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New database key to fighting deforestation

Indonesia is seen by many to have unreliable forestry data because of poor data management, poor data access and conflicting data sets

Hans Nicholas Jong (The Jakarta Post)
Jakarta
Sat, November 29, 2014

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New database key to fighting deforestation

I

ndonesia is seen by many to have unreliable forestry data because of poor data management, poor data access and conflicting data sets.

Although the government has fought against deforestation for years, it lacks a clear and cohesive benchmark on which a masterplan could be based.

But this is all about to change as Indonesia is poised to for the first time submit data to the global benchmark known as the forest reference emissions level (FREL).

FREL is a benchmark required for a country to measure its achievement in reducing emissions from deforestation and forest degradation (REDD+).

'€œIn order to reduce deforestation, we have to know first the projection of our business-as-usual deforestation rate in upcoming years,'€ Arief Darmawan of the REDD+ Agency (BP REDD+) technology, system and monitoring division told The Jakarta Post.

Launched in 2007, REDD+ is a mechanism aimed at slowing anthropogenic climate change by providing financial incentives to protect forests, as they absorb carbon from the atmosphere.

Arief said that the government had finished calculating its FREL and that it would submit the result to the United Nations Framework Convention on Climate Change (UNFCCC) during the 20th Conference of the Parties (COP) on Dec. 8 in Lima.

'€œIf we have submitted it and the technical assessment deems it to be good, that would be a game-changer,'€ he said. '€œThis will be our key to cooperate with other countries when they see that we finally have a reliable benchmark.'€

FREL is calculated by a team of experts from BP REDD+, the Environment and Forestry Ministry, the Agriculture Ministry, the Geospatial Information Agency (BIG), the Center for International Forestry Research (CIFOR), the Bogor Agriculture Institute (IPB), the United Nations Office for REDD+ Coordination in Indonesia (UNORCID) and The Nature Conservancy (TNC).

The team decided on 671,420 hectares per year as the benchmark for the country'€™s deforestation rate until 2020 after compiling deforestation data from the forestry ministry.

'€œThis FREL is calculated based on the average deforestation rate from 2000 to 2012. We are aiming to reduce our deforestation rate to below 671,420 hectares per year,'€ said Arief.

Indonesia is committed to reducing greenhouse gas emissions by 26 percent unilaterally or 41 percent with foreign assistance from current levels by 2020.

Arief said that he was confident of the reliability of the data; so much so that the country should have no problem defending its FREL during the COP.

'€œEven though data from the forestry ministry was obtained manually, we'€™re confident about it because we have already cross-checked it using digital data. It turns out to be consistent,'€ he said.

To obtain the data, the ministry used visual classification methods based on satellite imagery.

The ministry'€™s data was then compared with data from a study published in 2014 by Belinda Margono from the University of Maryland, which used high-resolution satellite imagery.

The team started working on the FREL in April 2014, but the process has not always been easy, especially after the forestry ministry released its own results on July 21.

'€œThey declared the FREL to be 0.816 gigatons of CO2, which is almost the actual figure. We were confused because the process [to calculate FREL] was still ongoing, but suddenly this number came out of nowhere,'€ he said.

It turned out that the ministry'€™s FREL was based on data from 2000 to 2006, which, by only taking into account carbon-heavy primary forest, overestimated the rate of deforestation in the country.

The ministry'€™s forestry planology director-general Bambang Soepijanto, meanwhile, said that the finalized FREl was a revision of the ministry'€™s version, and that the two studies had used the same set of data.

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