Satellite data applications in Indonesia

Transcription

1 Satellite data applications in Indonesia International Workshop on Inventory, Modeling, and Climate Impacts of GHG s and Aerosols in the Asian Region, Tsukuba, June 2013

2 Outline National priorities of monitoring forest for carbon accounting in Indonesia. Indonesia s National Carbon Accounting System (INCAS). Current methods for forest change mapping: Indonesia s National Carbon Accounting System (INCAS). CSIRO-Australia, Univ of Maryland-USA, Waganingen-Nederland, JAXA-Japan. Harmonization activities Closing remarks.

3 National priorities of monitoring forest for carbon accounting in Indonesia Indonesia GHG Emission Sources Source: Second National Communication on Climate Change (2009) Land clearing and forestry are significant components of Indonesia s emissions profile. There are large uncertainties in the knowledge of the time, location and size of land clearing events. A high quality, credible carbon accounting system needs to be established to show the success of REDD policies and interventions.

4 Indonesia s National Carbon Accounting System (INCAS) A Biomass Classification Annual Land Cover Change B C Classification of forests into groups (biomass classes) with common biomass characteristics (in undisturbed condition) Forest Disturbance Class Mapping E Carbon Accounting and Reporting Model (ICARM) Annual time-series defining areas of: Deforestation (permanent loss of forest cover) Degradation (forest clearance and regeneration or partial removal) C-mass Estimation D National map of forest disturbance classes at known date Minimal disturbance Moderate disturbance Heavy disturbance C-mass estimates for each biomass class (stock, growth/loss rate): Aboveground biomass Belowground biomass Litter Debris Soil

5 Land cover change actifities in Indonesia for INCAS International Collaboration CSIRO (Australia) Univ. Of Maryland (USA) Wageningen Univ. (Nederland) JAXA (Japan) Data Resolusion Area Periode Methodology Landsat 5/7 25 meter Indonesia Landsat 5/7 60 meter Indonesia ALOS PALSAR, RADAR SAT-2 50 meter Kalimantan ALOS PALSAR 25 meter Sumatera Bayesian probability Network Tree Class. algorithm Marcov Random Field Random Trees, SVM, MLP (Multi Layer Perceptron) Note: Collaboration between LAPAN and International Institution

6 Forest Cover Change based on CSIRO method

7 Forest Cover Change product from based on CSIRO method (Status on June 2013)

8 Sample forest cover change from 2000 to 2009 Dark Grey: Mapped as forest in Shades of red, orange and pink = Forest loss. Each shade corresponds to a different year (e.g , ,..., ) Shades of green and yellow = Forest gain. Each shade corresponds to a different year Light blue = Forest lost and gained (e.g disturbance and recovery or harvest and replanting). Backdrop (light greys): Landsat image composite

9 Landsat mosaic product (RGB 543) 2000, 2005, 2010 based on UMD (University of Maryland) method

10 Forest Cover Change product from based on UMD (University of Maryland) method FOREST GAIN Forest in 2000 Forest Gain Forest Gain FOREST LOSS Forest in 2000 Forest Loss Forest Loss

11 Land cover products based on Wageningen University method Legend: Lowland forest Riverine forest Swamp forest Mangrove forest Nipah mangrove forest Peat swamp pole forest Peat swamp padang forest Forest mosaics Shrubs Ferns / grass Grasslands, Alang-alang Cropland (upland) Cropland (irrigated) Plantations (Oil palm) Burnt peat swamp forest Open Water Layover /Shadow No strip coverage Montane forest Savannah grass Shrubs&Ferns New deforestation Degraded psf/f

12 Land cover products based on JAXA and LAPAN method ALOS PALSAR IMAGERIES Random SVM Trees Classifier Classifier

13 Sample Land cover products 2010, based on JAXA and LAPAN method (random trees Classifier) Forest (forest, swamp forest, acacia) Rubber Shrubs with trees + mangrove Oilpalm + coconut Shrubs Cropland Baresoil Settlement Water Location: Jambi & Harapan, Year 2010

14 Classification Results Comparison by Classifier Types for JAXA and LAPAN methode Classifier Classification Accuracy (%) MLP (Multi Layer Perceptron) LC 9 classes FNF Random Trees SVM (Support Vector Machine)

15 Harmonization approach Objective to bring together the different methodologies to form the one harmonized approach to deliver an assured time series analysis of land cover change which is capable of supporting the Government of Indonesia s carbon accounting and emissions reporting activities. Area : Kalimantan Participants: CSIRO, optical data Univ. of Maryland, optical data Wageningen University, SAR data Harmonization Workshop (Bogor, Nov 2012)

16 Forest in Landsat 2009 (CSIRO)

17 Forest in Landsat 2010 University of Maryland

18 F FM NF Forest in Palsar 2009 (WU)

19 FOREST: Palsar (F) Landsat-CSIRO Landsat-UMD

20 FOREST: Palsar (F+FM) Landsat-CSIRO Landsat-UMD

21 Area summaries report of harmonization result In the PALSAR maps the green classes are considered forest (F), the yellow classes (FM) could be included in the forest class but also in the non-forest class, depending on the definitions used. The red classes are non-forest (N).

22 Closing Remarks Next steps and expectations : Accuracy assessments using High Resolution imageries The same understanding about Forest, forest degradation, and deforestation definitions Strengt h and weakness analysis of all three products. Integration of optical and radar techniques.

23 Thank You for Your Attention

24 Jeremy Wallace Acknowledgements Commonwealth Scientific and Research Organization (CSIRO) - Australia Suzanne Furby CSIRO - Australia Dr. Matthew Hanson University of Maryland - USA Dirk Hoekman Wageningen University - Nederland