Descripteur
Documents disponibles dans cette catégorie (4899)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Harvested area did not increase abruptly-how advancements in satellite-based mapping led to erroneous conclusions / Johannes Breidenbach in Annals of Forest Science, vol 79 n° 1 (2022)
[article]
Titre : Harvested area did not increase abruptly-how advancements in satellite-based mapping led to erroneous conclusions Type de document : Article/Communication Auteurs : Johannes Breidenbach, Auteur ; David Ellison, Auteur ; Hans Petersson, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] changement climatique
[Termes IGN] données spatiotemporelles
[Termes IGN] Finlande
[Termes IGN] image à haute résolution
[Termes IGN] image Landsat
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] précision de l'estimation
[Termes IGN] récolte de bois
[Termes IGN] Suède
[Termes IGN] surface forestière
[Termes IGN] Union EuropéenneRésumé : (Auteur) Using satellite-based maps, Ceccherini et al. (Nature 583:72-77, 2020) report abruptly increasing harvested area estimates in several EU countries beginning in 2015. Using more than 120,000 National Forest Inventory observations to analyze the satellite-based map, we show that it is not harvested area but the map’s ability to detect harvested areas that abruptly increases after 2015 in Finland and Sweden. Numéro de notice : A2022-068 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01120-4 Date de publication en ligne : 22/02/2022 En ligne : https://doi.org/10.1186/s13595-022-01120-4 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100013
in Annals of Forest Science > vol 79 n° 1 (2022) . - n° 2[article]Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning / Aboubakar Sani-Mohammed in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)
[article]
Titre : Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning Type de document : Article/Communication Auteurs : Aboubakar Sani-Mohammed, Auteur ; Wei Yao, Auteur ; Marco Heurich, Auteur Année de publication : 2022 Article en page(s) : n° 100024 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre mort
[Termes IGN] Bavière (Allemagne)
[Termes IGN] bois sur pied
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] gestion forestière durable
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image infrarouge couleur
[Termes IGN] peuplement mélangé
[Termes IGN] puits de carbone
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation. Numéro de notice : A2022-871 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100024 Date de publication en ligne : 10/11/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102165
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100024[article]Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia / Mitiku Badasa Moisa in Applied geomatics, vol 14 n° 4 (December 2022)
[article]
Titre : Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia Type de document : Article/Communication Auteurs : Mitiku Badasa Moisa, Auteur ; Indale Niguse Dejene, Auteur ; Dessalegn Obsi Gemeda, Auteur Année de publication : 2022 Article en page(s) : pp 653 - 667 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] changement d'occupation du sol
[Termes IGN] climat urbain
[Termes IGN] espace vert
[Termes IGN] étalement urbain
[Termes IGN] Ethiopie
[Termes IGN] flore urbaine
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] modèle de régression
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression multiple
[Termes IGN] surface imperméable
[Termes IGN] urbanisation
[Termes IGN] utilisation du solRésumé : (auteur) Rapid urbanization and population growth are the main problems faced by developing countries that lead to natural resource depletion in the periphery of the city. This research attempts to analyze the impacts of urban land use land cover (LULC) change on land surface temperature (LST) from 1991 to 2021 in Jimma city, southwestern Ethiopia. Landsat Thematic Mapper (TM) 1991, Landsat Enhanced Thematic Mapper Plus (ETM +) 2005, and Landsat-8 Operational land imagery (OLI)/Thermal Infrared Sensor (TIRS) 2021 were used in this study. Multispectral bands and thermal infrared bands of Landsat images were used to calculate LULC change, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and LST. The LULC of the study area was classified using a supervised classification method with the maximum likelihood algorithm. The results of this study clearly showed that there is a negative correlation between vegetation cover and LST. The decrease in vegetation coverage and expansion of impervious surfaces lead to elevated LST in urban areas. The loss of vegetation cover contributed to the increasing trend of LST. Moreover, the conversion of vegetation cover to impervious surfaces aggravates the problem of LST. The results revealed that the built-up area was increased at a rate of 0.4 km2/year from 1991 to 2021. The vegetation cover in the city declined due to urban expansion to the periphery of the city. Consequently, the dense vegetation and sparse vegetation were converted into built-up areas by approximately 5.2 km2 during the study period. The mean LST of the study area increased by 10.3 °C from 1991 to 2021 during the winter season in daytime. To improve the problems of climate change around urban areas, all stakeholders should work together to increase the urban green space coverage, which will contribute a significant role in mitigating LST and the urban heat island effect. More specifically, all residents could be accessible to public green spaces around big cities. Numéro de notice : A2022-893 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-022-00463-x Date de publication en ligne : 22/08/2022 En ligne : https://doi.org/10.1007/s12518-022-00463-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102241
