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GEDI4R: an R package for NASA’s GEDI level 4 A data downloading, processing and visualization

Authors:
Elia Vangi at University of Florence
Elia Vangi
Giovanni D'Amico at University of Florence
Giovanni D'Amico
Saverio Francini at University of Bologna
Saverio Francini
Gherardo Chirici at University of Florence
Gherardo Chirici
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Abstract and Figures

Forest ecosystems’ structure and biomass monitoring are crucial for understanding the contribution of forests to the global greenhouse gas balance. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission collects waveform lidar data to estimate Above Ground Biomass Density (AGBD). While of great interest, GEDI data are challenging to download and pre-process and require coding expertise, limiting their usage. In this paper, we introduce GEDI4R, an open-source R package providing efficient methods for downloading, reading, clipping, visualizing, and exporting GEDI data. GEDI4R was tested over the whole of Italy, and more than 11 million GEDI pulses were downloaded in less than 10 hours. The GEDI pulse density in forests ranged between 132 per km2 (in the Friuli Venezia Giulia Italian administrative region) and 61 pulses per km2 (in Trentino Alto-Adige). A regional-level comparison between the official growing stock volume estimates reported in the last Italian forest inventory and the AGBD extracted from the GEDI data acquired over the forest revealed large correlations (r2 = 0.77). Our package facilitates the usage of GEDI AGBD data, which provides innovative information to monitor carbon cycle dynamics at the global scale.
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/ Publishedonline:14 December 2022
Earth Science Informatics (2023) 16:1109–1117
SOFTWARE
https://doi.org/10.1007/s12145-022-00915-3
Raupach 2008). Accurate measurements of forest variables
at large spatial scales are essential for understanding the
global carbon cycle and achieving eective carbon mitiga-
tion strategies (Chen et al. 2016). Indeed, three-dimensional
forest structure data acquired by Light Detection And Rang-
ing (lidar) sensors are the most valuable to assess forest
biomass and biomass changes due to human activities or
natural hazards mainly related to climate change (Silva et al.
2021). On the other hand, the extensive survey and process-
ing cost limit such information, which, as a result, is usually
available just over small areas limiting consistent and large-
scale monitoring of forest height and biomass (Dubayah et
al. 2020).
The Global Ecosystem Dynamics Investigation (GEDI)
is the rst satellite mission conceived explicitly for retriev-
ing vertical vegetation structure and has been collecting
unique data on vegetation structure since April 2019 for a
nominal two-year mission onboard the International Space
Station (ISS). GEDI is equipped with a geodetic-class laser
altimeter/waveform lidar comprised of three lasers that pro-
duce eight transects (beams) of structural information, pro-
viding 25-meter resolution measurements of forest height in
temperate and tropical forests (between 51.6° N and 51.6°
Introduction
Forest ecosystems cover about one-third of the Earth’s lands
and play a crucial role in the global carbon balance (FAO
2018), absorbing almost 3 billion tons of anthropogenic
carbon annually, or 30% of the total emissions associated
with fossil fuel burning and net deforestation (Canadell and
Communicated by H. Babaie
Saverio Francini
saverio.francini@uni.it
1 Dipartimento di Scienze e Tecnologie Agrarie, Alimentari,
Ambientali e Forestali, , Università degli Studi di Firenze,
Florence, Italy
2 Dipartimento di Bioscienze e Territorio, Università degli
Studi del Molise, Campobasso, Italy
3 CREA Research Centre for Forestry and Wood, Arezzo, Italy
4 Fondazione per il Futuro delle Città, Firenze, Italy
5 Forest Modelling Laboratory, Institute for Agriculture and
Forestry Systems in Mediterranean, National Research
Council of Italy (CNR-ISAFOM), Perugia, Italy
Abstract
Forest ecosystems’ structure and biomass monitoring are crucial for understanding the contribution of forests to the global
greenhouse gas balance. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission collects waveform lidar data
to estimate Above Ground Biomass Density (AGBD). While of great interest, GEDI data are challenging to download
and pre-process and require coding expertise, limiting their usage. In this paper, we introduce GEDI4R, an open-source R
package providing ecient methods for downloading, reading, clipping, visualizing, and exporting GEDI data. GEDI4R
was tested over the whole of Italy, and more than 11 million GEDI pulses were downloaded in less than 10 hours. The
GEDI pulse density in forests ranged between 132 per km2 (in the Friuli Venezia Giulia Italian administrative region)
and 61 pulses per km2 (in Trentino Alto-Adige). A regional-level comparison between the ocial growing stock volume
estimates reported in the last Italian forest inventory and the AGBD extracted from the GEDI data acquired over the forest
revealed large correlations (r2 = 0.77). Our package facilitates the usage of GEDI AGBD data, which provides innovative
information to monitor carbon cycle dynamics at the global scale.
