The models that produce most existing forest height maps minimize mean individual prediction errors quantified by metrics such as Root Mean Square Error (RMSE). However, when predictor data do not explain all height variability in the training sample, this objective function leads to prediction toward the mean, distorting population-level prediction of height range and variability. Several existing forest height maps have used training data from NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission, and while the mission's retrievals are subject to measurement error, we used GEDI to: 1) evaluate population-level errors of three prominent global height maps; and 2) produce new maps designed to better represent the full height distribution. Specifically, we introduce the GEDI L4D Imputed Waveform product, which was created using a nearest neighbor algorithm. This model assigned a high-quality waveform, represented by 11 Relative Height metrics and other measurements, to each 30 m pixel in the tropical and temperate domain. Covariates included synthetic Landsat bands derived from time series fit to all clear imagery. While previous maps systematically homogenized tree heights over large regions, L4D did not, and tests verified that L4D preserved appropriate covariance among GEDI-derived forest vertical structure metrics. Validation shows that L4D top height RMSE values were up to approximately 1.1 m greater than other maps. Applications such as predicting timber yield, modeling species distributions, and simulating fire spread may benefit from more demographically accurate tree height maps. Moving forward, attention to population-level accuracy metrics may increase the practical value of remotely sensed maps.

Aboveground biomass (AGB) estimation in savanna ecosystems remains challenging due to structural heterogeneity and phenological dynamics of woody vegetation. This study developed two physically–based models integrating multi-frequency SAR observations from ALOS-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) over Kruger National Park (KNP), South Africa, and the Injune Landscape Collaborative Project (ILCP), Australia. The Additive Saturation Model combines backscatter and interferometric coherence as independent exponential terms, while the Hybrid Allometric–Backscatter Model integrates canopy height derived from Random Volume over Ground (RVoG) inversion with unified backscatter. Under sitespecific calibration, both models achieved comparable accuracy (R2 = 0.63–0.65). Under combined calibration, the Additive Model degraded (R2 = 0.612, RMSE = 19.77 Mg ha−1, CCC = 0.756) because independent saturation terms could not adapt to site-dependent scattering mechanisms. The Hybrid Model maintained stronger performance under combined calibration (R2 = 0.731, RMSE = 16.35 Mg ha−1, CCC = 0.841). Shapley sensitivity analysis revealed that L-band backscatter dominated under site-specific calibration (ϕ = 0.74–0.83), while RVoG-derived C-band height metrics became the primary contributor under combined calibration at 40–80 Mg ha−1 where backscatter sensitivity diminishes. The allometric height–AGB relationship follows fundamental growth principles consistent across the pooled AGB gradient, supporting the robustness of the Hybrid Model under multi-site calibration.

Mangroves are critical coastal ecosystems known for their carbon storage capacity, biodiversity, and role in shoreline stabilization. In Suriname, mangroves develop within a dynamic coastal setting shaped by migrating mudbanks and high sedimentation rates. This study examines how 30 structural metrics derived from the Global Ecosystem Dynamics Investigation (GEDI) vary across gradients of mangrove stand age and seaward distance. Forest stand age and yearly coastline positions were derived from Landsat time series data, enabling the inte gration of temporal and spatial drivers to uncover patterns of mangrove succession and structural development. Nonlinear growth models, more specifically the Chapman-Richards function, captured early growth and stabiliza tion phases, while Generalized Additive Models (GAMs) provided flexibility to represent more complex structural changes observed in mature and decaying stands. Results show that structural metrics related to forest growth, such as canopy height and aboveground biomass density, increase rapidly during early successional stages but plateau beyond approximately 12 years or 2 km from the coastline. Complexity-oriented metrics, such as Foliage Height Diversity (FHD) and the Waveform Structural Complexity Index (WSCI), continue to evolve, reflecting increased vertical stratification in mature stands. By combining GEDI spaceborne LiDAR with Landsat-derived chronosequences, this study demonstrates how remote sensing can be used to monitor mangrove successional trajectories and structural complexity, including in inaccessible coastal regions. Our findings extend traditional mangrove successional models by quantifying how both temporal (age) and spatial (seaward distance) gradients jointly determine mangrove structure across the Surinamese coastline.

