Global Ecosystem Dynamics Investigation Research Articles

Impact of fire severity on forest structure and biomass stocks using NASA GEDI data. Insights from the 2020 and 2021 wildfire season in Spain and Portugal

Wildfires have been progressively shrinking the C sink capacity of forest accelerating climate change effects on forest biodiversity, especially where megafires are recurrent and of increased frequency such as in the Mediterranean. Data from The Global Ecosystem Dynamics Investigation (GEDI) mission can inform on changes on forest structure to inform on fire impacts on vegetation. In this study, we assessed the performance of GEDI at measuring biomass and structural change from wildfires using the 2020/21 summer fire seasons in Spain and Portugal. The GEDI hybrid-inference method was used to calculate mean and total biomass in pre- and post-fire stages, while GEDI footprint data was further used to explain the fire severity classes derived from optical data. Our results showed the increasing impact of wildfires on biomass stocks and GEDI ecological metrics by increasing fire severity. More than over biomass stocks, severe fires substantially altered trends in structural metrics such as plant area volume density. The integration of GEDI metrics to explain fire severity had an accuracy of 52% considering five severity classes and an accuracy of 69% when considering the three main classes: unburned, moderate and high. Structural metrics from GEDI can be used to improve optical-based fire severity estimates used globally and to evaluate potential fire impacts based on time-series of GEDI tracks as showcased in the study, but also to measure forest recovery between fire seasons. The extension of GEDI is a major support for wildfire mapping efforts, integrated approaches to capture the increasing impact of fire on forest biodiversity and the monitoring of changes in carbon stocks.

Repeat GEDI footprints measure the effects of tropical forest disturbances

More of the Amazon rainforest is disturbed each year than completely deforested, but the impact of these disturbances on the carbon cycle remains poorly understood. Recent algorithmic advances using optical and radar remote sensing have improved detection of disturbances at fine spatiotemporal resolution, but quantifying changes in forest structure and biomass associated with these detected disturbances has proven challenging. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne lidar mission collecting data from 2019 to 2023, provides an opportunity to address this problem. GEDI captured billions of measurements of forest height, plant area, and understory structure within ∼25-m diameter footprints scattered across the tropics. Though the instrument had no guaranteed repeat cycle, it sometimes sampled nearby locations twice; some of these spatially near-coincident footprints happened to measure forest structure before and after a detected disturbance, providing information on how disturbance affected aboveground biomass. In this study, we developed an efficient general-purpose open-source pipeline for identifying spatially coincident footprints, which is a computationally complex task, and used the pipeline to find over 13,700 footprint pairs with intervening disturbance events across the Amazon biome. We also identified a set of ∼65,000 spatially near-coincident footprint pairs that lacked an intervening disturbance but came from regions with similar disturbance threat; these provided a control dataset to evaluate the effectiveness of estimating forest structure and biomass changes with nearby footprints. Analysis of this Amazon-wide dataset demonstrated that GEDI was able to measure statistically significant canopy height and biomass deficits following non-stand-replacing disturbances as small as 900 m2 (30 m × 30 m). GEDI's unique three-dimensional view of forest structure also reflected the effects of different intensities of fire disturbance, including 20% of burned areas where the upper canopy retained most of its height, but the understory suffered substantial foliage losses. Finally, we modeled the relationship between Landsat and Sentinel-1 disturbance detection parameters and GEDI-estimated percent biomass loss, showing that certain satellite-derived intensity metrics are correlated with increasing biomass loss and identifying temporal trends in biomass loss and recovery following disturbance. This work represents an important step towards the development of a pan-tropical, spatially explicit system for tracking carbon losses and structural changes arising from forest disturbance.

