008 358° 40.1 40.0 29.5 33.5 37.8 36.1

Training on all data and residuals separated for different headings. Algorithm 3 has smallest residuals, but all algorithms perform similar.

Independent validation; Remningstorp data

Test site data Date Heading

Alg 1 RMSE (t/ha)

Alg 2 RMSE (t/ha)

Alg 3 RMSE (t/ha)

Alg 4 RMSE (t/ha)

Alg 5 RMSE (t/ha)

Alg 6 RMSE (t/ha)

Stands Mar 2007 179/200° 161.8 102.5 99.2 103.5 58.6 33.2

Stands Apr 2007 179/200° 116.3 74.4 90.4 96.4 45.8 29.2

Stands May 2007 179/200° 95.3 62.7 76.2 81.6 48.3 31.2

Plots 80x80 Mar 2007 179/200° 235.4 127.1 163.0 165.0 73.6 35.1

Plots 80x80 Apr 2007 179/200° 164.1 86.4 149.2 154.1 62.5 38.1

Plots 80x80 May 2007 179/200° 120.2 64.8 126.4 129.4 62.3 42.4

Algorithm 6 (HV, pol-ratio, topo-corr) significantly better than the others

Biomass retrieval performance

Algorithm 5 Algorithm 6

Algorithms trained with all data from Krycklan and evaluated with data from Remningstorp.Validation results from Remningstorp are shown in the plots above.

T = standsV = plots 80 m x 80 m

Conclusions

• Single-pol P-band backscatter shows significant variability when data from different test sites, headings and dates are pooled

• Six different regression models have been trained and validated on BioSAR-1/2 data over boreal forests in Sweden

• Results show that multiple polarisations and topographic corrections significantly improve biomass retrieval

• Algorithm 5 and 6 perform significantly better

• Algorithm 6 includes HV, polarisation ratio and topographic corrections and gave the best results (RMSE 30-40 tons/ha)

• Algorithm 5 includes HV and polarisation ratio and gave the second best results (RMSE 50-70 tons/ha)

• The study shows importance of including polarisation ratio and topographic corrections besides HV-pol