Explore the vast range of titles available.

Determining the relative influence of biotic and abiotic processes in structuring communities at local to large spatial scales is best understood using a biogeographic comparative‐experimental approach. Using this approach, previous work suggests that intertidal community dynamics (top‐down and bottom‐up effects) vary unimodally along an upwelling‐based productivity gradient, termed the Intermittent Upwelling Hypothesis (IUH). Evidence consistent with the IUH comes from the sessile invertebrate/predator (SIP) subweb in certain rocky intertidal communities, but whether this pattern extends to macrophyte/herbivore (MH) subwebs is unknown. Here we ask: Are MH subwebs also structured as predicted by the IUH? What is the relative importance of herbivory and predation in structuring these communities? Under what conditions do ecological subsidies like nutrients or propagule production drive community dynamics? And are omnivorous interactions important? We hypothesize that MH subwebs are driven by a new construct, the Grazing‐Weakening Hypothesis (GWH), which states that MH interactions weaken monotonically with increasing nutrients, with strong (weak) herbivory and low (high) macrophyte productivity at low (high) nutrients. We explored local‐to‐large spatial scale dynamics of both subwebs using a biogeographic comparative‐experimental factorial field experiment testing joint and separate effects of herbivores and predators between two continents. Experiments at 10 sites ranging from persistent upwelling to persistent downwelling regimes ran for 26–29 months in Oregon and California, and New Zealand (NZ) South Island. For the MH subweb, results were consistent with the GWH: herbivory declined and macrophytes increased with increasing nutrients. As expected, results for the SIP subweb were consistent with the IUH: predator effect size was unimodally related to upwelling. Overall, herbivory explained more variation in community structure than did predation, especially in NZ. Omnivory was weak, sessile invertebrates outcompeted macrophytes, and ocean‐driven subsidies provided the basic template driving ecosystem dynamics. We propose a unified meta‐ecosystem dynamics model combining MH and SIP results: with increased upwelling, sessile invertebrates and underlying dynamics vary unimodally (as in the IUH), while herbivory decreases and macrophytes generally increase. While this model was based on research in temperate ecosystems varying in upwelling regime, its wider applicability remains to be tested.

The Global Ecosystem Dynamics Investigation (GEDI) lidar is a multibeam laser altimeter on the International Space Station (ISS). GEDI is the first spaceborne instrument designed to measure vegetation height and to quantify aboveground carbon stocks in temperate and tropical forests and woodlands. This document describes the algorithm theoretical basis underpinning the development of the GEDI Level‐4A (GEDI04_A) footprint aboveground biomass density (AGBD) data product. The GEDI04_A data product contains estimates of AGBD for individual GEDI footprints and associated prediction intervals. The algorithm uses GEDI02_A relative height metrics and 13 linear models to predict AGBD in 32 combinations of plant functional type and world region within the observation limits of the ISS. GEDI04_A models for the release 1 and release 2 data products were developed using 8,587 quality‐filtered simulated GEDI waveforms associated with field estimates of AGBD in 21 countries. Although this is the most geographically comprehensive data available for the development of AGBD models using lidar remote sensing, important regions are underrepresented, including the forests of continental Asia, deciduous broadleaf forests and savannas of the dry tropics, and evergreen broadleaf forests north of Australia. We describe the scientific and statistical assumptions required to develop globally representative estimates of AGBD using GEDI lidar, including generalization beyond training data, and exclusion of GEDI02_A observations that do not meet requirements of the GEDI04_A algorithm. The footprint‐level predictions generated by this process provide globally comprehensive estimates of AGBD. These footprint‐level predictions are a prerequisite for the GEDI04_B gridded AGBD data product. Plain Language Summary The amount of carbon stored in aboveground vegetation is uncertain. This uncertainty limits our ability to calculate fluxes of carbon between the land surface and the atmosphere, and prevents rigorous carbon offset crediting in forests. Much of this uncertainty is attributed to inconsistent measurement techniques and the use of Earth‐observation methods that were not designed to quantify carbon density. The Global Ecosystem Dynamics Investigation (GEDI) can largely overcome these challenges by producing measurements of vegetation height using a lidar sensor on the International Space Station. This document describes methods developed by the GEDI Science Team to convert spaceborne measurements of vegetation height into estimates of aboveground biomass density. The algorithms depend on the geographic world region and the type of vegetation that is present at a sampled location. For example, evergreen broadleaf forests of the humid tropics in South America and deciduous broadleaf forests of Europe use different algorithms. Statistical models were developed using comprehensive field measurements and simulated GEDI data. This document describes the importance of filtering GEDI data to reduce the impact of measurement artifacts on aboveground biomass predictions. Quality flags and ancillary data contained in the GEDI04_A data product ensure that the best predictions can be used. Key Points Global Ecosystem Dynamics Investigation (GEDI) aboveground biomass density is from models trained on a comprehensive database of field measurements and simulated GEDI waveforms On‐orbit prediction requires stratification by plant functional type and world region Quality flags and metrics distinguish GEDI measurements that are representative of the conditions under which models were developed

