recover for redd and sustainable forest management eu recover project: remote sensing services to...
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ReCover for REDD and sustainable forest management
EU ReCover project: Remote sensing services to support REDD and sustainable forest management in Fiji
Pacific Island GIS&RS conference 2012,
27 – 30 November 2012, Suva
Johannes Reiche, Martin Herold: Wageningen UniversityDonata Pedrazzani: GMV
Fabian Enßle: Freiburg University
ReCover for REDD and sustainable forest management
Outline
1. ReCover project objective
2. ALOS PALSAR change detection and time-series analysis
3. MODIS time-series analysis for forest change detection
4. ICESat/GLAS space borne laser ranging for forest height & biomass
5. ReCover workshop and field work (October 2012)
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ReCover for REDD and sustainable forest management
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1. EU ReCover project objective
• To develop beyond state-of-the-art service capabilities to support reducing deforestation and forest degradation in the tropical regions:– Research project driven by REDD+ monitoring needs– Monitoring system of forest cover, forest cover
changes and biomass mapping including accuracy assessment.
– Capabilities are based on utilizing earth observation and in-situ data
– Using multiple remote sensing data sources– Involvement of national and regional partners, and
user organizations
ReCover for REDD and sustainable forest management
2. ALOS PALSAR change detection and time-series analysis
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• ALOS PALSAR– L-band SAR system (sensitive to biomass)– SAR is not affected by clouds– Fine Beam Dual data was ordered and processed to 25 m resolution
• Country-wide mosaic for 2010 (25 m) (will be completed)
False colour image RGBR: HH polarisationG: HV polarisationB: HH/HV ratio
ReCover for REDD and sustainable forest management
ALOS PALSAR: Dual-temporal (2007,2010) coverage of west Viti Levu
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2007-08/092010-08/09
2. ALOS PALSAR change detection and time-series analysis
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ClassificationStep 1: water mask (HH-07&10)Step 2: Vegetation cover change (HV difference 2007-2010)Step 3: Differentiating deforestation and other vegetation decrease, such as agriculture (HH-HV difference 2007)
Water mask
Positive change (e.g. reforestation)
Negative change
Forest/dense vegetation -> non-forest
Other vegetation decrease
Forest land cover change detection (Viti Levu west) 2007 - 2010 (first results, need to be evaluated)
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Water mask
Positive change (e.g. reforestation)
Negative change
Forest/dense vegetation -> non-forest
Other vegetation decrease
ReCover for REDD and sustainable forest management
Time-series examples
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Stable forest
2. ALOS PALSAR change detection and time-series analysis
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Deforestation of pine plantagen
Time-series examples
2. ALOS PALSAR change detection and time-series analysis
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RegrowthTime-series examples
2. ALOS PALSAR change detection and time-series analysis
ReCover for REDD and sustainable forest management
• BFAST: – time-series analysis package that detects changes as breaks in the time-series – Developed by Dr. Jan Verbesselt, Wageningen University (Netherlands)– BFAST R package is open source and free of charge ('http://bfast.r-forge.r-project.org/)
3. MODIS NDVI time-series for forest change detection using BFAST algorithm (Verbesselt et al.)
ReCover for REDD and sustainable forest management
• Input: 16 day MODIS NDVI composites (250m)– Complete country-wide time-series for 2000 – 2012– MODIS data is freely downloadable
• Settings:– Historical period: 01/2000-12/2004– Monitoring period: 01/2005-01/2012
ND
VI
Stable tropical forest pixel
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3. MODIS NDVI time-series for forest change detection using BFAST algorithm (Verbesselt et al.)
ReCover for REDD and sustainable forest management
ND
VI
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Deforestation pixel
3. MODIS NDVI time-series for forest change detection using BFAST algorithm (Verbesselt et al.)
• Input: 16 day MODIS NDVI composites (250m)– Complete country-wide time-series for 2000 – 2012– MODIS data is freely downloadable
• Settings:– Historical period: 01/2000-12/2004– Monitoring period: 01/2005-01/2012
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Deforestation pixel
• If break detected -> Output:(1) Date of change
(2) Magnitude of Change (compared to historical period)
3. MODIS NDVI time-series for forest change detection using BFAST algorithm (Verbesselt et al.)
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MODIS NDVI analysis analysis Fiji – Results
Year of change
ReCover for REDD and sustainable forest management
Apply MODIS NDVI time-series algorithm at Landsat time-series (30m pixel resolution)
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2000-2012, Intensive cloud cover
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4. ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping
• Geoscience Laser Altimeter System (GLAS)
• 1 precision surface lidar (1064nm)
• 1 cloud and aerosol lidar (523nm)
http://earthobservatory.nasa.gov/Features/ICESat/
• Mission life time 2003-2009
• Developed by NASA
• One scientific instrument
• Ice sheets; vegetation
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• 3 Lasers of non-continuous
• 40 shots per second
• 33-day to 56-day campaigns,
• footprint ~52m to 148m (70m)
• Laser spot separation
along track ~175m
4. ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping
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• Data distribution by National Snow and Ice Data Centre
• 15 standard GLAS products, binary file format
• GLA01 product
• Transmitted and received waveform parameters
• GLA14 product
• Global land surface altimetry data
• Up to 6 Gaussian peaks fitted to waveform
• Range increments
• Quality flags (cloud, saturation, range correction..)
4. ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping
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signal begin
signal end
ground
GLAS derived canopy height
4. ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping
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ICESat’s heights (pink & green ellipses = footprint)Airborne Laser Scanning (ALS) point cloud (blue)Digital terrain model by ALS data
4. ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping
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Vegetation height map
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5. ReCover workshop and field trip (October 2012)
• ReCover workshop– Participants: Forestry, GIZ, SOPAC and ReCover team– Presenting the ReCover project and status of remote
sensing based products– Joint work & data exchange with Forestry and SOPAC
• Joint ReCover field trip (SOPAC & ReCover team)
• ReCover work will be continued– Product refinement and validation– Joint work and data exchange
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Vinaka vaka levu!
http://www.vtt.fi/sites/recover/?lang=en