The enduring world forest carbon sink.

The uptake of carbon dioxide (CO2) by terrestrial ecosystems is critical for moderating climate change1. To provide a ground-based long-term assessment of the contribution of forests to terrestrial CO2 uptake, we synthesized in situ forest data from boreal, temperate and tropical biomes spanning three decades. We found that the carbon sink in global forests was steady, at 3.6 ± 0.4 Pg C yr-1 in the 1990s and 2000s, and 3.5 ± 0.4 Pg C yr-1 in the 2010s. Despite this global stability, our analysis revealed some major biome-level changes. Carbon sinks have increased in temperate (+30 ± 5%) and tropical regrowth (+29 ± 8%) forests owing to increases in forest area, but they decreased in boreal (-36 ± 6%) and tropical intact (-31 ± 7%) forests, as a result of intensified disturbances and losses in intact forest area, respectively. Mass-balance studies indicate that the global land carbon sink has increased2, implying an increase in the non-forest-land carbon sink. The global forest sink is equivalent to almost half of fossil-fuel emissions (7.8 ± 0.4 Pg C yr-1 in 1990-2019). However, two-thirds of the benefit from the sink has been negated by tropical deforestation (2.2 ± 0.5 Pg C yr-1 in 1990-2019). Although the global forest sink has endured undiminished for three decades, despite regional variations, it could be weakened by ageing forests, continuing deforestation and further intensification of disturbance regimes1. To protect the carbon sink, land management policies are needed to limit deforestation, promote forest restoration and improve timber-harvesting practices1,3.

Global patterns of tree wood density.

Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01° (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions and slightly higher values in Siberia forests, western United States, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49%-63% of spatial variations, followed by vegetation characteristics (25%-31%) and edaphic properties (11%-16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change.

Toward a forest biomass reference measurement system for remote sensing applications.

Forests contribute to climate change mitigation through carbon storage and uptake, but the extent to which this carbon pool varies in space and time is still poorly known. Several Earth Observation missions have been specifically designed to address this issue, for example, NASA's GEDI, NASA-ISRO's NISAR and ESA's BIOMASS. Yet, all these missions' products require independent and consistent validation. A permanent, global, in situ, site-based forest biomass reference measurement system relying on ground data of the highest possible quality is therefore needed. Here, we have assembled a list of almost 200 high-quality sites through an in-depth review of the literature and expert knowledge. In this study, we explore how representative these sites are in terms of their coverage of environmental conditions, geographical space and biomass-related forest structure, compared to those experienced by forests worldwide. This work also aims at identifying which sites are the most representative, and where to invest to improve the representativeness of the proposed system. We show that the environmental coverage of the system does not seem to improve after at least the 175 most representative sites are included, but geographical and structural coverages continue to improve as more sites are added. We highlight the areas of poor environmental, geographical, or structural coverage, including, but not limited to, Canada, the western half of the USA, Mexico, Patagonia, Angola, Zambia, eastern Russia, and tropical and subtropical highlands (e.g. in Colombia, the Himalayas, Borneo, Papua). For the proposed system to succeed, we stress that (1) data must be collected and processed applying the same standards across all countries and continents; (2) system establishment and management must be inclusive and equitable, with careful consideration of working conditions; and (3) training and site partner involvement in downstream activities should be mandatory.

Estimating the global distribution of field size using crowdsourcing.

There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.

Mapping global cropland and field size.

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

War drives forest fire risks and highlights the need for more ecologically-sound forest management in post-war Ukraine.

Since 24 February 2022, Ukraine has experienced full-scale military aggression initiated by the Russian Federation. The war has had a major negative impact on vegetation cover of war-affected regions. We explored interactions between pre-war forest management and the impacts of military activities in three of the most forested Ukrainian areas of interest (AOI), affected by the war. These were forests lying between Kharkiv and Luhansk cities (AOI 'East'), forests along the Dnipro River delta (AOI 'Kherson'), and those of the Chornobyl Exclusion Zone (AOI CEZ). We used Sentinel satellite imagery to create damaged forest cover masks for the year 2022. We mapped forests with elevated fire hazard, which was defined as a degree of exposure to the fire-supporting land use (mostly an agricultural land, a common source of ignitions in Ukraine). We evaluated the forest disturbance rate in 2022, as compared to pre-war rates. We documented significant increases in non-stand replacing disturbances (low severity fires and non-fire disturbances) for all three of the AOIs. Damaged forest cover varied among the AOIs (24,180 ± 4,715 ha, or 9.3% ± 1.8% in the 'East' AOI; 7,293 ± 1,925 ha, or 15.7% ± 4.1% in the 'Kherson' AOI; 7,116 ± 1,274 ha, or 5.0% ± 0.9% in the CEZ AOI). Among the forests damaged in 2022, the 'Kherson' AOI will likely have the highest proportion of an area with elevated fire hazard in the coming decades, as compared to other regions (89% vs. 70% in the 'East' and CEZ AOIs respectively). Future fire risks and extensive war-related disturbance of forest cover call for forest management to develop strategies explicitly addressing these factors.

Downgrading recent estimates of land available for biofuel production.

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development.

Drivers of tropical forest loss between 2008 and 2019.

During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform ( www.geo-wiki.org ) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public.