Mapping global forest canopy height through integration of GEDI and Landsat data

The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring

Forest canopy height mapping is critical for mapping and modeling bio-geophysical and ecological factors, including forest aboveground biomass, carbon reserves, forest carbon emissions, habitat diversity, forest degradation, and restoration success. The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne Light Detection and Ranging (LiDAR) sensor designed specifically to collect data on forest ecosystems worldwide. However, the information obtained by GEDI is not wall-to-wall, requiring data fusion approaches to map spatially continuous canopy heights. This study, for the first time, presents canopy height models for the entire country of Nepal based on interpolated GEDI tree heights fusing Sentinel-2 multispectral imagery with Sentinel-1 synthetic aperture radar (SAR), creating species-specific continuous canopy height models for Nepal at 10 m resolution. Forest plot field data, collected from a nationwide campaign, provided data on species identity, which was used for species mapping and accuracy evaluation. Differences in canopy-architecture and leaf-level traits mean that species-specific models are needed to interpolate GEDI tree heights using the Sentinel optical and SAR data. The national forest height map was compared with an independent set of GEDI data (RMSE = 2.4 m, R 2 = 0.92, intercept (c) = 0.53 m and slope (m) = 0.98) and fully independent field data (RMSE = 3.7 m, R 2 = 0.74, c = 4.1 m, and m = 0.89). The developed forest type map and canopy height models have the potential to aid in both operational monitoring and hindcasting of historical forest height and its dynamics. Local and national forest management initiatives and international climate and sustainable development projects require this kind of capacity.

Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest monitoring tools. Research studies have been carried out to assess the use of optical and radar images for producing forest height or biomass maps. The main limitations are the quantity, quality and representativeness of the reference data for model training. The Global Ecosystem Dynamics Investigation (GEDI) mission (full waveform LiDAR on board the International Space Station) has provided an unprecedented number of forest canopy height samples from 2019. These samples could be used to improve reference datasets. This paper aims to present and validate a method for estimating forest dominant height from open access optical and radar satellite images (Sentinel-1, Sentinel-2 and ALOS-2 PALSAR-2), and then to assess the use of GEDI samples to replace field height measurements in model calibration. Our approach combines satellite image features and dominant height measurements, or GEDI metrics, in a Support Vector Machine regression algorithm, with a feature selection process. The method is tested on mixed uneven-aged broadleaved and coniferous forests in France. Using dominant height measurements for model training, the cross-validation shows 7.3 to 11.6% relative Root Mean Square Error (RMSE) depending on the forest class. When using GEDI height metrics instead of field measurements for model training, errors increase to 12.8–16.7% relative RMSE. This level of error remains satisfactory; the use of GEDI could allow the production of dominant height maps on large areas with better sample representativeness. Future work will focus on confirming these results on new study sites, improving the filtering and processing of GEDI data, and producing height maps at regional or national scale. The resulting maps will help forest managers and public bodies to optimise forest resource inventories, as well as allow scientists to integrate these cartographic data into climate models.

Estimating consistent large-scale tropical forest height using remote sensing is essential for understanding forest-related carbon cycles. The Global Ecosystem Dynamics Investigation (GEDI) light detection and ranging (LiDAR) instrument employed on the International Space Station has collected unique vegetation structure data since April 2019. Our study shows the potential value of using remote-sensing (RS) data (i.e., optical Sentinel-2, radar Sentinel-1, and radar PALSAR-2) to extrapolate GEDI footprint-level forest canopy height model (CHM) measurements. We show that selected RS features can estimate vegetation heights with high precision by analyzing RS data, spaceborne GEDI LiDAR, and airborne LiDAR at four tropical forest sites in South America and Africa. We found that the GEDI relative height (RH) metric is the best at 98% (RH98), filtered by full-power shots with a sensitivity greater than 98%. We found that the optical Sentinel-2 indices are dominant with respect to radar from 77 possible features. We proposed the nine essential optical Sentinel-2 and the radar cross-polarization HV PALSAR-2 features in CHM estimation. Using only ten optimal indices for the regression problems can avoid unimportant features and reduce the computational effort. The predicted CHM was compared to the available airborne LiDAR data, resulting in an error of around 5 m. Finally, we tested cross-validation error values between South America and Africa, including around 40% from validation data in training to obtain a similar performance. We recommend that GEDI data be extracted from all continents to maintain consistent performance on a global scale. Combining GEDI and RS data is a promising method to advance our capability in mapping CHM values.

