Past decade above-ground biomass change comparisons from four multi-temporal global maps

Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change (Delta AGB) products has increased in recent years. Here we assessed the past decade net Delta AGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local Delta AGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels <10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall AAGB map cross-correlation between maps varied in the range 0.11-0.29 (r). Reported AAGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that AAGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently, AAGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

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)

Integrated global assessment of the natural forest carbon potential

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system 1. Remote-sensing estimates to quantify carbon losses from global forests 2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced 6 and satellite-derived approaches 2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea 2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets

Integrated global assessment of the natural forest carbon potential

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets

Integrated global assessment of the natural forest carbon potential

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2,3,4,5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets

Towards nationwide mapping of bamboo resources in the Philippines : testing the pixel-based and fractional cover approaches

In tropical and subtropical countries, the awareness on the importance of bamboos to the environment and economy is increasing and so is the demand for spatial bamboo information. However, mapping bamboos especially those naturally grown has been challenging, as these grasses are often mixed with other land-use and land-cover (LULC). In this study, we used Sentinel 1 and Sentinel 2 remote sensing (RS) images, and their vegetation indices to accurately map the bamboos of Iloilo province in the Philippines using: (1) pixel-based method that mapped bamboos and other LULC at 10 m resolution, and (2) fractional cover method that mapped bamboo cover at 100 m resolution (% ha−1). A random forest model was trained for each method and then validated per hectare basis using a 50:50 training-validation ratio of a stratified random sample. The fractional cover method showed 0.34 higher Nash-Sutcliffe Efficiency (NSE) and 5.10% lower Root Mean Square Error (RMSE) than the pixel-based method. Further validation within upland and lowland sites also favoured the fractional cover method, but the results of the two methods were closer in the upland site (bamboo plantation). Errors at 10 m resolution especially in the lowlands were mostly commission errors, likely because of the spectral similarity and proximity between bamboos and > 14 vegetations. Averaging the RS inputs into 100 m resulted in at most 12% separation of reflectance values among bamboos, forests, and other vegetations. Using the bamboo cover map, a total of 14,795 (± 1,283) ha bamboos and 7.45 (± 4.20) million harvestable culms (poles) were estimated for the whole province, where 54% come from the lowland. We suggest using the fractional cover method for nationwide baselining of bamboo resources.</p

Intra-Annual Identification of Local Deforestation Hotspots in the Philippines Using Earth Observation Products

Like many other tropical countries, the Philippines has suffered from decades of deforestation and forest degradation during and even after the logging era. Several open access Earth Observation (EO) products are increasingly being used for deforestation analysis in support of national and international initiatives and policymaking on forest conservation and management. Using a combination of annual forest loss and near-real time forest disturbance products, we provide a comprehensive analysis of the deforestation events in three forest frontiers of the Philippines. A space-time pattern mining approach was used to map quarterly deforestation hotspots at 1 km pixel size (100 hectares), where hotspots are classified according to the spatial and temporal variability of the 2000–2020 deforestation in the study area. Our results revealed that 79–81% of the hotspots overlap with primary forests and 27–29% are inside the state-declared protected areas. The intra-annual analysis of deforestation in 2020 revealed an alarming trend, where most deforestation occurred between the 1st and 2nd quarter (92–94% in hotspot forests; 87–89% in non-hotspot forests), highly overlapping within the slash-and-burn farming season. We also found “new” hotspots (2020) formed mostly from landslide scars and partly from selective logging, the latter is believed to be underestimated. Our study paves the way for rapid and regular assessment of the country’s deforestation, useful for the respective environmental institutions who convene several times a year. Moreover, our findings assert the imperative of alternative livelihoods to upland farmers, efficient forest protection activities, and even the mitigation of landslide risks

The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

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)

Past decade above-ground biomass change comparisons from four multi-temporal global maps

Peer reviewe

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement