Integration of Alos PalSAR and LIDAR IceSAT data...

Integration of Alos PalSAR and LIDAR IceSAT data in a multistep approach for wide area biomass

mapping Marcela Quinones, SarVision, [email protected]

Carina Van der Laan, Utrecht University, [email protected] Dirk Hoekman, Wageningen University. [email protected]

Vincent Schut, SarVision. [email protected]

.

mailto:[email protected]




Importance • Supporting UNFCC KP, REDD+, • Monitoring of PES contracts for carbon as environmental service. • Information over carbon emissions by (bio)agricultural production

Above Ground Biomass (carbon) mapping and monitoring:

Marcela Quinones, SarVision, [email protected] Carina Van der Laan, Utrecht University, [email protected] Dirk Hoekman, Wageningen University. [email protected] Vincent Schut, SarVision. [email protected]

LIVING PLANET, Edimburg,

September 2013





Above Ground Biomass mapping Technical challenges

• Traditional methods like field base measurements are costly and incomplete mostly do not include spatial variation with in a Forest type

• Field biomass estimates accuracy depends on • Field measurement accuracy (DBH and/or height) (range of measurements above 10 cm

DBH or above 5 cm dBH) • The equation used for the estimations ( type of ecosystem, mean wood density and

biomass expansion Factors)

• Radar Remote sensing is a good candidate for biomass mapping due to the wave-vegetation interaction mechanisms but also has limitations

• Different radar frequencies saturate at different levels of estimated biomass.(L band saturates at 200 ton /ha while P band can saturate at 300 ton /ha).

• Speckle challenges direct biomass inversion from radar images!

• Forest structure, terrain roughness and soil moisture has a strong effect in the biomass estimations with radar

• Use of combined Radar and LIDAR sensors is proposed to overcome radar saturation inversion is done directly form the height

• Accuracy of biomass mapping using RS data is scale dependent. Biomass sampling in the field should match RS spatial resolution.



September 2013





Above Ground Biomass mapping Proposed methodology

1. Pre-processing of SLC Alos PALSAR data and inter-calibration between strips

2. Use of pixel based classification techniques for forest structural mapping of the 2007-2008 Alos

PALSAR wide area mosaics. Created in step 1. Validation using geo-referenced field photos.

3. Processing of all available IceSAT Glas LIDAR data for 2008 for the whole area of Borneo. (25.000 points) Stratification of the data according to Forest structural types of step 2

4. Integration of Radar HV backscatter and LIDAR ICESAT derived heights per Forest structural type. Use of a histogram matching procedure .

5. Use of Independent field biomass for validation procedure: Dedicated radar design n campaign in order to sample all variations in radar backscatter. Field samples on homogenous areas (0.2 ha) including all trees above 5 cm DBH and 2 mt height.

6. Use of Chave et al. 2005 equation



September 2013





Radar Alos Palsar strips: Pre-Processing chain

SLC Multi-looked Geometric

Terrain corrected Radiometric

Terrain corrected

Inter-calibration between the strips K&C data



September 2013





Pixel based Forest structural type classification over radar mosaics

•Slope correction for flooded

classes

•Integration of MODIS

Use of Masks

Mangrove & cities & Plantations

Combination of unsupervised and

supervised classification

Vegetation Structural Type

1 Water

2 Degraded forest_Open canopy,

3 Peat swamp forest,

4 Agriculture dry,

5 High Grassland or bushes med bio,

6 High forest_close canopy 1

7 High forest_close canopy 2,

8 Recently cut low bio,

9 Plantations med development or woodland or shrubs med-low biomass,

10 plantations low biomass or shrubs low biomass,

11 Grassland low bio,

12 Plantations medium biomass or shrubs medium biomass,

13 Recently cut high bio,

14 Swamp forest,

15 Riparian Forest- close canopy,

16 Mangrove _ close canopy,

17 Degraded Forest_close canopy}

Post processing

Legend development process- cluster analysis

Validation Final Map



September 2013





Vegetation structural type Map- Borneo Vegetation Structural Type

1 Water



4 Agriculture dry,










14 Swamp forest,




Marcela Quinones, SarVision, [email protected];

Carina Van der Laan, Utrecht University, [email protected];

Dirk Hoekman, Wageningen University. [email protected]

LIVING PLANET, Edimburg, September 2013




Ground reference data collection: Geo-referenced photos and field notes at > 890

locations 2008

Validation for

Central Kalimantan

Validation procedure. Systematic and extensive Field observations on forest structure, Soil and flooding conditions



September 2013





Validation of final Map using polygon digitized and labeled according to Map

classes for 890 locations

Validation procedure. Confusion matrix for most of classes

Aggregation of classes into structural types increases accuracy

GROPING BY STRUCTURAL types



September 2013





Difference 1000m and 50m spatial resolution

500-1000m maps are not useful at the plantation level…

R-sampling methods and change in spatial resolution affects strongly the information contents

and the spatial patterns

1000m spatial resolution 50m spatial resolution



September 2013





Use vegetation height estimation form

ICEsat – GLAS LIDAR The GLA-14 file from the GLAS products was used to calculate the

vegetation height using the 2 fitted Gaussians as the one depicting

the top of canopy level, and the 'start of signal' altitude as the ground

altitude. The difference between those should give a good estimation

of the maximum vegetation height in the measured footprint area

[Sun et al. 2008]. Sun G. et al., 2008, Forest vertical structure from GLAS: An evaluation using LVIS and

SRTM data, Remote Sensing of Environment 112 (2008) 107-117G.

