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Tracing drought effects from the tree to the stand growth in temperate and Mediterranean forests: insights and consequences for forest ecology and management / Hans Pretzsch in European Journal of Forest Research, vol 141 n° 4 (August 2022)
[article]
Titre : Tracing drought effects from the tree to the stand growth in temperate and Mediterranean forests: insights and consequences for forest ecology and management Type de document : Article/Communication Auteurs : Hans Pretzsch, Auteur ; Miren del Rio, Auteur ; Rüdiger Grote, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 727 - 751 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bavière (Allemagne)
[Termes IGN] coefficient de Gini
[Termes IGN] croissance des arbres
[Termes IGN] écologie forestière
[Termes IGN] Espagne
[Termes IGN] Fagus sylvatica
[Termes IGN] forêt méditerranéenne
[Termes IGN] forêt tempérée
[Termes IGN] gestion forestière
[Termes IGN] mortalité
[Termes IGN] Picea abies
[Termes IGN] sécheresse
[Termes IGN] stress hydrique
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) How drought affects tree and stand growth is an old question, but is getting unprecedented relevance in view of climate change. Stress effects related to drought have been mostly studied at the individual tree level, mostly investigating dominant trees and using their responses as indicator for the impact at the stand level. However, findings at tree and stand level may differ, as the stand responses include interactions and feedbacks that may buffer or aggravate what is observed at the individual tree level. Here, we trace drought effects on growth and development from tree to the stand scale. Therefore, we analyse annually measured data from long-term experiments in temperate and Mediterranean forests. With this analysis, we aim to disclose how well results of dominant tree growth reflect stand-level behaviour, hypothesizing that drought resistance of dominant trees’ can strongly deviate from the overall sensitivity of the stand. First, we theoretically derive how drought responses at the stand level emerge from the tree-level behaviour, thereby considering that potential drought resistance of individual trees is modulated by acclimation and tree–tree interactions at the stand level and that the overall stress response at the stand level results from species-specific and size-dependent individual tree growth and mortality. Second, reviewing respective peer-reviewed literature (24 papers) and complementing findings by own measurements (22 experiments) from temperate and Mediterranean monospecific and mixed-species forests, we are able to reveal main causes for deviations of tree-level and stand-level findings regarding drought stress responses. Using a long-term experiment in Norway spruce (Picea abies (L.) KARST.) and European beech (Fagus sylvatica L.), we provide evidence that the species-dependent and size-dependent reactions matter and how the size–frequency distribution affects the scaling. We show by examples that tree-level derived results may overestimate growth losses by 25%. Third, we investigate the development of the growth dominance coefficient based on measurements gathered at the Bavarian forest climate stations. We show that drought changes stand biomass partitioning in favour of small trees, reduce social differentiation, and homogenize the vertical structure of forests. Finally, we discuss the drought-related consequences of the social class-specific growth reaction patterns for inventory and monitoring and highlight the importance of these findings for understanding site-specific stand dynamics, for forest modelling, and for silvicultural management. Numéro de notice : A2022-640 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10342-022-01451-x Date de publication en ligne : 07/05/2022 En ligne : https://doi.org/10.1007/s10342-022-01451-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101447
in European Journal of Forest Research > vol 141 n° 4 (August 2022) . - pp 727 - 751[article]Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020 / Rong Zhang in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)
[article]
Titre : Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020 Type de document : Article/Communication Auteurs : Rong Zhang, Auteur ; Mingming Jia, Auteur ; Zongming Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102918 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme de Otsu
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] Chine
[Termes IGN] dynamique de la végétation
[Termes IGN] image Sentinel-MSI
[Termes IGN] mangrove
[Termes IGN] réserve naturelleRésumé : (auteur) Mangrove National Nature Reserves (MNNRs) play an extraordinarily significant role in conserving mangrove forests and their habitats. In China, one-fourth of the total mangrove forests were located in MNNRs. Understanding annual spatial distributions and conversions of these mangrove forests are important for precision conservation and rehabilitation efforts. However, to date, annual land cover maps of China’s MNNRs are still unavailable. Here, we proposed a rapid and robust approach to produce annual maps of each MNNRs for the time period of 2016–2020 based on 10-m resolution Sentinel-2 imagery. The proposed approach was developed using object-based image analysis, Otsu and Random Forest algorithm. Results showed that 1) during 2016–2020, areal extents of mangrove forest in all the MNNRs continuously increased from 5912 ha to 6128 ha; 2) obvious increase were found in Zhanjiang Mangrove National Nature Reserve where mangrove forest increased by 127 ha, accounted for 59% of national total increases; 3) newly grown mangrove forests were mainly converted from tidal flats and aquaculture ponds. Our annual maps of China’s MNNRs could provide a basis for managing mangrove ecosystems and supporting the implementation of Sustainable Development Goals related to coastal development. Numéro de notice : A2022-583 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.102918 En ligne : https://doi.org/10.1016/j.jag.2022.102918 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101348