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%).

Current and planned space missions will produce aboveground biomass density data products at varying spatial resolution. Calibration and validation of these data products is critically dependent on the existence of field estimates of aboveground biomass and coincident remote sensing data from airborne or terrestrial lidar. There are few places that meet these requirements, and they are mostly in the northern hemisphere and temperate zone. Here we summarize the potential for low-altitude drones to produce new observations in support of mission science. We describe technical requirements for producing high-quality measurements from autonomous platforms and highlight differences among commercially available laser scanners and drone aircraft. We then describe a case study using a heavy-lift autonomous helicopter in a temperate mountain forest in the southern Czech Republic in support of calibration and validation activities for the NASA Global Ecosystem Dynamics Investigation. Low-altitude flight using drones enables the collection of ultra-high-density point clouds using wider laser scan angles than have been possible from traditional airborne platforms. These measurements can be precise and accurate and can achieve measurement densities of thousands of points · m−2. Analysis of surface elevation measurements on a heterogeneous target observed 51 days apart indicates that the realized range accuracy is 2.4 cm. The single-date precision is 2.1–4.5 cm. These estimates are net of all processing artifacts and geolocation errors under fully autonomous flight. The 3D model produced by these data can clearly resolve branch and stem structure that is comparable to terrestrial laser scans and can be acquired rapidly over large landscapes at a fraction of the cost of traditional airborne laser scanning.

Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of ‘transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact.

Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.

Vegetation structure is a key component in assessing habitat quality for wildlife and carbon storage capacity of forests. Studies conducted at global scale demonstrate the increasing pressure of the agricultural frontier on tropical forest, endangering their continuity and biodiversity within. The Paraguayan Chaco has been identified as one of the regions with the highest rate of deforestation in South America. Uninterrupted deforestation activities over the last 30 years have resulted in the loss of 27% of its original cover. The present study focuses on the assessment of vegetation structure characteristics for the complete Paraguayan Chaco by fusing Sentinel-1, -2 and novel spaceborne Light Detection and Ranging (LiDAR) samples from the Global Ecosystem Dynamics Investigation (GEDI). The large study area (240,000 km2) calls for a workflow in the cloud computing environment of Google Earth Engine (GEE) which efficiently processes the multi-temporal and multi-sensor data sets for extrapolation in a tile-based random forest (RF) regression model. GEDI-derived attributes of vegetation structure are available since December 2019, opening novel research perspectives to assess vegetation structure composition in remote areas and at large-scale. Therefore, the combination of global mapping missions, such as Landsat and Sentinel, are predestined to be combined with GEDI data, in order to identify priority areas for nature conservation. Nevertheless, a comprehensive assessment of the vegetation structure of the Paraguayan Chaco has not been conducted yet. For that reason, the present methodology was developed to generate the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index and Foliage-Height-Diversity-Index. The complex ecosystems of the Paraguayan Chaco ranging from arid to humid climates can be described by canopy height values from 1.8 to 17.6 m and canopy covers from sparse to dense (total canopy cover: 0 to 78.1%). Model accuracy according to median R2 amounts to 64.0% for canopy height, 61.4% for total canopy cover, 50.6% for Plant-Area-Index and 48.0% for Foliage-Height-Diversity-Index. The generated maps of vegetation structure should promote environmental-sound land use and conservation strategies in the Paraguayan Chaco, to meet the challenges of expanding agricultural fields and increasing demand of cattle ranching products, which are dominant drivers of tropical forest loss.

