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Automated detection of individual Juniper tree location and forest cover changes using Google Earth Engine / Sudeera Wickramarathna in Annals of forest research, vol 64 n° 1 (2021)
[article]
Titre : Automated detection of individual Juniper tree location and forest cover changes using Google Earth Engine Type de document : Article/Communication Auteurs : Sudeera Wickramarathna, Auteur ; Jamon Van Den Hoek, Auteur ; Bogdan Mihai Strimbu, Auteur Année de publication : 2021 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert forestier
[Termes IGN] croissance des arbres
[Termes IGN] détection d'arbres
[Termes IGN] détection de changement
[Termes IGN] extraction de la végétation
[Termes IGN] Google Earth Engine
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] juniperus (genre)
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Normalized Difference Water Index
[Termes IGN] Oregon (Etats-Unis)
[Termes IGN] réflectanceRésumé : (auteur) Tree detection is the first step in the appraisal of a forest, especially when the focus is monitoring the growth of tree canopy. The acquisition of annual very high-resolution aerial images by the National Agriculture Imagery Program (NAIP) and their accessibility through Google Earth Engine (GEE) supports the delineation of tree canopies and change over time in a cost and time-effective manner. The objectives of this study are to develop an automated method to detect the crowns of individual western Juniper (Juniperus occidentalis) trees and to assess the change of forest cover from multispectral 1-meter resolution NAIP images collected from 2009 to 2016, in Oregon, USA. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Vegetation Index (RVI), were calculated from the NAIP images, in addition to the red-green-blue-near infrared bands. To identify the most suitable approach for individual tree crown identification, we created two training datasets: one considering yearly images separately and one merging all images, irrespective of the year. We segmented individual tree crowns using a random forest algorithm implemented in GEE and seven rasters, namely the reflectance of four spectral bands as recorded by the NAIP images (i.e., the red-green-blue-near infrared) and three calculated indices (i.e., NDVI, NDWI, and RVI). We compared the estimated location of the trees, computed as the centroid of the crown, with the visually identified treetops, which were considered as validation locations. We found that tree location errors were smaller when years were analyzed individually than by merging the years. Measurements of completeness (74%), correctness (94%), and mean accuracy detection (82 %) show promising performance of the random forest algorithm in crown delineation, considering that only four original input bands were used for crown segmentation. The change in the calculated crown area for western juniper follows a sinusoidal curve, with a decrease from 2011 to 2012 and an increase from 2012 to 2014. The proposed approach has the potential to estimate individual tree locations and forest cover area dynamics at broad spatial scales using regularly collected airborne imagery with easy-to-implement methods. Numéro de notice : A2021-779 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15287/afr.2020.2145 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.15287/afr.2020.2145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98846
in Annals of forest research > vol 64 n° 1 (2021) . - pp 61 - 72[article]Beach morphology and its dynamism from remote sensing for coastal management support / Carlos Cabezas Rabadán (2021)
Titre : Beach morphology and its dynamism from remote sensing for coastal management support Type de document : Thèse/HDR Auteurs : Carlos Cabezas Rabadán, Auteur ; Josep E. Pardo Pascual, Directeur de thèse ; Miguel Rodilla Alamá, Directeur de thèse Editeur : Valencia : Universitat politécnica de Valencia Année de publication : 2021 Importance : 188 p. Format : 21 x 30 cm Note générale : bibliographie
Thesis dissertation submitted in fulfillment of the requirements for the Degree of Doctor of Philosophy at Universitat Politècnica de ValènciaLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] détection de changement
[Termes IGN] érosion côtière
[Termes IGN] géomorphologie
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] plage
[Termes IGN] sédiment