Deforestation detection from spaceborne full-waveform laser altimetry, incorporating terrain effects: A case study in Porto Velho, Brazil

Forest structure, notably the canopy height above the ground, can be obtained from the full-waveform data acquired by satellite laser altimetry. On sloped terrain, the laser pulse can simultaneously reflect from both the vegetation and the ground within their intersecting vertical extents, causing their signals to overlap. This overlap complicates the separation of canopy and ground in the analysis. Consequently, accurately obtaining canopy height and its variation, while considering the influence of terrain factors, poses a significant challenge.In this paper, to counter this, we introduce an innovative approach to remove terrain effects for more reliable identification of canopy and ground. Overlapping footprints from the Ice, Cloud, and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) campaigns are extracted as coincidental observations. Utilizing the different footprint sizes and by minimizing the center and width of each decomposed Gaussian component pair, optimized ground returns can be obtained. Validation using airborne LiDAR data from the Bartlett Experimental Forest, New Hampshire, USA, indicate that the proposed approach can reduce the ground elevation root-mean-square error (RMSE) by approximately 3 m, compared to the prevalent methods and standard products. While the GEDI product algorithms exhibit a certain accuracy in canopy height retrieval, the proposed approach presents a more constrained error band, signifying less impact from ground elevation discrepancies.While the proposed method enhances canopy height precision, its reliance on overlapping footprints means sacrificing some observations. However, the proposed method is effective in scenarios such as change detection that require repeat observations. As a case study, we applied the proposed method in Porto Velho, Rondônia, Brazil, which is a deforestation hotspot. Using the GlobeLand30 dataset, we investigated the land-cover dynamics from co-located laser footprints, identifying significant forest loss areas and validating our findings with the Hansen Global Forest Change dataset, achieving an accuracy of 83.81 %.In conclusion, this research emphasizes the significance of terrain topography effects in vegetation structure analysis, and positions satellite laser altimetry as instrumental for forest monitoring.

Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

ABSTRACT Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests.

Assessing canopy height measurements from ICESat-2 and GEDI orbiting LiDAR across six different biomes with G-LiHT LiDAR

The height of woody plants is a defining characteristic of forest and shrubland ecosystems because height responds to climate, soil and disturbance history. Orbiting LiDAR instruments, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation LiDAR (GEDI), can provide near-global datasets of plant height at plot-level resolution. We evaluate canopy height measurements from ICESat-2 and GEDI with high resolution airborne LiDAR in six study sites in different biomes from dryland shrub to tall forests, with mean canopy height across sites of 0.5–40 m. ICESat-2 and GEDI provide reliable estimates for the relative height with RMSE and mean absolute error (MAE) of 7.49 and 4.64 m (all measurements ICESat-2) and 6.52 and 4.08 m (all measurements GEDI) for 98th percentile relative heights. Both datasets slightly overestimate the height of short shrubs (1–2 m at 5 m reference height), underestimate that of tall trees (by 6–7 m at 40 m reference height) and are highly biased (>3 m) for reference height <5 m, perhaps because of the difficulty of distinguishing canopy from ground signals. Both ICESat-2 and GEDI height estimates were only weakly sensitive to canopy cover and terrain slope (R 2 < 0.06) and had lower error for night compared to day samples (ICESat-2 RMSE night: 5.57 m, day: 6.82 m; GEDI RMSE night: 5.94 m, day: 7.03 m). For GEDI, the day versus night differences varied with differences in mean sample heights for the day and night samples and had little effect on bias. Accuracy of ICESat-2 and GEDI canopy heights varies among biomes, and the highest MAE was observed in the tallest, densest forest (GEDI: 7.85 m; ICESat-2: 7.84 m (night) and 12.83 m (day)). Improvements in canopy height estimation would come from better discrimination of canopy photons from background noise for ICESat-2 and improvements in the algorithm for decomposing ground and canopy returns for GEDI. Both would benefit from methods to distinguish outlier samples.

