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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 GEDI data with a deep learning approach / Martin Schwartz in Earth System Science Data, vol 15 n° inconnu (2023)  

Public [article]  inEarth System Science Data > vol 15 n° inconnu (2023) Titre : 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 GEDI data with a deep learning approach Type de document : Article/Communication Auteurs : Martin Schwartz, Auteur ; Philippe Ciais, Auteur ; Aurélien de Truchis, Auteur ; Jérôme Chave, Auteur ; Catherine Ottle, Auteur ; Cédric Vega , Auteur ; Jean-Pierre Wigneron, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] biomasse aérienne [Termes IGN] données allométriques [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur des arbres [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] inventaire forestier national (données France) [Termes IGN] modèle numérique de surface de la canopée [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote sensing measurements (NASA’s GEDI LiDAR mission and ESA’s Copernicus Sentinel 1 & 2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha-1) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from Airborne Laser Scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 Mg ha-1 and 19.6 Mg.ha-1 respectively, providing greater performance than other AGBD products sampled over France. These results highlight the importance of coupling remote sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from https://doi.org/10.5281/zenodo.7840108 (Schwartz et al., 2023). Numéro de notice : A2023-179 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-2023-196 En ligne : https://doi.org/10.5194/essd-2023-196 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103341 [article]

Detection of growth change of young forest based on UAV RGB images at single-tree level / Xiaocheng Zhou in Forests, vol 14 n° 1 (January 2023)  

Public [article]  inForests > vol 14 n° 1 (January 2023) . - n° 141 Titre : Detection of growth change of young forest based on UAV RGB images at single-tree level Type de document : Article/Communication Auteurs : Xiaocheng Zhou, Auteur ; Hongyu Wang, Auteur ; Chongcheng Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 141 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Abies (genre) [Termes IGN] âge du peuplement forestier [Termes IGN] Chine [Termes IGN] croissance des arbres [Termes IGN] détection de changement [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] jeune arbre [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] surveillance forestière Résumé : (auteur) With the rapid development of Unmanned Aerial Vehicle (UAV) technology, more and more UAVs have been used in forest survey. UAV (RGB) images are the most widely used UAV data source in forest resource management. However, there is some uncertainty as to the reliability of these data when monitoring height and growth changes of low-growing saplings in an afforestation plot via UAV RGB images. This study focuses on an artificial Chinese fir (Cunninghamia lancelota, named as Chinese Fir) young forest plot in Fujian, China. Divide-and-conquer (DAC) and the local maximum (LM) method for extracting seedling height are described in the paper, and the possibility of monitoring young forest growth based on low-cost UAV remote sensing images was explored. Two key algorithms were adopted and compared to extract the tree height and how it affects the young forest at single-tree level from multi-temporal UAV RGB images from 2019 to 2021. Compared to field survey data, the R2 of single saplings’ height extracted from digital orthophoto map (DOM) images of tree pits and original DSM information using a divide-and-conquer method reached 0.8577 in 2020 and 0.9968 in 2021, respectively. The RMSE reached 0.2141 in 2020 and 0.1609 in 2021. The R2 of tree height extracted from the canopy height model (CHM) via the LM method was 0.9462. The RMSE was 0.3354 in 2021. The results demonstrated that the survival rates of the young forest in the second year and the third year were 99.9% and 85.6%, respectively. This study shows that UAV RGB images can obtain the height of low sapling trees through a computer algorithm based on using 3D point cloud data derived from high-precision UAV images and can monitor the growth of individual trees combined with multi-stage UAV RGB images after afforestation. This research provides a fully automated method for evaluating the afforestation results provided by UAV RGB images. In the future, the universality of the method should be evaluated in more afforestation plots featuring different tree species and terrain. Numéro de notice : A2023-115 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f14010141 Date de publication en ligne : 10/01/2023 En ligne : https://doi.org/10.3390/f14010141 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102482 [article]

Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)  