Vegetation heights were extracted for 100.000 points in all the

island of Borneo, data was stratified using the Vegetation

structural classes from the radar Map. For each vegetation cover

type a distribution of the GLAS derived height histogram was

created



September 2013





ALOS PALSAR radar image subset

black areas: sand,

higher areas,

often covered with

low/grassy vegetation

magenta

areas:

shrubland

blue areas:

ferns/grass

green areas:

lowland with peat swamp

forest

•Extensive observations on land cover in west, central and east

Kalimantan were made. In areas corresponding to different structural

types from the radar based map

• Observations are use to define realistic (field based) height ranges

for each vegetation structural type, for each GLAS-height derived

histogram

Definitions of “Useful ranges” for each of the GLAS-height derived histograms, based on ground observations



September 2013





Biomass Mapping: Unsupervised approach

Multistep procedure- data fusion

GLAS: LIDAR height-derived

distributions for each vegetation structural type

HV Alos FBD data 2008 Height map Inversion of Height Map using 3

available allometric equations

Bio1

Woodhouse, I. Predicting backscatter-

biomass and height-biomass trends

using a macroecology model

2006 In : IEEE Transactions on

Geoscience and Remote Sensing. 44, 4,

p. 871-877 7 p.Research output:

Contribution to journal › Article

Bio1[iX]=Height^1.68

Bio2[iX]=0.06328*(Height^2.4814)

Bio3[iX]=19.875+0.04552*(Height^2.5734

Statistical

histogram

matching of

GLAS derived

height

histograms and

and HV

histograms for

all structural

classes



September 2013

Bio 2 Bio 3

http://www.research.ed.ac.uk/portal/en/persons/iain-woodhouse(93b0cc34-b616-4dd1-9a6f-1a166a6240ba).html

http://www.research.ed.ac.uk/portal/en/persons/iain-woodhouse(93b0cc34-b616-4dd1-9a6f-1a166a6240ba).html

http://www.research.ed.ac.uk/portal/en/publications/predicting-backscatterbiomass-and-heightbiomass-trends-using-a-macroecology-model(c4a6c3f6-7f30-4eb0-be66-67435d7f845e).html













Validation using Dedicated Field data collection for radar studies Structural – biomass and soil conditions database

•

Field data recording 54

Geo-reference plots

with transects A and B

measurements.

Field recordings-

database (Excel files)

Includes pictures and

terrain observations Field estimated biomass

database: estimated with 3

different equations

54 Structural vertical profiles

54 Transect 100X20

Trees > 10 cm dB

DBH

Height

Height first branch

Specie

54 Transect 50 X 2

Trees > 1.0 cm dB

DBH

Height

Specie

Ketterings et al., 2001 BIO = 0,066*D^2,59

Kenzo et al., 2009 BIO = 0.0829 x D^2.43

Brown, 1997 BIO = 0.118 * D^2.53



September 2013

References Ketterings QM, Coe R, van Noordwijk M et al. (2001) Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. Forest Ecology and Management, 146, 199-209. Brown, S., 1997. Estimating Biomass and Biomass Change of Tropical Forests: a Primer (FAO Forestry Paper-134), FAO, United Nations, Rome. Kenzo, T., Furutani, R., Hattori, D., Kendawang, J. J., Tanaka, S., Sakurai, K., & Ninomiya, I. (2009). Allometric equations for accurate estimation of above-ground biomass in logged-over tropical rainforests in Sarawak, Malaysia. Journal of Forest Research, 14(6), 365-372. doi:10.1007/s10310-009-0149-1





Validation using Dedicated Field data collection for radar studies

Structural – biomass and soil conditions database

RMSE

Kenzo et al., 2009 Ketterings et al., 2001 Brown, 1997

Bio1[iX]=Height^1.68

10.47 10.69 10.37

Bio2[iX]=0.06328*(Height^2.4814 10.97 10.27 12.37

Bio3[iX]=19.875+0.04552*(Height^2.5734) 10.51 10.28 11.27



September 2013







September 2013





50m spatial resolution

Vegetation structural Map and Biomass GLAS- Alos derived Map are proposed to be used in combination

for REDD+ and other applications

At 50 m resolution is possible to see ecological patterns in both Maps



September 2013






1 Water



4 Agriculture dry,










14 Swamp forest,




•Biomass and Vegetation structural Maps are complementary and should be used

together for monitoring.

•When using in time series this maps can show degradation processes within one

land cover class

•Different land cover can show similar biomass levels

Biomass Range (ton/ha) Colour

0

0.1-5.0

5.1-10.0

10.1-25.0

25.1-50.0

50.1-75.0

75.1-100.0

100.1-150.0

150.1-200.0

200.1-250.0

250.1-300.0

300.1-350.0

350.1- 400.0

400.0-600.0

•The same land cover class can have different biomass levels

like the palm plantations

•With in one ecosystem different biomass patterns can

emerge


1 Water



4 Agriculture dry,










14 Swamp forest,






September 2013





Integration of Alos PalSAR and LIDAR IceSAT data...

Documents

Transcript of Integration of Alos PalSAR and LIDAR IceSAT data...