Stratified burn severity assessment by integrating spaceborne spectral and waveform attributes in Great Xing'an Mountain

Burn severity assessment is critical for understanding the pattern of post-fire vegetation recovery and ecosystem resilience. Previous studies proposed various field criteria (e.g., Composite Burn Index (CBI)) to quantify burn severity from strata level to total site level, yet suffering from surveyors' subjective interpretation across site conditions. High-resolution passive remote sensing allows for more objective assessment based on the strong relation between fire damages and spectral features. Importantly, burn severity generally characterizes differences between forest overstory and understory layers due to their discrepancies in vegetation structures and environmental conditions. Spatially explicit mapping of strata-level burn severity is vital for post-fire forest management and ecological function evaluation. Until now, almost no available spaceborne remote sensing method can concurrently assess overstory and understory burn severity over heterogeneous forests. In this study, we proposed a Hybrid Composite Burn Index (HCBI) that comprehensively indicates the fire-induced spectral and structural changes along the vertical profile by integrating spectral and waveform attributes from both active and passive remote sensing data. Firstly, we introduced two remote sensing-based rating factors namely relative spectral change (RSC) and relative waveform change (RWC), and established HCBI through weighting and scoring the rating factors. Subsequently, we evaluated the effectiveness and generality of overstory, understory, and total site HCBI based on various pairs of simulated remote sensing data of pre-fire and post-fire forest scenes. Thirdly, we derived the spatially discontinuous maps of overstory, understory, and total site HCBI of the Xiushan fire site in Great Xing'an Mountain using WorldView-2 (WV-2) multispectral imagery and Global Ecosystem Dynamics Investigation (GEDI) full-waveform LiDAR data. Finally, we produced wall-to-wall HCBI maps using multiple predictive variables from pre-fire and post-fire Sentinel-2 MultiSpectral Instrument (MSI) images with a Random Forest (RF) algorithm. The predicted overstory, understory, and total site HCBI were validated by field-surveyed CBI. The assessment results based on simulation data showed HCBI was sensitive to the fire damage regardless of burn severity levels and vegetation cover levels (R2 of larger than 0.97 and RMSE of <0.15). The prediction results based on RF models achieved reliable HCBI of the total site, overstory, and understory levels (R2 of around 0.85 and RMSE of around 0.30). We found the wall-to-wall maps of HCBI captured the subtle horizontal and vertical variation even in the case of understory burn alone. We concluded that the newly proposed HCBI can advance the remote sensing-based assessment of stratified burn severity, offering opportunities for making fine-scale forest management decisions.

How natural are the forests in Rajiv Gandhi (Nagarhole) Tiger Reserve? A multi-source data approach.

This study aimed to monitor long-term land use dynamics for understanding the natural forestintegrity and intactness of the Rajiv Gandhi (Nagarhole) Tiger Reserve (RTR) pre- and post-declarations as TR. We employed multi-source data from Survey of India Toposheets (1:50k), Landsat-7, and Sentinel-2A along with Global Ecosystem Dynamics Investigation (GEDI) vegetation canopy height (10m) data, a high-spatial resolution CORONA (1970) images and temporal Google Earth Pro images for mapping and validation. To map vegetation type, land use and land cover (LULC) transitions, and fragmentation (1980-2022) we employed a hybrid classification approach. This study also analyzed decadal forest dynamics within TRsusing India's State of Forest Reports (ISFR). Findings reveal significant forest fragmentation and habitat loss due to anthropogenic activities in the TR. Mono-plantations (teak and eucalyptus) were found inside TR, while the buffer (1km) was occupied mainly with coffee and orange plantations which indicates the prevalence of human footprint. The overall accuracy of the current period (2022) is 92.0% with a kappa coefficient value of 0.90. These plantations were established during the British colonial period (early 1900s) for commercial purposes by clearing natural forests. The present study highlights that mono-plantations have not transitioned into natural forests even after a century. This lack of transformation could potentially compromise the integrity of the native forest. Despite its ecological significance, RTR has experienced disturbance due to human footprint. Hence, there is a need for an action plan to protect this vital landscape by replacing mono-plantations with suitable species to preserve the integrity of the forest. These issues extend beyond the protected areas, impacting surrounding regions and require regular monitoring. The proposed methods can be applied to other protected areas facing similar problems in the countryand world.

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

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 &gt;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%).

