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An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data / Van-Tho Nguyen in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 2 (June 2021) . - Article 32 Titre : An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data Type de document : Article/Communication Auteurs : Van-Tho Nguyen, Auteur ; Thiéry Constant, Auteur ; Francis Colin, Auteur Année de publication : 2021 Article en page(s) : Article 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] détection d'anomalie [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] écorce [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] qualité du bois [Termes descripteurs IGN] Quercus sessiliflora [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] télémétrie laser terrestre [Termes descripteurs IGN] tronc Résumé : (Auteur) We designed a novel method allowing to automatically detect and measure defects on the surface of trunks including branches, branch scars, and epicormics from terrestrial LiDAR data by using only high-density 3D information. We could automatically detect and measure the defects with a diameter as small as 0.5 cm on either oak (Quercus petraea (Matt.) Liebl.) or beech (Fagus sylvatica L.) trees that display either rough or smooth bark. Context : Ground-based light detection and ranging (LiDAR) technology describes standing trees with a high level of detail. This provides an opportunity to assess standing tree quality and to use this information in forest inventory. Assuming the availability of a very high level of detail, we could extract information about the surface defects, mainly inherited from past ramification and having a strong impact on wood quality. Aims : Within the general framework of the development of a computing method able to detect, identify, and quantify the defects on the trunk surface described from 3D data produced by a terrestrial LiDAR, this study focuses on the relevance of the whole process for two tree species with contrasted bark roughness (Quercus petraea (Matt.) Liebl. and Fagus sylvatica L.) in terms of detection, identification of the defects, and comparison with measurements performed manually on the bark surface. Methods : First, a segmentation algorithm detected singularities on the trunk surface. Next, a Random Forests machine learning algorithm identified the most probable defect type and allowed the elimination of false detections. Finally, we estimated the position, horizontal, and vertical dimensions of each defect from 3D data, and we compared them to those observed directly on the trunk by an operator. Results : The defects were detected and classified with a high accuracy with an average F1 score (harmonic mean of precision and recall) of 0.74. There were differences in computed and observed defect areas, but a much closer agreement for the number of defects. Conclusion : The information about the defects present on the trunk surface measured from terrestrial LiDAR data can be used in an automated procedure for grading standing trees or roundwoods. Numéro de notice : A2021-326 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01022-3 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1007/s13595-020-01022-3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97484 [article]

Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101595 Titre : Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Masoud Minaei, Auteur ; Robert Gilmore Pontius Jr, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101595 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] extrapolation [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] Téhéran [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. Numéro de notice : A2021-274 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101595 date de publication en ligne : 16/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101595 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97357 [article]

Learning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 132 - 143 Titre : Learning from multimodal and multitemporal earth observation data for building damage mapping Type de document : Article/Communication Auteurs : Bruno Adriano, Auteur ; Naoto Yokoya, Auteur ; Junshi Xia, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 132 - 143 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] cyclone [Termes descripteurs IGN] dommage [Termes descripteurs IGN] données multitemporelles [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] observation de la Terre [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] séisme [Termes descripteurs IGN] surveillance d'ouvrage [Termes descripteurs IGN] tsunami Résumé : (auteur) Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis. Numéro de notice : A2021-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.016 date de publication en ligne : 17/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97343 [article]

