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Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information / Shaohui Zhang in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 114 (November 2022) . - n° 103072 Titre : Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information Type de document : Article/Communication Auteurs : Shaohui Zhang, Auteur ; Cédric Vega , Auteur ; Christine Deleuze, Auteur ; Sylvie Durrieu, Auteur ; Pierre Barbillon, Auteur ; Olivier Bouriaud , Auteur ; Jean-Pierre Renaud, Auteur Année de publication : 2022 Article en page(s) : n° 103072 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] empreinte [Termes IGN] gestion forestière [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier local [Termes IGN] inventaire forestier national (données France) [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation de la forêt [Termes IGN] placette d'échantillonnage [Termes IGN] Sologne (France) [Termes IGN] variogramme [Termes IGN] volume en bois Résumé : (auteur) The French National Forest Inventory provides detailed forest information up to large national and regional scales. Forest inventory for small areas of interest within a large population is equally important for decision making, such as for local forest planning and management purposes. However, sampling these small areas with sufficient ground plots is often not cost efficient. In response, small area estimation has gained increasing popularity in forest inventory. It consists of a set of techniques that enables predictions of forest attributes of subpopulation with the help of auxiliary information that compensates for the small field samples. Common sources of auxiliary information usually come from remote sensing technology, such as airborne laser scanning and satellite imagery. The newly launched NASA’s Global Ecosystem Dynamics Investigation (GEDI), a full waveform Lidar instrument, provides an unprecedented opportunity of collecting large-scale and dense forest sample plots given its sampling frequency and spatial coverage. However, the geolocation uncertainty associated with GEDI footprints create important challenges for their use for small area estimations. In this study, we designed a process that provides NFI measurements at plot level with GEDI auxiliary information from nearby footprints. We demonstrated that GEDI RH98 is equivalent to NFI dominant height at plot level. We stressed the importance of pairing NFI plots with nearby GEDI footprints, based on not only the distance in between but also their similarities, i.e., forest heights and forest types. Subsequently, these NFI-GEDI pairs were used for small area estimations following a two-phase sampling scheme. We showcased that, with an adequate sample size, small area estimation with GEDI auxiliary data can improve the accuracy of forest volume estimates. Numéro de notice : A2022-786 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.103072 Date de publication en ligne : 22/10/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103072 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101890 [article]

Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil / Adrián Pascual in Ecological Informatics, vol 70 (September 2022)  

Public [article]  inEcological Informatics > vol 70 (September 2022) Titre : Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil Type de document : Article/Communication Auteurs : Adrián Pascual, Auteur ; Frederico Tupinambá-Simões, Auteur ; Tiago de Conto, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte forestière [Termes IGN] Eucalyptus (genre) [Termes IGN] forêt tropicale [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur des arbres [Termes IGN] incertitude des données [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Mato Grosso [Termes IGN] modèle numérique de surface de la canopée [Vedettes matières IGN] Inventaire forestier Mots-clés libres : E. urograndis  E. urophylla x E. grandis, E. urophylla and E. camaldulensis x E. grandis Résumé : (auteur) The global monitoring of forest structure worldwide is increasingly being supported by refined and enhanced satellite mission datasets. Forest canopy height is a global metric to characterise and monitor dynamics in forest ecosystems worldwide. Satellite mapping missions as NASA's Global Ecosystem Dynamics Investigation (GEDI) are creating opportunities to refine global forest canopy height models adding forest structural information to time-series satellite imagery. A recent global canopy height model presented by Lang et al., (2022) using GEDI and 10-m Sentinel-2 and the map from Potapov et al., (2020) using GEDI and Landsat are both tested in this study using multi-temporal tree-level data collected over eucalypt plantations in Brazil. Our results at plot-level showed Lang et al., (2022)’s estimates of canopy height came short compared to 2020 maximum and mean tree height records in the plots, 7.6 and 3.6 m, respectively, but adding CHM standard deviation improves the agreement of ground records for maximum tree height. Higher errors were computed for the plots in 2019 using the Potapov's 30-m CHM: 14.2 and 9.5 m, respectively. Averaged stand values were more similar between the three sources tested. We report improvement from the 30-m CHM to the 10-m, but still height saturation problems were observed when accounting for height differences in tall eucalypt trees. As more global products for forest height and biomass are becoming available to users, more validation exercises as presented in this study are needed to assess the suitability of CHM products to forestry needs, and facilitate the uptake and actionability of the next generation of global height and biomass products. We provide recommendations and insights on the use of GEDI laser data for global mapping and on the potential of commercial forestry areas to benchmark the accuracy of satellite mapping missions focusing on tree height estimation in the tropics. Numéro de notice : A2022-615 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecoinf.2022.101748 En ligne : https://doi.org/10.1016/j.ecoinf.2022.101748 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101370 [article]

PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 123 - 139 Titre : PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data Type de document : Article/Communication Auteurs : Qi Zhang, Auteur ; Linlin Ge, Auteur ; Scott Hensley, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 123 - 139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse discriminante [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] bande L [Termes IGN] données lidar [Termes IGN] forêt boréale [Termes IGN] forêt tropicale [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur de la végétation [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] pansharpening (fusion d'images) [Termes IGN] polarimétrie radar [Termes IGN] pouvoir de résolution géométrique [Termes IGN] réseau antagoniste génératif [Termes IGN] semis de points Résumé : (auteur) This paper describes a deep-learning-based unsupervised forest height estimation method based on the synergy of the high-resolution L-band repeat-pass Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) and low-resolution large-footprint full-waveform Light Detection and Ranging (LiDAR) data. Unlike traditional PolInSAR-based methods, the proposed method reformulates the forest height inversion as a pan-sharpening process between the low-resolution LiDAR height and the high-resolution PolSAR and PolInSAR features. A tailored Generative Adversarial Network (GAN) called PolGAN with one generator and dual (coherence and spatial) discriminators is proposed to this end, where a progressive pan-sharpening strategy underpins the generator to overcome the significant difference between spatial resolutions of LiDAR and SAR-related inputs. Forest height estimates with high spatial resolution and vertical accuracy are generated through a continuous generative and adversarial process. UAVSAR PolInSAR and LVIS LiDAR data collected over tropical and boreal forest sites are used for experiments. Ablation study is conducted over the boreal site evidencing the superiority of the progressive generator with dual discriminators employed in PolGAN (RMSE: 1.21 m) in comparison with the standard generator with dual discriminators (RMSE: 2.43 m) and the progressive generator with a single coherence (RMSE: 2.74 m) or spatial discriminator (RMSE: 5.87 m). Besides that, by reducing the dependency on theoretical models and utilizing the shape, texture, and spatial information embedded in the high-spatial-resolution features, the PolGAN method achieves an RMSE of 2.37 m over the tropical forest site, which is much more accurate than the traditional PolInSAR-based Kapok method (RMSE: 8.02 m). Numéro de notice : A2022-195 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.02.008 Date de publication en ligne : 17/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99962 [article]

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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

Public [article]  inRemote sensing of environment > vol 268 (January 2022) . - n* 112760 Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] biomasse aérienne [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation bayesienne [Termes IGN] forme d'onde [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] semis de points Résumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495 [article]

A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms / Ibrahim Fayad in Remote sensing of environment, vol 265 (November 2021)  

Public [article]  inRemote sensing of environment > vol 265 (November 2021) . - n° 112652 Titre : A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms Type de document : Article/Communication Auteurs : Ibrahim Fayad, Auteur ; Dino Lenco, Auteur ; Nicolas Baghdadi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112652 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Brésil [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Eucalyptus (genre) [Termes IGN] forme d'onde [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur des arbres [Termes IGN] modèle de croissance végétale [Termes IGN] random-motion-over-ground model [Termes IGN] semis de points [Termes IGN] volume en bois Résumé : (auteur) Full waveform (FW) LiDAR systems have proven their effectiveness to map forest biophysical variables in the last two decades, owing to their ability of measuring, with high accuracy, forest vertical structures. The Global Ecosystem Dynamics Investigation (GEDI) system on board the International Space Station (ISS) is the latest FW spaceborne LiDAR instrument for the continuous observation of Earth's forests. FW systems rely on very sophisticated pre-processing steps to generate a priori metrics in order to leverage their capabilities for the accurate estimation of the aforementioned forest characteristics. The ever-expanding volume of acquired GEDI data, which to date comprises more than 25 billion acquired unfiltered shots, and along with the pre-processed data, amounting to more than 90 TB of data, raises new challenges in terms of adapted preprocessing methods for the suitable exploitation of such a huge and complex amount of LiDAR data. To overcome the issues related to the generation of relevant metrics from GEDI data, we propose a new metric-free approach to estimate canopy dominant heights (Hdom) and wood volume (V) of Eucalyptus plantations over five different regions in Brazil. To avoid metric computation, we leverage deep learning techniques and, more in detail, convolutional neural networks with the aim to analyze the GEDI Level 1B geolocated waveforms. Performance comparisons were conducted between four convolutional neural network (CNN) variants using GEDI waveform data (either untouched, or subsetted) and a metric based Random Forest regressor (RF). Additionally, we tested if our framework can improve the generalization of the models to different distant regions. First, the models were trained using data from all the study regions. Cross validated results showed that the CNN based models compared well against their RF counterpart for both Hdom and V. The RMSE on the estimation of Hdom from the CNN based models varied between 1.54 and 1.94 m with a coefficient of determination (R2) between 0.86 and 0.91, while the RF model produced an accuracy on Hdom estimates of 1.45 m (R2 = 0.92). For V, CNN based estimations ranged from 27.76 to 33.33 m3.ha−1 (R2 between 0.82 and 0.88), while for RF, the RMSE was 27.61 m3.ha−1 (R2 = 0.88). Next, model generalization was assessed by means of a spatial transfer experiment. For Hdom, both the CNN and RF approaches showed similar performances to a global model, however, the CNN based approach showed higher variability on the estimation accuracy, and the variability was related to the forest structure between the trained and tested data (similar tree heights yield better accuracies). For the estimation of V, considering both approaches, the accuracy was dependent on the allometric relationship between Hdom and V in the training and testing regions while lower accuracies on V were obtained when the testing and training regions exhibited a different allometric relationship. Numéro de notice : A2021-869 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112652 Date de publication en ligne : 31/08/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112652 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99118 [article]

Improving GEDI footprint geolocation using a high resolution digital terrain model / Anouk Schleich (2021)  

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Qualification des données LiDAR GEDI pour le suivi de l’impact climatique sur la forêt de Südharz / Iris Jeuffrard (2021) 

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Unit-level small area estimation of forest inventory with GEDI auxiliary information / Shaohui Zhang (2021)  

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Unit-level small area estimation of forest inventory with GEDI auxiliary information in France / Shaohui Zhang (2021) 

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