Connecting spaceborne lidar with NFI networks: A method for improved estimation of forest structure and biomass

Spaceborne lidar provides a unique opportunity to supplement the field plot measurements of national forest inventories (NFIs) by providing dense measurements of vertical canopy structure. For full waveform instruments such as the Global Ecosystem Dynamics Investigation (GEDI), measurements take the form of reflected energy as a function of height within an observed footprint. Many forest attributes cannot be directly computed from the waveforms, and thus statistical models relating the measurements to target attributes must be trained using field plot data. Because the discrete footprint samples produced by spaceborne lidar instruments have little chance of collocation with pre-existing field plots, model calibration remains a primary challenge. We leveraged the density and spatial correlation of GEDI observations to make predictions of the cumulative waveform over 326,787 NFI plots distributed across the contiguous United States. The predictions yield probability distributions, giving not only a predicted waveform but quantification of prediction uncertainty. The product of this work is a data set comprised of the NFI plots paired with the predictive waveform distributions. The predicted waveforms and their uncertainties can be used to train models with “error-in-variables” techniques that account for and filter the uncertainty in the predicted waveforms. This data set and the corresponding techniques allow statistically rigorous model training without requirements of collocation, yielding downstream forest attribute predictions that are consistent with NFI measurements and estimates. We demonstrate the data set by training a model for forest above ground biomass density (AGBD) and then use the model to produce a spatially complete 1 km map of AGBD for the contiguous United States. We further compare our AGBD predictions to US NFI estimates at a 64,000 hectare scale, showing the increase in precision to be proportional to the collection of almost 500,000 additional NFI plots across forested regions of CONUS.

Vicarious calibration of GEDI biomass with Landsat age data for understanding secondary forest carbon dynamics

The recovery of biomass in secondary forests plays a vital role in global carbon sequestration processes and carbon emission mitigation. However, accurately quantifying the accumulation rate of aboveground biomass density in these forests is challenging owing to limited longitudinal field data. An alternative monitoring strategy is characterizing the mean biomass at a single point in time across stands with a range of known ages. This chronosequence approach can also be used with remotely sensed data by combining biomass measured with platforms such as NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission with forest age strata provided by historic Landsat imagery. However, focusing on the low-biomass conditions common in newly regenerating forests will accentuate commonly observed over-prediction of low biomass values. We propose a vicarious calibration approach that develops a correction for GEDI’s biomass models in young forests, which may be mapped using Landsat time series, using an assumption that the aboveground biomass of newly cleared forests is zero. We tested this approach, which requires no additional local field data, in the U.S. Pacific Northwest, where extensive inventory data from the USDA Forest Service are available. Our results show that the calibration did not significantly improve the fit of predicted biomass as a function of age across 12 ecoregions (one-side t-test; p = 0.20), but it did significantly reduce bias for the youngest age groups with respect to reference data. Calibrated GEDI-based biomass estimates for < 20 year old forests were more accurate than 2006 IPCC defaults in most ecoregions (with respect to authoritative inventory estimates) and may represent a basis for refining carbon storage expectations for secondary forests globally.

Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

Accurate estimating of above-ground biomass (AGB) of vegetation in urbanized areas is essential for urban ecosystem services. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission can obtain precise terrestrial vegetation structure, which is very useful for AGB estimation in large forested areas. However, the spatial heterogeneity and sparse distribution of vegetation in urban areas lead to great uncertainty in AGB estimation. This study proposes a method for estimating vegetation heights by fusing GEDI laser observations with features extracted from optical images. GEDI is utilized to extract the accurate vegetation canopy height, and the optical images are used to compensate for the spatial incoherence of GEDI. The correlation between the discrete vegetation heights of GEDI observations and image features is constructed using Random Forest (RF) to obtain the vegetation canopy heights in all vegetated areas, thus estimating the AGB. The results in Xuzhou of China using GEDI observations and image features from Sentinel-2 and Landsat-8 satellites indicate that: (1) The method of combining GEDI laser observation data with optical images is effective in estimating AGB, and its estimation accuracy (R2 = 0.58) is higher than that of using only optical images (R2 = 0.45). (2) The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg/ha, and it is the highest in the coniferous forest at 46.60 Mg/ha. And the highest average AGB occurs in October–December at 59.55 Mg/ha in Xuzhou. (3) The near-infrared band has a greater influence on inverted AGB, followed by textural features. Although more precise information about vegetation should be considered, this paper provides a new method for the AGB estimation and also a way for the evaluation and utilization of urban vegetation space.

Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine.

Wetlands are amongst Earth's most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination (R2) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies.

Non fire-adapted dry forest of Northwestern Madagascar: Escalating and devastating trends revealed by Landsat timeseries and GEDI lidar data.

Ankarafantsika National Park (ANP), the last significant remnant of Northwestern Madagascar's tropical dry forests, is facing rapid degradation due to increased incidences of fire. This poses severe threats to biodiversity, local livelihoods, and vital ecosystem services. Our study, conducted on 3,052-ha of ANP's pristine forests, employed advanced remote-sensing techniques to assess fire impacts during the past 37 years. Our aims were to understand historical fire patterns and evaluate forest recovery and susceptibility to repeated fires following initial burns. Using data from multiple Landsat satellite sensors, we constructed a time series of fire events since 1985, which revealed no fire activity before 2014. The Global Ecosystem Dynamics Investigation (GEDI) lidar sensor data were used to observe forest structure in both post-fire areas and undisturbed zones for comparison. We recorded six fire incidents from 2014-2021, during which the fire-affected area exponentially grew. A significant fire incident in October 2021 impacted 1,052 hectares, 59% of which had experienced at least one fire in two-to-four years prior, with 60% experiencing two preceding incidents: one in 2017 and another in 2019. The initial fire drastically reduced plant cover and tree height, with subsequent fires causing minor additional loss. Post-fire recovery was negligible within the initial four years, even in patches without recurrent fires. The likelihood for an initial burn to trigger subsequent fires within a few years was high, leading to larger, more severe fires. We conclude that ANP's dry forests exhibit high vulnerability and low resilience to anthropogenic fires. Prompt preventive measures are essential to halt further fire spread and conserve the park's unique and invaluable biodiversity.

Maximum tree height in European Mountains decreases above a climate-related elevation threshold

Mountain forests face important threats from global change and spatio-temporal variation in tree height can help to monitor these effects. In this study, we used the Global Ecosystem Dynamics Investigation space-borne laser sensor to examine the relationship between maximum tree height and elevation, and the role of climate, in the main European mountain ranges. We found a piecewise relationship between elevation and maximum tree height in all mountain ranges, supporting the existence of a common breakpoint that marks the beginning of tree development limitations. Temperature and precipitation were identified as the most important drivers of tree height variation. Additionally, we predicted significant upward displacement of the breakpoint for the period 2080-2100 under climate change scenarios, potentially increasing the area without growth limitations for trees. These findings contribute to understanding the impacts of global warming on mountain forest ecosystems and provide insights for their monitoring and management.

Forestry Applications of Space-Borne LiDAR Sensors: A Worldwide Bibliometric Analysis.

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. Nowadays, these sensors offer novel opportunities for mapping terrain and canopy heights and estimating aboveground biomass (AGB) across local to regional scales. This study aims to analyze the scientific impact of these sensors on large-scale forest mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. A worldwide bibliometric analysis of this topic was carried out based on up to 412 publications indexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commissioning of two new LiDAR space missions: Ice, Cloud, and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions have been providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing" and among the leading contributors, the top five countries in terms of publications were the USA, China, the UK, France, and Germany. The upward trajectory in the number of publications categorizes this subject as a highly trending research topic, particularly in the context of improving forest resource management and participating in global climate treaty frameworks that require monitoring and reporting on forest carbon stocks. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.