Forest canopy height is a fundamental parameter of forest structure, and plays a pivotal role in understanding forest biomass allocation, carbon stock, forest productivity, and biodiversity. Spaceborne LiDAR (Light Detection and Ranging) systems, such as GEDI (Global Ecosystem Dynamics Investigation), provide large-scale estimation of ground elevation, canopy height, and other forest parameters. However, these measurements may have uncertainties influenced by topographic factors. This study focuses on the calibration of GEDI L2A and L1B data using an airborne LiDAR point cloud, and the combination of Sentinel-2 multispectral imagery, 1D convolutional neural network (CNN), artificial neural network (ANN), and random forest (RF) for upscaling estimated forest height in the Guangxi Gaofeng Forest Farm. First, various environmental (i.e., slope, solar elevation, etc.) and acquisition parameters (i.e., beam type, Solar elevation, etc.) were used to select and optimize the L2A footprint. Second, pseudo-waveforms were simulated from the airborne LiDAR point cloud and were combined with a 1D CNN model to calibrate the L1B waveform data. Third, the forest height extracted from the calibrated L1B waveforms and selected L2A footprints were compared and assessed, utilizing the CHM derived from the airborne LiDAR point cloud. Finally, the forest height data with higher accuracy were combined with Sentinel-2 multispectral imagery for an upscaling estimation of forest height. The results indicate that through optimization using environmental and acquisition parameters, the ground elevation and forest canopy height extracted from the L2A footprint are generally consistent with airborne LiDAR data (ground elevation: R2 = 0.99, RMSE = 4.99 m; canopy height: R2 = 0.42, RMSE = 5.16 m). Through optimizing, ground elevation extraction error was reduced by 45.5% (RMSE), and the canopy height extraction error was reduced by 30.3% (RMSE). After training a 1D CNN model to calibrate the forest height, the forest height information extracted using L1B has a high accuracy (R2 = 0.84, RMSE = 3.13 m). Compared to the optimized L2A data, the RMSE was reduced by 2.03 m. Combining the more accurate L1B forest height data with Sentinel-2 multispectral imagery and using RF and ANN for the upscaled estimation of the forest height, the RF model has the highest accuracy (R2 = 0.64, RMSE = 4.59 m). The results show that the extrapolation and inversion of GEDI, combined with multispectral remote sensing data, serve as effective tools for obtaining forest height distribution on a large scale.

The advent of new-generation spaceborne Light Detection and Ranging (lidar) systems, exemplified by the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), has opened up an unprecedented opportunity for observing forest canopy structures. However, forest canopy height derived from ICESat-2 ATL08 land and vegetation products and GEDI L2A geolocated elevation and height products exhibit varying accuracy across different regions. Low data accuracy limits the broader application of these systems. Moreover, the canopy height detection abilities of the two spaceborne lidar systems in the ecologically important forests of Northeast China require further investigation. In this study, airborne lidar data were used to evaluate the performance of canopy height retrievals from ICESat-2 ATL08 and GEDI L2A, as well as their influencing factors. In addition, a canopy height improvement method, based on a random forest model, was proposed to enhance the accuracy and consistency of canopy height data derived from ICESat-2 and GEDI. The results indicate that the performance of strong beams surpassed that of weak beams in detecting canopy height, with only weak beam data collected during the day recommended for exclusion in ICESat-2 applications. In contrast, for the GEDI mission, the advantages of the power beam were not pronounced, and its performance was better during the day than at night. Compared to ICESat-2, GEDI exhibited lower accuracy in canopy height detection under nighttime conditions or in evergreen needle-leaved forests, but showed greater sensitivity to slope. Moreover, the proposed method increased the coefficient of determination (R2) for ICESat-2 canopy height accuracy from 0.53 to 0.82, and reduced the root mean square error (RMSE) from 3.98 m to 2.00 m. Similarly, the R2 for GEDI improved from 0.52 to 0.80, while RMSE decreased from 4.45 m to 2.41 m. The consistency between ATL08 and GEDI L2A was also improved, with the RMSE reduced by 3.14 m. The findings of this study could provide valuable guidance for the selection and utilization of the two spaceborne lidar data. Canopy height data derived from the improved strategy may enable new opportunities for forest canopy height mapping in Northeast China and support further applications, such as the quantification of aboveground carbon stocks in forests.

