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One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.

Tree restoration is an effective way to store atmospheric carbon and mitigate climate change. However, large-scale tree-cover expansion has long been known to increase evaporation, leading to reduced local water availability and streamflow. More recent studies suggest that increased precipitation, through enhanced atmospheric moisture recycling, can offset this effect. Here we calculate how 900 million hectares of global tree restoration would impact evaporation and precipitation using an ensemble of data-driven Budyko models and the UTrack moisture recycling dataset. We show that the combined effects of directly enhanced evaporation and indirectly enhanced precipitation create complex patterns of shifting water availability. Large-scale tree-cover expansion can increase water availability by up to 6% in some regions, while decreasing it by up to 38% in others. There is a divergent impact on large river basins: some rivers could lose 6% of their streamflow due to enhanced evaporation, while for other rivers, the greater evaporation is counterbalanced by more moisture recycling. Several so-called hot spots for forest restoration could lose water, including regions that are already facing water scarcity today. Tree restoration significantly shifts terrestrial water fluxes, and we emphasize that future tree-restoration strategies should consider these hydrological effects.

Wälder spielen global eine entscheidende Rolle bei der Regulierung des Weltklimas, da sie aktiv Kohlenstoff speichern und binden. Trotz der Bemühungen durch internationale Programme nehmen die Waldschäden weiter zu. Entwaldung und Walddegradierung sind zwei unterschiedliche Prozesse, die sich auf die globalen Wälder auswirken. Entwaldung ist eine klar definierte Umwandlung oder Abholzung der Waldflächen, während Degradierung subtiler, vorübergehend und variabel sein kann und daher schwer zu detektieren ist. Walddegradierung wird im Allgemeinen als eine funktionale Verringerung der Fähigkeit von Wäldern Ökosystemleistungen zu erbringen identifiziert. Sie wird nicht als Veränderung der Landbedeckung oder Entwaldung klassifiziert. Daraus folgt keine deutliche Verringerung der Waldfläche, sondern eher eine Abnahme der Qualität und des Zustands. Diese Veränderung kann, wie die Entwaldung dennoch mit einer signifikanten Verringerung der oberirdischen Biomasse und damit miterheblichen Treibhausgasemissionen verbunden sein. Die Schätzungen der Kohlenstoffemissionen aus Waldstörungen liegen zwischen 12 und 20 % aller weltweit emittierten Emissionen. Durch eine fehlende einheitliche Definition oder Methode zur Quantifizierung der Degradation, der Vielzahl an Einflussfaktoren und der Unsicherheit bei der Schätzung der Biomasse variieren die Werte stark. Die von der Walddegradierung betroffene Fläche könnte in der Tat viel größer sein als die der Entwaldung, die ohnehin jedes Jahr auf eine Fläche von etwa der Größe Islands geschätzt wird. Die REDD+-Mechanismen zur Finanzierung von Emissionsreduktionen zur Minderung des Klimawandels erfordern robuste, transparente und skalierbare Methoden zur Quantifizierung der Walddegradierung, zusammen mit der Erfassung der damit verbundenen Treibern. Da die Degradierung oft der Entwaldung vorausgeht, kann ein schnelles Monitoring mit einer Beurteilung der Waldschäden und ihren Treibern ein wichtiges Frühwarnsystem sein. Nur so können Maßnahm, Global forests play a crucial role in regulating global climate by actively storing and sequestering carbon. Despite efforts to mitigate climate through international efforts, human-caused forest disturbance and forest-related greenhouse gas emissions continue to rise. Deforestation and forest degradation are two different processes affecting global forests. Deforestation is a clearly defined conversion or removal of forest cover, while degradation can be more subtle, temporary, variable, and therefore difficult to detect. Forest degradation is generally identified as a functional reduction in the capacity of forests to provide ecosystem services, that does not qualify as a change in land cover or forest clearing. That means no clear reduction of the forest area, but rather a decrease in quality and condition. This change, like deforestation can still be associated with significant reductions in above-ground biomass and therefore considerable greenhouse gas emissions. Estimates of carbon emissions from forest degradation and disturbance range anywhere from 12-20% of all emissions emitted globally with values varying widely because of a lack of uniform definition or method for quantifying degradation, the broad number of influencing factors, and uncertainty in biomass estimates. The area affected by forest degradation could in fact be much larger than that of deforestation, which is already estimated to be an area about the size of Iceland every year. The REDD+ mechanisms of financing emissions reductions to mitigate climate change require robust, transparent and scalable methods for quantifying degradation, along with a quantification of associated direct drivers. Furthermore, as degradation often precedes deforestation, timely monitoring and assessment of forest degradation and changes in drivers can provide crucial early warning to engage interventions to keep forests intact, benefitting nature and biodiversity as well as the livelihoods, health and well-being of