in International journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102918[article]Transfer learning from citizen science photographs enables plant species identification in UAV imagery / Salim Soltani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)
[article]
Titre : Transfer learning from citizen science photographs enables plant species identification in UAV imagery Type de document : Article/Communication Auteurs : Salim Soltani, Auteur ; Hannes Feilhauer, Auteur ; Robbert Duker, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données naturalistes
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] distribution spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] espèce végétale
[Termes IGN] filtrage de la végétation
[Termes IGN] identification de plantes
[Termes IGN] image captée par drone
[Termes IGN] orthoimage couleur
[Termes IGN] science citoyenne
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter scale acquired with Unoccupied Aerial Vehicles (UAV). However, such tasks often require ample training data, which is commonly generated in the field via geocoded in-situ observations or labeling remote sensing data through visual interpretation. Both approaches are laborious and can present a critical bottleneck for CNN applications. An alternative source of training data is given by using knowledge on the appearance of plants in the form of plant photographs from citizen science projects such as the iNaturalist database. Such crowd-sourced plant photographs typically exhibit very different perspectives and great heterogeneity in various aspects, yet the sheer volume of data could reveal great potential for application to bird’s eye views from remote sensing platforms. Here, we explore the potential of transfer learning from such a crowd-sourced data treasure to the remote sensing context. Therefore, we investigate firstly, if we can use crowd-sourced plant photographs for CNN training and subsequent mapping of plant species in high-resolution remote sensing imagery. Secondly, we test if the predictive performance can be increased by a priori selecting photographs that share a more similar perspective to the remote sensing data. We used two case studies to test our proposed approach with multiple RGB orthoimages acquired from UAV with the target plant species Fallopia japonica and Portulacaria afra respectively. Our results demonstrate that CNN models trained with heterogeneous, crowd-sourced plant photographs can indeed predict the target species in UAV orthoimages with surprising accuracy. Filtering the crowd-sourced photographs used for training by acquisition properties increased the predictive performance. This study demonstrates that citizen science data can effectively anticipate a common bottleneck for vegetation assessments and provides an example on how we can effectively harness the ever-increasing availability of crowd-sourced and big data for remote sensing applications. Numéro de notice : A2022-488 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100016 Date de publication en ligne : 23/05/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100956
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 5 (August 2022) . - n° 100016[article]UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment / Katerina Trepekli in Natural Hazards, vol 113 n° 1 (August 2022)
[article]
Titre : UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment Type de document : Article/Communication Auteurs : Katerina Trepekli, Auteur ; Thomas Balstrøm, Auteur ; Thomas Friborg, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 423 - 451 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] antenne GNSS
[Termes IGN] centrale inertielle
[Termes IGN] faisceau laser
[Termes IGN] Ghana
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] risque naturel
[Termes IGN] semis de points
[Termes IGN] Triangulated Irregular Network
[Termes IGN] zone urbaineRésumé : (auteur) In this study, we present the first findings of the potential utility of miniaturized light and detection ranging (LiDAR) scanners mounted on unmanned aerial vehicles (UAVs) for improving urban flood modelling and assessments at the local scale. This is done by generating ultra-high spatial resolution digital terrain models (DTMs) featuring buildings and urban microtopographic structures that may affect floodwater pathways (DTMbs). The accuracy and level of detail of the flooded areas, simulated by a hydrologic screening model (Arc-Malstrøm), were vastly improved when DTMbs of 0.3 m resolution representing three urban sites surveyed by a UAV-LiDAR in Accra, Ghana, were used to supplement a 10 m resolution DTM covering the region’s entire catchment area. The generation of DTMbs necessitated the effective classification of UAV-LiDAR point clouds using a morphological and a triangulated irregular network method for hilly and flat landscapes, respectively. The UAV-LiDAR data enabled the identification of archways, boundary walls and bridges that were critical when predicting precise run-off courses that could not be projected using the coarser DTM only. Variations in a stream’s geometry due to a one-year time gap between the satellite-based and UAV-LiDAR data sets were also observed. The application of the coarser DTM produced an overestimate of water flows equal to 15% for sloping terrain and up to 62.5% for flat areas when compared to the respective run-offs simulated from the DTMbs. The application of UAV-LiDAR may enhance the effectiveness of urban planning by projecting precisely the locations, extents and run-offs of flooded areas in dynamic urban settings. Numéro de notice : A2022-704 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s11069-022-05308-9 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.1007/s11069-022-05308-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101567