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) Beaches are coastal spaces that perform numerous environmental functions. They provide important benefits to society and coastal communities, including the ecological function, the provision of protection for coastal territories, and constitute a basic resource for the tourism industry. Due to climate change and human actions that alter the natural dynamism of the coast, beaches are experiencing increasingly harmful erosive processes that affect their physical integrity and the maintenance of their ecological functions. Beach management is often not adapted to the particularities of the different coastal segments. Decision-making is not based on sufficient information about characteristics, dynamism, and current state of beaches, resulting in short or ineffective solutions. Geomorphological characteristics are essential in the development of beach functions as they condition their physical dimensions and their behavior in response to the action of the sea. Therefore, their detailed and updated characterization is necessary to carry out efficient actions, allowing a more ecosystemic and sustainable coastal management. Remote sensing techniques have a great capacity for acquiring data from the land surface. In particular, Sentinel-2 and Landsat (5, 7, and 8) satellites freely provide medium resolution images with global coverage and high-revisit frequency. The algorithms for extracting the water/land interface recently developed by the Geo-Environmental Cartography and Remote Sensing Group (CGAT – UPV) allow defining the position of the shoreline on these images, constituting potentially useful data to describe beach morphology and dynamics. Universalizing their application requires testing and validation at different coastal types. For this purpose, the extraction process has been adapted for exploitation in tidal environments, and the resulting shorelines have been assessed under different oceanographic conditions offering an accuracy close to 5 m RMSE (Root-Mean-Square Error). From these shorelines, and taking into account the existing information needs for management, it is proposed to derive indicators to characterize the geomorphology of the beaches and to monitor their changes. To this end, the proposed methodologies ensure the efficient management of large volumes of shorelines, being able to characterize the beaches along broad coastal segments and periods. Thus, beach width and sediment grain size are derived as objective and easily understandable indicators of the beach geomorphology. Spatial-temporal modeling of the state and changes of shoreline position and beach width makes it possible to monitor the response to storms and anthropogenic actions, allowing to analyze changes that occur every few days or over decades. The large spatial coverage together with the integration with other cartographic databases allows characterizing the influence of beach geomorphology in the performance of its functions, offering a holistic view of the coast from a regional scale. The methodologies developed in this thesis and the indicators derived from remote sensing provide support and criteria for prioritizing the actions of managers. This contributes to fill the gap between the availability of techniques to obtain remote information and its application in the coastal decision-making process. Note de contenu : 1- General introduction
2- Assessing user’s expectations and perceptions on different beach types and the need for diverse management frameworks
3- Satellite-derived shorelines at an exposed mesotidal beach
4- Characterizing beach changes using satellite-derived shorelines
5- Detecting problematic beach widths for the recreational function from subpixel shoreline
6- Shoreline variability from Sentinel-2: an approach for estimating beach sediment size?
7- Conclusions, management implications and future perspectivesNuméro de notice : 28599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics : Valencia, Spain : 2021 DOI : 10.4995/Thesis/10251/165076 En ligne : https://doi.org/10.4995/Thesis/10251/165076 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99405 Change detection of land use and land cover, using landsat-8 and sentinel-2A images / Mohammed Abdulmohsen Alhedyan (2021)
Titre : Change detection of land use and land cover, using landsat-8 and sentinel-2A images Type de document : Thèse/HDR Auteurs : Mohammed Abdulmohsen Alhedyan, Auteur Editeur : Leicester [Royaume-Uni] : University of Leicester Année de publication : 2021 Importance : 228 p. Format : 21 x 30 cm Note générale : bibliographie
Thesis submitted for the degree of PhD at the University of LeicesterLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse vectorielle
[Termes IGN] Arabie Saoudite
[Termes IGN] Corine (base de données)
[Termes IGN] détection de changement
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] Royaume-Uni
[Termes IGN] utilisation du solRésumé : (auteur) The main theme of this research is the development of a new hybrid method for change detection of land use and land cover (LULC). LULC change detection is one of most widely used applications of remote sensing. This study used data from two different optical sensors, Landsat-8 images and Sentinel-2A images. Given the newly developed capabilities of these remote sensing satellites, it was necessary to devise appropriate techniques to realise the benefits that they offer. Therefore, three effective change detection methods have been tested, comprehensively analysed, and used to inform the design and development of a new hybrid method of change detection. The studied change detection methods were change vector analysis (CVA), multi-index integrated change analysis (MIICA), and the comprehensive change detection method (CCDM). Case