Abstract
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.
Highlights
It is estimated that 12%–20% of global greenhouse gas emissions between 1990 and 2000 were from forest loss, primarily in tropical regions [1]
The aim of this paper is to evaluate the suitability of the Global Forest Change datasets for national-level MRV reporting, and the objectives are: (a) to assess the percent tree canopy cover threshold in the Global Forest Change data that best corresponds to the forest definition for Guyana, and (b) to compare the Global Forest Change dataset forest change estimates against the Guyana-MRV maps for the period between 2001 and 2017
The results identify an overestimation of forest cover in the Global Forest Change dataset in the year 2000 tree canopy cover percent, even though errors in forest loss tend to be averaged out over the period 2001–2017
Summary
It is estimated that 12%–20% of global greenhouse gas emissions between 1990 and 2000 were from forest loss, primarily in tropical regions [1]. Federici et al [4] estimated a global forest-related emissions value of 1.10 109 tC year−1 for forest-related emissions from 1991 to 2015 based on modelling of country data derived from the Food and Agricultural Organization of the United Nations (FAO). This compares favourably with a study by Le Quéré et al that quantifies all major fluxes of the global carbon cycle and reports an annual emission value of 1.0 ± 0.5 109 tC year−1 for land use change between 2000 to 2014 [5]. The reader should note the large uncertainty attached to the estimates of annual carbon loss and of annual global emissions; the differences in quantitative estimates are essentially due to the quantity, quality and global availability of reliable land cover change data, which are the focus of this paper
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