in Natural Hazards > vol 113 n° 1 (August 2022) . - pp 423 - 451[article]Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand / Kiatkulchai Jitt-Aer in Natural Hazards, vol 113 n° 1 (August 2022)
[article]
Titre : Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand Type de document : Article/Communication Auteurs : Kiatkulchai Jitt-Aer, Auteur ; Graham Wall, Auteur ; Dylan Jones, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 185 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse spatiale
[Termes IGN] ArcGIS
[Termes IGN] figuration de la densité
[Termes IGN] gestion de crise
[Termes IGN] interpolation spatiale
[Termes IGN] planification côtière
[Termes IGN] population
[Termes IGN] prévention des risques
[Termes IGN] Thaïlande
[Termes IGN] tsunamiRésumé : (auteur) The 2004 Indian Ocean tsunami led to improvements in Thailand’s early warning systems and evacuation procedures. However, there was no consideration of better aid delivery, which critically depends on estimates of the affected population. With the widespread use of geographical information systems (GIS), there has been renewed interest in spatial population estimation. This study has developed an application to determine the number of disaster-impacted people in a given district, by integrating GIS and population estimation algorithms, to facilitate humanitarian relief logistics. A multi-stage spatial interpolation is used for estimating the affected populations using ArcGIS software. We present a dasymetric mapping approach using a population-weighted technique coupled with remote sensing data. The results in each target area show the coordinates of each shelter location for evacuees, with the minimum and maximum numbers of people affected by the tsunami inundation. This innovative tool produces not only numerical solutions for decision makers, but also a variety of maps that improve visualisation of disaster severity across neighbourhoods. A case study in Patong, a town of Phuket, illustrates the application of this GIS-based approach. The outcomes can be used as key decision-making factors in planning and managing humanitarian relief logistics in the preparedness and response phases to improve performance with future tsunami occurrences, or with other types of flood disaster. Numéro de notice : A2022-703 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-022-05295-x Date de publication en ligne : 09/03/2022 En ligne : https://doi.org/10.1007/s11069-022-05295-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101566
in Natural Hazards > vol 113 n° 1 (August 2022) . - pp 185 - 211[article]A model development on GIS-driven data to predict temporal daily collision through integrating Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) algorithms; case study: Tehran-Qazvin freeway / Reza Sanayeia in Geocarto international, vol 37 n° 14 ([20/07/2022])PermalinkSegmentation and sampling method for complex polyline generalization based on a generative adversarial network / Jiawei Du in Geocarto international, vol 37 n° 14 ([20/07/2022])PermalinkGNSSseg, a statistical method for the segmentation of daily GNSS IWV time series / Annarosa Quarello in Remote sensing, vol 14 n° 14 (July-2 2022)PermalinkAbout tree height measurement: Theoretical and practical issues for uncertainty quantification and mapping / Samuele De petris in Forests, vol 13 n° 7 (July 2022)PermalinkCan machine learning improve small area population forecasts? A forecast combination approach / Irina Grossman in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkA comparison of three multi-criteria decision-making models in mapping flood hazard areas of Northeast Penang, Malaysia / Rofiat Bunmi Mudashiru in Natural Hazards, vol 112 n° 3 (July 2022)PermalinkDetection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks / Gensheng Hu in Geocarto international, vol 37 n° 12 ([01/07/2022])PermalinkDiscriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition / Tiantian Yan in Pattern recognition, vol 127 (July 2022)PermalinkEffects of offsets and outliers on the sea level trend at Antalya 2 tide gauge within the Eastern Mediterranean Sea / Mehmet Emin Ayhan in Marine geodesy, vol 45 n° 4 (July 2022)PermalinkEstimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkEvaluation of the GSRM2.1 and the NUVEL1-A values in Europe using SLR and VLBI based geodetic velocity fields / Mina Rahmani in Survey review, vol 54 n° 385 (July 2022)PermalinkA framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method / Yongyang Xu in Computers, Environment and Urban Systems, vol 95 (July 2022)PermalinkFusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco bay area and Southern California / Huineng Yan in Journal of geodesy, vol 96 n° 7 (July 2022)PermalinkGANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)PermalinkGeodesic geometry on graphs / Daniel Cizma in Discrete & computational geometry, vol 68 n° 1 (July 2022)PermalinkGeographic knowledge graph attribute normalization: Improving the accuracy by fusing optimal granularity clustering and co-occurrence analysis / Chuan Yin in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)PermalinkGlobal forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis / Jinpei Chen in GPS solutions, vol 26 n° 3 (July 2022)PermalinkImpact of offsets