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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 descript...
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... exhaustive meta-analysis of previous biomass modeling efforts, such as in Zolkos et al. (2013) is beyond our scope here, but a sample of previous studies (Table 1) helps to illustrate the breadth of geographic domain, ecosystems, statistical algorithms, sensors, and associated accuracies. This diversity of models, data, and methods highlights the enormous task of creating a parsimonious set of calibration models appropriate for global biomass predictions using GEDI. ...
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... general, local studies (e.g., project-level airborne lidar collection) tend to include more predictor variables, and have higher reported accuracies (e.g. see Zolkos et al., 2013 and Table 1). In contrast, regional or larger area studies (continental, pantropical, global) have lower reported accuracies due to broader domains that cover an enormous range of edaphic, topographic, floristic and climatic gradients that can create local variations in canopy structure, and hence aboveground biomass Meyer et al., 2019;Xu et al., 2017). ...
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... previous efforts outlined in Table 1 from more than 20 years of research underscore the complexity of the task any space mission must face towards developing calibration models for biomass. The GEDI mission has a primary science goal of mapping aboveground woody biomass across Earth's temperate, subtropical, and tropical forests. ...
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... lidar point clouds were processed through a GEDI waveform simulator , Fig 1b) to produce GEDI-like waveforms and derived metrics commensurate with field plot data. An exhaustive set of models was fit to predict field AGBD as a function of simulated RH metrics, with permutations in candidate predictor metrics (all possible Table 1 Examples of previous lidar biomass studies in forested ecosystems provide context for the unique geographic extent and spatial resolution of GEDI footprint-level biomass (GEDI04_A) models. Models are listed from local to pantropical studies, using a range of input data (discrete return lidar, airborne full waveform (LVIS and SLICER)), and spaceborne waveform lidar (GLAS). ...
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... field data used in the development of the GEDI04_A data product were from 74 sites (Fig. 2), taken from a total of 142 sites or projects that contributed data to this research (Table S1). These datasets were assembled by an international consortium of researchers and represent both publicly available and privately managed datasets (Fig. S1). ...
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... tree measurements including diameter at breast height (DBH) or above basal deformities, tree height and species (as available) and plot geometries were used to match simulated GEDI footprints to field plots. We predicted individual tree AGB from DBH, and wood density based on taxonomic information, as well as height for tropical datasets, where available, using available broadly applicable allometric models (Table S1). While these allometric models are known to have high uncertainties ( Vorster et al., 2020), e.g. for estimation of large tree biomass ( Disney et al., 2020), the set of allometric models adopted for GEDI04_A was the most generalized available for the geographic scale of the GEDI04_A models. ...
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... of the projects/sites included in the training database (Table S1) Fig. 1. Flow chart of the GEDI04_A modeling process. ...
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... Creek site in Australia or Barro Colorado Island in Panama) were divided into several GEDI footprint-sized plots placed side-by-side in a tighter range of biogeographic conditions. Plot-level AGBD was calculated by summing all individual tree AGB (above a minimum DBH threshold, see Table S1) in a GEDI footprint-sized plot, but this summation took two general forms; 1) summing all trees when the plot was approximately GEDI sized (~25 m diameter), or 2) dividing stemmapped plots into GEDI footprint-sized plots prior to summing all trees. Total plot-level AGB was then divided by plot area to produce estimates of AGBD. ...
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... field data with spatially coincident airborne lidar data collected during the leaf-on season within two years of field data acquisition were used for GEDI's footprint-level AGBD models. The lidar data were from a range of instruments (Table S1). Lidar datasets were filtered to ensure sufficient sampling density of returns were available (>4 pulses m -2 ) to simulate GEDI waveforms Hancock et al. (2019). ...
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... to model fitting, several filters were applied to identify erroneous outliers and ensure the training dataset (Fig. 2, Table S1) was not biased toward large plots. To prevent model training being weighted too heavily to plots with a larger number of footprints, we fit OLS models with a weighting factor based on the number of simulated footprints per plot (i.e. ...