NASA’s Global Ecosystem Dynamic Investigation (GEDI) mission provides billions of lidar samples of canopy structure over the Earth’s temperate and pantropical forests. Using the GEDI sample data alone, gridded height and biomass products have been created at a spatial resolution of 1 km or coarser. However, this resolution may be too coarse for some applications. In this study, we present a new method of mapping high spatial resolution forest height across large areas using fusion of data acquired by GEDI and TanDEM-X (TDX) Interferometric Synthetic Aperture Radar (InSAR). Our method utilizes GEDI waveforms to provide vertical profiles of scatterers needed to invert a physically-based InSAR model to solve for canopy height. We then use 2-year GEDI canopy height and dynamic wavenumber (kZ)-based calibration models to reduce errors in the inverted canopy height caused by the limited penetration capability of the X-band signal in dense tropical forests and the impact of terrain. We apply this dynamic method over large areas including Gabon, Mexico, French Guiana and most of the Amazon basin, and generate continuous forest height products at 25m and 100 m. After validating against airborne lidar data, we find that our canopy height products have a bias of 0.31 m and 0.46 m, and a root mean square error (RMSE) of 8.48 m (30.02%) and 6.91 m (24.08%) at 25 m and 100 m respectively, for all sites combined. Compared to existing data products that integrate GEDI with passive optical data using machine learning approaches, our method reduces bias, has a lower RMSE, and does not saturate for tall canopy heights up to 56 m. A key feature of this study is that our canopy height product is complemented with an uncertainty of prediction map which provides information on the predictor’s uncertainty around the actual value —an advancement over the standard error maps used in earlier studies, which provide uncertainty around the expectation of the predicted value. This integration approach enables the first-ever accurate and high-resolution mapping of forest canopy heights at unprecedented large areas using GEDI and TDX InSAR data, serving as an essential foundation for pantropical aboveground biomass mapping.

Forests worldwide are undergoing large-scale and unprecedented changes in terms of structure and composition due to land use change and natural disturbances. We have some understanding of how disturbances impact forest structure. Still, we lack knowledge of the structural impact at fine spatial and temporal resolution, as well as across large spatial extents. Here, we provide a perspective on new approaches to observe, quantify and understand forest disturbances and recovery from space by using time series of the most detailed 3D virtual forest models that aim to digitise real-life forests fully. These virtual forests are important for enhancing our fundamental understanding of how we observe forest disturbance and recovery monitoring from space. We define virtual forests in the context of this paper as explicit 3D reconstructed models that are parameterised so they can be used and manipulated for radiative transfer modelling. Realistic virtual forests can be created through empirical reconstruction of explicit forest structure measured by terrestrial laser scanning, coupled with radiometric parameterisation. We argue that these realistic virtual forests, capturing the temporal dimension of forest disturbances, combined with physically-based radiative transfer modelling, provide a critical link between detailed in situ observations and large spatial coverage from satellite observations.

Industries such as forestry, environmental consulting, and carbon credit markets increasingly benefit from laser scanning (LiDAR) to create a comprehensive forest inventory. These sectors require accurate, individual tree-level data for critical applications in ecological monitoring, carbon estimation, and resource management. However, a significant gap persists between academic research and practical, at-scale industrial deployment. Many current methods, including deep learning approaches, struggle with the reality of industrial settings, often requiring large, manually labeled datasets that are expensive to create and may not generalize well to new environments. To bridge this gap, we introduce a novel suite of Topological Data Analysis (TDA) techniques tailored for forest point cloud processing. Our approach is encapsulated in an end-to-end workflow designed for direct application, requiring minimal manual input. Our framework features a modular design, allowing practitioners to construct tailored workflows for diverse applications, from rapid tree detection to detailed structural analysis, enhancing its utility across different industrial needs. The entire framework is delivered as an open-source library, libTTS (https://github.com/alexxsun/libTTS), with seamless Python integration, lowering the barrier to adoption and modification within existing industrial data pipelines. This work aims to provide a practical, topology-driven solution that directly addresses the challenges faced by the geospatial industry.

Accurate estimation and modeling of InSAR coherence are important for surface InSAR-based surface deformation, velocity mapping, and the characterization of forest vertical structure. This study analyses the temporal decorrelation over a deciduous forest at C- and L-band wavelengths. Primarily, the analysis focuses on the seasonal component of the decorrelation. Further, it explores the periodic nature of seasonal coherence over very long temporal baselines spanning more than 4 years. In this work, the performance of a recently proposed seasonal coherence model [1] is evaluated using 142 Sentinel-1 C-band and 11 ALOS-2 ScanSAR L-band InSAR pairs acquired over a test site located at Nallamala Forest, India. The RMSE of modeled coherence is reduced to 50% in the C-band and 25% in the L-band with the seasonal coherence model compared to the exponential model with a R-2 = 0.97. Further, for a very long temporal baseline dataset of 4.5 years, there is an improvement of more than 30% RMSE with R-2 = 0.9. Furthermore, four seasonal peaks (one per year) were observed with a minimum amplitude of vertical bar gamma vertical bar = 0.4, confirming the significant contribution of the seasonal component even at high temporal baselines.

