The UN's REDD+ (Reducing Emissions from Deforestation and Forest Degradation) programme requires accurate national forest monitoring. Peru has the largest extent of Amazon Basin rainforest after Brazil – 60% of the nation's total land area is humid tropical forest. The country's capital, Lima, is also the site of the UNFCCC negotiations taking place this week.

"While several methods for forest monitoring have been tested in Peru, they have shortcomings regarding replicability, cost and/or timeliness," said Peter Potapov of the University of Maryland, US, and colleagues in a press release. "The Peruvian Ministry of Environment (Ministerio del Ambiente MINAM), which is responsible for forest monitoring, is searching for an efficient, easy to implement and repeatable protocol for annual and multi-year forest monitoring."

With that in mind, MINAM teamed up with the University of Maryland, State University of New York, Conservation International, Peru's Programa Nacional de Conservación de Bosques, and the US SilvaCarbon and USAID Forest Carbon, Markets and Communities programmes to develop and test such a tool.

The group employed a technique developed by the University of Maryland, initially for looking at forest cover at a global scale, which employs Landsat remote sensing data freely available from the United States Geological Survey. The University of Maryland researchers provided a series of training sessions to MINAM specialists, who used their method to map forest-cover extent and loss within Peru.

"According to the IPCC, tropical-forest loss and degradation is responsible for more than 10% of total greenhouse-gas emissions, the primary driver of global climate change," said Potapov. "Peru is losing forest to agricultural, oil-palm plantation [and] mining."

The team quantified forest-cover loss in Peru from 2000–2011 using all of the Landsat images available – a total of 11,654 – and a version of the University of Maryland data processing system modified to suit a national scale. The researchers used higher-resolution Rapid Eye images to validate their findings; the user's accuracy was 92.2%.

"The high-resolution data used for validation not only confirmed the high accuracy of the Landsat-based map, but also allowed us to disaggregate forest loss by region, at annual intervals and by disturbance type – anthropogenic clearing versus natural disturbance," said the researchers. "Our results have been reported in an official national memorandum, providing baseline information for Peru's reporting of deforestation-related greenhouse-gas emissions to the UN. The computer software and training provided to MINAM specialists enables future implementation of the approach within an operational monitoring context."

Potapov reckons that the method may be implemented at the national level for any country requiring basic information on forest extent and change, whether for REDD+ or other forest-management purposes.

"Our approach has been demonstrated in other countries, such as the Democratic Republic of Congo, but not within a government agency in an operational mode," he said. "The next objective of our joint University of Maryland–MINAM project is to perform annual forest-loss updates for Peru. Our collaboration is an example of the successful porting of research methods developed within the scientific community to operational settings within a government agency."

The team reported its findings in Environmental Research Letters(ERL).

• This article was amended on 10/12/14 to correct the figure for forest loss in the first sentence.

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