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... explored three levels of geographic stratification to produce globally representative models. For vegetation type classification we used PFT, a broadly adopted classification of ecosystem structure and function (Diaz and Cabido, 1997 Table 1). We also stratified our database by geographic region as it has been well documented that forest composition and structure both vary within and between continents (Carlucci et al., 2017;Corlett and Primack, 2006;Feldpausch et al., 2012;Friis and Balslev, 2005). ...
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... height was directly related to the AGB of trees and therefore RH98 usually had a relationship with AGBD at the plot level, where the biomass may be a product of many smaller trees or a few large trees at the size of GEDI footprints (Fig. 7e). However, the 90th percentile lidar height is often used in the literature instead of maximum height for AGBD prediction (Table 1). We compared RH98 and RH90 across geographic regions, and found that while they were highly correlated, there were often large differences between the two metrics (Fig. 7h, i). ...
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... lidar specifically designed for measuring forest structure has not been available at a footprint size of 25 m, so generating a set of globally representative models is a relatively novel endeavor (with the exception of the pantropical studies listed in Table 1). GEDI04_A models performed comparably to other wide area AGBD modeling efforts (Table 1), but generally did not produce as accurate results as local studies where models are specifically developed for the area of interest ( Ploton et al., 2020). ...
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... lidar specifically designed for measuring forest structure has not been available at a footprint size of 25 m, so generating a set of globally representative models is a relatively novel endeavor (with the exception of the pantropical studies listed in Table 1). GEDI04_A models performed comparably to other wide area AGBD modeling efforts (Table 1), but generally did not produce as accurate results as local studies where models are specifically developed for the area of interest ( Ploton et al., 2020). We found that accuracies varied considerably by geographic strata (Section 4.1), but that variable selection was fairly consistent and primarily used high (RH98, RH90) and low (RH10, RH20) height metrics (Section 4.2). ...
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... ENT North America forests included the highest plot-level AGBD values in the database, but this stratum includes an exceptionally wide range of AGBD, and is dominated by shorter trees, which sum to a lower AGBD, rather than the giant redwood stands in the Western United States (Fig. 2). The general model performance in these strata was as expected (Table 1), but there remains ample room for improvement, particularly in tropical EBT regions, and most notably in Asia. This may include the improvement of reference data (e.g better allometric models, more training data), the inclusion of new predictors (e.g. ...
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... waveform lidar-based studies have differed in the selection of RH metrics, where Huang et al., 2013;Sun et al., 2011 found RH50 and RH75 the most useful, Swatantran et al., 2011 used RH75, RH100 and canopy cover, Ni-Meister et al., 2010 used RH50, RH100, and canopy cover, Drake et al., 2003 used RH50 (HOME) alone. However, many lidar studies have demonstrated the importance of canopy cover (Table 1), and low RH metrics may be particularly sensitive to canopy cover (Fig. 7g). RH10 and RH20 should also be sensitive to terrain slope, which may in turn be correlated with AGBD (Ferry et al., 2010). ...
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Citations
... This is particularly true for GEDI, where the mean estimated stratum-wise difference is ∼12 Mg ha −1 , and largest in either the fragmented, vegetation-sparse or mountainous forests. The lack of training data in the GEDI L4A models in plantation forests (Duncanson et al 2022) may explain some of these minor discrepancies. For the CCI product, the extent of Spain was classified as a single stratum for the construction of allometric models (Santoro et al 2023a), whereby minor differences to NFI estimates could arise from constraining AGBD to a single maximum value across varying ecological conditions from north to south of the country. ...
Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of `transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first Global Stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation (GEDI) and ESA Climate Change Initiative (CCI), describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact.
... than 90% of the plot was covered with intact forest . For each of the plots, the mean biomass density according to Global Ecosystem Dynamics Investigation (GEDI) L4B product (Dubayah et al., 2022) and the mean canopy height (Potapov et al., 2021) were recorded to compare the relationship between the EII score and these variables. ...