Keywords Lidar · Forest · Biomass · Ecosystem · Remote sensing · Open access
Received: 13 April 2022 / Accepted: 3 December 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
GEDI4R: an R package for NASA’s GEDI level 4A data downloading,
processing and visualization
EliaVangi1,2,5· GiovanniD’Amico1,3· SaverioFrancini1,4· GherardoChirici1,4
1 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... The MA framework (McRoberts et al., 2016) represents the benchmark for RS-based estimates of forest variables (Chirici, Giannetti, Mazza, et al., 2020;Francini et al., 2020Francini et al., , 2021Vangi et al., 2022Vangi et al., , 2023 such as CO 2 equivalent absorbed. In the MA estimation, a model exploiting RS data is used as auxiliary information to enhance the inference, while the estimation variance is based on the probability sample (Särndal et al., 1992). ...
... Herein we examined a novel Field Independent (FI) spatial technique for mapping and quantifying CO 2 equivalent emissions in tree plantations by combining remote sensing data with the knowledge and expertise of plantation owners. Aggregating pixel predictions, as in the context of small area estimation (Chirici, Giannetti, Mazza, et al., 2020;Vangi et al., 2022), FI results were comparable to well-established estimators like the Model Assisted (MA) and the Design Based (DB). However, since FI does not rely on reference data, the resulting numbers are not statistically rigorous estimates and should be taken with caution. ...
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... In this context, satellite data has become crucial for periodic, detailed, and accurate remotely sensed monitoring of vegetation conditions (Alvites et al., 2021(Alvites et al., , 2022Francini et al., 2023a;Liang et al., 2019;Vangi et al., 2023). Several NASA missions, including ICEsat-2 (Ice, Cloud and Land Elevation Satellite) and GEDI (Global Ecosystem Dynamics Investigation), have focused on the extensive canopy height monitoring from space, facilitating global studies on biomass and vegetation patterns (Dubayah, 2021;Liu et al., 2021). ...
Canopy Height Mapper: a Google Earth Engine application for predicting global canopy heights combining GEDI with multi-source data.
Article
  • Nov 2024
  • ENVIRON MODELL SOFTW
Spatially and temporally discontinuous canopy height footprints collected by NASA's GEDI (Global Ecosystem Dynamics Investigation) mission are accessible on the Google Earth Engine (GEE) cloud computing platform. This study introduces an open-source, user-friendly, code-free GEE web application called Canopy Height Mapper (CH-GEE), available at https://ee-calvites1990.projects.earthengine.app/view/ch-gee, which automatically generates (10 m) high-resolution canopy height maps for a specific area by integrating GEDI with multi-source remote sensing data: Copernicus and topographical data from the GEE data catalogue. CH-GEE generates local-to-country scale calibrated canopy height maps worldwide using machine learning algorithms and leveraging the GEE platform's big data and cloud computing capabilities. CH-GEE allows customization of geographic area, algorithms and time windows for GEDI and predictors. Canopy heights generated by CH-GEE were validated using the Italian National Forest Inventory across 110,000 km2 at multiple scales (Country-based R-squared = 0.89, RMSE = 17%). CH-GEE's accuracy and scalability make it suitable for forest monitoring.