in Applied geomatics > vol 14 n° 4 (December 2022) . - pp 653 - 667[article]Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)
[article]
Titre : Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Arnaud Le Bris , Auteur ; Ali Asghar Darvishsefat, Auteur ; Fardin Moradi, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 1759 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] aire protégée
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SARRésumé : (auteur) Considering the importance of accurate and up-to-date land use/cover (LULC) maps and in a situation of fast LULC changes, an accurate mapping of complex landscapes requires real-time high-resolution remote sensed data and powerful classification algorithms. The new ESA Copernicus satellites Sentinel-1 (S-1) and Sentinel-2 (S-2) have contributed to the effective monitoring of the Earth’s surface. This paper aims at assessing the potential of mono-temporal S-1 and S-2 satellite images and three common classification algorithms including maximum likelihood (ML), support vector machine (SVM), and random forest (RF) for LULC classification. The research methodology consists of a sequence of tasks including data collection and preprocessing, the extraction of texture and spectral features, the definition of several feature set configurations, classification, and accuracy assessment. Based on the results, using S-1 data alone leads to quite poor results, even though dual polarimetric C-band and texture features increased the classification accuracy. The S-2 data outperformed the S-1 data in terms of overall and class level accuracies. A combined use of S-1 and S-2 satellite images involving extracted features from both sources led to the best result for identifying all classes. This emphasizes the critical importance of using multi-modal datasets and different features in the LULC classification. Among classification algorithms, the SVM led to the highest accuracies irrespective of the dataset. To sum it up, according to the applied methodology and results, S-1 and S-2 data can provide optimal and up-to-date information for LULC mapping using non-parametric classifiers as SVM or RF. Numéro de notice : A2022-699 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12517-022-11035-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s12517-022-11035-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102253
in Arabian Journal of Geosciences > vol 15 n° 24 (December 2022) . - n° 1759[article]Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution / Zhenfeng Shao in Geo-spatial Information Science, vol 25 n° 4 (December 2022)
[article]
Titre : Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution Type de document : Article/Communication Auteurs : Zhenfeng Shao, Auteur ; Yuan Zhang, Auteur ; Cheng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 550 - 567 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] approche hiérarchique
[Termes IGN] Chine
[Termes IGN] distribution spatiale
[Termes IGN] image Gaofen
[Termes IGN] image Landsat-OLI
[Termes IGN] scène urbaine
[Termes IGN] surface imperméableRésumé : (auteur) Impervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas. Numéro de notice : A2022-890 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2022.2028535 Date de publication en ligne : 02/03/2022 En ligne : https://doi.org/10.1080/10095020.2022.2028535 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102237
in Geo-spatial Information Science > vol 25 n° 4 (December 2022) . - pp 550 - 567[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)PermalinkReconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)PermalinkSea 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)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])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)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)PermalinkA high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation / Fang Gao in Computers & geosciences, vol 168 (November 2022)PermalinkMachine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)PermalinkMapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)PermalinkDriving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression / Ines Grigorescu in Geocarto international, vol 37 n° 24 ([20/10/2022])PermalinkModelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach / Abebe Debele Tolche in Geocarto international, vol 37 n° 24 ([20/10/2022])PermalinkChallenging the link between functional and spectral diversity with radiative transfer modeling and data / Javier Pacheco-Labradora in Remote sensing of environment, vol 280 (October 2022)PermalinkComparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping / Dang Hung Bui in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkDeep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lin Zhou in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkDeep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)PermalinkDSNUNet: An improved forest change detection network by combining Sentinel-1 and Sentinel-2 images / Jiawei Jiang in Remote sensing, vol 14 n° 19 (October-1 2022)PermalinkEvaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks / Abdelkrim Bouasria in Geo-spatial Information Science, vol 25 n° 3 (October 2022)Permalink