Observations from the NASA Global Ecosystem Dynamics Investigation (GEDI) provide global information on forest structure and biomass. Footprint-level predictions of aboveground biomass density (AGBD) in the GEDI mission are based on training data sourced from sparsely distributed field plots coincident with airborne laser scanning surveys. National Forest Inventories (NFI) are rarely used to calibrate GEDI footprint biomass models because their sampling and positional accuracy prevent accurate colocation with GEDI or ALS. This omission can limit the harmonization of jurisdictional biomass estimates from NFI's and GEDI; however, there are methods available to improve the colocation of NFI plots with GEDI footprints. Focusing on Mediterranean forests in Spain, we compared different approaches to the collocation of NFI and GEDI data: (i) simulated waveforms from ALS; (ii) nearest-neighbor on-orbit GEDI waveforms; and (iii) imputed GEDI waveforms imputed to NFI plot locations using a novel geostatistical method. These methods are potential solutions to improve the local performance of biomass models and address potential local systematic deviations between GEDI and NFI estimates. We assess the advantages and limitations of these methods to locally calibrate GEDI biomass models and quantify the impact of geolocation errors in reference NFI plot data. The new biomass models from each method were used to predict footprint level AGBD, which were then gridded for a province in the North-West of Spain. It was found that the imputation approach is not sensitive to common errors in NFI plot geolocation, but it can outperform ALS-based simulation in some cases, highlighting the benefit of information from multiple GEDI footprints proximate to NFI plots for improving biomass predictions. This research provides users with benchmark of available techniques to locally-calibrate GEDI footprint biomass models.

Forest structural complexity metrics integrate multiple canopy attributes into a single value that reflects habitat quality and ecosystem function. Spaceborne lidar from the Global Ecosystem Dynamics Investigation (GEDI) has enabled mapping of structural complexity in temperate and tropical forests, but its sparse sampling limits continuous high-resolution mapping. We present a scalable, deep learning framework fusing GEDI observations with multimodal Synthetic Aperture Radar (SAR) datasets to produce global, high-resolution (25 m) wall-to-wall maps of forest structural complexity. Our adapted EfficientNetV2 architecture, trained on over 130 million GEDI footprints, achieves high performance (global R2 = 0.82) with fewer than 400,000 parameters, making it an accessible tool that enables researchers to process datasets at any scale without requiring specialized computing infrastructure. The model produces accurate predictions with calibrated uncertainty estimates across biomes and time periods, preserving fine-scale spatial patterns. It has been used to generate a global, multi-temporal dataset of forest structural complexity from 2015 to 2022. Through transfer learning, this framework can be extended to predict additional forest structural variables with minimal computational cost. This approach supports continuous, multi-temporal monitoring of global forest structural dynamics and provides tools for biodiversity conservation and ecosystem management efforts in a changing climate.

Reliable predictions of ecosystem dynamics and carbon stocks depend on accurate initialization of ecosystem states in process-based model simulations. Unlike traditional potential vegetation simulations which assume that ecosystems equilibrate with long-term climate, observation-initialized simulations integrate the impacts of previous history of disturbance events and human activities on ecosystem structure and composition. However, observation-constrained initialization is challenging at regional scales due to limited availability of spatially-comprehensive measurement data. In this study, we assimilate remote-sensing estimates of canopy structure from Global Ecosystem Dynamics Investigation (GEDI) and canopy composition from AVIRIS imaging spectrometry into Ecosystem Demography version 2 (ED2), a cohort-based Terrestrial Biosphere Model. We drive model simulations with future climate scenarios and rising atmospheric CO2 concentrations to predict ecosystem responses to environmental changes over an elevational transect region in California’s Sierra Nevada by the end of the century. Our simulations suggest that predictions are significantly impacted by ecosystem initial condition at the multi-decadal (50+ year) scale. The impacts are stronger in dense-canopy forests at mid-to-high elevations than woody savannahs at low elevations. Under a hotter and drier future climate with CO2 enrichment, ecosystems across the elevational transect are predicted to act as a net carbon sink but with marked changes in composition. Aboveground biomass (AGB) is predicted to increase at low elevations due to increasing abundance in both deciduous and coniferous trees. However, at mid-to-high elevations, AGB increases are caused by increasing abundance of coniferous trees but large declines in the abundance of deciduous trees. Our research demonstrates how large-scale remote-sensing data can be assimilated into process-based model simulations to improve future predictions of ecosystem dynamics.