... The benefit of the EII score is that it provides a single and simple measure that can be used to monitor progress and inform management planning without having to measure the full array of metrics related to forest integrity. Such benefit of an ecosystem integrity composite index and exercise of validation considering the correlation with other metrics has been previously used for other ecosystem integrity indices, such as the forest landscape integrity index (Duncanson et al., 2022). Here, we present an example of a validation exercise demonstrating how EII score is correlated with field measurements related to forest conditions, specifically canopy height and biomass. ...
Introduction
An unprecedented amount of Earth Observations and in-situ data has become available in recent decades, opening up the possibility of developing scalable and practical solutions to assess and monitor ecosystems across the globe. Essential Biodiversity Variables are an example of the integration between Earth Observations and in-situ data for monitoring biodiversity and ecosystem integrity, with applicability to assess and monitor ecosystem structure, function, and composition. However, studies have yet to explore how such metrics can be organized in an effective workflow to create a composite Ecosystem Integrity Index and differentiate between local plots at the global scale.
Methods
Using available Essential Biodiversity Variables, we present and test a framework to assess and monitor forest ecosystem integrity at the global scale. We first defined the theoretical framework used to develop the workflow. We then measured ecosystem integrity across 333 forest plots of 5 km ² . We classified the plots across the globe using two main categories of ecosystem integrity (Top and Down) defined using different Essential Biodiversity Variables.
Results and discussion
We found that ecosystem integrity was significantly higher in forest plots located in more intact areas than in forest plots with higher disturbance. On average, intact forests had an Ecosystem Integrity Index score of 5.88 (CI: 5.53–6.23), whereas higher disturbance lowered the average to 4.97 (CI: 4.67–5.26). Knowing the state and changes in forest ecosystem integrity may help to deliver funding to priority areas that would benefit from mitigation strategies targeting climate change and biodiversity loss. This study may further provide decision- and policymakers with relevant information about the effectiveness of forest management and policies concerning forests. Our proposed method provides a flexible and scalable solution that facilitates the integration of essential biodiversity variables to monitor forest ecosystems.
... This includes everything from canopy height, canopy cover, basal area, tree density and aboveground biomass (Asner and Mascaro, 2014;Jucker et al., 2018b;Lefsky et al., 2002). Programs such as NASA's GEDI missionthe first spaceborne LiDAR sensor designed specifically to map forest ecosystems ) -have expanded this capability even further, capturing fine-scale variation in habitat 3D structure across entire regions and biomes (Duncanson et al., 2022;Schneider et al., 2020). However, to realise the full potential of these novel technologies to guide ecosystem conservation and restoration, we first need to develop robust approaches for integrating data across platforms and spatial scales. ...
... Laser scanning, an active form of remote sensing commonly known as lidar, is suitable to characterize three-dimensional forest structural properties (Lefsky et al., 2002). The Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument has been providing unprecedented three-dimensional information of tropical and temperate forests worldwide (Dubayah et al., 2020Duncanson et al., 2022) and has made it possible to investigate forest structure over large areas in the Amazon and other forest biomes worldwide. ...
The Brazilian Amazon has been a focus of land development with large swaths of forests converted to agriculture. Forest degradation by selective logging and fires has accompanied the agricultural frontier and has resulted in significant impacts on Amazonian ecosystems. Changes in forest structure resulting from forest disturbances have large impacts on the surface energy balance, including on land surface temperature (LST) and evapotranspiration (ET). This study’s objective is to assess the effects of forest disturbances on water fluxes and forest structure in a transitional forest site in the Southern Amazon. We used ET and LST products from MODIS and Landsat 8 and GEDI‐derived forest structure data to address our research questions. We found that disturbances induced seasonal water stress, more pronounced in croplands/pastures than in forests (differences up to 20% in the dry season), and more pronounced in second‐growth and recently burned areas than in logged and intact forests (differences up to 12% in the dry season). Moreover, ET and LST were negatively related, with more consistent relationships across disturbance classes in the dry season (R ² : 0.41‐0.87) than in the wet season (R ² : 0.18‐0.49). Forest and cropland and pasture classes showed contrasting relationships in the dry season. Finally, we found that forest structure exhibited stronger relationships with ET and LST in the most disturbed forests (R ² : 0.01‐0.43) than in the least disturbed forests (R ² <0.05). Our findings help to elucidate degraded forests functioning under a changing climate and to improve estimates of water and energy fluxes in Amazonian degraded forests.
