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Individual tree segmentation from airborne laser scanning data is a longstanding and important challenge in forest remote sensing. Tree segmentation algorithms are widely available, but robust intercomparison studies are rare due to the difficulty of obtaining reliable reference data. Here we provide a benchmark data set for temperate and tropical broadleaf forests generated from labelled terrestrial laser scanning data. We compared the performance of four widely used tree segmentation algorithms against this benchmark data set. All algorithms performed reasonably well on the canopy trees. The point cloud-based algorithm AMS3D (Adaptive Mean Shift 3D) had the highest overall accuracy, closely followed by the 2D raster based region growing algorithm Dalponte2016 +. However, all algorithms failed to accurately segment the understory trees. This result was consistent across both forest types. This study emphasises the need to assess tree segmentation algorithms directly using benchmark data, rather than comparing with forest indices such as biomass or the number and size distribution of trees. We provide the first openly available benchmark data set for tropical forests and we hope future studies will extend this work to other regions.

Allometric equations for calculation of tree above-ground biomass (AGB) form the basis for estimates of forest carbon storage and exchange with the atmosphere. While standard models exist to calculate forest biomass across the tropics, we lack a standardized tool for computing AGB across boreal and temperate regions that comprise the global extratropics. Here we present an integrated R package, allodb, containing systematically selected published allometric equations and proposed functions to compute AGB. The data component of the package is based on 701 woody species identified at 24 large Forest Global Earth Observatory (ForestGEO) forest dynamics plots representing a wide diversity of extratropical forests. A total of 570 parsed allometric equations to estimate individual tree biomass were retrieved, checked and combined using a weighting function designed to ensure optimal equation selection over the full tree size range with smooth transitions across equations. The equation dataset can be customized with built-in functions that subset the original dataset and add new equations. Although equations were curated based on a limited set of forest communities and number of species, this resource is appropriate for large portions of the global extratropics and can easily be expanded to cover novel forest types.

Describing the heterogeneous structure of forests is often challenging. One possibility is to analyze forest biomass in different plots and to derive plot-based frequency distributions. However, these frequency distributions depend on the plot size and thus are scale dependent. This study provides insights about transferring them between scales. Understanding the effects of scale on distributions of biomass is particularly important for comparing information from different sources such as inventories, remote sensing and modeling, all of which can operate at different spatial resolutions. Reliable methods to compare results of vegetation models at a grid scale with field data collected at smaller scales are still missing.The scaling of biomass and variables, which determine the forest biomass, was investigated for a tropical forest in Panama. Based on field inventory data from Barro Colorado Island, spanning 50 ha over 30 years, the distributions of aboveground biomass, biomass gain and mortality were derived at different spatial resolutions, ranging from 10 to 100 m. Methods for fitting parametric distribution functions were compared. Further, it was tested under which assumptions about the distributions a simple stochastic simulation forest model could best reproduce observed biomass distributions at all scales. Also, an analytical forest model for calculating biomass distributions at equilibrium and assuming mortality as a white shot noise process was tested.Scaling exponents of about −0.47 were found for the standard deviations of the biomass and gain distributions, while mortality showed a different scaling relationship with an exponent of −0.3. Lognormal and gamma distribution functions fitted with the moment matching estimation method allowed for consistent parameter transfers between scales. Both forest models (stochastic simulation and analytical solution) were able to reproduce observed biomass distributions across scales, when combined with the derived sca

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AG

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha-1 / normalized RMSE 18.8% / R² 0.76; 50 ha: 22.8 trees ha-1 / 6.2% / 0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha-1, bias 0.8 m² ha-1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

The Amazon rainforest plays an important role in the global carbon cycle. However, due to its structural complexity, current estimates of its carbon dynamics are very imprecise. The aim of this study was to determine the forest productivity and carbon balance of the Amazon, particularly considering the role of canopy height complexity. Recent satellite missions have measured canopy height variability in great detail over large areas. Forest models are able to transform these measurements into carbon dynamics. For this purpose, about 110 million lidar waveforms from NASA’s GEDI mission (footprint diameters of ~25 m each) were analyzed over the entire Amazon ecoregion and then integrated into the forest model FORMIND. With this model–data fusion, we found that the total gross primary productivity (GPP) of the Amazon rainforest was 11.4 Pg C a−1 (average: 21.1 Mg C ha−1 a−1) with lowest values in the Arc of Deforestation region. For old-growth forests, the GPP varied between 15 and 45 Mg C ha−1 a−1. At the same time, we found a correlation between the canopy height complexity and GPP of old-growth forests. Forest productivity was found to be higher (between 25 and 45 Mg C ha−1 a−1) when canopy height complexity was low and lower (10–25 Mg C ha−1 a−1) when canopy height complexity was high. Furthermore, the net ecosystem exchange (NEE) of the Amazon rainforest was determined. The total carbon balance of the Amazon ecoregion was found to be −0.1 Pg C a−1, with the highest values in the Amazon Basin between both the Rio Negro and Solimões rivers. This model–data fusion reassessed the carbon uptake of the Amazon rainforest based on the latest canopy structure measurements provided by the GEDI mission in combination with a forest model and found a neutral carbon balance. This knowledge may be critical for the determination of global carbon emission limits to mitigate global warming.

