ReCover for REDD and sustainable forest management EU ReCover project: Remote sensing services to support REDD and sustainable forest management in Fiji

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


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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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



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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


ALOS PALSAR: Dual-temporal (2007,2010) coverage of west Viti Levu

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2007-08/092010-08/09



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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


Time-series examples

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Stable forest



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Deforestation of pine plantagen

Time-series examples



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RegrowthTime-series examples



• 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.)

http://bfast.r-forge.r-project.org/


• 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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ND

VI

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Deforestation pixel


• 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)



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MODIS NDVI analysis analysis Fiji – Results

Year of change


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



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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..)



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signal begin

signal end

ground

GLAS derived canopy height



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ICESat’s heights (pink & green ellipses = footprint)Airborne Laser Scanning (ALS) point cloud (blue)Digital terrain model by ALS data



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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

Documents

ReCover for REDD and sustainable forest management EU ReCover project: Remote sensing services to support REDD and sustainable forest management in Fiji