Public [article]  inGIScience and remote sensing > vol 60 n° 1 (2023) . - n° 2171706 Titre : Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data Type de document : Article/Communication Auteurs : Ying Quan, Auteur ; Mingze Li, Auteur ; Yuanshuo Hao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2171706 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] espèce végétale [Termes IGN] extraction de traits caractéristiques [Termes IGN] forêt secondaire [Termes IGN] image captée par drone [Termes IGN] image hyperspectrale [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] semis de points Résumé : (auteur) Recent growth in unmanned aerial vehicle (UAV) technology have promoted the detailed mapping of individual tree species. However, the in-depth mining and comprehending of the significance of features derived from high-resolution UAV data for tree species discrimination remains a difficult task. In this study, a state-of-the-art approach combining UAV-borne light detection and ranging (LiDAR) and hyperspectral was used to classify 11 common tree species in a typical natural secondary forest in Northeast China. First, comprehensive relevant structural and spectral features were extracted. Then, the most valuable feature sets were selected by using a hybrid approach combining correlation-based feature selection with the optimized recursive feature elimination algorithm. The random forest algorithm was used to assess feature importance and perform the classification. Finally, the robustness of features derived from point clouds with different structures and hyperspectral images with different spatial resolutions was tested. Our results showed that the best classification accuracy was obtained by combining LiDAR and hyperspectral data (75.7%) compared to that based on LiDAR (60.0%) and hyperspectral (64.8%) data alone. The mean intensity of single returns and the visible atmospherically resistant index for red-edge band were the most influential LiDAR and hyperspectral derived features, respectively. The selected features were robust in point clouds with a density not lower than 5% (~5 pts/m2) and a resolution not lower than 0.3 m in hyperspectral data. Although canopy surface features were slightly different from original LiDAR features, canopy surface information was also important for tree species classification. This study proved the capabilities of UAV-borne LiDAR and hyperspectral data in natural secondary forest tree species discrimination and the potential for this approach to be transferable to other study areas. Numéro de notice : A2023-194 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/15481603.2023.2171706 Date de publication en ligne : 03/02/2023 En ligne : https://doi.org/10.1080/15481603.2023.2171706 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103075 [article]

Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)  

Public [article]  iniForest, biogeosciences and forestry > vol 15 n° 6 (December 2022) . - pp 451-457 Titre : Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy Type de document : Article/Communication Auteurs : Mauro Maesano, Auteur ; Giovanni Santopuoli, Auteur ; Federico Valerio Moresi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 451-457 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] Calabre [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] gestion forestière durable [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] régression [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha-1 and from 34.2 to 22.1 Mg ha-1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model. Numéro de notice : A2022-903 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3832/ifor3781-015 Date de publication en ligne : 03/11/2022 En ligne : https://doi.org/10.3832/ifor3781-015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102299 [article]

Assessment of camera focal length influence on canopy reconstruction quality / Martin Denter in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100025 Titre : Assessment of camera focal length influence on canopy reconstruction quality Type de document : Article/Communication Auteurs : Martin Denter, Auteur ; Julian Frey, Auteur ; Teja Kattenborn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100025 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie [Termes IGN] Abies alba [Termes IGN] Acer pseudoplatanus [Termes IGN] Allemagne [Termes IGN] canopée [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Fagus sylvatica [Termes IGN] image captée par drone [Termes IGN] Larix decidua [Termes IGN] longueur focale [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] parcelle forestière [Termes IGN] Picea abies [Termes IGN] reconstruction d'image [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) Unoccupied aerial vehicles (UAV) with RGB-cameras are affordable and versatile devices for the generation of a series of remote sensing products that can be used for forest inventory tasks, such as creating high-resolution orthomosaics and canopy height models. The latter may serve purposes including tree species identification, forest damage assessments, canopy height or timber stock assessments. Besides flight and image acquisition parameters such as image overlap, flight height, and weather conditions, the focal length, which determines the opening angle of the camera lens, is a parameter that influences the reconstruction quality. Despite its importance, the effect of focal length on the quality of 3D reconstructions of forests has received little attention in the literature. Shorter focal lengths result in more accurate distance estimates in the nadir direction since small angular errors lead to large positional errors in narrow opening angles. In this study, 3D reconstructions of four UAV-acquisitions with different focal lengths (21, 35, 50, and 85 mm) on a 1 ha mature mixed forest plot were compared to reference point clouds derived from high quality Terrestrial Laser Scans. Shorter focal lengths (21 and 35 mm) led to a higher agreement with the TLS scans and thus better reconstruction quality, while at 50 mm, quality losses were observed, and at 85 mm, the quality was considerably worse. F1-scores calculated from a voxel representation of the point clouds amounted to 0.254 with 35 mm and 0.201 with 85 mm. The precision with 21 mm focal length was 0.466 and 0.302 with 85 mm. We thus recommend a focal length no longer than 35 mm during UAV Structure from Motion (SfM) data acquisition for forest management practices. Numéro de notice : A2022-870 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100025 Date de publication en ligne : 09/11/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102164 [article]

A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)  

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GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK / Morteza Pourreza in Forests, vol 13 n° 11 (November 2022)  

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Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)  

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Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR / Zhenyang Hui in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)  

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Riparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds / Elena Belcore in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)  

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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)  

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Modeling merchantable wood volume using airborne LiDAR metrics and historical forest inventory plots at a provincial scale / Antoine Leboeuf in Forests, vol 13 n° 7 (July 2022)  

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Simulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading / Štefan Kohek in International journal of applied Earth observation and geoinformation, vol 111 (July 2022)  

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Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review / André Duarte in Forests, vol 13 n° 6 (June 2022)  

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Unveiling the complex canopy spatial structure of a Mediterranean old-growth beech (Fagus sylvatica L.) forest from UAV observations / Francesco Solano in Ecological indicators, vol 138 (May 2022)  

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