Accurate monitoring of forest canopy height (FCH) is highly important to gain a proper understanding of ecosystem dynamics, biodiversity, and carbon sequestration processes. This study presents an effective approach that integrates Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) with Global Ecosystem Dynamics Investigation (GEDI) data for enabling accurate large-scale forest structure mapping. This approach aims at predicting canopy height by making use of machine learning (ML) algorithms in Pongara National Park, Gabon. For canopy height modelling, the relative height at 100% energy return (RH100) from GEDI is used as the primary reference metric, and the three regression models, namely Random Forest (RF), Classification and Regression Tree (CART), and Gradient Tree Boost (GTB), are applied using features derived from PolInSAR data as input. Furthermore, this research incorporates the Random Volume over Ground (RVoG) inversion model, which is a physics-based model using the PolInSAR data to derive forest height estimates, enabling a comparison with ML algorithms as a data-driven approach. Feature importance analysis revealed that DEM height, incidence angle, and backscattered HV power are the most influential predictors in all models. The research findings exhibited that the best training performance was for CART (RMSE = 3.011 m, R2 = 0.963), while GTB demonstrated superior generalisation during validation (RMSE = 7.713 m, R2 = 0.757), suggesting it as the most robust model. Overall, RF was very competitive, with close conformity to GTB in terms of bias, accuracy, and correlation. In comparison, the RVoG model laid out a strong correlation with the RF model (up to 0.91) and tended to estimate higher canopy heights than other ML results, reflecting its sensitivity to vertical forest structure.

The development of high resolution mapping models of forest attributes based on employing machine or deep learning techniques has increasingly accelerated in the last couple of years. The consequence of this is the widespread availability of multiple sources of information, which can either lead to a potential confusion, or to a possibility to get an "extended” insight into the state of our forests by interpreting these sources jointly. This contribution aims at addressing the latter, by relying on the Bayesian model averaging (BMA) approach.BMA is a method that can be used in building a consensus from an ensemble of different model predictions. It can be seen as weighted mean of different predictions with weights reflecting the predictive performances of different models, or as a finite mixture model which estimates the probability that each observation from the independent validation dataset has been generated by one of the models belonging to the ensemble. BMA can thus be used to diagnose and understand the difference in the predictions and to possibly interpret them.The predictions in our case are the forest canopy height estimations for the metropolitan France coming from 5 different AI models [1-5], while the independent validation dataset comes from the French National Forest Inventory (NFI) disposing with some 6000 plots per year, distributed across the territory of interest. For every plot we have several measurements/estimations of the forest canopy height out of which the following two are considered in this study: h_m – the maximum total height (from the tree's base level to the terminal bud of the tree's main stem) measured within the plot, and h_dom – the average height of the seven largest dominant trees per hectare.In this contribution we present for every considered plot the dominant model with respect to both references i.e. the model having the highest probability to be the one generating measurements/estimations at NFI plot (h_m and h_dom). We present as well as the respective inter-model and the intra-model variance estimations, allowing us to propose a series of hypotheses concerning the established differences between predictions of individual models in function of their specificities.[1] Schwartz, M., et al.: FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach, Earth Syst. Sci. Data, 15, 4927–4945, 2023, https://doi.org/10.5194/essd-15-4927-2023[2] Lang, N., et al.: A high-resolution canopy height model of the Earth, Nat Ecol Evol 7, 1778–1789, 2023. https://doi.org/10.1038/s41559-023-02206-6[3] Morin, D. et al.: Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process, Remote Sens., 14, 2079. 2022, https://doi.org/10.3390/rs14092079[4] Potapov, P., et al.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sensing of Environment, 253, 2021, https://doi.org/10.1016/j.rse.2020.112165.[5] Liu, S. et al.: The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe, Sci. Adv. 9, eadh4097, 2023, 10.1126/sciadv.adh4097.

