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Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 138 - 151 Titre : Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data Type de document : Article/Communication Auteurs : Sugandh Chauhan, Auteur ; Roshanak Darvishzadeh, Auteur ; Mirco Boschetti, Auteur ; Andrew Nelson, Auteur Année de publication : 2020 Article en page(s) : pp 138 - 151 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] agrégation [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] courbure [Termes descripteurs IGN] gestion prévisionnelle [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] Italie [Termes descripteurs IGN] matrice de confusion [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] surveillance agricole Résumé : (auteur) Crop lodging - the bending of crop stems from their upright position or the failure of root-soil anchorage systems - is a major yield-reducing factor in wheat and causes deterioration of grain quality. The severity of lodging can be measured by a lodging score (LS)- an index calculated from the crop angle of inclination (CAI) and crop lodged area (LA). LS is difficult and time consuming to measure manually meaning that information on lodging occurrence and severity is limited and sparse. Remote sensing-based estimates of LS can provide more timely, synoptic and reliable information on crop lodging across vast areas. This information could improve estimates of crop yield losses, inform insurance loss adjusters and influence management decisions for subsequent seasons. This research - conducted in the 600 ha wheat sown area in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy - evaluated the performance of RADARSAT-2 and Sentinel-1 data to discriminate and classify lodging severity based on field measured LS. We measured temporal crop status characteristics related to lodging (e.g. lodged area, CAI, crop height) and collected relevant meteorological data (wind speed and rainfall) throughout May-June 2018. These field measurements were used to distinguish healthy (He) wheat from lodged wheat with different degrees of lodging severity (moderate, severe and very severe). We acquired multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 and Sentinel-1 (40°) images and derived multiple metrics from them to discriminate and classify lodging severity. As a part of our data exploration, we performed a correlation analysis between the image-based metrics and LS. Next, a multi-temporal discriminant analysis approach, including a partial least squares (PLS-DA) method, was developed to classify lodging severities. We used the area under the curve-receiver operating characteristics (AUC-ROC) and confusion matrices to evaluate the accuracy of the PLS-DA classification models. Results show that (1) volume scattering components were highly correlated with LS at low incidence angles while double and surface scattering was more prevalent at high incidence angles; (2) lodging severity was best classified using low incidence angle R-FQ8 data (overall accuracy 72%) and (3) the Sentinel-1 data-based classification model was able to correctly identify 60% of the lodging severity cases in the study site. The results from this first study on classifying lodging severity using satellite-based SAR platforms suggests that SAR-based metrics can capture a substantial proportion of the observed variation in lodging severity, which is important in the context of operational crop lodging assessment in particular, and sustainable agriculture in general. Numéro de notice : A2020-276 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.012 date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.012 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95087 [article]

Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data / Shivangi Srivastava in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1117 - 1136 Titre : Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data Type de document : Article/Communication Auteurs : Shivangi Srivastava, Auteur ; John E. Vargas-Muñoz, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2020 Article en page(s) : pp 1117 - 1136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données urbaines [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données localisées libres [Termes descripteurs IGN] Ile-de-France [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] image terrestre [Termes descripteurs IGN] information géographique [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] réseau social Résumé : (auteur) We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization. Numéro de notice : A2020-269 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542698 date de publication en ligne : 18/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542698 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95041 [article]

Guided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Inconnu (June 2020)    

