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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)
[article]
Titre : A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds Type de document : Article/Communication Auteurs : Xiaoqiang Liu, Auteur ; Qin Ma, Auteur ; Xiaoyong wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] Chine
[Termes IGN] couvert forestier
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] écosystème forestier
[Termes IGN] entropie
[Termes IGN] estimation par noyau
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) Forest canopy structural complexity (CSC) describes the three-dimensional (3D) arrangement of canopy elements, and has become an emergent forest attribute mediating forest ecosystem functioning along with species diversity. Light detection and ranging (lidar), especially the emerging near-surface lidar platforms (e.g., terrestrial laser scanning/TLS, backpack laser scanning/BLS, unmanned aerial vehicle laser scanning/ULS), can depict 3D canopy information with high efficiency and accuracy, providing an ideal data source for forest CSC quantification. However, current existing lidar-based CSC quantification indices may share common limitations of getting saturated in structurally complex forest stands and not fully capturing within-canopy structural variations. In this study, we introduced the concept of entropy into forest CSC quantification, and proposed a new forest CSC index, namely canopy entropy (CE). Two major bottlenecks were addressed in the CE calculation procedure, including (1) using a Mann-Kendall (MK) test-based resampling strategy to address the issue of incongruent sampling chances of canopy elements at different locations from different lidar systems, and (2) using a kernel density estimation (KDE)-based method to reduce its dependence on point density. The effectiveness and generality of CE were evaluated by simulating TLS and ULS point clouds from nine forest stands and collecting TLS, BLS, and ULS point clouds from 110 field plots distributed in five forest sites, covering a large variety of forest types and forest CSC conditions. The results showed that CE was an effective forest CSC quantification index that successfully captured CSC variations caused by both tree density and the number of vertical canopy layers. It had significant positive correlations with four widely used CSC indices (i.e., canopy cover, foliage height diversity, canopy top rugosity, and fractal dimension; R2: 0.32 to 0.67), but outperformed them by overcoming their common limitations. CE estimates from multiplatform lidar point clouds agreed well with each other (R2 ≥ 0.70, RMSE ≤0.10), indicating it has generality in cross-platform forest CSC quantification practices. We believe the proposed CE index has great potential to help us unravel the correlations among forest CSC, species diversity, and forest ecosystem functions, and therefore improve our understanding on forest ecosystem processes. Numéro de notice : A2022-795 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113280 Date de publication en ligne : 26/09/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101930
in Remote sensing of environment > vol 282 (December 2022) . - n° 113280[article]Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al. / Guido Ceccherini in Annals of Forest Science, vol 79 n° 1 (2022)
[article]
Titre : Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al. Type de document : Article/Communication Auteurs : Guido Ceccherini, Auteur ; Grégory Duveiller, Auteur ; Giacomo Grassi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 31 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] exploitation forestière
[Termes IGN] Finlande
[Termes IGN] foresterie
[Termes IGN] image Landsat
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] placette d'échantillonnage
[Termes IGN] récolte de bois
[Termes IGN] ressources forestières
[Termes IGN] Suède
[Termes IGN] surface forestière
[Termes IGN] Union EuropéenneRésumé : (auteur) The timely and accurate monitoring of forest resources is becoming of increasing importance in light of the multi-functionality of these ecosystems and their increasing vulnerability to climate change. Remote sensing observations of tree cover and systematic ground observations from National Forest Inventories (NFIs) represent the two major sources of information to assess forest area and use. The specificity of two methods is calling for an in-depth analysis of their strengths and weaknesses and for the design of novel methods emerging from the integration of satellite and surface data. On this specific debate, a recent paper by Breidenbach et al. published in this journal suggests that the detection of a recent increase in EU forest harvest rate—as reported in Nature by Ceccherini et al.—is largely due to technical limitations of satellite-based mapping. The article centers on the difficulty of the approaches to estimate wood harvest based on remote sensing. However, it does not discuss issues with the robustness of validation approaches solely based on NFIs. Here we discuss the use of plot data as a validation set for remote sensing products, discussing potentials and limitations of both NFIs and remote sensing, and how they can be used synergistically. Finally, we highlight the need to collect in situ data that is both relevant and compatible with remote sensing products within the European Union. Numéro de notice : A2022-630 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01150-y Date de publication en ligne : 13/07/2022 En ligne : https://doi.org/10.1186/s13595-022-01150-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101393
in Annals of Forest Science > vol 79 n° 1 (2022) . - n° 31[article]Reconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)
[article]
Titre : Reconstructing compact building models from point clouds using deep implicit fields Type de document : Article/Communication Auteurs : Zhaiyu Chen, Auteur ; Hugo Ledoux, Auteur ; Seyran Khademi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 58 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] Bâti-3D
[Termes IGN] champ aléatoire de Markov
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de modèle
[Termes IGN] image à haute résolution
[Termes IGN] maillage par triangles
[Termes IGN] optimisation (mathématiques)
[Termes IGN] polygone