Allometric equations for calculation of tree aboveground biomass (AGB) form the basis for estimates of forest carbon storage and exchange with the atmosphere. While standard models exist to calculate forest biomass across the tropics, we lack a standardized tool for computing AGB across boreal and temperate regions that comprise the global extratropics. Here we present an integrated R package, allodb, containing systematically selected published allometric equations and proposed functions to compute AGB. The data component of the package is based on 701 woody species identified at 24 large Forest Global Earth Observatory (ForestGEO) forest-dynamics plots representing a wide diversity of extratropical forests. A total of 570 parsed allometric equations to estimate individual tree biomass were retrieved, checked, and combined using a weighting function designed to ensure optimal equation selection over the full tree size range with smooth transitions across equations. The equation dataset can be customized with built-in functions that subset the original dataset and add new equations. Although equations were curated based on a limited set of forest communities and number of species, this resource is appropriate for large portions of the global extratropics and can easily be expanded to cover novel forest types.

Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD—that forest stands with different AGBD may produce highly similar waveforms—and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non-uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7–411 Mg ha−1, average of 167 Mg ha−1). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

Forests are an important component in the earth system. They cover nearly one third of the land surface, store about as much carbon as the entire atmosphere and host more than half of the planet’s biodiversity. Forests provide ecosystem services such as climate regulation and water cycling and they supply resources. However, forests are increasingly at risk worldwide, due to anthropogenic deforestation, degradation and climate change. Concepts for counteracting this development require abilities to monitor forests and predict possible future developments. Given the vast size of forest cover along with the variety of forest types, field measurements and experiments alone cannot provide the solution for this task. Remote sensing and forest modeling enable a broader and deeper understanding of the processes that shape our planet’s forests. Wälder sind ein wichtiger Bestandteil des Systems Erde. Sie bedecken fast ein Drittel der Landoberfläche, speichern etwa so viel Kohlenstoff wie die gesamte Atmosphäre und beherbergen mehr als die Hälfte der biologischen Vielfalt des Planeten. Wälder bieten Ökosystemdienstleistungen wie die Regulierung von Klima und Wasserkreisläufen und liefern Ressourcen. Allerdings sind Wälder weltweit zunehmend gefährdet durch anthropogene Abholzung, Degradierung und den Klimawandel. Konzepte zur Bekämpfung dieser Entwicklung erfordern die Fähigkeit, Wälder zu monitoren und mögliche zukünftige Entwicklungen vorherzusagen. Angesichts der riesigen Waldflächen und der Vielfalt der Waldtypen können Feldmessungen und Experimente allein keine Lösungen für diese Aufgaben bieten. Fernerkundung und Waldmodellierung ermöglichen ein breiteres und tieferes Verständnis der Prozesse, die die Wälder unseres Planeten prägen.

Forest aboveground biomass is a key variable in remote sensing based forest monitoring. Active sensor systems, such as lidar, can generate detailed canopy height products. Relationships between canopy height and biomass are commonly established via regression analysis using information from ground-truth plots. In this way, many site-specific height-biomass relationships have been proposed in the literature and applied for mapping in regional contexts. However, such relationships are only valid within the specific forest type for which they were calibrated. A generalized relationship would facilitate biomass estimation across forest types and regions. In this study, a combination of lidar-derived and ancillary structural descriptors is proposed as an approach for generalization between forest types. Each descriptor is supposed to quantify a different aspect of forest structure, i.e., mean canopy height, maximum canopy height, maximum stand density, vertical heterogeneity and wood density. Airborne discrete return lidar data covering 194 ha of forest inventory plots from five different sites including temperate and tropical forests from Africa, Europe, North, Central and South America was used. Biomass predictions using the best general model (nRMSE = 12.4%, R2 = 0.74) were found to be almost as accurate as predictions using five site-specific models (nRMSE = 11.6%, R2 = 0.78). The results further allow interpretation about the importance of the employed structure descriptors in the biomass estimation and the mechanisms behind the relationships. Understanding the relationship between canopy structure and aboveground biomass and being able to generalize it across forest types are important steps towards consistent large scale biomass mapping and monitoring using airborne and potentially also spaceborne platforms.