on assessing the low-frequency stochastic properties of geodetic time series / Kevin Gobron in Journal of geodesy, vol 96 n° 7 (July 2022)PermalinkImproving remote sensing classification: A deep-learning-assisted model / Tsimur Davydzenka in Computers & geosciences, vol 164 (July 2022)PermalinkInvestigating the role of image retrieval for visual localization / Martin Humenberger in International journal of computer vision, vol 130 n° 7 (July 2022)PermalinkLidar point-to-point correspondences for rigorous registration of kinematic scanning in dynamic networks / Aurélien Brun in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkA lightweight network with attention decoder for real-time semantic segmentation / Kang Wang in The Visual Computer, vol 38 n° 7 (July 2022)PermalinkMixed geographically and temporally weighted regression for spatio-temporal deformation modelling / Zhijia Yang in Survey review, vol 54 n° 385 (July 2022)PermalinkOutliers and uncertainties in GNSS ZTD estimates from double-difference processing and precise point positioning / Katarzyna Stępniak in GPS solutions, vol 26 n° 3 (July 2022)PermalinkPolyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)PermalinkA second-order attention network for glacial lake segmentation from remotely sensed imagery / Shidong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkSemantic 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)PermalinkSimulation-driven 3D forest growth forecasting based on airborne topographic LiDAR data and shading / Štefan Kohek in International journal of applied Earth observation and geoinformation, vol 111 (July 2022)PermalinkStreet-view imagery guided street furniture inventory from mobile laser scanning point clouds / Yuzhou Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)PermalinkTemporal transitions of demographic dot maps / Jeff Allen in International journal of cartography, vol 8 n° 2 (July 2022)PermalinkValidation of regional and global ionosphere maps from GNSS measurements versus IRI2016 during different magnetic activity / Ahmed Sedeek in Journal of applied geodesy, vol 16 n° 3 (July 2022)PermalinkEncoder-decoder structure with multiscale receptive field block for unsupervised depth estimation from monocular video / Songnan Chen in Remote sensing, Vol 14 n° 12 (June-2 2022)PermalinkEstimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique / Syaza Rozali in Geocarto international, vol 37 n° 11 ([15/06/2022])PermalinkRisk assessment and prediction of forest health for effective geo-environmental planning and monitoring of mining affected forest area in hilltop region / Narayan Kayet in Geocarto international, vol 37 n° 11 ([15/06/2022])Permalink3D browsing of wide-angle fisheye images under view-dependent perspective correction / Mingyi Huang in Photogrammetric record, vol 37 n° 178 (June 2022)Permalink3D modeling method for dome structure using digital geological map and DEM / Xian-Yu Liu in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkAjustement en bloc des données de stations totales et de récepteurs GNSS dans les études de déformation / Joël Van Cranenbroeck in XYZ, n° 171 (juin 2022)PermalinkAnalysis of structure from motion and airborne laser scanning features for the evaluation of forest structure / Alejandro Rodríguez-Vivancos in European Journal of Forest Research, vol 141 n° 3 (June 2022)PermalinkArtificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data / Saeideh Sahebi Vayghan in Geocarto international, vol 37 n° 10 ([01/06/2022])PermalinkAssessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])PermalinkBeyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification / Yongqiang Mao in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)PermalinkCharacteristics of disease maps of zoonoses: A scoping review and a recommendation for a reporting guideline for disease maps / Inthuja Selvaratnam in Cartographica, vol 57 n° 2 (Summer 2022)PermalinkCombination of Sentinel-1 and Sentinel-2 data for tree species classification in a Central European biosphere reserve / Michael Lechner in Remote sensing, vol 14 n° 11 (June-1 2022)PermalinkConstraint-based evaluation of map images generalized by deep learning / Azelle Courtial in Journal of Geovisualization and Spatial Analysis, vol 6 n° 1 (June 2022)PermalinkContext-aware network for semantic segmentation toward large-scale point clouds in urban environments / Chun Liu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkCoupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkDART-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)PermalinkDetecting interchanges in road networks using a graph convolutional network approach / Min Yang in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)PermalinkDetecting spatiotemporal traffic events using geosocial media data / Shishuo Xu in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkDiffusionNet: discretization agnostic learning on surfaces / Nicholas Sharp in ACM Transactions on Graphics, TOG, Vol 41 n° 3 (June 2022)PermalinkExploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference / Xiao Huang in Transactions in GIS, vol 26 n° 4 (June 2022)PermalinkExtracting the urban landscape features of the historic district from street view images based on deep learning: A case study in the Beijing Core area / Siming Yin in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkFeature-selection high-resolution network with hypersphere embedding for semantic segmentation of VHR remote sensing images / Hanwen Xu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkGIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)PermalinkGIS-based assessment of long-term traffic accidents using spatiotemporal and empirical Bayes analysis in Turkey / Saffet Erdoğan in Applied geomatics, vol 