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

Transcription

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

2 Above Ground Biomass (carbon) mapping and monitoring: Importance Supporting UNFCC KP, REDD+, Monitoring of PES contracts for carbon as environmental service. Information over carbon emissions by (bio)agricultural production

3 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 wavevegetation 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.

4 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 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. ( 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 equation

5 Radar Alos Palsar strips: Pre-Processing chain SLC Multi-looked Geometric Terrain corrected Radiometric Terrain corrected K&C data Inter-calibration between the strips

6 Pixel based Forest structural type classification over radar mosaics Combination of unsupervised and supervised classification Legend development processcluster analysis Post processing Validation Final Map Vegetation Structural Type 1 Water 2 Degraded forest_open canopy, 3 Peat swamp forest, 4 Agriculture dry, Slope correction for flooded classes Integration of MODIS 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, Use of Masks Mangrove & cities & Plantations 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}

7 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} ; ;

8 Validation procedure. Systematic and extensive Field observations on forest structure, Soil and flooding conditions Ground reference data collection: Geo-referenced photos and field notes at > 890 locations 2008 Validation for Central Kalimantan

9 Validation procedure. Confusion matrix for most of classes Aggregation of classes into structural types increases accuracy Validation of final Map using polygon digitized and labeled according to Map classes for 890 locations GROPING BY STRUCTURAL types

10 R-sampling methods and change in spatial resolution affects strongly the information contents and the spatial patterns Difference 1000m and 50m spatial resolution m maps are not useful at the plantation level 1000m spatial resolution 50m spatial resolution

11 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) G. Vegetation heights were extracted for 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

12 Definitions of Useful ranges for each of the GLAS-height derived histograms, based on ground observations green areas: lowland with peat swamp forest black areas: sand, higher areas, often covered with low/grassy vegetation blue areas: ferns/grass magenta areas: shrubland ALOS PALSAR radar image subset 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

13 Biomass Mapping: Unsupervised approach Multistep procedure- data fusion GLAS: LIDAR height-derived distributions for each vegetation structural type Bio1 Bio 2 Bio 3 HV Alos FBD data 2008 Statistical histogram matching of GLAS derived height histograms and and HV histograms for all structural classes Height map Inversion of Height Map using 3 available allometric equations Bio1[iX]=Height^1.68 Bio2[iX]= *(Height^2.4814) Bio3[iX]= *(Height^ Woodhouse, I. Predicting backscatterbiomass and height-biomass trends using a macroecology model 2006 In : IEEE Transactions on Geoscience and Remote Sensing. 44, 4, p p.research output: Contribution to journal Article

14 Validation using Dedicated Field data collection for radar studies Structural biomass and soil conditions database Ketterings et al., 2001 BIO = 0,066*D^2,59 Kenzo et al., 2009 Brown, 1997 BIO = x D^2.43 BIO = * D^2.53 Field data recording 54 Geo-reference plots with transects A and B measurements. 54 Transect 100X20 54 Transect 50 X 2 Trees > 10 cm db DBH Height Height first branch Specie Trees > 1.0 cm db DBH Height Specie Field recordingsdatabase (Excel files) Includes pictures and terrain observations 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, Brown, S., 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 loggedover tropical rainforests in Sarawak, Malaysia. Journal of Forest Research, 14(6), doi: /s Field estimated biomass database: estimated with 3 different equations 54 Structural vertical profiles

15 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^ Bio2[iX]= *(Height^ Bio3[iX]= *(Height^2.5734)

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17 At 50 m resolution is possible to see ecological patterns in both Maps 50m spatial resolution Vegetation structural Map and Biomass GLAS- Alos derived Map are proposed to be used in combination for REDD+ and other applications

18 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 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}

19 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} Biomass Range (ton/ha) Colour