studies were conducted in two regions, Bristol (United Kingdom) and Hail (Saudi Arabia), to provide sufficient variety of inputs to enable the response of more LULC varieties to be recorded. Finally, the Coordination of Information on the Environment (Corine) land cover scheme was used to identify land cover types and LULC changes. In the study area of Bristol, the new hybrid change detection method achieved an overall accuracy of 90% and 0.81 kappa, while the results for the study area of Hail were 74% overall accuracy and 0.40 kappa. The change detection results obtained by the new hybrid method constitute a significant improvement over the implementation of the existing CVA, MIICA and CCDM methods at the two study areas while using Landsat-8 and Sentinel-2A images. Note de contenu : 1- Introduction
2- Literature review
3- Classification system, study areas, data sources and data preparation process
4- Evaluation of existing change detection
5- The hybrid change detection method
6- Discussion
7- ConclusionNuméro de notice : 28466 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Leicester : Geography, Geology, and Environment : 2021 DOI : 10.25392/leicester.data.16988440.v1 En ligne : https://doi.org/10.25392/leicester.data.16988440.v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99094 Deep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)
Titre : Deep learning for wildfire progression monitoring using SAR and optical satellite image time series Type de document : Thèse/HDR Auteurs : Puzhao Zhang, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 2021 Importance : 100 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-91-7873-935-6 Note générale : bibliographie
Doctoral Thesis in GeoinformaticsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alberta (Canada)
[Termes IGN] apprentissage profond
[Termes IGN] bande C
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] détection de changement
[Termes IGN] gestion des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie de forêt
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] Sydney (Nouvelle-Galles du Sud)
[Termes IGN] zone sinistréeRésumé : (auteur) Wildfires have coexisted with human societies for more than 350 million years, always playing an important role in affecting the Earth's surface and climate. Across the globe, wildfires are becoming larger, more frequent, and longer-duration, and tend to be more destructive both in lives lost and economic costs, because of climate change and human activities. To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even real-time. Satellite remote sensing enables cost-effective, accurate, and timely monitoring on the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8 and Sentinel-1/2 data opens the new era for global land surface monitoring, providing an opportunity to analyze wildfire impacts around the globe. The advances in both cloud computing and deep learning empower the automatic interpretation of spatio-temporal remote sensing big data on a large scale. The overall objective of this thesis is to investigate the potential of modern medium resolution earth observation data, especially Sentinel-1 C-Band synthetic aperture radar (SAR) data, in wildfire monitoring and develop operational and effective approaches for real-world applications. This thesis systematically analyzes the physical basis of earth observation data for wildfire applications, and critically reviews the available wildfire burned area mapping methods in terms of satellite data, such as SAR, optical, and SAR-Optical fusion. Taking into account its great power in learning useful representations, deep learning is adopted as the main tool to extract wildfire-induced changes from SAR and optical image time series. On a regional scale, this thesis has conducted the following four fundamental studies that may have the potential to further pave the way for achieving larger scale or even global wildfire monitoring applications. To avoid manual selection of temporal indices and to highlight wildfire-induced changes in burned areas, we proposed an implicit radar convolutional burn index (RCBI), with which we assessed the roles of Sentinel-1 C-Band SAR intensity and phase in SAR-based burned area mapping. The experimental results show that RCBI is more effective than the conventional log-ratio differencing approach in detecting burned areas. Though VV intensity itself may perform poorly, the accuracy can be significantly improved when phase information is integrated using Interferometric SAR (InSAR). On the other hand, VV intensity also shows the potential to improve VH intensity-based detection results with RCBI. By exploiting VH and VV intensity together, the proposed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the 2017 Thomas Fire and the 2018 Carr Fire. For the scenario of near real-time application, we investigated and demonstrated the potential Sentinel-1 SAR time series for wildfire progression monitoring with Convolutional Neural Networks (CNN). In this study, the available pre-fire SAR time series were exploited to compute temporal