View
... GEDI footprint and gridded datasets come in different spatial resolutions for 3D Earth observation [45]. Since we were focused on canopy height, we used the GEDI-L2A (Level-2A) top-of-canopy and relative height metrics. ...
High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data
Article
Full-text available
  • Apr 2024
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%).
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... accessed 12 May 2022). We used the 'GEDI4R' package (Vangi et al 2022) to process the Landsat-derived maps of the year of stand-initiating disturbance. In this study, stand age is used to stratify biomass predictions, and median age-specific biomass numbers are used to understand post-disturbance AGBD accumulation rates. ...
Vicarious calibration of GEDI biomass with Landsat age data for understanding secondary forest carbon dynamics
Article
Full-text available
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 (AGBD) 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 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.
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Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy
Article
Full-text available
Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r² = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems.
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Aboveground Biomass Density Models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) Lidar Mission
Article
Full-text available
  • Mar 2022
  • REMOTE SENS ENVIRON
NASAs Global Ecosystem Dynamics Investigation (GEDI) is collecting space-borne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDIs footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDIs waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we select the best input predictor variables, data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favors combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) does not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and that the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends
Article
Full-text available
  • Jul 2021
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Light detection and ranging (lidar) data acquired from airborne or spaceborne platforms have revolutionized measurement and mapping of forest attributes. Airborne data are often either acquired using multiple overlapped flight lines to provide complete coverage of an area of interest, or using transects to sample a given population. Spaceborne lidar datasets are unique to each sensor and are sample-or profile-based with characteristics driven by acquisition mode and orbital parameters. To leverage the wealth of accurate vegetation structural data from these lidar systems, a number of approaches have been developed to extend these observations over broader areas, from local landscapes to the globe. In this review we examine studies that have utilised modelling approaches to extend air-or space-based lidar data with the aim of communicating methods, outcomes, and accuracies , and offering guidance on linking lidar metrics and lidar-derived forest attributes with broad-area predictors. Modelling approaches are developed for a variety of applications. In some cases, generation of spatially-exhaustive layers may be useful for forest management purposes, driving management and inventory decisions over smaller focus areas or regions. In other cases, outputs are designed for monitoring at regional or global scales, and may be-due to the spatial grain of the structural estimates-insufficiently accurate or reliable for management. From the reviewed studies, we found height, aboveground biomass and volume, derived from either upper proportions of a large-footprint full-waveform lidar profiles, or statistically modelled from discrete return small-footprint lidar point clouds, to be the most commonly extended forest attributes, followed by canopy cover, basal area and stand complexity. Assessment of the accuracy and bias of the extrapolated forest attributes varied with both independent and model-derived estimates. The coefficient of determination (R 2) was the most often reported, followed by absolute and relative (i.e., as a proportion of the mean) root mean square error (RMSE and RMSE% respectively). Compilation of the stated accuracies suggested that the variance explained in predictions of forest height ranged from R 2 = 0.38 to 0.90 (mean = 0.64), RMSE from 2 to 6m and RMSE% from 12 to 34%. For volume, R 2 ranged from 0.25 to 0.72 (mean = 0.53) and RMSE from 60 to 87 m 3 /ha and for aboveground biomass (AGB) R 2 ranged from 0.35 to 0.78 (mean = 0.55) and RMSE from 28 to 44 Mg/ha. There was no consensus on the level of accuracy required to support successful extension over larger areas. Ultimately, the review suggests that the information need motivating the spatial extension over larger areas drives the choice of the type of lidar data, spatial datasets and related grain. We conclude by discussing future directions and the outlook for new approaches including new lidar-derived response variables, advances in modelling approaches, and assessment of change.