ESA WorldCover 10 m 2021 v200 The European Space Agency (ESA) WorldCover 10 m 2021 product provides a global land cover map for 2021 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project. The ESA WorldCover 10m 2021 v200 product updates the existing ESA WorldCover 10m 2020 v100 product to 2021 but is produced using an improved algorithm version (v200) compared to the 2020 map. Consequently, since the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps should be treated with caution, as they include both real changes in land cover and changes due to the algorithms used. The WorldCover 2021 v200 product is developed by a consortium lead by VITO Remote Sensing together with partners Brockmann Consult, Gamma Remote Sensing AG, IIASA and Wageningen University

The dataset contains four csv files that were used to produce the results and other figures in the paper. NFI availability and characteristics data: data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. National biomass intercomparison data: national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. NFI plot design characteristics: data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. NFI years: data on NFI years of the latest NFI in 46 tropical countries., The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at https://doi.org/10.1016/j.scitotenv.2022.157788. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below. NFI availability and characteristics data: The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. National biomass intercomparison data: The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha). The da

This article describes a data-driven framework based on spatiotemporal machine learning to produce distribution maps for 16 tree species (Abies alba Mill., Castaneasativa Mill., Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H.Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestrisL., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercuss uber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence datafor a total of three million of points was used to train different algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 305 coarse and high resolution covariates representing spectral reflectance, different biophysical conditions and biotic competition was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to tune and train an ensemble model based on stacking with a logistic regressor as a meta-learner.An ensemble model was trained for each species: probability and model uncertainty maps of realized distribution were produced for each species using a time window of4 years for a total of six distribution maps per species, while for potential distributions only one map per species was produced. Results of spatial cross validation show that the ensemble model consistently outperformed or performed as good as the best individual model in both potential and realized distribution tasks,with potential distribution models achieving higher predictive performances(TSS = 0.898, R 2logloss = 0.857) than realized distribution ones on average(TSS = 0.874, R 2logloss = 0.839). Ensemble models for Q. suber achieved the best performances in both potential (TSS = 0.968, R2logloss = 0.952) and realized(TSS = 0.959, R 2logloss = 0.949) distribut

Probability and uncertainty maps showing the potential and realized distribution for the common hazel (Corylus avellana, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the silver fir (Abies alba, Mill.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

National forest inventories (NFIs) are a reliable source for national forest measurements. However, they are usually not developed for linking with remotely sensed (RS) biomass information. There are increasing needs and opportunities to facilitate this link towards better global and national biomass estimation. Thus, it is important to study and understand NFI characteristics relating to their integration with space-based products; in particular for the tropics where NFIs are quite recent, less frequent, and partially incomplete in several countries. Here, we (1) assessed NFIs in terms of their availability, temporal distribution, and extent in 236 countries from FAO's Global Forest Resources Assessment (FRA) 2020; (2) compared national forest biomass estimates in 2018 from FRA and global space-based Climate Change Initiative (CCI) product in 182 countries considering NFI availability and temporality; and (3) analyzed the latest NFI design characteristics in 46 tropical countries relating to their integration with space-based biomass datasets. We observed significant NFI availability globally and multiple NFIs were mostly found in temperate and boreal countries while most of the single NFI countries (94 %) were in the tropics. The latest NFIs were more recent in the tropics and many countries (35) implemented NFIs from 2016 onwards. The increasing availability and update of NFIs create new opportunities for integration with space-based data at the national level. This is supported by the agreement we found between country biomass estimates for 2018 from FRA and CCI product, with a significantly higher correlation in countries with recent NFIs. We observed that NFI designs varied greatly in tropical countries. For example, the size of the plots ranged from 0.01 to 1 ha and more than three-quarters of the countries had smaller plots of ≤0.25 ha. The existing NFI designs could pose specific challenges for statistical integration with RS data in the tropics. Future NFI

Probability and uncertainty maps showing the potential and realized distribution for the Turkey oak (Quercus cerris, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the sweet chestnut (Castanea sativa, Mill.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