... Here, we use spaceborne lidar from NASA's Global Ecosystem Dynamics Instrument (GEDI), which provides near-global (between 51.6 • S and 51.6 • N latitude; Fig. 1) estimates of forest structure (see Dubayah et al., 2020 for GEDI details). Although GEDI has been used for applications such as estimating forest canopy heights (Liu et al., 2021;Potapov et al., 2021), estimating biomass and fuel loads across large areas Duncanson et al., 2022;Leite et al., 2022), and coupling the structural information provided by GEDI with additional datasets to predict the biodiversity of trees and birds (Burns et al., 2020;Marselis et al., 2022), to our knowledge, our study is the first effort to examine the relative roles of structural and species diversity in explaining aboveground carbon storage with GEDI data. Building on previous work that found positive associations between structural diversity and net primary production using either lidar (Gough et al., 2019;Hardiman et al., 2011) or forest inventory data (Dȃnescu et al., 2016;LaRue et al., 2023), our study integrates GEDI and forest inventory data to examine diversity-carbon storage relationships across the entire USA. ...
Since biodiversity often increases ecosystem functioning, changes in tree species diversity could substantially influence terrestrial carbon cycling. Yet much less is known about the relationships between forest structural diversity (i.e., the number and physical arrangement of vegetation elements in a forest) and carbon cycling, and the factors that mediate these relationships. We capitalize on spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) and on-the-ground forest inventory and analysis (FIA) data from 1796 plots across the contiguous United States to assess relationships among the structural and species diversity of live trees and aboveground carbon storage. We found that carbon storage was more strongly correlated with structural diversity than with species diversity, for both forest inventory-based metrics of structural diversity (e.g., height and DBH diversity) and GEDI-based canopy metrics (i.e., foliage height diversity (FHD)). However, the strength of diversity‑carbon storage relationships was mediated by forest origin and forest types. For both plot-based and GEDI-based metrics, the relationship between structural diversity (i.e., height diversity, DBH diversity, and FHD) and carbon storage was positive in natural forests for all forest types (broadleaf, mixed, conifer). For planted forests, structural diversity showed positive relationships in planted conifer forests but not in planted mixed forests. Species diversity did not show strong associations with carbon storage in natural forests but showed a positive relationship in mixed coniferous-broadleaf planted forests. Although plot-based structural diversity metrics refine our understanding of drivers of forest carbon balances at the plot scale, remotely sensed metrics such as those from GEDI can help extend that understanding to regional/national scales in a spatially continuous manner. Carbon storage showed stronger associations with plot-based structural diversity than with stand age, soil variables, or climate variables. Incorporating structural diversity into management and restoration strategies could help guide efforts to increase carbon storage and mitigate climate change as nature-based solutions.
... The GEDI on the International Space Station was orbiting on board and measured canopy height, vertical structure, and elevation between 51.6 • S and 51.6 • N [34]. GEDI offered four different types of products, which included raw waveforms; footprint-level ground and canopy heights; grid-form heights; and biomass [34,36]. The raw GEDI waveform data were a Level-1 product [36]. ...
... GEDI offered four different types of products, which included raw waveforms; footprint-level ground and canopy heights; grid-form heights; and biomass [34,36]. The raw GEDI waveform data were a Level-1 product [36]. Recently released on the Google Earth Engine (GEE), the GEDI Level-2A data offered canopy-relative height (RH) metrics, RH0-RH100 [37]. ...
... The GEDI on the International Spac Station was orbiting on board and measured canopy height, vertical structure, an elevation between 51.6°S and 51.6°N [34]. GEDI offered four different types of product which included raw waveforms; footprint-level ground and canopy heights; grid-form heights; and biomass [34,36]. The raw GEDI waveform data were a Level-1 product [36 The GEDI Level-2A data contained 2 versions (Version 1 and Version 2). ...
