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Automatic tuning of segmentation parameters for tree crown delineation with VHR imagery / Camile Sothe in Geocarto international, Vol 36 n° 19 ([18/10/2021])  

Public [article]  inGeocarto international > Vol 36 n° 19 [18/10/2021] . - pp 2241 - 2259 Titre : Automatic tuning of segmentation parameters for tree crown delineation with VHR imagery Type de document : Article/Communication Auteurs : Camile Sothe, Auteur ; Claudia Maria de Almeida, Auteur ; Marcos Benedito Schimalski, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2241 - 2259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] Brésil [Termes IGN] délimitation [Termes IGN] forêt tropicale [Termes IGN] houppier [Termes IGN] identification de plantes [Termes IGN] image à très haute résolution [Termes IGN] image Worldview [Termes IGN] méthode heuristique [Termes IGN] orthoimage [Termes IGN] segmentation d'image Résumé : (auteur) In the case of tree species delineation with very high spatial resolution (VHR) images, is desirable that each segment corresponds to one individual tree crown (ITC). However, in order to have a segmentation algorithm that generates segments matching to ITCs, its parameters ought to be properly tuned. Aiming to avoid time-consuming trial-and-error procedures associated with this task, some initiatives for the automatic search of segmentation parameters have been developed, such as metaheuristic methods. The objective of this work was to test the automatic tuning of segmentation parameters of three segmentation algorithms for the delineation of ITCs belonging to a native endangered species in a subtropical forest area, comparing this method with the traditional trial-and-error approach. Two datasets (WorldView-2 and an orthoimage) and three segmentation algorithms (multiresolution, mean-shift and graph-based) were tested. For the automatic approach, a hybrid metaheuristic method was applied to accomplish the automatic search of parameters for the segmentation algorithms, while for the trial-and-error, a visual assessment was conducted for each set of parameters tested. Four supervised metrics were used to assess the quality of the segmentation results for the optimization approach and for the final set of parameters chosen in the trial-and-error approach. Results showed that none of the algorithms, datasets or approaches differ too much. The evaluation metrics values were lower, indicating that the reference ITCs polygons matched with the segmentation results. Despite the similar results, the automatic tuning of segmentation parameters proved to be a feasible alternative to reduce the subjectivity and the human effort in the choice of segmentation parameters as compared to the trial-and error approach. Numéro de notice : A2021-765 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1690056 Date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1690056 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98810 [article]

Mapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September - 2 2021)  

Public [article]  inRemote sensing > vol 13 n° 18 (September - 2 2021) . - n° 3777 Titre : Mapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches Type de document : Article/Communication Auteurs : He Zhang, Auteur ; Marijn Bauters, Auteur ; Pascal Boeckx, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 3777 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] Congo (bassin) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt tropicale [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] photogrammétrie aérienne [Termes IGN] point d'appui [Termes IGN] semis de points [Termes IGN] structure-from-motion [Termes IGN] surveillance forestière Résumé : (auteur) Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests. Numéro de notice : A2021-754 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13183777 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.3390/rs13183777 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98746 [article]

Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 35 - 49 Titre : Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data Type de document : Article/Communication Auteurs : Laura Elena Cué La Rosa, Auteur ; Camile Sothe, Auteur ; Raul Queiroz Feitosa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 35 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Brésil [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] densité de la végétation [Termes IGN] données d'apprentissage [Termes IGN] espèce végétale [Termes IGN] étiquetage sémantique [Termes IGN] forêt tropicale [Termes IGN] image captée par drone [Termes IGN] image hyperspectrale Résumé : (Auteur) This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoders, one for the semantic segmentation output and one for the distance map regression. We report that introducing the complementary task boosts the semantic segmentation performance compared to the single-task counterpart in up to 11% reaching an average user’s accuracy of 88.63% and an average producer’s accuracy of 88.59%, achieving state-of-art performance for tree species classification in tropical forests. Numéro de notice : A2021-575 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.001 Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98175 [article]

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The real potential of current passive satellite data to map aboveground biomass in tropical forests / Nidhi Jha in Remote sensing in ecology and conservation, vol 7 n° 3 (September 2021)  

