GFOI Expert Workshop. Sensor interoperability, complementarity, and the temporal component. Francesco Holecz
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1 GFOI Expert Workshop Sensor interoperability, complementarity, and the temporal component Francesco Holecz Woods Hole Research Centre, MA, USA June, 2014
2 On sensor interoperability Single-date vs. multi-year The signal processing aspect Single- date PALSAR- 1 HH- HV (15m) HH, HV, HH-HV Mul9- year PALSAR- 1 HH- HV (15m) HH, HV, HH-HV
3 On sensor interoperability Single-date vs. multi-year The signal processing aspect 1- day InSAR Cosmo- SkyMed (3m) 1-day coh, mean HH, int change Mul9- year PALSAR- 1 HH- HV (15m) HH, HV, HH-HV
4 On sensor interoperability Single-date vs. multi-year The thematic aspect 1- day InSAR Cosmo- SkyMed (3m) Mul9- year PALSAR- 1 HH- HV (15m)
5 On sensor complementarity Single-sensor The thematic aspect multi-temporal L-band, 15m ALOS PALSAR-1, Aug 2008 HH, HV, HH-HV Multi-temporal C-band, 15m ENVISAT ASAR AP, Feb 2010 HH, HV, HH-HV 1-day InSAR X-band, 3m Cosmo-SkyMed, Dec 2009 coherence, mean HH, int change
6 On sensor complementarity Single-sensor The thematic aspect ALOS PALSAR-1, Aug 2008 Cosmo-SkyMed, Dec 2009
7 On sensor complementarity Single-sensor The thematic aspect ALOS PALSAR-1, Aug 2008 Cosmo-SkyMed, Dec 2009
8 On sensor complementarity Single-sensor vs. multi-sensor The thematic aspect Mul9- year PALSAR- 1 HH- HV 1- day InSAR Cosmo- SkyMed Seasonal ENVISAT ASAR
9 On sensor complementarity Single-sensor vs. multi-sensor The thematic aspect Multi-year PALSAR-1 data in dry season 1-day InSAR CSK at SoS Seasonal ASAR data after SoS Potential crop extent (15m) Potential crop area at SoS (3m) Crop growth extent (15m) Cultivated Area (15m)
10 On sensor complementarity Single-sensor vs. multi-sensor The thematic aspect
11 Don t forget the temporal component
12 The Gambia case study
13 Objective A valuable and unexploited data source is provided by spaceborne SAR archive data. In this work, it will be demonstrated that the integration of multiyear and multi-sensor data allows the generation of a consistent national baseline Land Cover Map.
14 Multi-year, multi-sensor approach multi-year ALOS PALSAR-1 multi-year ENVISAT ASAR grouping of the data: dry season wet season whole season grouping of the data: dry season wet season whole season computation of temporal features for: dry season wet season whole season computation of temporal features for: dry season wet season whole season knowledge based classification data Land Cover Map processing products
15 Intensity processing 1. Strip mosaicing of single frames in slant range geometry (if zero-doppler) and multi-looking 2. Grouping of the strip mosaics acquired with the same geometry 3. DEM based orbital correction of one reference image for each group, when necessary 4. Co-registration 5. De Grandi time series speckle filtering 6. Terrain geocoding and radiometric calibration 7. Radiometric normalisation 8. Anisotropic Non-Linear Diffusion filtering
16 Multi-year, multi-sensor mosaic at 1 hectare ALOS PALSAR-1 ScanSAR HH dry season ENVISAT ASAR Wide Swath HH dry season ENVISAT ASAR Wide Swath HH wet and dry season
17 Agricultural extent at 1 hectare
18 Multi-year, multi-sensor mosaic at 15 meter ALOS PALSAR-1 mean HV dry season ENVISAT ASAR mean HH dry season ENVISAT ASAR HH difference wet and dry season
19 Land cover map at 15 meter Agricultural area Mangrove - Sandbanks Water Bare soil-weak vegetation (low biomass) Medium vegetation (medium biomass) Strong vegetation (high biomass)
20 Land cover map at 3 meter (based on seasonal CSK) Rice Crop 1 Crop 2 Water Forest
21 Vegetation Productivity Index (250m) high CNR-IREA / sarmap The VPI has been derived from Aqua and Terra MODIS 250m It is relative to Mid September (approximately peak of season) of each year low
22 The Malawi case study
23 Objective The objective is to demonstrate, at country-level, the multi-purpose use of ALOS PALSAR-1 data, particularly of multi-year ALOS PALSAR-1 Intensity data and their synergetic use with other spaceborne SAR data. Following products are targeted: Forest map Cultivated area map Digital Elevation Model
24 Coherence or Intensity? Not suitable for thematic purposes PALSAR- 1 HH coherence PALSAR- 1 HV coherence during dry season during dry season perpendicular baseline 280m Mul1- year PALSAR- 1 HH- HV intensity during dry season
25 Intensity or coherence? Disadvantages: reduced spatial resolution coherence at 44 days during the wet season is unpredictable Mul1- year PALSAR- 1 HH- HV during dry season PALSAR- 1 HH coh & int during wet season PALSAR- 1 HH coherence during wet season
26 Forest area Mul9- year PALSAR- 1 HH- HV during dry season
27 Cultivated area Mul1- year PALSAR- 1 HH- HV during dry season ASAR HH PALSAR HV ASAR HH ASAR data acquired during wet season
28 Forest area Validation forest sparse veg other Total Omission error (%) Urban Sugarcane Crop Forest Other Total K-coeff 0.75 Commission error (%) overall accuracy 87% PALSAR-1 HH-HV forest sparse veg other Total Omission error (%) Urban Sugarcane Crop Forest Other Total K-coeff 0.82 Commission error (%) overall accuracy 91% PALSAR-1 HH-HV Cultivated area (ASAR HH-HV) forest sparse veg other Total Omission error (%) Urban Sugarcane Crop Forest Other Total K-coeff 0.84 Commission error (%) overall accuracy 92% PALSAR-1 HH-HV Cultivated area (ASAR HH-HV) ASAR HH-HV
29 Digital Elevation Model average Δh < 5m PALSAR- 1 HH coherence during dry season PALSAR- 1 HH InSAR DEM SRTM
30 Publication, K&C Phase 3 report
31 On technical challenges and R&D requirements Mul9- temporal systema9c acquisi9ons are oien not enough. The use of the exis9ng archive data, even if not op9mal for the targeted applica9on, provides a valuable data source, enabling the genera9on of consistent countrywide land cover maps. Automa9on is, to some extent, feasible as long as a mul9- temporal synerge9c approach is used. No general solu9on but biome AND product (end user requirements!) specific solu9ons are possible.
32 On-going and future actions The processing of large mul9- temporal mul9- sensor data implies: Suppor9ng the processing of all spaceborne SAR sensors and acquisi9on modes; Processing solu9ons on local cluster AND cloud compu9ng. Moving from CPU to GPU. The products are con9nuously developed in coopera9on with end users. Know- how transfer is on- going (as example refer to RIICE). A similar ini9a9ve is on- going with FSC (TransparentForests).
33 TransparentForests EO products
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