Stand age is a significant factor when investigating forest resource management. How to obtain age data at a sub-compartment level on a large regional scale conveniently and in real time has become an urgent scientific challenge in forestry research. In this study, we established two strategies for stand-age estimation at sub-compartment and pixel levels, specifically object-based and pixel-based approaches. First, the relationship between canopy height and stand age was established based on field measurement data, which was achieved at the Mao’er Mountain Experimental Forest Farm in 2020 and 2021. The stand age was estimated using the relationship between the canopy height, the stand age, and the canopy-height map, which was generated from multi-resource remote sensing data. The results showed that the validation accuracy of the object-based estimation results of the stand age and the canopy height was better than that of the pixel-based estimation results, with a root mean squared error (RMSE) increase of 40.17% and 33.47%, respectively. Then, the estimated stand age was divided into different age classes and compared with the forest inventory data (FID). As a comparison, the object-based estimation results had better consistency with the FID in the region of the broad-leaved forests and the coniferous forests. In addition, the pixel-based estimation results had better accuracy in the mixed forest regions. This study provided a reference for estimating stand age and met the requirements for stand-age data at the pixel and sub-compartment levels for studies involving different forestry applications.
... Credible, accurate and reliable monitoring of stocks and changes in forest structure are critical for achieving international goals and national commitments to forest conservation, management, climate change and sustainable development [1,2]. Achieving this requires accurate measurements of tree height, which is used as a predictor of biomass [3] and informs the subsequent retrieval of carbon stocks, structure, function and biodiversity [4]. Estimating forest structure, however, is problematic in tidally and seasonally flooded forests where tree height estimates vary relative to sub-canopy tidal/flooding conditions. ...
Flooding controls wetland carbon cycling and hinders accurate measurements of ecosystem structure from remotely sensed data. Single measurements over a changeable surface can yield large uncertainties, exacerbated by insufficient repeat sampling of inundation dynamics across globally distributed locations. In forested wetlands, flood frequency and duration is critical to controlling carbon cycling, but high canopy cover obscures the view of inundation stage where large fluctuations in flooding directly impact measurements of ecosystem structure.
... While the first and third assumptions were addressed by Patterson et al. (2019) during GEDI's pre-launch phase, the extent to which the L4A models are unbiased everywhere is not wellestablished (Dubayah et al., 2022b;Duncanson L. et al., 2022). This possibility is acknowledged by Duncanson L. et al. (2022), and represents a substantial challenge in developing globally representative prediction models due to geographic gaps in training data used to calibrate the L4A models that must be transferable to entire continents. ...
... While the first and third assumptions were addressed by Patterson et al. (2019) during GEDI's pre-launch phase, the extent to which the L4A models are unbiased everywhere is not wellestablished (Dubayah et al., 2022b;Duncanson L. et al., 2022). This possibility is acknowledged by Duncanson L. et al. (2022), and represents a substantial challenge in developing globally representative prediction models due to geographic gaps in training data used to calibrate the L4A models that must be transferable to entire continents. Such an assessment requires validation of GEDI's AGBD estimates with independent reference data, however standardized reference data does not exist to validate a global biomass map at 1 km resolution . ...
