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Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (January - June 2022)
[article]
Titre : Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data Type de document : Article/Communication Auteurs : Fardin Moradi, Auteur ; Seyed Mohammad Moein Sadeghi, Auteur ; Hadi Beygi Heidarlou, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 165 - 182 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] allométrie
[Termes IGN] biomasse aérienne
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] forêt méditerranéenne
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Iran
[Termes IGN] Quercus brantii
[Termes IGN] taillisRésumé : (auteur) Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: MultiLayer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson’s correlation coefficient between AGB and the extracted values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R2 = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots. Numéro de notice : A2022-876 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15287/afr.2022.2390 Date de publication en ligne : 29/06/2022 En ligne : https://doi.org/10.15287/afr.2022.2390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102180
in Annals of forest research > vol 65 n° 1 (January - June 2022) . - pp 165 - 182[article]An extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways / Yimin Chen in Computers, Environment and Urban Systems, vol 91 (January 2022)
[article]
Titre : An extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways Type de document : Article/Communication Auteurs : Yimin Chen, Auteur Année de publication : 2022 Article en page(s) : n° 101727 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] Canton (Kouangtoung)
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] construction
[Termes IGN] croissance urbaine
[Termes IGN] données socio-économiques
[Termes IGN] Kouangtoung (Chine)
[Termes IGN] modèle de simulation
[Termes IGN] urbanisationRésumé : (auteur) Most contemporary urban cellular automata (CA) models primarily focus on the simulation of urban land expansion, and cannot effectively simulate vertical urban growth. This study addresses this drawback by extending a patch-based urban CA model with a component that can predict the building volumes of an urban land expansion scenario. The proposed model is evaluated through a case study in the Guangzhou-Foshan metropolitan area, China. The horizontal urban growth simulations achieve a mean ‘Figure-of-merit’ value of 0.1406 at the cell level and an agreement of 97% at the pattern level. The building volume prediction made by the methods of random forest and k-nearest-neighbor has a testing R2 of 0.90 and a mean percentage absolute error of 22%. The proposed model is applied to the urban growth projections under the shared socioeconomic pathways (SSPs). The results successfully reflect the influences that different SSPs have on vertical urban developments. These results also complement related research of urbanization projections under the SSPs, because most existing studies consider the impacts of horizontal urban growth only. As building volumes and heights are fundamental parameters to urban climate modeling, the ability of the proposed model to project future change in vertical urban developments can support the mitigation of climate change effects on human settlements. Numéro de notice : A2022-008 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101727 Date de publication en ligne : 21/10/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101727 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99049
in Computers, Environment and Urban Systems > vol 91 (January 2022) . - n° 101727[article]Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)
[article]
Titre : Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data Type de document : Article/Communication Auteurs : Fardin Moradi, Auteur ; Ali Asghar Darvishsefat, Auteur ; Manizheh Rajab Pourrahmati, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] Carpinus betulus
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] image Sentinel-MSI
[Termes IGN] Iran
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificielNuméro de notice : A2022-080 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13010104 Date de publication en ligne : 12/01/2022 En ligne : https://doi.org/10.3390/f13010104 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99472
in Forests > vol 13 n° 1 (January 2022) . - n° 104[article]A hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds / Yetao Yang in Computers & geosciences, vol 157 (December 2021)
[article]
Titre : A hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Yetao Yang, Auteur ; Rongkui Tang, Auteur ; Jinglei Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104932 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification par réseau neuronal
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] itération
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] structure hiérarchique de donnéesRésumé : (auteur) Airborne LiDAR point clouds classification has been a challenging task due to the characteristics of point clouds and the complexity of the urban environment. Recently, methods that directly act on unordered point set have achieved satisfactory results in point clouds classification. However, the existing methods that directly consume point clouds pay little attention to the interaction between the deep layers, which makes the feature learning insufficient in complex environments. In this paper, we propose a deep neural network for semantic labeling task. It iteratively learns deep features in a hierarchical structure, and provides a simple but efficient way to make interactions between different hierarchical levels. Since iteration process will greatly increase the number of layers, we employ the residual network to improve the performance. In addition, we also introduce dilated k-nearest neighbors and multi-scale grouping to increase the receptive field. The experiments on both Vaihingen 3D dataset and Dayton Annotated LiDAR Earth Scan (DALES) dataset demonstrate the effectiveness of the proposed method in point cloud classification. Numéro de notice : A2021-867 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104932 Date de publication en ligne : 04/09/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104932 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99098
in Computers & geosciences > vol 157 (December 2021) . - n° 104932[article]OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry / Xuan Fang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)
[article]
Titre : OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry Type de document : Article/Communication Auteurs : Xuan Fang, Auteur ; Jincheng Li, Auteur ; Ying Zhu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 805 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse d'image orientée objet
[Termes IGN] Chine
[Termes IGN] classification barycentrique
[Termes IGN] dommage matériel
[Termes IGN] données de terrain
[Termes IGN] érosion
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] pente
[Termes IGN] photogrammétrie aérienne
[Termes IGN] segmentation d'image
[Termes IGN] surface cultivée
[Termes IGN] terrasseRésumé : (auteur) Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China. Numéro de notice : A2021-882 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120805 Date de publication en ligne : 27/11/2021 En ligne : https://doi.org/10.3390/ijgi10120805 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99178
in ISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 805[article]Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])PermalinkGaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds / Longjie Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)PermalinkSurface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])PermalinkExtracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches / Kim Lowell in Marine geodesy, vol 44 n° 4 (July 2021)PermalinkAn incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkAutomatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)PermalinkComplémentarité des images optiques Sentinel-2 avec les images radar Sentinel-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique / Siham Acharki in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)Permalink3D change detection using adaptive thresholds based on local point cloud density / Dan Liu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkAnalysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science, vol 78 n° 1 (March 2021)PermalinkFOSTER - An R package for forest structure extrapolation / Martin Queinnec in Plos one, vol 16 n° 1 (January 2021)Permalink