SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 Author/Presenter Indonesia MRV & Reporting Status & Related Space Data Needs Nikki Fitzgerald Technical support for the Indonesian National Carbon Accounting System (INCAS)

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 Introduction The Indonesian National Carbon Accounting System has been supported through the Indonesia – Australian Forest Carbon partnership since July The executing agency of this programme is in the Ministry of Forestry, but the remote sensing component is managed by the National Institute of Aeronautics and Space (LAPAN). The Space Act, mandating that LAPAN supply Remote Sensing data used by all government agencies was established on August 6, 2013 A presidential decree to establish a REDD+ Agency was signed on September 2, 2013.

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 National Carbon Accounting Ambitions The INCAS programme was designed to: – monitor annual changes in sources and sinks for the land sector – have sufficient flexibility to support all emissions reporting requirements, both international & domestic – quantify impact of past & current Indonesian policies and land management practices – support modelling of future scenarios to establish a credible Reference Emission Level (REL) – be a flexible system framework with a built-in continuous improvement plan On a path toward Tier 3 / Approach 3 accounting.

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 Status of National MRV Implementation Indonesia has previously submitted National Communications to the UNFCCC. – The Ministry with responsibility for these reports has recently agreed that it will use INCAS to report emissions from the land sector in its 3 rd National Communication (December 2014). Indonesia also has a national action plan to implement domestic MRV projects in each of the 33 provinces. – INCAS is expected to be the mandatory system to produce consistent estimates.

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 – wall-to-wall coverage of Landsat – annual reporting of emissions – coverage of all carbon pools and all greenhouse gases – reporting at fine scales – scalable to allow nesting – ability to test different land use and management scenarios – spatial and temporal consistency MRV system design : to integrate remote sensing data of forest cover change with on-ground biomass and other site data to quantify annual changes in forest carbon stock across the archipelago.

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 Annual Land Cover Change B Annual time-series defining areas of: ▪ Deforestation (permanent loss of forest cover) C-mass Estimation D C-mass estimates for each biomass class (stock, growth/loss rate): Aboveground biomass Belowground biomass Litter Debris Soil Biomass Classification A Classification of forests into groups (biomass classes) with common biomass characteristics (in undisturbed condition) Forest Disturbance Class Mapping C National map of forest disturbance classes at known date - Minimal disturbance - Moderate disturbance - Heavy disturbance Carbon Accounting and Reporting Model (ICARM) E  Degradation (forest clearance and regeneration or partial removal)

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 – Annual forest cover change from for Kalimantan, Sumatera, Papua & Sulawesi islands – Timeseries processing of Java, Nusa Tenggara Timor & Maluku islands are currently in progress – Capacity building through training in image processing – Purchase of high-resolution images targeting areas of uncertain forest condition and type, to guide the Landsat classification and perform verification of the results – Installation of a high performance computing system Remote Sensing Achievements Kalimantan forest cover change since 2000.

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 Next Steps – Land Cover Change Analysis 1.Obtaining Landsat data for 2011 and Performing time series land cover change analysis for the periods of 1990 to 1999, and 2010 to Analysing the land cover change of the remote sensing to deliver land use change (activity data) for integration and modelling in the complete system. 4.Use of existing high resolution imagery to validate the results. The GFOI could consider adding this verification step to the guidance. The GFOI could consider developing tools that report certainty statistics from the classification

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 LAPAN has established a National Remote Sensing Data Bank to ensure that the large quantities of data generated by this initiative are efficiently stored, archived and made available for dissemination – Landsat and SPOT data are acquired through local ground stations. – There is a need for assistance to design IT (storage, network, retrieval) systems and data management protocols The SDCG could consider support for in-country data management for all countries receiving data through CEOS mechanism(s). Data Management

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 Lower resolution, higher frequency satellite data are candidates to deliver rapid assessment tools to help identify forest management information (e.g. from selective logging & fire), yet the historical data requirement poses a problem. To use this information with the time series activity data from Landsat there is a need to generate consistent products since As such, it will be necessary to use multiple sensors. The SDCG could consider developing a methodology for cross- calibration &/or disaggregation between sensors such as MODIS and NOAA Data for rapid assessment

SDCG-4, Caltech, CA, USA 4 th -6 th September 2013 Classifying “Forest / Non-forest” using Landsat data is an operational methodology employed by both the Australian and Indonesian systems. These transitions are currently intersected with available forest type data, almost guaranteed to be created by a different source for a different purpose. One improvement would be to extend the Landsat classification to identify different cover types at each time step. (eg natural forest converted to plantation forest) The UNFCCC requirement to report transitions between non- forest land-uses (eg. Grassland to Cropland or Settlement) is also currently reliant on external data sources. (link to GEOGLAM?) Land Use Mapping (future R&D)