Atmospheric CO 2 concentrations are dependent on land-atmosphere carbon fluxes resultant from forest dynamics and land-use changes. These fluxes are not well-constrained, in part because reliable baseline estimates of forest carbon stocks and the associated uncertainties are lacking. NASA's Global Ecosystem Dynamics Investigation (GEDI) produces estimates of aboveground biomass density (AGBD) that are unique because GEDI's hybrid estimation framework enables formal uncertainty calculations that accompany the biomass estimates. However, GEDI's estimates are not without issue; a recent validation using design-based AGBD estimates from the US Forest Inventory and Analysis (FIA) program revealed systematic differences between GEDI and FIA estimates within a hexagon tessellation of the continental United States. Here, we explored these differences and identified two issues impacting GEDI's estimation process: incomplete filtering of low quality GEDI observations and regional biases in GEDI's footprint-level biomass models. We developed a solution to each, in the form of improved data filtering and GEDI-FIA fusion AGBD models, developed in a scale-invariant small area estimation framework, that were compatible with hybrid estimation. We calibrated 10 regional Fay-Herriot models at the hexagon scale for application at the unit scale of GEDI footprints, for which we provide a mathematical justification and empirical testing of the models' scale-invariance. These models predicted realistic distributions of unit level AGBD, with equal or improved performance relative to GEDI's L4A models for all regions. We then produced GEDI-FIA fusion estimates that were more precise than the FIA estimates and resulted in a bias reduction of 86.7% relative to the original GEDI estimates: 19.3% due to improved data filtering and 67.5% due to the new AGBD models. Our findings indicate that (1) small area estimation models trained in a scale-invariant framework can produce realistic predictions of AGBD, and (2) there is substantial spatial variability in the relationship between GEDI forest structure metrics and AGBD. This work is a step toward achieving reliable baseline forest carbon stocks, provides a viable methodology for training remote sensing biomass models, and may serve as a reference for other investigations of GEDI AGBD estimates.
... Finally, we compared the GEDI RH98 values to TanDEM-X height values to determine how comparable height estimates from each sensor are in the Niger Delta. For this comparison, we chose to compare to the RH98 since other work has found this to be a more stable parameter for estimating canopy height (Duncanson et al., 2022). ...
Invasive species are a leading threat to biodiversity worldwide. Nypa palm ( Nypa fruticans ) has emerged as the predominant invasive species in the Niger Delta region of Nigeria. While endemic mangroves have high rates of carbon sequestration, stabilize coastlines and protect biodiversity, Nypa does not provide these services outside its native region of Southeast Asia. Oil exploration and urbanization in this region also exacerbate mangrove loss and Nypa spread. As Nypa is difficult to distinguish from endemic mangrove species in remotely sensed data, estimates of mangrove and ecosystem services losses in Nigeria are highly uncertain. Here, we analyse multisensor satellite data with machine learning to quantify the rapid expansion of Nypa from 2015 to 2020 in Nigeria. Using Landsat imagery and random forest classification, we quantify total potential Nypa extent in Nigeria in 2019. We then produced a Nypa extent map using iterative combinations of Sentinel‐1 SAR, Sentinel‐2 MSI and ALOS PALSAR. Random forest classifications using SAR data from ALOS and Sentinel‐1 were best suited for mapping Nypa extent with similar accuracies (78% and 75%, respectively). Based on data availability and accuracy, we focussed our change analysis on Sentinel‐1 SAR. Our results show ~28 000 ha of mangroves were converted to Nypa in Nigeria by 2020 and covered a larger extent than endemic mangroves, compounding the effect of the existing degradation and deforestation in the region. We also compared forest height and complexity estimates from Global Ecosystem Dynamics Investigation LiDAR to further distinguish between endemic mangroves and Nypa in three dimensions. Nypa structural variability, measured by top‐of‐canopy height, vegetation cover, plant area index, and foliage height diversity, was lower than that of mangroves. At current rates of Nypa expansion, the entire area of study would be invaded by Nypa by 2028, with potentially detrimental consequences to the ecosystem services provided by mangroves.
... Within our model assessments, we found moderate to high predictive performance for a set of eight GEDI structure metrics across our diverse study region. Much of the existing GEDI literature has focused on accuracy assessments of the GEDI waveform geolocations and structure measures (Adam et al., 2020;Li et al., 2023), developing footprint level biomass models (Duncanson et al., 2022), or leveraged simulated GEDI data in lieu of actual footprint samples (Burns et al., 2020;Silva et al., 2021). Studies have begun to investigate GEDI footprint samples as the basis for scaling up forest structure measures by leveraging fusions of passive and active satellite earth observations, although the majority of those studies have been focused on elevation, canopy height, or biomass Potapov et al., 2021;Shendryk, 2022). ...
Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R ² values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.
