Global Forest Change datasets have the potential to assist countries with national forest measuring, reporting and verification (MRV) requirements. This paper assesses the accuracy of the Global Forest Change data against nationally derived forest change data by comparing the forest loss estimates from the global data with the equivalent data from Guyana for the period 2001–2017. To perform a meaningful comparison between these two datasets, the initial year 2000 forest state needs first to be matched to the definition of forest land cover appropriate to a local national setting. In Guyana, the default definition of 30% tree cover overestimates forest area is by 483,000 ha (18.15%). However, by using a tree canopy cover (i.e., density of tree canopy coverage metric) threshold of 94%, a close match between the Guyana-MRV non-forest area and the Global Forest Change dataset is achieved with a difference of only 24,210 ha (0.91%) between the two maps. A complimentary analysis using a two-stage stratified random sampling design showed the 94% tree canopy cover threshold gave a close correspondence (R² = 0.98) with the Guyana-MRV data, while the Global Forest Change default setting of 30% tree canopy cover threshold gave a poorer fit (R² = 0.91). Having aligned the definitions of forest for the Global Forest Change and the Guyana-MRV products for the year 2000, we show that over the period 2001–2017 the Global Forest Change data yielded a 99.34% overall Correspondence with the reference data and a 94.35% Producer’s Accuracy. The Guyana-MRV data yielded a 99.36% overall Correspondence with the reference data and a 95.94% Producer’s Accuracy. A year-by-year analysis of change from 2001–2017 shows that in some years, the Global Forest Change dataset underestimates change, and in other years, such as 2016 and 2017, change is detected that is not forest loss or gain, hence the apparent overestimation. The conclusion is that, when suitably calibrated for percentage tree cover, the Global Forest Change datasets give a good first approximation of forest loss (and, probably, gains). However, in countries with large areas of forest cover and low levels of deforestation, these data should not be relied upon to provide a precise annual loss/gain or rate of change estimate for audit purposes without using independent high-quality reference data.

Background and Methods: Degradation of forests in developing countries results from multiple activities and is perceived to be a key source of greenhouse gas emissions, yet there are not reliable methodologies to measure and monitor emissions from all degrading activities. Therefore, there is limited knowledge of the actual extent of emissions from forest degradation. Degradation can be either in the forest interior, with a repeatable defined pattern within areas of forest, as with timber harvest, or on the forest edge and immediately bounding areas of deforestation. Forest edge degradation is especially challenging to capture with remote sensing or to predict from proxy factors. This paper addresses forest edge degradation and: (1) proposes a low cost methodology for assessing forest edge degradation surrounding deforestation; (2) using the method, provides estimates of gross carbon emissions from forest degradation surrounding and caused by alluvial mining in Guyana, and (3) compares emissions from mining degradation with other sources of forest greenhouse gas emissions. To estimate carbon emissions from forest degradation associated with mining in Guyana, 100 m buffers were located around polygons pre-mapped as mining deforestation, and within these buffers rectangular transects were established. Researchers collected ground data to produce estimates of the biomass damaged as a result of mining activities to apply to the buffer area around the mining deforestation. Results: The proposed method to estimate emissions from forest edge degradation was successfully piloted in Guyana, where 61% of the transects lost 10 Mg C ha⁻¹ or less in trees from mining damage and 46% of these transects lost 1 Mg C ha⁻¹ or less. Seventy percent of the damaged stems and 60% of carbon loss occurred in the first 50 m of the transects. The median loss in carbon stock from mining damage was 2.2 Mg C ha⁻¹ (95% confidence interval: 0.0–10.2 Mg C ha⁻¹). The carbon loss from mining degradation represented 1.0% of mean total aboveground carbon stocks, with emissions from mining degradation equivalent to ~2% of all emissions from forest change in Guyana. Conclusions: Gross carbon emissions from forest degradation around mining sites are of little significance regardless of persistence and potential forest recovery. The development of cost- and time-effective buffers around deforestation provides a sound approach to estimating carbon emissions from forest degradation adjacent to deforestation including surrounding mining. This simple approach provides a low-cost method that can be replicated anywhere to derive forest degradation estimates.