Public [article]  contenu dans HAL Hyper articles en ligne / Centre pour la Communication Scientifique Directe CCSD (2000)   inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > Inconnu (June 2020) . - 9 p. Titre : Guided feature matching for multi-epoch historical image blocks pose estimation Type de document : Article/Communication Auteurs : Lulin Zhang, Auteur ; Ewelina Rupnik , Auteur ; Marc Pierrot-Deseilligny , Auteur Année de publication : 2020 Article en page(s) : 9 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] Pézenas [Termes descripteurs IGN] SIFT (algorithme) [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Historical aerial imagery plays an important role in providing unique information about evolution of our landscapes. It possesses many positive qualities such as high spatial resolution, stereoscopic configuration and short time interval. Self-calibration reamains a main bottleneck for achieving the intrinsic value of historical imagery, as it involves certain underdeveloped research points such as detecting inter-epoch tie-points. In this research, we present a novel algorithm to detecting inter-epoch tie-points in historical images which do not rely on any auxiliary data. Using SIFT-detected keypoints we perform matching across epochs by interchangeably estimating and imposing that points follow two mathematical models: at first a 2D spatial similarity, then a 3D spatial similarity. We import GCPs to quantitatively evaluate our results with Digital Elevation Models (DEM) of differences (abbreviated as DoD) in absolute reference frame, and compare the results of our method with other 2 methods that use either the traditional SIFT or few virtual GCPs. The experiments show that far more correct inter-epoch tie-points can be extracted with our guided technique. Qualitative and quantitative results are reported. Numéro de notice : C2020-003 Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans date de publication en ligne : 05/05/2020 En ligne : https://hal.archives-ouvertes.fr/hal-02563454/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95081 [article]

Documents numériques

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Modelling housing rents using spatial autoregressive geographically weighted regression: a case study in cracow, Poland / Mateusz Tomal in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 20 p. Titre : Modelling housing rents using spatial autoregressive geographically weighted regression: a case study in cracow, Poland Type de document : Article/Communication Auteurs : Mateusz Tomal, Auteur Année de publication : 2020 Article en page(s) : 20 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] autorégression [Termes descripteurs IGN] bien immobilier [Termes descripteurs IGN] Cracovie (Pologne) [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] économétrie [Termes descripteurs IGN] évaluation foncière [Termes descripteurs IGN] gestion foncière [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] régression géographiquement pondérée Résumé : (auteur) The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space. Numéro de notice : A2020-314 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060346 date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.3390/ijgi9060346 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95169 [article]

Under-canopy UAV laser scanning for accurate forest field measurements / Eric Hyyppä in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 41 - 60 Titre : Under-canopy UAV laser scanning for accurate forest field measurements Type de document : Article/Communication Auteurs : Eric Hyyppä, Auteur ; Juha Hyyppä, Auteur ; Teemu Hakala, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 41 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] balayage laser [Termes descripteurs IGN] canopée [Termes descripteurs IGN] densité du bois [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] hauteur à la base du houppier [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] inventaire forestier local [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] Simultaneous Localisation And Mapping [Termes descripteurs IGN] télédétection aérienne [Termes descripteurs IGN] télémètre laser terrestre [Termes descripteurs IGN] télémétrie laser aéroporté [Termes descripteurs IGN] tronc Résumé : (auteur) Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m 32 m test sites that were characterized as sparse ( = 42 trees) and obstructed ( = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories. Numéro de notice : A2020-240 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.021 date de publication en ligne : 11/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94994 [article]

Validation of Sentinel-3A SRAL coastal sea level data at high posting rate: 80 Hz / Ana Aldarias in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

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Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland / Izabela Karsznia in Geocarto international, vol 35 n° 7 ([15/05/2020])  

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Assessment of the accuracy of DTM river bed model using classical surveying measurement and LiDAR: a case study in Poland / Pawel Kotlarz in Survey review, vol 52 n° 372 (May 2020)  

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City information modeling, Rennes s'offre un jumeau numérique / Marielle Mayo in Géomètre, n° 2180 (mai 2020)

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Intertidal topography mapping using the waterline method from Sentinel-1 & -2 images: The examples of Arcachon and Veys Bays in France / Edward Salameh in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)  

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Deformation detection through the realization of reference frames / Nestoras Papadopoulos in Journal of applied geodesy, vol 14 n° 2 (April 2020)  

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Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis / T. Poblete in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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Multitemporal analysis of gully erosion in olive groves by means of digital elevation models obtained with aerial photogrammetric and LIDAR data / Tomás Fernández in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

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Street-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Can mixed pine forests conserve understory richness by improving the establishment of understory species typical of native oak forests? / Daphne Lopez-Marcos in Annals of Forest Science [en ligne], Vol 77 n° 1 (March 2020)  

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