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Harvested area did not increase abruptly-how advancements in satellite-based mapping led to erroneous conclusions / Johannes Breidenbach in Annals of Forest Science [en ligne], vol 79 n° 1 (2022)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 79 n° 1 (2022) . - n° 2 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 Européenne Résumé : (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 [article]

Improving remote sensing classification: A deep-learning-assisted model / Tsimur Davydzenka in Computers & geosciences, vol 164 (July 2022)  

Public [article]  inComputers & geosciences > vol 164 (July 2022) . - n° 105123 Titre : Improving remote sensing classification: A deep-learning-assisted model Type de document : Article/Communication Auteurs : Tsimur Davydzenka, Auteur ; Pejman Tahmasebi, Auteur ; Mark Carroll, Auteur Année de publication : 2022 Article en page(s) : n° 105123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image à haute résolution [Termes IGN] modèle stochastique [Termes IGN] précision de la classification Résumé : (auteur) In many industries and applications, obtaining and classifying remote sensing imagery plays a crucial role. The accuracy of classification, in particular the machine learning methods, mainly depends on a multitude of factors, among which one of the most important ones is the amount of training data. Obtaining sufficient amounts of training data, however, can be very difficult or costly, and one must find alternative ways to improve the accuracy of predictions. To this end, a possible solution that we provide in this study is to use a stochastic method for producing variations of the training images that will retain the important class-wide features and thereby enrich the machine learning's “understanding” of the variabilities. As such, we applied a stochastic algorithm to produce additional realizations of the limited input imagery and thereby significantly increase the final overall accuracy in a deep learning method. We found that by enlarging the initial training set by additional realizations, we are able to consistently improve classification accuracy, compared with generic image augmentation approaches. The results of this study show that there is a great opportunity to increase the accuracy of predictions when enough data are not available. Numéro de notice : A2022-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105123 Date de publication en ligne : 29/04/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100672 [article]

Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery / Qian Shen in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 78 - 94 Titre : Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Qian Shen, Auteur ; Jiru Huang, Auteur ; Min Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 78 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] données qualitatives [Termes IGN] estimation quantitative [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] jeu de données [Termes IGN] réseau neuronal siamois Résumé : (auteur) In the field of remote sensing applications, semantic change detection (SCD) simultaneously identifies changed areas and their change types by jointly conducting bitemporal image classification and change detection. It facilitates change reasoning and provides more application value than binary change detection (BCD), which offers only a binary map of the changed/unchanged areas. In this study, we propose a multitask Siamese network, named the semantic feature-constrained change detection (SFCCD) network, for building change detection in bitemporal high-spatial-resolution (HSR) images. SFCCD conducts feature extraction, semantic segmentation and change detection simultaneously, where change detection and semantic segmentation are the main and auxiliary tasks, respectively. For the segmentation task, ResNet50 is used to conduct image feature extraction, and the extracted semantic features are provided to execute the change detection task via a series of jump connections. For the change detection task, a global channel attention (GCA) module and a multiscale feature fusion (MSFF) module are designed, where high-level features offer training guidance to the low-level feature maps, and multiscale features are fused with multiple convolutions that possess different receptive fields. In bitemporal HSR images with different view angles, high-rise buildings have different directional height displacements, which generally cause serious false alarms for common change detection methods. However, known public building change detection datasets often lack buildings with height displacement. We thus create the Nanjing Dataset (NJDS) and design the aforementioned network structures and modules to target this issue. Experiments for method validation and comparison are conducted on the NJDS and two additional public datasets, i.e., the WHU Building Dataset (WBDS) and Google Dataset (GDS). Ablation experiments on the NJDS show that the joint utilization of the GCA and MSFF modules performs better than several classic modules, including atrous spatial pyramid pooling (ASPP), efficient spatial pyramid (ESP), channel attention block (CAB) and global attention upsampling (GAU) modules, in dealing with building height displacement. Furthermore, SFCCD achieves higher accuracy in terms of the OA, recall, F1-score and mIoU measures than several state-of-the-art change detection methods, including deeply supervised image fusion network (DSIFN), the dual-task constrained deep Siamese convolutional network (DTCDSCN), and multitask U-Net (MTU-Net). Numéro de notice : A2022-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.05.001 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.05.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100762 [article]

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Human cognition based framework for detecting roads from remote sensing images / Naveen Chandra in Geocarto international, vol 37 n° 8 ([22/06/2022])  