National-scale assessments of post-deforestation land-use are crucial for decreasing deforestation and forest degradation-related emissions. In this research, we assess the potential of different satellite data modalities (single-date, multi-date, multi-resolution, and an ensemble of multi-sensor images) for classifying land-use following deforestation in Ethiopia using the U-Net deep neural network architecture enhanced with attention. We performed the analysis on satellite image data retrieved across Ethiopia from freely available Landsat-8, Sentinel-2 and Planet-NICFI satellite data. The experiments aimed at an analysis of (a) single-date images from individual sensors to account for the differences in spatial resolution between image sensors in detecting land-uses, (b) ensembles of multiple images from different sensors (Planet-NICFI/Sentinel-2/Landsat-8) with different spatial resolutions, (c) the use of multi-date data to account for the contribution of temporal information in detecting land-uses, and, finally, (d) the identification of regional differences in terms of land-use following deforestation in Ethiopia. We hypothesize that choosing the right satellite imagery (sensor) type is crucial for the task. Based on a comprehensive visually interpreted reference dataset of 11 types of post-deforestation land-uses, we find that either detailed spatial patterns (single-date Planet-NICFI) or detailed temporal patterns (multi-date Sentinel-2, Landsat-8) are required for identifying land-use following deforestation, while medium-resolution single-date imagery is not sufficient to achieve high classification accuracy. We also find that adding soft-attention to the standard U-Net improved the classification accuracy, especially for small-scale land-uses. The models and products presented in this work can be used as a powerful data resource for governmental and forest monitoring agencies to design and monitor deforestation mitigation measures and data-driven land-use

Probability and uncertainty maps showing the potential and realized distribution for the goat willow (Salix caprea, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by Heising and Hengl (2020) and absence data from the LUCAS (in-situ source) dataset. A set of 50 different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by Parente et al. (2021). We overlaid the points with the probability maps for the classes: 311: Broad-leaved forest, 312: Coniferous forest, 313: Mixed forest, 323: Sclerophyllous forest, 324: Transitional woodland-shrub, 333: Sparsely vegetated area.

Probability and uncertainty maps showing the potential and realized distribution for the holm oak (Quercus ilex L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the sweet cherry (Prunus avium, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the Norway spruce (Picea abies, L. H. Karst.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the cork oak (Quercus suber, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the Scots pine (Pinus sylvestris, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the stone pine (Pinus pinea, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the pedunculate oak (Quercus robur, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100 ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual t

Probability and uncertainty maps showing the potential and realized distribution for the European beech (Fagus sylvatica, Mill.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the Austrian pine (Pinus nigra J. F. Arnold) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the Aleppo pine (Pinus halepensis, Mill.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

Probability and uncertainty maps showing the potential and realized distribution for the olive tree (Olea europaea, L.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020.

The sustainable management of aquatic resources requires spatially explicit information on the water and vegetation presence of aquatic ecosystems. Previous Global Aquatic Land Cover (GALC) mapping has been focused on water bodies while lacking information on vegetation, and aquatic types have always been characterized by low accuracies in global land cover products, calling for specific attention to improve GALC mapping. The availability of a wealth of open Earth Observation (EO) data on cloud-computing platforms provides opportunities to map aquatic land cover globally. This study aims to evaluate the potential of multi-source freely available EO data, including optical, Synthetic Aperture Radar (SAR), and various ancillary datasets, for improving the characterization of aquatic land cover comprising both water and vegetation types on a global scale. Using different combinations of features derived from these data, the classification performance of five land cover classes (i.e., trees, shrubs, herbaceous cover, bare/sparsely vegetated lands, and water bodies) in aquatic areas was cross-validated. Results showed that Sentinel-2 data alone achieved similarly good overall accuracy as those combining multi-source data. However, the single-sensor Sentinel-2 data cannot discriminate highly mixed and spectrally similar types, such as shrubs, trees, and herbaceous vegetation. Integrating SAR features from the ALOS/PALSAR mosaic and Sentinel-1 data with optical features provided by Sentinel-2 data could help address this limitation to some extent. Although with a lower spatial and temporal resolution, the ALOS/PALSAR mosaic had a stronger impact on GALC classification than Sentinel-1 data when they were synergistically used. Features provided by ancillary datasets did not lead to significant improvement in the overall GALC classification. At class-level, topographic and soil features helped to reduce the commission error of shrubs, and climate variables were useful to impr

Results of the study ' Shifts in regional water availability due to global tree restoration' (https://doi.org/10.1038/s41561-022-00935-0). The data presents how global large-scale tree restoration could impact water fluxes. The data includes the evaporation (E) and streamflow (Q) under current land-cover and the change in precipitation (P), evaporation (E) and streamflow (Q) with global large-scale tree restoration. Data at 1/10 (0.1) degree spatial resolution.