average and standard deviation for characterizing SAR backscatter behaviors over time and highlighting the changes with kMap. Trained with binarized kMap time series in a progression-wise manner, CNN showed good capability in detecting wildfire burned areas and capturing temporal progressions as demonstrated on three large and impactful wildfires with various topographic conditions. Compared to the pseudo masks (binarized kMap), CNN-based framework brought an 0.18 improvement in F1 score on the 2018 Camp Fire, and 0.23 on the 2019 Chuckegg Creek Fire. The experimental results demonstrated that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals. For continuous wildfire progression mapping, we proposed a novel framework of learning U-Net without forgetting in a near real-time manner. By imposing a temporal consistency restriction on the network response, Learning without Forgetting (LwF) allows the U-Net to learn new capabilities for better handling with newly incoming data, and simultaneously keep its existing capabilities learned before. Unlike the continuous joint training (CJT) with all available historical data, LwF makes U-Net learning not dependent on the historical training data any more. To improve the quality of SAR-based pseudo progression masks, we accumulated the burned areas detected by optical data acquired prior to SAR observations. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019-2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also found that the SAR cross-polarization ratio (VH/VV) can be very useful in highlighting burned areas when VH and VV have diverse temporal change behaviors. SAR-based change detection often suffers from the variability of the surrounding background noise, we proposed a Total Variation (TV)-regularized U-Net model to relieve the influence of SAR-based noisy masks. Considering the small size of labeled wildfire data, transfer learning was adopted to fine-tune U-Net from pre-trained weights based on the past wildfire data. We quantified the effects of TV regularization on increasing the connectivity of SAR-based areas, and found that TV-regularized U-Net can significantly increase the burned area mapping accuracy, bringing an improvement of 0.0338 in F1 score and 0.0386 in IoU score on the validation set. With TV regularization, U-Net trained with noisy SAR masks achieved the highest F1 (0.6904) and IoU (0.5295), while U-Net trained with optical reference mask achieved the highest F1 (0.7529) and IoU (0.6054) score without TV regularization. When applied on wildfire progression mapping, TV-regularized U-Net also worked significantly better than vanilla U-Net with the supervision of noisy SAR-based masks, visually comparable to optical mask-based results. On the regional scale, we demonstrated the effectiveness of deep learning on SAR-based and SAR-optical fusion based wildfire progression mapping. To scale up deep learning models and make them globally applicable, large-scale globally distributed data is needed. Considering the scarcity of labelled data in the field of remote sensing, weakly/self-supervised learning will be our main research directions to go in the near future. Note de contenu : 1- Introduction
2- Literature review
3- Study areas and data
4- Metodology
5- Results and discussionNuméro de notice : 28309 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics : RTK Stockholm : 2021 DOI : sans En ligne : http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1557429 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98130 Détection de changement d’occupation du sol à l’aide de données Sentinel en contexte tropical / Lucas Martelet (2021)
Titre : Détection de changement d’occupation du sol à l’aide de données Sentinel en contexte tropical Type de document : Mémoire Auteurs : Lucas Martelet, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2021 Importance : 57 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de fin d'étude, cycle des Ingénieurs diplômés de l’ENSG 3ème année, Information Géographique, Analyse Spatiale et TélédétectionLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] changement d'occupation du sol
[Termes IGN] dégradation de la flore
[Termes IGN] détection de changement
[Termes IGN] forêt tropicale
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] milieu tropicalIndex. décimale : IGAST Mémoires du Master Information Géographique, Analyse Spatiale et Télédétection Résumé : (Auteur) Avec l’accessibilité facilité de données satellites optique et radar et l’augmentation du dynamisme de changement d’occupation du sol d’origine entropique, les méthodes de détection automatique de changement d’occupation du sol sont de plus en plus étudiées. Ce rapport présente un état de l’art de la diversité des méthodes existantes pour ce type de détection appliquée à la dégradation forestière en milieu tropicale. Trois des méthodes présentées sont ensuite adaptées pour tenir compte des classes d’occupation recherchées et des données disponibles et utilisées sur une zone d’exploitation forestière en Amazonie. Note de contenu :
Introduction
1. SITE D’ETUDES ET DONNEES UTILISEES
