Guyana possesses one of the largest tracts of untouched rainforest in South America. In 2009, the Governments of Norway and Guyana embarked on one of the first national-scale REDD+ initiatives to preserve Guyana’s forests. The bilateral agreement sets out how the two countries would work together to achieve the objective of Guyana conserving its forest stocks and helping reduce global carbon emissions.
The aim was to create a REDD+ MRV system with a solid methodology for the detection and reporting of national forest change. Potentially, the methods employed could serve as model that could assist other countries to progress their REDD+ MRV initiatives off the ground. Initially, the Guyana Forestry Commission (GFC) found the wall-to-wall mapping and monitoring program to be a major undertaking, due to the size of the country (~215,000 km2). It has since found a way to effectively establish processes to streamline operations and standardize outputs that enables annual reporting of forest change.
Until 2009, the only cost-effective and reliable source of imagery to accomplish the goals of this project was Landsat. Forest degradation was impossible to assess from the first year dataset due to the low resolution of the Landsat imagery. As a result, the degradation estimates for the first measurement year (2010) were based on the assumption that forest degradation radiates from deforested sites for a distance of 500 meters. In order to maintain continuity and improve the quality of the detection the GFC and Indufor team considered using RapidEye imagery.
The GFC and Indufor team decided that high resolution imagery was required to improve the detection of small-scale degradation events. A methodology was developed using five meter RapidEye imagery. The method adopted considered the visual characteristics of degradation including, size, proximity and its spectral characteristics. In 2011, RapidEye imaged 60% of Guyana over a four month period. In 2012, this was expanded to the entire country.
An Eye on Results
Guyana has established an annual, nation-wide MRV system. The historical analysis (1990-2009) has shown that the country lost about 0.02% of its forest area every year. The incorporation of RapidEye and improved forest change routines into the MRV system have resulted in improved detection and classification of both deforestation and degradation events.
In 2011, the RapidEye constellation was tasked over a four month period to collect approximately 12 million hectares (120,000 km2) of new imagery over all previously identified year one (2009-2010) forest loss areas. Using the higher resolution RapidEye imagery as opposed to strictly Landsat, a higher level of accuracy in the mapping was achieved. This is particularly apparent in determining the cause and the extent of both forest loss and degradation.
While the overall change identification process was consistent from year 1 to year 2, modifications were made to accommodate the shift from 30 m Landsat to 5 m RapidEye imagery. The most notable change was the two stage evaluation approach that was implemented for year two.
For the first stage, a grid the size of a RapidEye tile (25 × 25 km) was used to manually screen for change. The second stage used a combination of automated and manual processes to detect change. Each change event was systematically evaluated by casting a 1 × 1 km grid over the image. The outcome showed that changes could easily be identified in stage one, while accurately quantifying and attributing a cause to the change was carried out in stage two.
Since 2010 an independent in-country validation of the forest change estimates has been conducted by the University of Durham, England. The review process evaluates the accuracy of the mapping by scrutinizing the methodology and developing a statistical sampling approach to verify the results. Their audit concluded that the estimation of the 2011 forest loss was the same as reported by the GFC and Indufor team’s analysis. The overall map accuracy (for both deforestation and forest degradation) was 99.2%.
The University of Durham attributed the extremely high accuracy rate to the manual multi-stage methods when validating forest loss, the five meter high resolution RapidEye imagery and the meticulous work of the GFC and Indufor team.
In the University’s recommendations and comments, they strongly suggested that RapidEye data be used to image the entire country of Guyana in future years, as it is “of excellent quality and ideally suited for the task”. They also stated that RapidEye data was “…of sufficient spatial resolution to identify deforestation and the main drivers of deforestation.”
The Guyana Forestry Commission and Indufor team have already received RapidEye coverage over all of Guyana for 2012. This data will also be used to continue the REDD+ MRV assessment of forest loss and degradation. These measurements are used to prove that Guyana has met or exceeded the forest management benchmarks established with the government of Norway that trigger incentive payments.
Figure 1. RapidEye Imagery of Guyana
About RapidEye for REDD+
As part of its effort to assist participating countries in becoming “REDD-ready”, RapidEye offers an extensive and very recent archive of imagery, which can provide users with a wealth of information. Maps can be created showing current land use or land cover in regions, while multiple coverages can show changes that have happened to an area over time.
Whether identifying which areas are forested or tracking the change of forested land over multiple years, RapidEye is the perfect partner. Wall-to-wall coverages of most REDD countries are available in the RapidEye archive for National REDD+ initiatives. Contact email@example.com for more information.