Pacific island gis rs conference 2012 27 30 november 2012 suva
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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.

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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
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
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
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 viti levu
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 viti levu west 2007 2010 first results need to be evaluated
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
Time-series examples

2. ALOS PALSAR change detection and time-series analysis

Stable forest


Time series examples1
Time-series examples

2. ALOS PALSAR change detection and time-series analysis

Deforestation of pine plantagen


Time series examples2
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
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 al1
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 al2
    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 al3
    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



    Apply modis ndvi time series algorithm at landsat time series 30m pixel resolution
    Apply MODIS NDVI time-series algorithm at Landsat time-series (30m pixel resolution)

    2000-2012, Intensive cloud cover


    4. time-series (30m pixel resolution)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. time-series (30m pixel resolution)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. time-series (30m pixel resolution)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. time-series (30m pixel resolution)ICESat/GLAS: space borne laser ranging for vegetation height and biomass mapping

    signal begin

    ground

    signal end

    GLAS derived canopy height


    4. time-series (30m pixel resolution)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 time-series (30m pixel resolution)


    5 time-series (30m pixel resolution). 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! time-series (30m pixel resolution)

    http://www.vtt.fi/sites/recover/?lang=en


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