Assessing Forest Degradation using Time Series of Fine Spatial Resolution Imagery in Africa

Transcription

1 Assessing Forest Degradation using Time Series of Fine Spatial Resolution Imagery in Africa A. Verhegghen, B. Desclée, H. Eva, P. Mayaux, F. Achard European Commission Joint Research Centre Sentinel-2 for Science Workshop May 2014 ESA-ESRIN Frascati (Rome) Italy

2 ReCaREDD project REDD + : reducing emissions from deforestation, forest degradation, conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries ReCaREDD project - Strengthening national and regional capacities for reporting on the mitigation actions of the forest sector in tropical countries TREES 3: Tropical Forest Cover Change Assessment

3 Lead institution & project name FAO FRA-RSS JRC TREES-3 JRC/CIRAD/ OFAC FAO UN-REDD WB (Grants) EC/AUC & MESA UCL FCPF AMESD Key products of project for MRV of REDD+ Deforestation estimates by regions Regional forest map Deforestation estimates at national level Deforestation estimates at national level Spatial Coverage of project Tropics (JRC) / Global (FAO) Central Africa 16 countries (with Cent. Africa) Capacity building Regional workshops Project embedded in COMIFAC Support to National Progr. No maps 12 countries Support to National Progr. Real-time Environmental monitoring Africa (Regional Economic Comm.) In regional centres Policy support REDD phase 1 Yes Yes Yes Yes Specific Comments Spatial information. Deforestation only Spatial information. Biodiversity safeguard considered Based on existing methods Based on existing methods Complementary to forest-cover (fires, ) FAO FRA Country Survey Global report from national statistics Global - - No spatial information WB REDD Allometric equations Central Africa Workshops Yes Regional and training Capacity-building sessions JRC ReCaREDD Regional Forest maps Degradation Focus on selected In regional and national REDD phases 1 Spatial information for estimates at regional regions and institutions & 2 and national levels countries all activities 3

4 ReCaREDD projet Funded by the European Commission ( ) Joint Research Center (Ispra and Brussel) in collaboration with target countries (Africa, South America, South East Asia) Focusing on Forest Degradation Joint methodological development for the monitoring of forest degradation Strengthening of regional forest observatories and national institutions Provision of information of forest-cover evolution to policy makers Sentinel 2

5 Forest Degradation Complex definition FAO (2009) : Reduction of the capacity of a forest to provide goods and services UN-REDD: Reduction of carbon stocks Important to define the spatial and temporal scales 2 month before exploitation 1 month after exploitation 2 years after exploitation

6 Landsat ETM 30 m SPOT-5 10 m 6

7 Identification of partner countries Analysis of needs and opportunities: access to data, capacities, institutions, techniques Interest of partner-countries (technical and political) Commitment of partner-countries in forest political process (FLEGT, REDD, ) and operational initiatives (FCPF, UN-REDD ), existence of Climate plan, of C.N. REDD, Complementarity with other projects Libreville March

8 Partner countries in Africa Libreville March 2014 Ivory Coast/Cameroon/Democratic Republic of Congo/Republic of Congo Forest degradation no technical expertise yet Work on case studies with different causes of degradation Collection of fuel wood, charcoal production Shifting cultivation Grazing Selective Logging Forest fires 8

9 Methodological development Identification of partner countries and institutions Definition of a joint working program Data acquisition Development of new methodologies for degradation monitoring Forest cover and forest cover changes maps Transfer of technology with open source softwares

10 RapidEye image (5m) World-View image (0.5 m) Challenges for forest degradation monitoring by remote sensing 1) Complex forest cover change Reference state: Regional/National forest types maps Small areas of change Various change trajectories 2) Exploit higher resolution (10 m) to detect and quantify areas of degraded forests 3) Multi-temporal: Exploit new sensors with higher frequency acquisition From single image to time series analysis 4) Automation 10

11 Jan 2005 Mar 2005 Jan 2007 SPOT Image 20 March 2014 Detected gap Logged tree 11

12 SPOT4Take5 Congo-2 site Logging concessions selective logging Exploitation in 2013 Concomitant with Spot4Take5 acquisition ha

13 SPOT4Take5 Congo-2 site Time series of 20 images acquired between 1/02/2013 and 16/06/2013 CNES/CESBIO V2.0 version (march 2014) Improvement in the orthorectification Cloud mask, still haze and small clouds Landsat 8 and Rapideye images 04/06/

14 SPOT4Take5 Congo-2 site 1) Can we detect degradation? 2) Can we detect the direct area affected? 3) Can we estimate the area of indirect impact? 4) How do we estimate potential emissions? Work on the detection and the definition of degradation on Central Africa 4 June

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16 Analysis of the 6 best images INPE Spectral unmixing Enhance logging tracks and logging gaps Segmentation on Soil fraction + NDVI detection of the logging tracks and gaps for each image 04/06/

17 Difference in area of changes detected ha ha ha ha ha ha ha Cumulative impact observed ha ha ha ha ha ha Single image analysis

18 Changes happen fast

19 Time series analysis with the 20 images NDVI Soil Textural Change detected

20 Estimate the area of indirect impact Interpret the area of change detected How do we estimate potential emissions Logged tree Detected gap Baccini et al. (2012) 20

21 Conclusions and perspectives Spot 4 Take 5 the high temporal frequency give the opportunities to detect the fast changes in humid tropical forest In situ data (logging company) Necessity of various methods for variety of degradation types Time series analysis where available Automated approaches Interest of partner-countries and capacity building 4 June