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de pointsRésumé : (auteur) While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open problem. In this paper, we present a novel framework for reconstructing compact, watertight, polygonal building models from point clouds. Our framework comprises three components: (a) a cell complex is generated via adaptive space partitioning that provides a polyhedral embedding as the candidate set; (b) an implicit field is learned by a deep neural network that facilitates building occupancy estimation; (c) a Markov random field is formulated to extract the outer surface of a building via combinatorial optimization. We evaluate and compare our method with state-of-the-art methods in generic reconstruction, model-based reconstruction, geometry simplification, and primitive assembly. Experiments on both synthetic and real-world point clouds have demonstrated that, with our neural-guided strategy, high-quality building models can be obtained with significant advantages in fidelity, compactness, and computational efficiency. Our method also shows robustness to noise and insufficient measurements, and it can directly generalize from synthetic scans to real-world measurements. The source code of this work is freely available at https://github.com/chenzhaiyu/points2poly. Numéro de notice : A2022-824 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.09.017 Date de publication en ligne : 17/10/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.09.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102001
in ISPRS Journal of photogrammetry and remote sensing > vol 194 (December 2022) . - pp 58 - 73[article]Relevé 2D & 3D du marégraphe de Marseille / Emmanuel Clédat in XYZ, n° 173 (décembre 2022)
[article]
Titre : Relevé 2D & 3D du marégraphe de Marseille Type de document : Article/Communication Auteurs : Emmanuel Clédat , Auteur ; Clovis Bergeret, Auteur ; Marius Dahuron, Auteur ; Lilian Wecker, Auteur ; Frédéric Ye, Auteur Année de publication : 2022 Article en page(s) : pp 55 - 62 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] géoréférencement
[Termes IGN] image captée par drone
[Termes IGN] marégraphe
[Termes IGN] Marseille
[Termes IGN] modélisation 2D
[Termes IGN] modélisation 3D
[Termes IGN] semis de pointsRésumé : (Editeur) Le marégraphe de Marseille est un monument historique de l’IGN. Pour permettre au plus grand nombre de le visiter (virtuellement), et pour préparer d’éventuels travaux de restauration, l’association des amis du marégraphe a commandité une modélisation 3D. Effectués par les élèves de l’ENSG-Géomatique en utilisant les méthodes de photogrammétrie et de scanner laser terrestre, ces relevés ont permis de produire un modèle 3D intérieur et extérieur, mais aussi des produits 2D : coupes, plans, écorchés. Numéro de notice : A2022-912 Affiliation des auteurs : ENSG (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102270
in XYZ > n° 173 (décembre 2022) . - pp 55 - 62[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2022041 RAB Revue Centre de documentation En réserve L003 Disponible Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)
[article]
Titre : Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach Type de document : Article/Communication Auteurs : Hakan Oktay Aydınlı, Auteur ; Ali Ekincek, Auteur ; Mervegül Aykanat-Atay, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 669 - 678 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] détection de changement
[Termes IGN] données Copernicus
[Termes IGN] image Aqua-MODIS
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle de simulation
[Termes IGN] Noire, mer
[Termes IGN] optimisation (mathématiques)
[Termes IGN] série temporelle
[Termes IGN] température de surface de la merRésumé : (auteur) High temporal resolution remote sensing images provide continuous data about the marine environment, which is critical for gaining extensive knowledge about the aquatic environment and marine species. Sea surface temperature (SST) is one of the basic parameters that can be obtained with the help of remote sensing. Long-term alterations in the SST can affect the aquatic environment and marine species, such as the life expectancy of anchovies in the Black Sea. Forecasting the dynamics of SSTs is crucial for detecting and eliminating the SST-oriented impacts. The goal of the current study is to construct a predictive model to estimate the daily SST value for the mid-Black Sea using a machine learning approach by employing time-series satellite data from 2008 to 2021. Turkey’s mid-Black Sea coastal line, comprising Ordu, Samsun, and Sinop stations, was chosen as the study area. The SST predictive model was represented by applying the recurrent neural network (RNN) long- and short-term memory (LSTM). Adam stochastic optimization was used for validation, and the mean square error (MSE) for each location was found to be 0.914, 0.815, and 0.802, respectively. The findings indicate that our model is significantly promising for accurate and effective short- and midterm daily SST prediction. Numéro de notice : A2022-894 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-022-00462-y Date de publication en ligne : 23/08/2022 En ligne : https://doi.org/10.1007/s12518-022-00462-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102242
in Applied geomatics > vol 14 n° 4 (December 2022) . - pp 669 - 678[article]A semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) / Masoud Azad in Applied geomatics, vol 14 n° 4 (December 2022)PermalinkSpatio-temporal patterns of wildfires in Siberia during 2001–2020 / Oleg Tomshin in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkThe simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)PermalinkUrban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis / Das Subhasis in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkVine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging / Igor Petrovic in Remote sensing, vol 14 n° 22 (November-2 2022)PermalinkAn advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations / Kai Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)PermalinkBuilding a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images / Emilio Chuvieco in Science of the total environment, vol 845 (November 1 2022)PermalinkCross-guided pyramid attention-based residual hyperdense network for hyperspectral image pansharpening / Jiahui Qu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)PermalinkExploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)PermalinkForeground-aware refinement network for building extraction from remote sensing images / Zhang Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 11 (November 2022)Permalink