Forest aboveground biomass (AGB) products serve as essential references for research on carbon cycle and climate change. However, significant uncertainties exist regarding forest AGB products and their evaluation methods. This study aims to evaluate AGB products in the context of discrepancies in plot size and product scales, while also investigate the applicability of large-scale AGB products at a regional level. The National Aeronautics and Space Administration (NASA)’s Global Ecosystem Dynamics Investigation (GEDI) and the European Space Agency (ESA)’s Climate Change Initiative (CCI) biomass data were evaluated using sample plots from the National Forest Inventory (NFI). The study was conducted in Jilin Province, located in Northeast China, which is predominantly covered by natural forests. Spatial representativeness evaluation indicators for sample plots were established, followed by a comprehensive representativeness assessment and the selection of sample plots based on the criteria importance through the intercriteria correlation (CRITIC) method. Additionally, the study conducted an overall evaluation of the products, as well as evaluations across different biomass ranges and various forest types. The results indicate that the accuracy metrics demonstrated improved performance when using representative plots compared to all plots, with the R2 increasing by 15.38%. Both products demonstrated optimal accuracy and stability in the 50–150 Mg/ha range. GEDI and CCI biomass data indicated an overall underestimation, with biases of −25.68 Mg/ha and −83.95 Mg/ha, respectively. Specifically, a slight overestimation occurred in the <50 Mg/ha range, while a gradually increasing underestimation was observed in the ≥50 Mg/ha range. This study highlights the advantages of spatial representativeness analysis in mitigating evaluation uncertainties arising from scale mismatches and enhancing the reliability of product evaluation. The accuracy trends of AGB products offer significant insights that could facilitate improvements and enhance their application.

Vegetation phenology represents vegetation dynamics following seasonal climatic variations. Phenological monitoring allows us to understand the vegetation responses to the effects of climate change. This study examines tropical forest phenology in the Brazilian Amazon using the Global Ecosystem Dynamics Investigation (GEDI), which observes tropical and temperate forests using a self-contained laser altimeter on the International Space Station. The plant area index (PAI) from the GEDI laser product, measured at every 5 m from the ground, was used to create an upper canopy and understory time-series PAI at 2° spatial resolution. Spline algorithms were employed to produce PAI time-series free of time gaps. These data were then utilized to identify the phenological timing of both the upper canopy and understory layers. The results revealed that upper canopy PAI consistently increased during early dry seasons, while understory PAI likely increased due to sunlight that penetrated the upper canopy during its leaf abscission period in the late dry season. The phenological timing within the Brazilian Amazon varied in both space and year. Upper canopy and understory phenological cycles sometimes widen or narrow, occasionally resulting in two cycles per year in some regions. This study demonstrated the effectiveness of using GEDI data to study the phenology of the Brazilian Amazon.

Expert reasoning about ecosystems requires a focus on the dynamics of the system, including the inherent processes, change over time, and responses to disturbances. However, students often bring assumptions to thinking about ecosystems that may limit their developing expertise. Cognitive science research has shown that novices often reduce ongoing patterns and processes to events across diverse science concepts. A robust, event-based focus may exacerbate student difficulties with reasoning about ecosystems in terms of resilience and change over time. In this study, we investigated middle-school students' initial reasoning about ecosystem dynamics and analyzed promising shifts in their reasoning after they interacted with a virtual environment with features designed to support thinking about change over time. Some students adopted a domino narrative pattern—a sequential story about the events and processes. The findings suggest that educators should consider the possibility that novices will bring event-based framing to their ecosystems learning.