Pacific Island GIS&RS conference 2012, 27 – 30 November 2012, Suva

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 University Donata Pedrazzani: GMV Fabian Enßle: Freiburg University

Outline • ReCover project objective • ALOS PALSAR change detection and time-series analysis • MODIS time-series analysis for forest change detection • ICESat/GLAS space borne laser ranging for forest height & biomass • ReCover workshop and field work (October 2012)

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

2. ALOS PALSAR change detection and time-series analysis • 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 RGB R: HH polarisation G: HV polarisation B: HH/HV ratio

ALOS PALSAR: Dual-temporal (2007,2010) coverage of west VitiLevu 2. ALOS PALSAR change detection and time-series analysis 2010-08/09 2007-08/09

Forest land cover change detection (VitiLevu west) 2007 - 2010 (first results, need to be evaluated) Classification Step 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 Water mask Positive change (e.g. reforestation) Positive change (e.g. reforestation) Negative change Negative change Forest/dense vegetation -> non-forest Forest/dense vegetation -> non-forest Other vegetation decrease 6 Other vegetation decrease

Time-series examples 2. ALOS PALSAR change detection and time-series analysis Stable forest

Time-series examples 2. ALOS PALSAR change detection and time-series analysis Deforestation of pine plantagen

Time-series examples 2. ALOS PALSAR change detection and time-series analysis Regrowth

3. MODIS NDVI time-series for forest change detection using BFAST algorithm (Verbesselt et al.) • 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.) • 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 Stable tropical forest pixel NDVI

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 Deforestation pixel NDVI

3. MODIS NDVI time-series for forest change detection using BFAST algorithm (Verbesselt et al.) • If break detected -> Output: • Date of change • Magnitude of Change (compared to historical period) Deforestation pixel

MODIS NDVI analysis analysis Fiji – Results Year of change

Apply MODIS NDVI time-series algorithm at Landsat time-series (30m pixel resolution) 2000-2012, Intensive cloud cover

4. ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping • Developed by NASA • Mission life time 2003-2009 • Ice sheets; vegetation • One scientific instrument http://earthobservatory.nasa.gov/Features/ICESat/ • Geoscience Laser Altimeter System (GLAS) • 1 precisionsurfacelidar (1064nm) • 1 cloudandaerosollidar (523nm)

4. ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping • 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 • 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 signal begin ground signal end GLAS derived canopy height

4. ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping ICESat’sheights (pink & green ellipses = footprint) Airborne Laser Scanning (ALS) point cloud (blue) Digital terrain model by ALS data

Vegetation height map

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

Vinaka vaka levu! http://www.vtt.fi/sites/recover/?lang=en

Pacific Island GIS&RS conference 2012, 27 – 30 November 2012, Suva