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A density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January-2 2021)  

Public [article]  inRemote sensing > Vol 13 n° 2 (January-2 2021) . - n° 322 Titre : A density-based algorithm for the detection of individual trees from LiDAR data Type de document : Article/Communication Auteurs : Melissa Latella, Auteur ; Fabio Sola, Auteur ; Carlo Camporeal, Auteur Année de publication : 2021 Article en page(s) : n° 322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre (flore) [Termes IGN] comptage [Termes IGN] densité de la végétation [Termes IGN] détection d'arbres [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt de feuillus [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] semis de points [Termes IGN] sous-étage Résumé : (auteur) Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management. Numéro de notice : A2021-196 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13020322 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/rs13020322 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97146 [article]

Using Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)  

Public [article]  inRemote sensing > Vol 13 n° 2 (January-2 2021) . - n° 320 Titre : Using Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas Type de document : Article/Communication Auteurs : José P. Granadeiro, Auteur ; João Belo, Auteur ; Mohamed Henriques, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 320 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bathymétrie [Termes IGN] carte bathymétrique [Termes IGN] écosystème [Termes IGN] estran [Termes IGN] Guinée-Bissao [Termes IGN] habitat (nature) [Termes IGN] image Sentinel-MSI [Termes IGN] modèle numérique de surface [Termes IGN] régression logistique [Termes IGN] topographie locale Résumé : (auteur) Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation. Numéro de notice : A2021-197 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article DOI : 10.3390/rs13020320 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/rs13020320 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97148 [article]

Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)  

Public [article]  inRemote sensing > Vol 13 n° 2 (January-2 2021) . - n° 289 Titre : Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series Type de document : Article/Communication Auteurs : Misganu Debella-Gilo, Auteur ; Arnt Kristian Gjertsen, Auteur Année de publication : 2021 Article en page(s) : n° 289 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] carte agricole [Termes IGN] carte d'utilisation du sol [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image Sentinel-MSI [Termes IGN] Norvège [Termes IGN] série temporelle [Termes IGN] Soil Adjusted Vegetation Index [Termes IGN] surface cultivée [Termes IGN] utilisation du sol [Termes IGN] variation saisonnière Résumé : (auteur) The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas. Numéro de notice : A2021-198 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13020289 Date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/rs13020289 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97149 [article]