A new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 97 (May 2021) . - n° 102303 Titre : A new small area estimation algorithm to balance between statistical precision and scale Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Jean-Pierre Renaud, Auteur ; Ankit Sagar , Auteur ; Olivier Bouriaud , Auteur Année de publication : 2021 Projets : LUE / Université de Lorraine, DIABOLO / Packalen, Tuula, ARBRE/CHM-era / Jolly, Anne Article en page(s) : n° 102303 Note générale : bibliographie This research was funded by The French Environmental Management Agency (ADEME), grant number 16-60-C0007. The methods and algorithms for processing photogrammetric data were supported by DIABOLO project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 633464, as well as CHM-ERA project from the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). Ankit Sagar received the financial support of the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE, through the project Impact DeepSurf. Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] arbre BSP [Termes descripteurs IGN] capital sur pied [Termes descripteurs IGN] données auxiliaires [Termes descripteurs IGN] données de terrain [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] réduction d'échelle [Termes descripteurs IGN] seuillage [Termes descripteurs IGN] surface terrière [Vedettes matières IGN] Sylviculture Résumé : (auteur) Combining national forest inventory (NFI) data with auxiliary information allows downscaling and improving the precision of NFI estimates for small domains, where normally too few field plots are available to produce reliable estimates. In most situations, small domains represent administrative units that could greatly vary in size and forested area. In small and poorly sampled domains, the precision of estimates often drop below expected standards. To tackle this issue, we introduce a downscaling algorithm generating the smallest possible groups of domains satisfying prescribed sampling density and estimation error. The binary space partitioning algorithm recursively divides the population of domains in two groups while the prescribed precision conditions are fulfilled. The algorithm was tested on two major forest attributes (i.e. growing stock and basal area) in an area of 7,500 km2 dominated by hardwood forests in the centre of France. The estimation domains consisted in 157 municipalities. The field data included 819 NFI plots surveyed during a 5 years period. The auxiliary data consisted in 48 metrics derived from a forest map, photogrammetric models and Landsat images. A model-assisted framework was used for estimation. For each forest attribute, the best model was selected using a best-subset approach using a Bayesian Information Criteria. The retained models explained 58% and 41% of the observed variance for the growing stocks and basal areas respectively. The performance of the algorithm was evaluated using a minimum of 3 NFI points per domain and estimation errors varying from 10 to 50%. For a target estimation error set to 10%, the algorithm led to a limited number of estimation domains ( The algorithm provides a flexible estimation framework for small area estimation. The key advantages of the approach are relying on its capacity to produce estimations based on a preselected precision threshold and to produce results over the whole area of interest, avoiding areas without any estimates. The algorithm could also be used on any kind of polygon layers (not only administrative ones), provided that the field sampling design enable estimation. This makes the proposed algorithm a convenient tool notably for decision makers and forest managers. Numéro de notice : A2021-067 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2021.102303 date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.jag.2021.102303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96992 [article]

Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future / Tamás Gál in Computers, Environment and Urban Systems, vol 87 (May 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101600 Titre : Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future Type de document : Article/Communication Auteurs : Tamás Gál, Auteur ; Sándor István Mahó, Auteur ; Norà Skarbit, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101600 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] espace vert [Termes descripteurs IGN] flore urbaine [Termes descripteurs IGN] forêt périurbaine [Termes descripteurs IGN] Hongrie [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] parc urbain [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) This paper focuses on urban green spaces in terms of climate and human thermal comfort containing their effect on heat load mitigation. It incorporates a modelling study in which the role of green spaces was investigated in terms of heat stress modification by applying MUKLIMO_3 model. During the experiment, the thermal effects of dense trees, scattered trees, grasslands and mixed green infrastructure has been investigated in the case of Szeged (Hungary) and assessed using different climate indices. The investigations encompassed 3 climatological time periods (1981–2010, 2021–2050 and 2071–2100) and two emission scenarios for future climate (RCP4.5 and RCP8.5). It was found that urban green spaces (e.g. parks) generally cool the environment, although, the cooling potential of the different green types differs. The highest reduction of heat load was found in the case of large urban parks comprising of dense trees near the downtown. The spatial extension of detected cooling was found small. However, it would increase during the future, especially in the case of grasslands. For urban planners, it is highly recommended to introduce new green sites within a city and to increase the spatial extension of the existing ones to mitigate and adapt to the impacts of climate change in the urban environment. Numéro de notice : A2021-276 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101600 date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101600 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97362 [article]

Performance evaluation of artificial neural networks for natural terrain classification / Perpetual Hope Akwensi in Applied geomatics, vol 13 n° 1 (May 2021)  

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A Voronoi-based method for land-use optimization using semidefinite programming and gradient descent algorithm / Vorapong Suppakitpaisarn in International journal of geographical information science IJGIS, vol 35 n° 5 (May 2021)  

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Electrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed / H.S. Virupaksha in Geocarto international, vol 36 n° 8 ([15/04/2021])  

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An iterative-mode scan design of terrestrial laser scanning in forests for minimizing occlusion effects / Linyuan Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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IWV observations in the Caribbean Arc from a network of ground-based GNSS receivers during EUREC4A / Olivier Bock in Earth System Science Data, vol 13 n° 4 (April 2021)  

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Des pixels et des peuples / Laurent Polidori in Géomètre, n° 2190 (avril 2021)

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Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring / Gopal Krishna in Geocarto international, vol 36 n° 5 ([15/03/2021])  

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Horizontal calibration of vessels with UASs / Casey O'Heran in Marine geodesy, vol 44 n° 2 (March 2021)  

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Aggregating land-use polygons considering line features as separating map elements / Sven Gedicke in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)  

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Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm / Siddhartho Shekhar Paul in Geocarto international, vol 36 n° 4 ([01/03/2021])  

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