Public [article]  inGeocarto international > vol 37 n° 8 [22/06/2022] . - pp 2365 - 2384 Titre : Human cognition based framework for detecting roads from remote sensing images Type de document : Article/Communication Auteurs : Naveen Chandra, Auteur ; Himadri Vaidya, Auteur ; Jayanta Kumar Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 2365 - 2384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse d'image numérique [Termes IGN] classification [Termes IGN] cognition [Termes IGN] extraction du réseau routier [Termes IGN] image à haute résolution [Termes IGN] interprétation (psychologie) [Termes IGN] représentation cognitive [Termes IGN] segmentation d'image Résumé : (auteur) The complete extraction of roads from remote sensing images (RSIs) is an emergent area of research. It is an interesting topic as it involves diverse procedures for detecting roads. The detection of roads using high-resolution-satellite-images (HRSi) is challenging because of the occurrence of several types of noise such as bridges, vehicles, and crossing lines, etc. The extraction of the correct road network is crucial due to its broad range of applications such as transportation, map updating, navigation, and generating maps. Therefore our paper concentrates on understanding the cognitive processes, reasoning, and knowledge used by the analyst through visual cognition while performing the task of road detection from HRSi. The novel process is performed emulating human cognition within cognitive task analysis which is carried out in five different stages. The suggested cognitive procedure for road extraction is validated with the fifteen HRSi of four different land cover patterns specifically developed-sub-urban (DSUr), developed-urban (DUr), emerging-sub-urban (ESUr), and emerging-urban (EUr). The experimental results and the comparative assessment prove the impact of the presented cognitive method. Numéro de notice : A2022-506 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1810330 Date de publication en ligne : 14/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1810330 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101027 [article]

How large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps / Marion E. Caduff in Forest ecology and management, vol 514 (15 June 2022)  

Public [article]  inForest ecology and management > vol 514 (15 June 2022) . - n° 120201 Titre : How large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps Type de document : Article/Communication Auteurs : Marion E. Caduff, Auteur ; Natalie Brožová, Auteur ; Andrea D. Kupferschmid, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Alpes [Termes IGN] avalanche [Termes IGN] bois mort [Termes IGN] dépérissement [Termes IGN] image à haute résolution [Termes IGN] modèle de simulation [Termes IGN] orthophotographie [Termes IGN] protection des forêts [Termes IGN] régénération (sylviculture) [Termes IGN] risque naturel [Termes IGN] santé des forêts [Termes IGN] Scolytinae [Termes IGN] Suisse [Termes IGN] xylophage Résumé : (auteur) Large-scale bark beetle outbreaks in spruce dominated mountain forests have increased in recent decades, and this trend is expected to continue in the future. These outbreaks have immediate and major effects on forest structure and ecosystem services. However, it remains unclear how forests recover from bark beetle infestations over the long term, and how different recovery stages fulfil the capacity of forests to protect infrastructures and human lives from natural hazards. The aim of this study was to investigate how a bark beetle infestation (1992–1997) in a spruce dominated forest in the Swiss Alps changed the forest structure and its protective function against snow avalanches. In 2020, i.e. 27 years after the peak of the outbreak, we re-surveyed the composition and height of new trees, as well as the deadwood height and degree of decay in an area that had been surveyed 20 years earlier. With the help of remote sensing data and avalanche simulations, we assessed the protective effect against avalanches before the disturbances (in 1985) and in 1997, 2007, 2014 and 2019 for a frequent (30-year return period) and an extreme (300-year return period) avalanche scenario. Post-disturbance regeneration led to a young forest that was again dominated by spruce 27 years after the outbreak, with median tree heights of 3–4 m and a crown cover of 10–30%. Deadwood covered 20–25% of the forest floor and was mainly in decay stages two and three out of five. Snags had median heights of 1.4 m, leaning logs 0.5 m and lying logs 0.3 m. The protective effect of the forest was high before the bark beetle outbreak and decreased during the first years of infestation (until 1997), mainly in the case of extreme avalanche events. The protective capacity reached an overall minimum in 2007 as a result of many forest openings. It partially recovered by 2014 and further increased by 2019, thanks to forest regeneration. Simulation results and a lack of avalanche releases since the infestation indicate that the protective capacity of post-disturbance forest stands affected by bark beetle may often be underestimated. Numéro de notice : A2022-349 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2022.120201 Date de publication en ligne : 08/04/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100536 [article]

DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images / Yingjie Wang in Remote sensing of environment, vol 274 (June 2022)  

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HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)  

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Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)  

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Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)  

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A context feature enhancement network for building extraction from high-resolution remote sensing imagery / Jinzhi Chen in Remote sensing, vol 14 n° 9 (May-1 2022)  

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Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data / Susmita Dasgupta in Science of the total environment, vol 839 (May 2022)  

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Research on machine intelligent perception of urban geographic location based on high resolution remote sensing images / Jun Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)  

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Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning / Jun Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)  

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Airborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([01/01/2022])  

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Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ / Zhimin Wang in Computers & geosciences, vol 158 (January 2022)  

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