Earth Observation (EO) data can provide added value to nations' assessments of vegetation aboveground biomass density (AGBD) with minimal additional costs. Yet, neither open access to global-scale EO datasets of vegetation heights or biomass, nor the availability of computational power, has proven sufficient for their wide uptake in climate policy-related assessments. Using Mexico as an example, one of the primary obstacles to enhancing their National Forest Inventory (NFI) with such global EO datasets is the lack of statistically defensible methodologies that do so, while addressing the nation's existing reporting needs and gaps. In collaboration with the Comisi & oacute;n Nacional Forestal (CONAFOR), this study develops a geostatistical model that integrates vegetation height and AGBD estimates from NASA's Global Ecosystem Dynamics Investigation (GEDI) and ESA's Climate Change Initiative (CCI) with Mexico's NFI to attain sub-national and geographically-explicit biomass predictions. The posited model includes spatially varying parameters, allowing flexibility to capture non-stationary relations between the EO-based covariates and NFI-estimated AGBD. Inference is conducted with Bayesian methods, allowing the computation of summary statistics, such as the standard deviations for single-location and area-wide predictions of AGBD. This enables the transparent disclosure and traceability of sources of uncertainty throughout the prediction approach. Results indicate strong model performance; the EO-based covariates explain 79% of the variance in NFI-estimated AGBD in a randomly withheld sample of 10% of observations and a heuristic root mean squared error (RMSE) of 21.55 Mg/ha. Approximately 96% of the observations falling within the 95% credible intervals of our predictions, with some systematic under-prediction observed at AGBD ranges of >100 Mg/ha. To ease the operational uptake of the model for policy purposes, source code based in the 'R' language with the optional use of urban and (non)forest masks for AGBD predictions is released. It includes demonstrations for predicting AGBD in Mexico's Natural Protected Areas, terrestrial ecological strata, and community forest management or payment for environmental services projects, which are commonly used delineations in its climate policy reports. For other nations considering the presented approach for policy purposes, the study discusses challenges concerning the use of EO-based covariates and the limitations of the model. It concludes with a broader call toward ensuring consistency in EO data streams, and prioritizing the co-development of EO-NFI integration approaches with nations in the future, thereby directly addressing their long-term climate policy needs.

The NISAR mission which in its most recent round of launch preparations was set to launch in the spring of 2024, and now delayed until later in the fall or early spring of 2025, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The two frequency, L- and S-band will full-polarimetric capability over a 250 km wide swath using the SweepSAR technique [1] will collect reliable set of observations (60 per year; 30 each for ascending and descending passes) on a continuing basis that will allow for the modeling and observation of time-varying processes that are prevalent in the living environment broadly described as Ecosystems. Among the prime science goals of the NISAR Ecosystems disciplines are in the characterization of agriculture, disturbance, biomass, forest structure and water dynamics seen in the world’s rivers, coasts, and permafrost regions. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

Continuous and operational monitoring of forest canopy structure plays an important role in assessing the global carbon budget, mapping forest disturbance, planning restoration activities, and informing decision-making. Several studies have taken advantage of synthetic aperture radar (SAR) for forest mapping and monitoring because of its regular reliable acquisitions and high sensitivity to the structural and dielectric properties of the forest. This work utilizes the Senitnel-1 C- and ALOS-2 PALSAR-2 L-band interferometric coherence for canopy height estimation in savanna woodlands. A simplified physics-based Random Volume over Ground (RVoG) model is used for the height estimation. This study uses datasets collected over two test sites, one in Injune, Australia, and the second in Kruger National Park (KNP), South Africa. The proposed method achieved an overall RMSE of 2.39m for canopy height with a Pearson coefficient, r = 0.83 by simultaneous use of both C- and L-band coherence.

Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration's (NASA's) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency's (ESA's) Climate Change Initiative (CCI). It also provides the underlying classification used by the IPCC as geospatial layers, delineating global forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (>20 years)). The approaches leverage complementary strengths of various EO-derived datasets that are compiled in an open-science framework through the Multi-mission Algorithm and Analysis Platform (MAAP). This transparency and flexibility enables the adoption of any new incoming datasets in the framework in the future. The EO-based AGBD estimates are expected to be an independent contribution to the IPCC Emission Factors Database in support of UNFCCC processes, and the forest classification expected to support the generation of other policy-relevant datasets while reflecting ongoing shifts in global forests with climate change.