Public [article]  inRemote sensing in ecology and conservation > vol 7 n° 3 (September 2021) . - pp 504 - 520 Titre : The real potential of current passive satellite data to map aboveground biomass in tropical forests Type de document : Article/Communication Auteurs : Nidhi Jha, Auteur ; Nitin Kumar Tripathi, Auteur ; Nicolas Barbier, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 504 - 520 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] biomasse aérienne [Termes IGN] forêt tropicale [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Worldview [Termes IGN] Thaïlande Résumé : (auteur) Forest biomass estimation at large scale is challenging and generally entails large uncertainty in tropical regions. With their wall-to-wall coverage ability, passive remote sensing signals are frequently used to extrapolate field estimates of forest aboveground biomass (AGB). However, studies often use limited reference data and/or flawed validation schemes and thus report unreliable extrapolation error estimates. Here, we compared the ability of three medium- to high-resolution passive satellite sensors, Landsat-8 (L8), Sentinel-2B (S2) and Worldview-3 (WV3), to map AGB in a forest landscape of Thailand. We used a large airborne LiDAR-derived AGB dataset as a reference to train and validate a random forest algorithm and conducted robust error assessments and variable selection using spatialized cross-validations. Our results indicate that the selected predictors strongly varied among the three sensors and between analyses restricted to low (≤200 Mg ha−1) and high (>200 Mg ha−1) AGB areas. WV3 and S2 data outperformed L8 data to extrapolate AGB (RMSE of 68 and 72 against 84 Mg ha−1, respectively) due to the inclusion of the red-edge band and, probably, to their higher spatial and spectral resolution. Sensitivity to large AGB values was higher for WV3 than for S2 and L8 with saturation point of 247 Mg ha−1 against 204 and 192 Mg ha−1. AGB values above these saturation points remained poorly predictable, especially for L8, indicating that several tropical forest AGB maps should be interpreted with extreme caution. However, predicted gradients of lower AGB values (≤200 Mg ha−1), i.e., in early forest successional stages, were fairly consistent among sensors (r > 0.70), even if the mean absolute difference between estimates was large when AGB predictions were extrapolated out of the calibration area at regional level (34%). We finally showed that calibrating the model only within the sensitivity AGB domain (e.g., Numéro de notice : A2021-731 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.203 En ligne : https://doi.org/10.1002/rse2.203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98676 [article]

Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia / Peter Kitin in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 2 (June 2021) . - Article 31 Titre : Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia Type de document : Article/Communication Auteurs : Peter Kitin, Auteur ; Edgard Espinoza, Auteur ; Hans Beeckman, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : Article 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] abattage (sylviculture) [Termes IGN] Afzelia (genre) [Termes IGN] analyse discriminante [Termes IGN] apprentissage non-dirigé [Termes IGN] bois [Termes IGN] espèce végétale [Termes IGN] forêt tropicale [Termes IGN] identification de plantes [Termes IGN] signature spectrale [Termes IGN] spectrométrie [Termes IGN] taxinomie [Termes IGN] temps réel Résumé : (Auteur) Distinct chemical fingerprints of the wood of Afzelia pachyloba and A. bipindensis demonstrated an effective method for identifying these two commercially important species. Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) allowed high-throughput examination of chemotypes with vast potential in taxonomic, ecological, and forensic research of wood. Context : Afzelia is a genus of valuable tropical timber trees. Accurate identification of wood is required for the prevention of illicit timber trade as well as for certification purposes in the forest and wood products industry. For many years, particular interest has been focused on attempts to distinguish the wood of A. bipindensis Harms from A. pachyloba Harms due to substantial differences in the commercial values of these two species. Aims : We investigated if wood chemical signatures and microscopy could identify the wood of A. bipindensis and A. pachyloba. Methods : We used two approaches, namely metabolome profiling by direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) and wood microstructure by light microscopy and SEM. In all, we analyzed samples from 89 trees of A. bipindensis, and A. pachyloba. Results : The two species could not be separated by the IAWA standard microscopic wood features. SEM analysis showed considerable variation in the morphology of vestured pits; however, this variation was not species-specific. In contrast, DART-TOFMS followed by unsupervised statistics (Discriminant Analysis of Principal Components) showed distinct metabolome signatures of the two species. Conclusion : DART-TOFMS provides a rapid method for wood identification that can be easily applied to small heartwood samples. Time- and cost-effective classification of wood chemotypes by DART-TOFMS can have potential applications in various research questions in forestry, wood science, tree-ecophysiology, and forensics. Numéro de notice : A2021-327 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01024-1 Date de publication en ligne : 31/03/2021 En ligne : https://doi.org/10.1007/s13595-020-01024-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97488 [article]

Potentialité des données satellitaires Sentinel-2 pour la cartographie de l’impact des feux de végétation en Afrique tropicale : application au Togo / Yawo Konko in Bois et forêts des tropiques, n° 347 ([02/04/2021])  

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Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)  

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Individual tree diameter growth modeling system for Dalat pine (Pinus dalatensis Ferré) of the upland mixed tropical forests / Bao Huy in Forest ecology and management, vol 480 (15 January 2021)  

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Near-real-time identification of the drivers of deforestation in French Guiana / Marie Ballère (2021)  

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SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery / Marie Ballère in Remote sensing of environment, Vol 252 (January 2021)  

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Sylviculture / Ana Cristina Goncalves (2021)    

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Mapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)  

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Wide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October 2020)  

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Evaluating the impact of declining tsetse fly (Glossina pallidipes) habitat in the Zambezi valley of Zimbabwe / Farai Matawa in Geocarto international, vol 35 n° 12 ([01/09/2020])  

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Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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