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Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 219 - 230 Titre : Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees Type de document : Article/Communication Auteurs : Hamid Hamraz, Auteur ; Nathan B. Jacobs, Auteur ; Marco A. Contreras, Auteur ; Chase H. Clark, Auteur Année de publication : 2019 Article en page(s) : pp 219 - 230 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] houppier [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] semis de points Résumé : (auteur) The purpose of this study was to investigate the use of deep learning for coniferous/deciduous classification of individual trees segmented from airborne LiDAR data. To enable processing by a deep convolutional neural network (CNN), we designed two discrete representations using leaf-off and leaf-on LiDAR data: a digital surface model with four channels (DSM × 4) and a set of four 2D views (4 × 2D). A training dataset of tree crowns was generated via segmentation of tree crowns, followed by co-registration with field data. Potential mislabels due to GPS error or tree leaning were corrected using a statistical ensemble filtering procedure. Because the training data was heavily unbalanced (~8% conifers), we trained an ensemble of CNNs on random balanced sub-samples. Benchmarked against multiple traditional shallow learning methods using manually designed features, the CNNs improved accuracies up to 14%. The 4 × 2D representation yielded similar classification accuracies to the DSM × 4 representation (~82% coniferous and ~90% deciduous) while converging faster. Further experimentation showed that early/late fusion of the channels in the representations did not affect the accuracies in a significant way. The data augmentation that was used for the CNN training improved the classification accuracies, but more real training instances (especially coniferous) likely results in much stronger improvements. Leaf-off LiDAR data were the primary source of useful information, which is likely due to the perennial nature of coniferous foliage. LiDAR intensity values also proved to be useful, but normalization yielded no significant improvement. As we observed, large training data may compensate for the lack of a subset of important domain data. Lastly, the classification accuracies of overstory trees (~90%) were more balanced than those of understory trees (~90% deciduous and ~65% coniferous), which is likely due to the incomplete capture of understory tree crowns via airborne LiDAR. In domains like remote sensing and biomedical imaging, where the data contain a large amount of information and are not friendly to human visual system, human-designed features may become suboptimal. As exemplified by this study, automatic, objective derivation of optimal features via deep learning can improve prediction tasks in such domains. Numéro de notice : A2019-547 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.10.011 date de publication en ligne : 03/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.10.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94192 [article]

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Impact of deadwood decomposition on soil organic carbon sequestration in Estonian and Polish forests / Ewa Blonska in Annals of Forest Science [en ligne], Vol 76 n° 4 (December 2019)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 76 n° 4 (December 2019) Titre : Impact of deadwood decomposition on soil organic carbon sequestration in Estonian and Polish forests Type de document : Article/Communication Auteurs : Ewa Blonska, Auteur ; Jaroslaw Lasota, Auteur ; Arvo Tullus, Auteur ; et al., Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] bois mort [Termes descripteurs IGN] déchet organique [Termes descripteurs IGN] Estonie [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] forêt tempérée [Termes descripteurs IGN] Pologne [Termes descripteurs IGN] teneur en carbone [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Key message : The deadwood of different tree species with different decomposition rates affects soil organic carbon sequestration in Estonian and Polish forests. In warmer conditions (Poland), the deadwood decomposition process had a higher rate than in cooler Estonian forests. Soil organic matter fractions analysis can be used to assess the stability and turnover of organic carbon between deadwood and soil in different experimental localities. Context : Deadwood is an important element of properly functioning forest ecosystem and plays a very important role in the maintenance of biodiversity, soil fertility, and carbon sequestration. Aims : The main aim was to estimate how decomposition of deadwood of different tree species with different decomposition rates affects soil organic carbon sequestration in Estonian and Polish forests. Methods : The investigation was carried out in six forests in Poland (51° N) and Estonia (58° N). The study localities differ in their mean annual air temperature (of 2 °C) and the length of the growing season (of 1 month). The deadwood logs of Norway spruce (Picea abies (L.) Karst.), common aspen (Populus tremula L.), and silver birch (Betula pendula Roth) were included in the research. Logs in three stages of decomposition (III–V) were selected for the analysis. Results : There were differences in the stock of soil organic carbon in two experimental localities. There was a higher soil carbon content under logs and in their direct vicinity in Polish forests compared to those in the cooler climate of Estonia. Considerable differences in the amount of soil organic matter were found. The light fraction constituted the greatest quantitative component of organic matter of soils associated with deadwood. Conclusion : A higher carbon content in surface soil horizons as an effect of deadwood decomposition was determined for the Polish (temperate) forests. More decomposed deadwood affected soil organic matter stabilization more strongly than less decayed deadwood. This relationship was clearer in Polish forests. Higher temperatures and longer growing periods primarily influenced the increase of soil organic matter free light fraction concentrations directly under and in close proximity to logs of the studied species. The slower release of deadwood decomposition products was noted in Estonian (hemiboreal) forests. The soil organic matter mineral fraction increased under aspen and spruce logs at advanced decomposition in Poland. Numéro de notice : A2019-530 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0889-9 date de publication en ligne : 29/10/2019 En ligne : https://doi.org/10.1007/s13595-019-0889-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94118 [article]

Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 108 - 123 Titre : Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits Type de document : Article/Communication Auteurs : Tawanda W. Gara, Auteur ; Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 108 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Leaf mass per area [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV  0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services. Numéro de notice : A2019-493 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.005 date de publication en ligne : 11/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93725 [article]

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Segmenting mangrove ecosystems drone images using SLIC superpixels / Edward Zimudzi in Geocarto international, vol 34 n° 14 ([30/10/2019])  

Public [article]  inGeocarto international > vol 34 n° 14 [30/10/2019] . - pp 1648 - 1662 Titre : Segmenting mangrove ecosystems drone images using SLIC superpixels Type de document : Article/Communication Auteurs : Edward Zimudzi, Auteur ; Ian Sanders, Auteur ; Nicholas Rollings, Auteur ; Christian Omlin, Auteur Année de publication : 2019 Article en page(s) : pp 1648 - 1662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de superpixels [Termes descripteurs IGN] classification par nuées dynamiques [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] Fidji [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orthophotoplan numérique [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] SLIC algorithm Résumé : (auteur) Mangrove ecosystems play a very important ecological role on land–ocean interfaces in tropical regions. These ecosystems comprise of various tree species and aquatic animals, protecting the environment and providing a habitat that supports many living organisms including humans. The identification of image regions in mangrove ecosystems plays a significant role in ecosystem monitoring and conservation. Recent studies have suggested oversegmentation of colour images using superpixels as a solution to the segmentation of image regions. This study used the SLIC superpixel algorithm and k-means clustering to segment images taken from a camera mounted on a drone from a mangrove ecosystem in Fiji. The SLIC superpixel algorithm performed well to demarcate image regions with similar colour and texture information into patches and to use k-means for the segmentation of the whole image. These results lend support to the use of superpixel algorithms for the segmentation of mangrove ecosystems. Understanding how superpixels can be used for the segmentation of drone images will assist conservation efforts in mangrove ecosystems. Numéro de notice : A2019-539 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1497093 date de publication en ligne : 22/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1497093 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94114 [article]

Estimating pasture biomass and canopy height in brazilian savanna using UAV photogrammetry / Juliana Batistoti in Remote sensing, Vol 11 n° 20 (2 October 2019)  

Public [article]  inRemote sensing > Vol 11 n° 20 (2 October 2019) . - 12 p. Titre : Estimating pasture biomass and canopy height in brazilian savanna using UAV photogrammetry Type de document : Article/Communication Auteurs : Juliana Batistoti, Auteur ; José Marcato, Auteur ; Luis Itavo, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : 12 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] canopée [Termes descripteurs IGN] couvert végétal [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] Poaceae [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] positionnement cinématique en temps réel Résumé : (auteur) The Brazilian territory contains approximately 160 million hectares of pastures, and it is necessary to develop techniques to automate their management and increase their production. This technical note has two objectives: First, to estimate the canopy height using unmanned aerial vehicle (UAV) photogrammetry; second, to propose an equation for the estimation of biomass of Brazilian savanna (Cerrado) pastures based on UAV canopy height. Four experimental units of Panicum maximum cv. BRS Tamani were evaluated. Herbage mass sampling, height measurements, and UAV image collection were simultaneously performed. The UAVs were flown at a height of 50 m, and images were generated with a mean ground sample distance (GSD) of approximately 1.55 cm. The forage canopy height estimated by UAVs was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The R2 between ruler height and UAV height was 0.80; between biomass (kg ha−1 GB—green biomass) and ruler height, 0.81; and between biomass (kg ha−1 GB) and UAV height, 0.74. UAV photogrammetry proved to be a potential technique to estimate height and biomass in Brazilian Panicum maximum cv. BRS Tamani pastures located in the endangered Brazilian savanna (Cerrado) biome Numéro de notice : A2019-556 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202447 date de publication en ligne : 22/10/2019 En ligne : https://doi.org/10.3390/rs11202447 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94212 [article]

Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Mapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines / Lauri Korhonen in Silva fennica, vol 53 n° 3 (2019)  

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Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia / Neil Flood in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Contribution à la connaissance phytosociologique de la végétation du pays de Sault (département de l’Aude, France) / Bruno de Foucault in Evaxiana, n° 6 (2019)

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Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

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Télédétection radar : de l'image d'intensité initiale au choix du mode de calibration des coefficients de diffusion / Jean-Paul Rudant in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - décembre 2019)

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Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])  

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Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators / Dinesh Babu Irulappa Pillai Vijayakumar in Remote sensing, vol 11 n° 8 (August 2019)  

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Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB‐II for 135 eastern U.S. trees / Matthew P. Peters in Ecology and evolution, vol 9 n° 15 (August 2019)  

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