Community-based monitoring (CBM) is one of the- most sustainable ways of establishing a national forest monitoring system for successful Reduce Emissions from Deforestation and Forest Degradation (REDD+) implementation. In this research, we present the details of the National Forest Conservation Program (PNCB—Programa Nacional de Conservación de Bosques para la Mitigación del Cambio Climático), Peru, from a satellite-based alert perspective. We examined the community’s involvement in forest monitoring and investigated the usability of 1853 CBM data in conjunction with 445 satellite-based alerts. The results confirm that Peru’s PCNB contributed significantly to the REDD+ scheme, and that the CBM data provided rich information on the process and drivers of forest change. We also identified some of the challenges faced in the existing system, such as delays in satellite-based alert transfer to communities, sustaining community participation, data quality and integration, data flow, and standardization. Furthermore, we found that mobile devices responding to alerts provided better and faster data on land-use, and a better response rate, and facilitated a more targeted approach to monitoring. We recommend expanding training efforts and equipping more communities with mobile devices, to facilitate a more standardized approach to forest monitoring. The automation and unification of the alert data flow and incentivization of the participating communities could further improve forest monitoring and bridge the gap between near-real-time (NRT) satellite-based and CBM systems. View Full-Text

Unoccupied aerial vehicle laser scanning (UAV-LS) has been increasingly used for forest structure assessment in recent years due to the potential to directly estimate individual tree attributes and availability of commercial solutions. However, standardised procedures for campaign planning are still largely missing. This study investigated scanner properties and flight planning to provide recommendations on minimising forest canopy occlusion and thereby maximise exploration of canopy volume. A flight campaign involving two UAV-LS systems was conducted over a dense, wet tropical forest at the Paracou research station (French Guiana). Four experiments on scanner properties and flight planning were conducted, analysed and recommendations derived. First, the scanner pulse repetition rate (PRR) should be at least 100 kHz per 1 m s−1 flight speed based on 360° FOV for exploration of middle canopy strata (5 m to 20 m). Higher PRR are beneficial for exploration of lower canopy (<5 m) but would need to be increased exponentially to achieve linear improvement. Alternatively, flight speed could be reduced within the constraints given by the inertial measurement unit (IMU), but would increase flight time. Second, the scanner maximum range was identified as a proxy for the laser pulse power, which positively impacts canopy exploration. This was particularly the case when using multi-return capabilities. No saturation could be observed when increasing the laser power, suggesting that this is currently a limiting factor. Additionally, a smaller laser beam divergence and pulse width were plausible reasons for better exploration of the upper canopy just below the top of canopy. Third, off-nadir scanning angles up to 20° were found to result in similar occlusions, suggesting a practical FOV of 40° in the investigated dense forest. This number might be larger for open canopies. UAV-LS systems with viewing geometries that focus laser pulses downwards and within optimal ranges should be

Handling multiple scales efficiently is one avenue for processing big remote sensing imagery data. Unfortunately, imagery is also affected by the infamous modifiable areal unit problem, which creates unpredictable errors at different scales. We developed a downsampling method that attempts to keep the data distribution in a downsampled image constant, reducing the modifiable areal unit problem. We tested our method against classic downsampling methods (mean, central pixel selection, random) under a range of typical remote sensing scenarios. Under our experimental conditions, our downsampling method consistently outperformed the classical downsampling methods within a 95% confidence level. The downsampling method can be used in most typical situations where downsampling is needed, but it is likely to shine when used as a pyramid building policy in geocomputing platforms, such as Google Earth Engine.

Land use efficiency (LUE), energy efficiency (EE), and air quality are key indicators when assessing urban-related Sustainable Development Goals, yet recent trends and trade-offs in and around urban areas worldwide remain largely unknown. We use an Earth Observation approach to map the land-energy-air sustainability nexus and highlight distinct urban-rural gradients worldwide (2000-2015). In the Global South, urban areas perform relatively better in land-energy-air sustainability trends than rural areas, which are the least sustainable in our global comparative analysis. Comparatively, urban areas in the Global North tend to be less sustainable than surrounding rural regions. Trade-offs among land-energy-air change directions are mostly related to EE versus air quality in urban areas, while spatial and temporal trade-offs between LUE and EE are more pronounced in suburban and rural areas. Integrating satellite data is crucial for tracking the progress of the land-energy-air nexus and can guide context-specific strategies to account for urban-rural differences in achieving sustainability and creating more livable environments.