Forest aboveground biomass estimation by GEDI and multi-source EO data fusion over Indian forest

ABSTRACT Monitoring changes in carbon stocks through forest biomass assessment is crucial for carbon cycle studies. However, challenges in obtaining timely and reliable ground measurements hinder creation of the spatially continuous maps of forest aboveground biomass density (AGBD). This study proposes an approach for generating spatially continuous maps of forest aboveground biomass density (AGBD) by combining Global Ecosystem Dynamics Investigation (GEDI) LiDAR-based data with open-access earth observation (EO) data. The key contribution of the study lies in the systematic evaluation of various model configurations to select the optimal model for AGBD generation. The evaluation considered various model configurations, including predictor sets, spatial resolution, beam selection, and sensitivity thresholds. We used a Random Forest model, trained through five-fold cross-validation on 80% of the total data, to estimate AGBD in the Indian forest region. Model performance was assessed using the 20% independent test dataset. Results, using Sentinel-1 and 2 predictors, yielded R2 values of 0.55 to 0.60 and RMSE of 48.5 to 56.3 Mg/ha. Incorporating forest and agroclimatic zone attributes improved performance (R2: 0.59 to 0.69, RMSE: 42.2 to 53.3 Mg/ha). The selection of the top 15 predictors, which favoured features from Sentinel-2, DEM, forest attributes, and agroclimatic zones, and GEDI data with sensitivity >0.98, yielded the optimal model with an R2 of 0.64 and RMSE of 46.59 Mg/ha. The results underscore the significance of incorporating attributes like forest and agro-climatic zones and the need for an optimal model selection considering predictor types and GEDI shot characteristics. The top-performing model is validated in Simdega, Jharkhand (R2: 0.74, RMSE: 39.3 Mg/ha), demonstrating the methodological potential of this approach. Overall, this study emphasizes the methodological prospects of integrating multi-source open-access EO data to produce spatially continuous aboveground biomass (AGB) maps through data fusion.

Definition criteria determine the success of old-growth mapping

Old-growth forests have been widely studied for decades. The extreme diversity of old forest characteristics has inspired an equally diverse set of old-growth definitions, and makes mapping old-growth difficult across large areas and different forest types. While the use of remote sensing in old-growth research is not new, there is a growing need for large scale mapping to improve understanding of old forest processes and to support old-growth conservation. Old-growth mapping requires definitions that are ecologically relevant to old forests while also transferable to remote sensing data. In this paper we develop a conceptual framework to evaluate three dimensions of old-growth—a temporal dimension related to tree ages, a physical dimension related to tree sizes, and a functional dimension related to forest processes. In the first part of our analysis, we classify forests throughout the eastern US as old or not with respect to each old-growth dimension using existing old-growth definitions and data from the US Forest Inventory and Analysis (FIA) program. We estimate the proportion of forest classified as old within a hexagon grid, resulting in a unique map of old forest proportion (OFP) for each dimension. Subsequently, we use spaceborne lidar data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to reproduce each OFP map in a modeling framework designed to 1) assess the extent to which each dimension of forest oldness can be mapped at large spatial scales, and 2) identify biophysical GEDI variables related to each dimension of forest oldness. We estimate that only 2% of forest classified as old in any dimension satisfied the old criteria in all three dimensions. We found substantial spatial variation in the mapped OFP estimates across the three dimensions, highlighting how definition criteria impacts old-growth maps. We also found that physically old forests were more effectively mapped using GEDI data than functionally or temporally old forests, and that physically old forests were more structurally similar to one another than temporally or functionally old forests. Our modeling results indicate that while remote sensing may be best suited to mapping physical old-growth characteristics, definitions that rely solely on physical characteristics do not adequately represent old forests throughout the eastern US. We propose that future efforts to map old-growth with spaceborne remote sensing data may maximize utility through collaboration between western and indigenous old-growth experts to determine broad yet nuanced approaches that are appropriately tailored to the target variable of old forests. These efforts should balance explicit and ecologically relevant old-growth definitions specifically for mapping that can be linked to remotely sensed data, 2) appropriate spatial resolutions, and 3) flexible quantitative frameworks that encompass the complexities and heterogeneity of old forests.