Forests represent an important pool in the global carbon cycle. However, biomass stocks and carbon fluxes are variable due to the fact that forest dynamics are driven by processes that act on different spatial and temporal scales. Estimating forest biomass and productivity for larger regions is therefore a major challenge. In this study, horizontal and vertical forest structure is used to improve forest ecosystem service mapping by remote sensing. By linking remote sensing techniques with vegetation modelling (here FORMIND) and forest inventories, forest structure maps were derived for Germany (resolution 4 km). Using these maps, the role of forest structure for selected ecosystem services of forests has been investigated. For forest state estimations (like biomass) horizontal forest structure plays a key role while for productivity estimations both horizontal and vertical structures are relevant. This concept of forest structure classification in combination with forest modelling and remote sensing has high potential for applications at continental scales as future remote sensing missions will provide information on forest structure.

Light detection and ranging (lidar) is currently the state-of-the-art remote sensing technology for measuring the 3D structures of forests. Studies have shown that various lidar-derived metrics can be used to predict forest attributes, such as aboveground biomass. However, finding out which metric works best at which scale and under which conditions requires extensive field inventories as ground-truth data. The goal of our study was to overcome the limitations of inventory data by complementing field-derived data with virtual forest stands from a dynamic forest model. The simulated stands were used to compare 29 different lidar metrics for their utility as predictors of tropical forest biomass at different spatial scales. We used the process-based forest model FORMIND, developed a lidar simulation model, based on the Beer-Lambert law of light extinction, and applied it to a tropical forest in Panama. Simulation scenarios comprised undisturbed primary forests and stands exposed to logging and fire disturbance regimes, resulting in mosaics of different successional stages, totaling 3.7 million trees on 4200 ha. The simulated forest was sampled with the lidar model. Several lidar metrics, in particular height metrics, showed good correlations with forest biomass, even for disturbed forest. Estimation errors (nRMSE) increased with decreasing spatial scale from < 10% (200-m scale) to > 30% (20-m scale) for the best metrics. At the often used 1-ha scale, the top-of-canopy height obtained from canopy height models with fine to relatively coarse pixel resolutions (1 to 10 m) yielded the most accurate biomass predictions, with nRMSE < 6% for undisturbed and nRMSE < 9% for disturbed forests. This study represents the first time dynamic modeling of a tropical forest has been combined with lidar remote sensing to systematically investigate lidar-to-biomass relationships for varyi

Global environmental change necessitates increased predictive capacity; for forests, recent advances in technology provide the response to this challenge. “Next-generation” remote-sensing instruments can measure forest biogeochemistry and structural change, and individual-based models can predict the fates of vast numbers of simulated trees, all growing and competing according to their ecological attributes in altered environments across large areas. Application of these models at continental scales is now feasible using current computing power. The results obtained from individual-based models are testable against remotely sensed data, and so can be used to predict changes in forests at plot, landscape, and regional scales. This model–data comparison allows the detailed prediction, observation, and testing of forest ecosystem changes at very large scales and under novel environmental conditions, a capability that is greatly needed in this time of potentially massive ecological change.

Freshwater turtle species are still poorly understood, and many species are in decline due to unsustainable trade as well as human alteration and degradation of freshwater ecosystems. Mauremys leprosa is a freshwater chelonian endemic to the Mediterranean Basin. Whereas the fossil record demonstrates that this species used to be distributed to well beyond the Spanish border in France, it is today restricted to the border region with Spain, at the Baillaury River in the Pyrenees, with some isolated observations from slightly farther into France. The species consequently holds an “Endangered” status according to the French IUCN Red list. Here we report for the first time the presence and demographic structure in its northern range and demonstrate that its distribution expands beyond the Pyrenees Mountains, throughout French Catalonia. Sequence analyses of the mitochondrial DNA (mtDNA) cytochrome b (cyt b) gene from 216 specimens mainly from France and Spanish Catalonia resulted in a patchwork pattern of haplotypes that supports a mixed origin of the species in France. We encountered two extreme haplotypes, with specimens with the endemic Spanish Catalonian haplotype A18 belonging to M. leprosa leprosa and others being clearly referable to M. leprosa saharica (cyt b haplotypes from clade B) that is otherwise typical from below the Atlas Mountain Range in Morocco. Short- and long-term directions for research as well as conservation management actions are suggested for this insufficiently studied species