Abstract. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).

Abstract. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground forest biomass (dry mass, AGB) with a spatial resolution of 1 ha. Using an extensive database of 110,897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high carbon stock forests with AGB > 250 Mg ha−1 where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country’s national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps, and identify major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the sub-tropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon and socio-economic modelling schemes, and provides a crucial baseline in future carbon stock changes estimates. The dataset is available at: https://doi.pangaea.de/10.1594/PANGAEA.894711 (Santoro, 2018).

Abstract. The Guyana Forestry Commission’s (GFC) Monitoring, Reporting and Verification System (MRVS) is a combined Geographic Information System (GIS) and field-based monitoring system, which has underpinned the conducting of a historical assessment of forest cover as well as eight national assessments of forest area change to date. The System seeks to provide the basis for measuring verifiable changes in Guyana’s forest cover and resultant carbon emissions from Guyana’s forests, which will provide the basis for results-based REDD+ compensation in the long-term. With the continuous compilation, analysis and dissemination of MRVS results on a typically annual basis, the GFC envisioned a larger role for this data, in informing national processes such as natural resources policy and management. This resulted in a significant broadening of the application of the MRVS data and products for purposes that are aligned or complementary to national REDD+ objectives and forest policy and management. These broader applications have allowed for a beneficial shift towards the increased use of remote sensing data and scientific reporting to inform forest management, governance and decision making on natural resource management across forested land. This has resulted in a transformation in the nature of data available to inform decision making on forest management and governance, and overall environmental oversight, from predominantly social science data and factors to now incorporating remote sensing and scientific observations and reporting. Primary decision makers are turning to scientific based reporting to determine best approaches for developmental initiatives in Guyana. This study shows how Guyana has demonstrated significant progress in making remote sensing products accessible and useful to policy makers in Guyana.

Supplement of manuscript The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations A.1 Auxiliary datasets The European Space Agency (ESA) Climate Change Initiative Land Cover (CCI-LC) dataset consists of annual (1992-2018) maps classifying the world's land cover into 22 classes (Table S6). The overall accuracy of the 2010 land cover dataset was 76% (Defourny et al., 2014), with the most relevant commission and omission errors in mixed classes or in regions of strongly heterogeneous land cover. The land cover maps were provided in equiangular projection with a pixel size of 0.00278888° in both latitude and longitude. In this study, we used the land cover map of 2010, version 2.07. The dataset was reprojected to the map geometry of our AGB dataset. The Global Ecological Zones (GEZ) dataset produced by the Food and Agriculture Organization (FAO, 2001) divides the land surface into 20 zones (Figure S1, Table S2) with "broad yet relatively homogeneous natural vegetation formations, similar (but not necessarily identical) in physiognomy" (FAO, 2001). In this study, the dataset has been rasterized to the geometry of the images requiring stratification by ecological zones. Spatially explicit datasets of GSV representative of dense forests were obtained to support the model calibration described in Sections A.2 and A.3. This dataset was first compiled by assigning a value to the centre of each tile in a regular 2°×2° grid. Where available, in situ measurements from field plots or spatially explicit datasets of GSV were used. The GSV of dense forests was then defined as the 90 th percentile of the histogram within the 2°×2° area (Santoro et al., 2011). Elsewhere, it was estimated with an empirical piece-wise linear function (Santoro et al., 2015a) starting from values of the average biomass reported at provincial or national level. For tiles including several provinces or nations, the average biomass representative for the tile was obtained by weighting the individual averages by the area of each within the tile. In regions where values based on inventory measurements were unavailable but we could gather more than one map of AGB (preferably based on laser scanning observations), we

Abstract. In 2010 Guyana started work on the development of a national Monitoring Reporting and Verification System (MRVs) to quantify and measure the changes in the country’s forest cover carbon and carbon emissions. A necessary part of this process involved the identification of reliable Earth Observation data of sufficient resolution to detect and quantify land use change in Guyana's forests. Over the past 10 years the MRVs has evaluated and integrated many data streams used to; map national-scale forest change, support the analysis, and importantly datasets suitable to determine the accuracy of the change area mapped. Guyana’s approach has evolved over time, to accommodate new technologies, but at its core the MRVs recognises the importance of local management, existing datasets and linking these elements to appropriate EO data such as Landsat, RapidEye, Sentinel, Planet Scope and very high spatial resolution aerial imagery. From the outset the MRVs development was divided into phases. This approach recognises that not all MRVs reporting functions can be satisfied immediately. For Phase 1 (Years 2010 to 2014) of the MRVS, historical change analysis was conducted using Landsat 30 m resolution imagery. Being a persistently cloudy country alternative EO data sources were included, with Landsat and DMC imagery largely superseded by 5 m resolution RapidEye imagery. After five years of monitoring the forest change baselines, methods, reporting processes and standard operating procedures had been well established and able to provide the required performance-based indicators. The focus of MRVs phase 2 (Years 2015 to 2019) was to retain the reporting standards already achieved while also streamlining processes, improving functionality and reducing operational costs (i.e. the reliance on commercial image data) post-2019. Process improvements and operational research targeted two areas; the feasibility of using freely available Landsat and 10 metre resolution Sentinel data to map countrywide deforestation, and development of a sample-based approach to estimate degradation from aerial imagery and Planet Scope (3–5 metres resolution). Guyana’s move to integrate multiple data streams has been driven by the need for higher temporal resolution, repeated monitoring, and the creation of a data agnostic system to supports multiple decision-making processes on forest management. While the originally the MRVs was intended to support REDD+ reporting, the flexibility of the system design has meant it is proving to be invaluable tool for natural resources management in Guyana.