Retrieval of Tree Height Percentiles over Rugged Mountain Areas via Target Response Waveform of Satellite Lidar

The retrieval of tree height percentiles from satellite lidar waveforms observed over mountainous areas is greatly challenging due to the broadening and overlapping of the ground return and vegetation return. To accurately represent the shape distributions of the vegetation and ground returns, the target response waveform (TRW) is resolved using a Richardson–Lucy deconvolution algorithm with adaptive iteration. Meanwhile, the ground return is identified as the TRW component within a 4.6 m ground signal extent above the end point of the TRW. Based on the cumulative TRW distribution, the height metrics of the energy percentiles of 25%, 50%, 75%, and 95% are determined using their vertical distances relative to the ground elevation in this study. To validate the proposed algorithm, we select the received waveforms of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the Pahvant Mountains of central Utah, USA. The results reveal that the resolved TRWs closely resemble the actual target response waveforms from the coincident airborne lidar data, with the mean values of the coefficient of correlation, total bias, and root-mean-square error (RMSE) taking values of 0.92, 0.0813, and 0.0016, respectively. In addition, the accuracies of the derived height percentiles from the proposed algorithm are greatly improved compared with the conventional Gaussian decomposition method and the slope-adaptive waveform metrics method. The mean bias and RMSE values decrease by the mean values of 1.68 m and 2.32 m and 1.96 m and 2.72 m, respectively. This demonstrates that the proposed algorithm can eliminate the broadening and overlapping of the ground return and vegetation return and presents good potential in the extraction of forest structure parameters over rugged mountainous areas.

Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI

Forest aboveground biomass (AGB) is integral to the global carbon cycle and climate change study. Local and regional AGB mapping is crucial for understanding global carbon stock dynamics. NASA’s global ecosystem dynamics investigation (GEDI) and combination of multi-source optical and synthetic aperture radar (SAR) datasets have great potential for local and regional AGB estimation and mapping. In this study, GEDI L4A AGB data and ground sample plots worked as true AGB values to explore their difference for estimating forest AGB using Sentinel-1 (S1), Sentinel-2 (S2), and ALOS PALSAR-2 (PALSAR) data, individually and in their different combinations. The effects of forest types and different true AGB values for validation were investigated in this study, as well. The combination of S1 and S2 performed best in forest AGB estimation with R2 ranging from 0.79 to 0.84 and RMSE ranging from 7.97 to 29.42 Mg/ha, with the ground sample plots used as ground truth data. While for GEDI L4A AGB product working as reference, R2 values range from 0.36 to 0.47 and RMSE values range from 31.41 to 37.50 Mg/ha. The difference between using GEDI L4A and ground sample plot as reference shows obvious dependence on forest types. In summary, optical dataset and its combination with SAR performed better in forest AGB estimation when the average AGB is less than 150 Mg/ha. The AGB predictions from GEDI L4A AGB product used as reference underperformed across the different forest types and study sites. However, GEDI can work as ground truth data source for forest AGB estimation in a certain level of estimation accuracy.

Performance of GEDI data combined with Sentinel-2 images for automatic labelling of wall-to-wall corn mapping

Corn is the dominant crop planted in Northeast China, and its accurate and timely mapping is important for food security and agricultural management in China. However, the absence of enough labels is challenging for corn accurate mapping in a regional area using machine learning methods or deep learning methods. In this study, an efficient way of automatic labelling and mapping of corn planted areas by combining Global Ecosystem Dynamics Investigation (GEDI) data and Sentinel-2 images is proposed. We explore the height and vertical structure differences between corn and other crops derived from GEDI features and generate labels automatically by referencing crop type products and transferring models from historical years. The trained learning networks of automatic labelling from GEDI points and the trained decision trees of the Random Forest (RF) classifier can be transferred to corn mapping in arbitrary target years. The Sentinel-2 features are combined to perform wall-to-wall corn mapping using a random forest algorithm and GEDI-based labels. This approach is used to map corn planted areas in Northeast China from 2019 to 2022, and the classification results are validated using independent labels collected in field campaigns in 2023, published maps, and official statistics. Our classification results reveal that our proposed method achieves high accuracy and robustness with an average overall accuracy of 0.91 validated using testing labels from spatial-type stratified sampling. The correlation coefficient (R2) between our classified result with the official statistical data and published classification results reach 0.96 and 0.98, respectively. These results demonstrate the potential of GEDI data for automatic label collection for vegetation with height difference and provide a new approach for efficient crop mapping on a large-scale.