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Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy
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Forest planning, forest management, and forest policy require updated, reliable , and harmonized spatial datasets. In Italy a national geographic Forest Information System (FIS) designed to store and facilitate the access and analysis of spatial datasets is still missing. Among the different information layers which are useful to start populating a FIS, two are essential for their multiple use in the assessment of forest resources: (i) forest mapping, and (ii) data from Airborne Laser Scanning (ALS). Both layers are not available wall-to-wall for Italy, though different local sources of information potentially useful for their implementation already exist. The objectives of this work were to: (i) review forest maps and ALS data availability in Italy; (ii) develop for the first time a high resolution forest mask of Italy which was validated against the official statistics of the Italian National Forest Inventory; (iii) develop the first mosaic of all the main ALS data available in Italy producing a consistent Canopy Height Model (CHM). An on-line geographic FIS with free access to both layers from (ii) and (iii) was developed for demonstration purposes. The total area of forest and other wooded lands computed from the forest mask was 102,608.82 km 2 (34% of the Italian territory), i.e., 1.9% less than the NFI benchmark estimate. This map is currently the best wall-to-wall forest mask available for Italy. We showed that only the 63% of the Italian territory (the 60% of the forest area) is covered by ALS data. These results highlight the urgent need for a national strategy to complete the availability of forest data in Italy.
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The Effect of Forest Mask Quality in the Wall-to-Wall Estimation of Growing Stock Volume
Article
Full-text available
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Information about forest cover and its characteristics are essential in national and international forest inventories, monitoring programs, and reporting activities [...]
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Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping
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Full-text available
  • Feb 2021
  • REMOTE SENS ENVIRON
Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.
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The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography
Article
Full-text available
  • Jun 2020
Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.
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Mapping global forest canopy height through integration of GEDI and Landsat data
Article
  • Nov 2020
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Consistent, large-scale operational monitoring of forest height is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. The Global Ecosystem Dynamics Investigation (GEDI) lidar instrument onboard the International Space Station has been collecting unique data on vegetation structure since April 2019. Here, we employed global Landsat analysis-ready data to extrapolate GEDI footprint-level forest canopy height measurements, creating a 30 m spatial resolution global forest canopy height map for the year 2019. The global forest height map was compared to the GEDI validation data (RMSE = 6.6 m; MAE = 4.45 m, R2 = 0.62) and available airborne lidar data (RMSE = 9.07 m; MAE = 6.36 m, R2 = 0.61). The demonstrated integration of GEDI data with time-series optical imagery is expected to enable multidecadal historic analysis and operational forward monitoring of forest height and its dynamics. Such capability is important to support global climate and sustainable development initiatives.
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Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference
Article
  • Oct 2016
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Remotely sensed data have been widely used in recent years for mapping and estimating biomass. However, the characterization of the uncertainty of mapped or estimated biomass in previous studies was either based on ad-hoc approaches (e.g., using model fitting statistics such root mean square errors derived from purposive samples) or mostly limited to the analysis of mean biomass for the whole study area. This study proposed a novel uncertainty analysis method that can characterize biomass uncertainty across multiple spatial scales and multiple spatial resolutions. The uncertainty analysis method built on model-based inference and can propagate errors from trees to field plots, individual pixels, and small areas or large regions that consist of multiple pixels (up to all pixels within a study area). We developed and tested this method over northern Minnesota forest areas of approximately 69,508 km 2 via a unique combination of several datasets for biomass mapping and estimation: wall-to-wall airborne lidar data, national forest inventory (NFI) plots, and destructive measurements of tree aboveground biomass (AGB). We found that the pixel-level AGB prediction error is dominated by lidar-based AGB model residual errors when the spatial resolution is near 380 m or finer and by model parameter estimate errors when the spatial resolution is coarser. We also found that the relative error of AGB predicted from lidar can be reduced to approximately 11% (or mean 5.1 Mg/ha; max 43.6 Mg/ha) at one-hectare scale (or at 100 m spatial resolution) over our study area. Because our uncertainty analysis method uses model-based inference and does not require probability samples of field plots, our methodology has potential applications worldwide, especially over tropics and developing countries where NFI systems are not well-established.
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