Post on 23-Jun-2020
Integration of Alos PalSAR and LIDAR IceSAT data in a multistep approach for wide area biomass
mapping Marcela Quinones, SarVision, quinones@sarvision.nl
Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl
Vincent Schut, SarVision. Schut@sarvision.nl
.
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, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
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.
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
September 2013
Vegetation structural type Map- Borneo 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}
Marcela Quinones, SarVision, quinones@sarvision.nl;
Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl;
Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
September 2013
Bio 2 Bio 3
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
September 2013
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
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
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
September 2013
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}
•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
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}
Marcela Quinones, SarVision, quinones@sarvision.nl Carina Van der Laan, Utrecht University, C.vanderLaan@uu.nl Dirk Hoekman, Wageningen University. Dirk.Hoekman@wur.nl Vincent Schut, SarVision. Schut@sarvision.nl
LIVING PLANET, Edimburg,
September 2013