Abstract. This paper presents a global-to-local fusion approach combining spaceborne synthetic aperture radar (SAR) interferometry (InSAR) and lidar to create large-scale mosaics of forest stand height. The forest height estimates are derived based on a semi-empirical InSAR scattering model, which links the forest height to repeat-pass InSAR coherence magnitudes. The sparsely yet extensively distributed lidar samples provided by the Global Ecosystem Dynamics Investigation (GEDI) mission enable the parameterization of the signal model at a finer spatial scale. The proposed global-to-local fitting strategy allows for the efficient use of lidar samples to determine the adaptive model at a regional scale, leading to improved forest height estimates by integrating InSAR–lidar under nearly concurrent acquisition conditions. This is supported by fusing the second generation of the Advanced Land Observing Satellite (ALOS-2) and GEDI data at several representative forest sites. This approach is further applied to the open-access ALOS InSAR data to evaluate its large-scale mapping capabilities. To address temporal mismatch between the GEDI and ALOS acquisitions, disturbances such as deforestation are identified by integrating ALOS-2 backscatter products and GEDI data. A modified signal model is further developed to account for natural forest growth over temperate forest regions where the intact forest landscape, along with forest height, remains quite stable and only changes slightly as trees grow. In the absence of detailed statistical data on forest growth, the modified signal model can be well approximated using the original model at the regional scale via local fitting. To validate this, two forest height mosaic maps based on the open-access ALOS-1 data were generated for the entire northeastern regions of the US and China with total area of 18 and 152 million ha, respectively. The validation of the forest height estimates demonstrates improved accuracy achieved by the proposed approach compared to the previous efforts, i.e., reducing from a 4.4 m RMSE at a few-hectare pixel size to 3.8 m RMSE at a sub-hectare pixel size. This updated fusion approach not only fills in the sparse spatial sampling of individual GEDI footprints, but also improves the accuracy of forest height estimates by 20 % compared to the interpolated GEDI maps. Extensive evaluation of forest height inversion against Land, Vegetation, and Ice Sensor (LVIS) lidar data indicates an accuracy of 3–4 m over flat areas and 4–5 m over hilly areas in the New England region, whereas the forest height estimates over northeastern China are best compared with small-footprint lidar validation data even at an accuracy of below 3.5 m and with a coefficient of determination (R2) mostly above 0.6. Given the achieved accuracy for forest height estimates, this fusion prototype offers a cost-effective solution for public users to obtain wall-to-wall forest height maps at a large scale using freely accessible spaceborne repeat-pass L-band InSAR (e.g., forthcoming NISAR) and spaceborne lidar (e.g., GEDI) data. These products are available via https://doi.org/10.5281/zenodo.11640299 (Yu and Lei, 2024).

Canopy height is a key physiognomic parameter of biodiversity, productivity and other ecosystem functions in high-elevation alpine ecosystems. However, little is known as to how altitude influences canopy height in these ecosystems. This study makes use of an open-access global forest canopy height map with a spatial resolution of 30 m that integrates Global Ecosystem Dynamics Investigation (GEDI)–Light Detection and Ranging (LiDAR) data (April–October 2019) and Landsat analysis-ready time-series data (year 2019). The variation in canopy height was quantified for each 100 m elevation band starting 500 metres below the alpine treeline ecotone at 3780 masl and extending up to 500 m above the alpine treeline ecotone. The global forest height map was compared to the in situ data (root-mean-square error [RMSE] = 6.6 m; mean absolute error [MAE] = 4.45 m). We observed a strong negative correlation (R2 = 0.96) between altitude and LiDAR-estimated canopy height. The altitude alone explained 96% of the variation in canopy height (p < 0.001). This chapter provides the first of its kind landscape-level quantification of the rate at which canopy height decreases with the increase in altitudinal gradient across the Indian Himalayan treeline.KeywordsAlpine ecosystemCanopy heightGEDI LiDARIndian Himalayan regionTreeline ecotone

State-wide forest canopy height and aboveground biomass map for New York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data

Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency (R2 = 0.85, RMSE = 2.2m, pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R2, RMSE, and pRMSE of 0.64, 3.38m, and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models.