14 n° 2 (June 2022)PermalinkGraph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)PermalinkHow can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? / Katja Kuhwald in Remote sensing in ecology and conservation, vol 8 n° 3 (June 2022)PermalinkInvariant structure representation for remote sensing object detection based on graph modeling / Zicong Zhu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 6 (June 2022)PermalinkLarge-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)PermalinkLine-based deep learning method for tree branch detection from digital images / Rodrigo L. S. Silva in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)PermalinkMapping monthly population distribution and variation at 1-km resolution across China / Zhifeng Cheng in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)PermalinkMulti-objective optimization of urban environmental system design using machine learning / Peiyuan Li in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkA phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images / Jing Zeng in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)PermalinkPrecise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)PermalinkSelf-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization / Seda Şalap-Ayça in Transactions in GIS, vol 26 n° 4 (June 2022)PermalinkThe effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events / Sidgley Camargo de Andrade in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)PermalinkThe promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning / Elie Morin in Ecological indicators, vol 139 (June 2022)PermalinkPermalinkTowards the automated large-scale reconstruction of past road networks from historical maps / Johannes H. Uhl in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkTrade-offs between sustainable development goals in systems of cities / Juste Raimbault in Journal of Urban Management, vol 11 n° 2 (June 2022)PermalinkTrue orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)PermalinkUncertainty of biomass stocks in Spanish forests: a comprehensive comparison of allometric equations / Aitor Ameztegui in European Journal of Forest Research, vol 141 n° 3 (June 2022)PermalinkAnalysis of massive imports of open data in Openstreetmap database: a study case for France / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)PermalinkAnalyzing spatio-temporal pattern of the forest fire burnt area in Uttarakhand using Sentinel-2 data / Shailja Mamgain in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)PermalinkApplication oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)PermalinkAutomatic training data generation in deep learning-aided semantic segmentation of heritage buildings / Arnadi Murtiyoso in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkClassification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)PermalinkDeep learning for the detection of early signs for forest damage based on satellite imagery / Dennis Wittich in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkEffect of label noise in semantic segmentation of high resolution aerial images and height data / Arabinda Maiti in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkEfficient dike monitoring using terrestrial SFM photogrammetry / Laurent Froideval in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkK-means clustering based on omnivariance attribute for building detection from airborne lidar data / Renato César Dos santos in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkLearning from the past: crowd-driven active transfer learning for semantic segmentation of multi-temporal 3D point clouds / Michael Kölle in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkRailway lidar semantic segmentation with axially symmetrical convolutional learning / Antoine Manier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkSemantic segmentation of urban textured meshes through point sampling / Grégoire Grzeczkowicz in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkVegetation cover mapping from RGB webcam time series for land surface emissivity retrieval in high mountain areas / Benedikt Hiebl in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)PermalinkA voxel-based method for the three-dimensional modelling of heathland from lidar point clouds: first results / N. Homainejad in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)PermalinkAn algorithm to assist the robust filter for tightly coupled RTK/INS navigation system / Zun Niu in Remote sensing, vol 14 n° 10 (May-2 2022)PermalinkComparative analysis of gradient boosting algorithms for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 37 n° 9 ([15/05/2022])PermalinkA new method to detect targets in hyperspectral images based on principal component analysis / Shahram Sharifi Hashjin in Geocarto international, vol 37 n° 9 ([15/05/2022])PermalinkNovel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping / A'Kif Al-Fugara in Geocarto international, vol 37 n° 9 ([15/05/2022])PermalinkResearch 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)PermalinkSpatial-temporal variation of satellite-based gross primary production estimation in wheat-maize rotation area during 2000–2015 / Wenquan Xie in Geocarto international, vol 37 n° 9 ([15/05/2022])Permalink3D building model simplification method considering both model mesh and building structure / Jiangfeng She in Transactions in GIS, vol 26 n° 3 (May 2022)Permalink3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation / Heyang Thomas Li in The Visual Computer, vol 38 n° 5 (May 2022)PermalinkAn empirical study on the effects of temporal trends in spatial patterns on animated choropleth maps / Paweł Cybulski in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)Permalink