1.1 Site d’étude
1.2 Données
2. TOUR D’HORIZON DES METHODES EXISTANTES
2.1 Méthodes de détection par comparaison de produit finaux
2.2 Méthodes de détection par analyse multi-temporelle
3. TRAITEMENTS ET METHODES
3.1 Traitements
3.2 Méthodes de détection des changements
4. RESULTATS
4.1 Données S1
4.2 Segmentation SLIC
4.3 Détection par classification de changement
4.4 Détection des par analyse de vecteur de changement
4.5 Détection avec la méthode LF
4.6 Approche bi-échelle
4.7 Recommandations
ConclusionNuméro de notice : 26688 Affiliation des auteurs : IGN (2020- ) Thématique : FORET/IMAGERIE Nature : Mémoire de fin d'études IT Organisme de stage : Office National des Forêts International ONFI Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99000 Documents numériques
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Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkA novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkAn integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis / Lei Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkUnsupervised change detection between SAR images based on hypergraphs / Jun Wang in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkGeomorphic Change Detection Using Cost-Effective Structure-from-Motion Photogrammetry: Evaluation of Direct Georeferencing from Consumer-Grade UAS at Orewa Beach (New Zealand) / Stéphane Bertin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 5 (May 2020)PermalinkConterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database / Collin Homer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkDeformation detection through the realization of reference frames / Nestoras Papadopoulos in Journal of applied geodesy, vol 14 n° 2 (April 2020)PermalinkTechniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network / Adil Alim in Geoinformatica, vol 24 n° 2 (April 2020)PermalinkUse of automated change detection and VGI sources for identifying and validating urban land use change / Ana-Maria Olteanu-Raimond in Remote sensing, vol 12 n° 7 (April 2020)PermalinkAssessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 / Anthony Campbell in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)PermalinkAssessment of the Baspa basin glaciers mass budget using different remote sensing methods and modeling techniques / Vinay Kumar Gaddam in Geocarto international, vol 35 n° 3 ([01/03/2020])PermalinkReducing shadow effects on the co-registration of aerial image pairs / Matthew Plummer in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)PermalinkSpectral–spatial–temporal MAP-based sub-pixel mapping for land-cover change detection / Da He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkMulti-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkA novel fire index-based burned area change detection approach using Landsat-8 OLI data / Sicong Liu in European journal of remote sensing, vol 53 n° 1 (2020)PermalinkSpatial visualization of quantitative landscape changes in an industrial region between 1827 and 1883. Case study Katowice, southern Poland / Paweł Cybulski in Journal of maps, vol 16 n° 1 ([02/01/2020])PermalinkConvolutional neural networks for change analysis in earth observation images with noisy labels and domain shifts / Rodrigo Caye Daudt (2020)PermalinkPermalinkPermalinkPermalinkRecherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois / Margarita Khokhlova (2020)PermalinkStreambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions / Benjamin U. Meinen in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)PermalinkUnsupervised satellite image time series analysis using deep learning techniques / Ekaterina Kalinicheva (2020)PermalinkAn implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkNovel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images / Zhi Yong Lv in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkRetours d'une campagne in-situ de VGI pour la mise à jour de données d'occupation du sol / Laurence Jolivet in Cartes & Géomatique, n° 241-242 (décembre 2019)PermalinkResidences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkConsidering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use / Alexis Comber in Transactions in GIS, Vol 23 n° 5 (October 2019)PermalinkUn été brûlant sous l’oeil des satellites / Laurent Polidori in Géomètre, n° 2173 (octobre 2019)PermalinkSaliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)PermalinkChange detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods / Jiří Šandera in Geodetski vestnik, vol 63 n° 3 (September - November 2019)PermalinkA factor model approach for the joint segmentation with between‐series correlation / Xavier Collilieux in Scandinavian Journal of Statistics, vol 46 n° 3 (September 2019)PermalinkLand-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images / Temulun Tangud in Geocarto international, vol 34 n° 11 ([15/08/2019])PermalinkObservation et suivi de déformations de surface d'origine anthropique par interférométrie radar satellitaire / Daniel Raucoules in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkMulti-temporal image