Space-based laser altimetry has revolutionized our capacity to characterize terrestrial ecosystems through the direct observation of vegetation structure and the terrain beneath it. Data from NASA's ICESat-2 mission provide the first comprehensive look at canopy structure for boreal forests from space-based lidar. The objective of this research was to create ICESat-2 aboveground biomass density (AGBD) models for the global entirety of boreal forests at a 30 m spatial resolution and apply those models to ICESat-2 data from the 2019–2021 period. Although limited in dense canopy, ICESat-2 is the only space-based laser altimeter capable of mapping vegetation in northern latitudes. Along each ICESat-2 orbit track, ground and vegetation height is captured with additional modeling required to characterize biomass. By implementing a similar methodology of estimating AGBD as GEDI, ICESat-2 AGBD estimates can complement GEDI's estimates for a full global accounting of aboveground carbon. Using a suite of field measurements with contemporaneous airborne lidar data over boreal forests, ICESat-2 photons were simulated over many field sites and the impact of two methods of computing relative height (RH) metrics on AGBD at a 30 m along-track spatial resolution were tested; with and without ground photons. AGBD models were developed specifically for ICESat-2 segments having land cover as either Evergreen Needleleaf or Deciduous Broadleaf Trees, whereas a generalized boreal-wide AGBD model was developed for ICESat-2 segments whose land cover was neither. Applying our AGBD models to a set of over 19 million ICESat-2 observations yielded a 30 m along-track AGBD product for the pan-boreal. The ability demonstrated herein to calculate ICESat-2 biomass estimates at a 30 m spatial resolution provides the scientific underpinning for a full, spatially explicit, global accounting of aboveground biomass.

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to in situ measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions.

Radiative transfer models (RTMs) are often used to retrieve biophysical parameters from earth observation data. RTMs with multi-temporal and realistic forest representations enable radiative transfer (RT) modeling for real-world dynamic processes. To achieve more realistic RT modeling for dynamic forest processes, this study presents the 3D-explicit reconstruction of a typical temperate deciduous forest in 2015 and 2022. We demonstrate for the first time the potential use of bitemporal 3D-explicit RT modeling from terrestrial laser scanning on the forward modeling and quantitative interpretation of: (1) remote sensing (RS) observations of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and canopy light extinction, and (2) the impact of canopy gap dynamics on light availability of explicit locations. Results showed that, compared to the 2015 scene, the hemispherical-directional reflectance factor (HDRF) of the 2022 forest scene relatively decreased by 3.8% and the leaf FAPAR relatively increased by 5.4%. At explicit locations where canopy gaps significantly changed between the 2015 scene and the 2022 scene, only under diffuse light did the branch damage and closing gap significantly impact ground light availability. This study provides the first bitemporal RT comparison based on the 3D RT modeling, which uses one of the most realistic bitemporal forest scenes as the structural input. This bitemporal 3D-explicit forest RT modeling allows spatially explicit modeling over time under fully controlled experimental conditions in one of the most realistic virtual environments, thus delivering a powerful tool for studying canopy light regimes as impacted by dynamics in forest structure and developing RS inversion schemes on forest structural changes.

Vertical forest structure is closely linked to multiple ecosystem characteristics, such as biodiversity, habitat, and productivity. Mixing tree species in planted forests has the potential to create diverse vertical forest structures due to the different physiological and morphological traits of the composing tree species. However, the relative importance of species richness, species identity and species interactions for the variation in vertical forest structure remains unclear, mainly because traditional forest inventories do not observe vertical stand structure in detail. Terrestrial laser scanning (TLS), however, allows to study vertical forest structure in an unprecedented way. Therefore, we used TLS single scan data from 126 plots across three experimental planted forests of a large-scale tree diversity experiment in Belgium to study the drivers of vertical forest structure. These plots were 9–11 years old young pure and mixed forests, characterized by four levels of tree species richness ranging from monocultures to four-species mixtures, across twenty composition levels. We generated vertical plant profiles from the TLS data and derived six stand structural variables. Linear mixed models were used to test the effect of species richness on structural variables. Employing a hierarchical diversity interaction modelling framework, we further assessed species identity effect and various species interaction effects on the six stand structural variables. Our results showed that species richness did not significantly influence most of the stand structure variables, except for canopy height and foliage height diversity. Species identity on the other hand exhibited a significant impact on vertical forest structure across all sites. Species interaction effects were observed to be site-dependent due to varying site conditions and species pools, and rapidly growing tree species tend to dominate these interactions. Overall, our results highlighted the importance of considering both species identity and interaction effects in choosing suitable species combinations for forest management practices aimed at enhancing vertical forest structure.