The vertical structure of forests, including the measurement of canopy height, helps researchers understand forest characteristics such as density and growth stages. It is one of the key variables for estimating forest biomass and is crucial for accurately monitoring changes in forest carbon storage. However, current technologies face challenges in achieving cost-effective, accurate measurement of canopy height on a widespread scale. This study introduces a method aimed at extracting accurate forest canopy height from The Global Ecosystem Dynamics Investigation (GEDI) data, followed by a comprehensive large-scale analysis utilizing this approach. Before mapping, verifying and analyzing the accuracy and sensitivity of parameters that may affect the precision of GEDI data extraction, such as slope, aspect, and vegetation coverage, can aid in assessment and decision-making, enhancing inversion accuracy. Consequently, a random forest method based on parameter sensitivity analysis is developed to break through the constraints of traditional issues and achieve forest canopy height inversion. Sensitivity analysis of influencing parameters surpasses the uniform parameter calculation of traditional methods by differentiating the effects of various land use types, thereby enhancing the precision of height inversion. Moreover, potential factors affecting the accuracy of GEDI data, such as vegetation cover density, terrain complexity, and data acquisition conditions, are thoroughly analyzed and discussed. Subsequently, large-scale forest canopy height estimation is conducted by integrating vegetation cover Normalized Difference Vegetation Index (NDVI), sun altitude angle and terrain data, among other variables, and accuracy validation is performed using airborne LiDAR data. With an R2 value of 0.64 and an RMSE of 8.62, the mapping accuracy underscores the resilience of the proposed method in delineating forest canopy height within the Changbai Mountain forest domain.

Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data

Climate change has notably altered the elevation of mountain glaciers, particularly in alpine regions. Alpine glaciers play a pivotal role not only as indicators of climate change but also as crucial elements for human and wildlife well-being, regulating freshwater supply and providing vital habitats in Europe. Consequently, continuous monitoring of these glaciers offers valuable insights into their changing structure and surface dynamics [1].&amp;#160;While Unmanned Aerial Vehicles (UAV) offer the most precise method for tracking glacier surface changes, their practicality is often hindered by cost limitations and challenging in-situ measurements in extreme weather or remote areas. Therefore, remote sensing and satellite altimetry emerge as a feasible alternative in such scenarios.&amp;#160;Numerous LiDAR and RADAR altimetry sensors, such as Jason-2 and 3, CryoSat, and ICESat-1 and 2, have been employed. However, the Global Ecosystem Dynamics Investigation (GEDI), a reliable source of altimetry data, has been overlooked due to its restricted latitude range of 51.6 and -51.6 [2]. GEDI has proven its efficacy in measuring forest and canopy top height, monitoring lakes and water resources and generating Digital Surface Models (DSM).&amp;#160;Google Earth Engine (GEE), a cloud-based platform renowned for its ability to integrate diverse datasets and potent analytical tools, has recently incorporated GEDI into its extensive repository [3].Our initial analysis aims to assess the accuracy of GEDI data for glacier monitoring. Firstly, we focus on detecting and eliminating outliers. Secondly, we compare the glacier levels obtained from GEDI with reference ground truth. Thus, we've chosen the Rutor and Belvedere glaciers in Northern Italy, where we have access to reference-level measurements from UAV DEMs.&amp;#160;The proposed outlier detection consists of two steps for each GEDI passage over the glacier surface.The first step relies on quality surface flags available within GEDI bands, In the subsequent phase, the outlier removal process was refined by employing the x-means algorithm, an unsupervised classifier available within GEE. This approach facilitated the identification and elimination of outliers within the GEDI data set, contributing to refining the dataset's accuracy for comparative analysis with the reference ground truth.After the above-mentioned outlier removals, we obtained a median difference of -0.27m and NMAD of 4.9 m for Rutor Glacier in 2021 from more than 500 footprints, whereas for Belvedere a median difference of -0.43 and NMAD of 3.7m were obtained. These underestimated values might be due to the nearly 2-month difference between the DEM and the GEDI acquisitions.&amp;#160;[1] Belloni, V., et al. (2023). High-resolution high-accuracy orthophoto map and digital surface model of Forni Glacier tongue (Central Italian Alps) from UAV photogrammetry. Journal of Maps, 19(1), 2217508.[2] Hamoudzadeh, A., et al.: Gedi Data Within Google Earth Engine: Potentials And Analysis For Inland Surface Water Monitoring, EGU General Assembly 2023, Vienna, Austria, EGU23-15083&amp;#160;[3] Hamoudzadeh, A., et al. (2023). GEDI data within google earth engine: preliminary analysis of a resource for inland surface water monitoring. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.