change mining based on evidential conflict reasoning / Fatma Haouas in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkLearning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery / Lichao Mou in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkMonitoring suspended particle matter using GOCI satellite data after the Tohoku (Japan) tsunami in 2011 / Audrey Minghelli in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 12 n° 2 (February 2019)PermalinkNear real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)PermalinkSynergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico / Fabiola D. Yepez-Rincon in Geocarto international, vol 34 n° 2 ([01/02/2019])PermalinkTanDEM-X digital surface models in boreal forest above-ground biomass change detection / Kirsi Karila in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkArchival aerial photogrammetric surveys, a data source to study land use/cover evolution over the last century : opportunities and issues / Arnaud Le Bris (2019)PermalinkPermalinkPermalinkPermalinkJoint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)PermalinkMultitemporal SAR images denoising and change detection : applications to Sentinel-1 data / Weiying Zhao (2019)PermalinkA spatiotemporal calculus for reasoning about land-use trajectories / Adeline Marinho Maciel in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)PermalinkSeparating the influence of vegetation changes in polarimetric differential SAR interferometry / Virginia Brancato in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkChange detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)PermalinkHow to calibrate historical aerial photographs : a change analysis of naturally dynamic boreal forest landscapes / Niko Kulha in Forests, vol 9 n° 10 (October 2018)PermalinkTowards a polyalgorithm for land use change detection / Rishu Saxena in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkAn experimental framework for integrating citizen and community science into land cover, land use, and land change detection processes in a national mapping agency / Ana-Maria Olteanu-Raimond in Land, vol 7 n° 3 (September 2018)PermalinkAn improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data / Li Zhuo in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkContextual classification using photometry and elevation data for damage detection after an earthquake event / Ewelina Rupnik in European journal of remote sensing, vol 51 n° 1 (2018)PermalinkSensitivity analysis of pansharpening in hyperspectral change detection / Seyd Teymoor Seydi in Applied geomatics, vol 10 n° 1 (March 2018)PermalinkDétection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018)PermalinkFrom Google Maps to a fine-grained catalog of street trees / Steve Branson in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)PermalinkModélisation spatio-temporelle multi-niveau à base d'ontologies pour le suivi de la dynamique en imagerie satellitaire / Fethi Ghazouani (2018)PermalinkUse of satellite image classifications to update and enhance a land cover database / Mohamed Touiti (2018)PermalinkDiscriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network / Wei Zhao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkThorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping / Wojciech Drzewiecki in Geodesy and cartography, vol 66 n° 2 (December 2017)PermalinkOccupancy modelling for moving object detection from Lidar point clouds: A comparative study / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W4 (September 2017)PermalinkForest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkChange detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications / Zhe Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkReducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkFusion of Landsat 8 OLI and sentinel-2 MSI data / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkChange detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)PermalinkChange detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)PermalinkMonitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkA review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures / Wallace Mukupa in Survey review, vol 49 n° 353 (June 2017)PermalinkA time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter / Jeffrey D. Ouellette in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkA simple but effective landslide detection method based on image saliency / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 5 (May 2017)PermalinkTrace coherence : a new operator for polarimetric and interferometric SAR images / Armando Marino in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkUnsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)Permalink3D change detection – Approaches and applications / Rongjun Qin in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)PermalinkCartographie de la dynamique de terroirs villageois à l’aide d’un drone dans les aires protégées de la République démocratique du Congo / Jean Semeki Ngabinzeke in Bois et forêts des tropiques, n° 330 (4e trimestre 2016)Permalink