National forest inventories (NFI) provide essential forest-related biomass and carbon information for country greenhouse gas (GHG) accounting systems. Several tropical countries struggle to execute their NFIs while the extent to which space-based global information on aboveground biomass (AGB) can support national GHG accounting is under investigation. We assess whether the use of a global AGB map as auxiliary information produces a gain in precision of subnational AGB estimates for the Peruvian Amazonia. We used model-assisted estimators with data from the country’s NFI and explored hybrid inferential techniques to account for the sources of uncertainty associated with the integration of remote sensing-based products and NFI plot data.Our results show that the selected global biomass map tends to overestimate AGB values across the Peruvian Amazonia. For most strata, directly using the map in its published form did not reduce the precision of AGB estimates. However, after calibrating the map using the NFI data, the precision of our map-assisted AGB estimates increased by up to 50% at stratum level and 20% at Amazonia level. We further demonstrate how different sources of uncertainties can be incorporated in the map-NFI integrated estimates. With the hybrid inferential analysis, we found that the small spatial support of the NFI plots compared to the remote sensing-based sample units of aggregated pixels (within block variability) contributed the most to the total uncertainty associated with the AGB estimates from our map-NFI integration. Uncertainties caused by measurement variability and allometric model prediction uncertainty were the second largest contributors. When these uncertainties were incorporated, the increase in precision of our calibrated map-assisted AGB estimates was negligible, probably hindered by the great contribution of the within block variability to our map-plot assessment. We developed a reproducible method that countries can build upon and fu, National forest inventories (NFI) provide essential forest-related biomass and carbon information for country greenhouse gas (GHG) accounting systems. Several tropical countries struggle to execute their NFIs while the extent to which space-based global information on aboveground biomass (AGB) can support national GHG accounting is under investigation. We assess whether the use of a global AGB map as auxiliary information produces a gain in precision of subnational AGB estimates for the Peruvian Amazonia. We used model-assisted estimators with data from the country’s NFI and explored hybrid inferential techniques to account for the sources of uncertainty associated with the integration of remote sensing-based products and NFI plot data. Our results show that the selected global biomass map tends to overestimate AGB values across the Peruvian Amazonia. For most strata, directly using the map in its published form did not reduce the precision of AGB estimates. However, after calibrating the map using the NFI data, the precision of our map-assisted AGB estimates increased by up to 50% at stratum level and 20% at Amazonia level. We further demonstrate how different sources of uncertainties can be incorporated in the map-NFI integrated estimates. With the hybrid inferential analysis, we found that the small spatial support of the NFI plots compared to the remote sensing-based sample units of aggregated pixels (within block variability) contributed the most to the total uncertainty associated with the AGB estimates from our map-NFI integration. Uncertainties caused by measurement variability and allometric model prediction uncertainty were the second largest contributors. When these uncertainties were incorporated, the increase in precision of our calibrated map-assisted AGB estimates was negligible, probably hindered by the great contribution of the within block variability to our map-plot assessment. We developed a reproducible method that countries can build upon and f

This dataset contains data underlying the publication: "Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR" and consists of the following data folders: 1_ReferenceMeasurementData (Destructive sampling tree AGB data): Destructive sampling measurement data of 29 large tropical trees from 3 sites (Indonesia, Peru and Guyana) for estimating tree wood volume and tree AGB. 2_AllometricEqInventoryData (Forest inventory data): Forest inventory data of 29 individual large tropical trees from 3 sites (Indonesia, Peru and Guyana) for estimating tree AGB with allometric equations. 3_QsmCylinderData (Quantitative Structure Models): Quantitative Structural Models (QSM) cylinder model outputs (3D tree architecture models), generated from the individual TLS point cloud data of 29 large tropical trees from 3 sites (Indonesia, Peru and Guyana). 4_LidarTreePoinCloudData (TLS tree point cloud data): TLS point cloud data for 29 large tropical trees in 3 study sites: Indonesia (peat swamp forest in Central Kalimantan, Borneo), Peru (lowland tropical moist forest in Madre de Dios) and Guyana (lowland tropical moist forest in Cayuni-Mazaruni).