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Structure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring / Edoardo Grottoli in Remote sensing, vol 13 n° 1 (January-1 2021)
[article]
Titre : Structure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring Type de document : Article/Communication Auteurs : Edoardo Grottoli, Auteur ; Mélanie Biausque, Auteur ; David Rogers, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 95 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] carte de profondeur
[Termes IGN] données lidar
[Termes IGN] dune
[Termes IGN] érosion côtière
[Termes IGN] filtrage de points
[Termes IGN] image captée par drone
[Termes IGN] image numérisée
[Termes IGN] modèle numérique de surface
[Termes IGN] reconstruction 3D
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] surveillance du littoralRésumé : (auteur) Recent advances in structure-from-motion (SfM) techniques have proliferated the use of unmanned aerial vehicles (UAVs) in the monitoring of coastal landform changes, particularly when applied in the reconstruction of 3D surface models from historical aerial photographs. Here, we explore a number of depth map filtering and point cloud cleaning methods using the commercial software Agisoft Metashape Pro to determine the optimal methodology to build reliable digital surface models (DSMs). Twelve different aerial photography-derived DSMs are validated and compared against light detection and ranging (LiDAR)- and UAV-derived DSMs of a vegetated coastal dune system that has undergone several decades of coastline retreat. The different studied methods showed an average vertical error (root mean square error, RMSE) of approximately 1 m, with the best method resulting in an error value of 0.93 m. In our case, the best method resulted from the removal of confidence values in the range of 0–3 from the dense point cloud (DPC), with no filter applied to the depth maps. Differences among the methods examined were associated with the reconstruction of the dune slipface. The application of the modern SfM methodology to the analysis of historical aerial (vertical) photography is a novel (and reliable) new approach that can be used to better quantify coastal dune volume changes. DSMs derived from suitable historical aerial photographs, therefore, represent dependable sources of 3D data that can be used to better analyse long-term geomorphic changes in coastal dune areas that have undergone retreat. Numéro de notice : A2021-079 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010095 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.3390/rs13010095 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96821
in Remote sensing > vol 13 n° 1 (January-1 2021) . - n° 95[article]The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January-1 2021)
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Titre : The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution Type de document : Article/Communication Auteurs : Dimitri I. Rukhovitch, Auteur ; Polina V. Koroleva, Auteur ; Danila D. Rukhovitch, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dégradation des sols
[Termes IGN] distribution spatiale
[Termes IGN] érosion
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Russie
[Termes IGN] surface cultivée
[Termes IGN] système d'information géographiqueRésumé : (auteur) Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps. Numéro de notice : A2021-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010155 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/rs13010155 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96810
in Remote sensing > vol 13 n° 1 (January-1 2021) . - n° 155[article]Displacement monitoring of upper Atbara dam based on time series InSAR / Q.Q. Wang in Survey review, vol 52 n° 375 (November 2020)
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Titre : Displacement monitoring of upper Atbara dam based on time series InSAR Type de document : Article/Communication Auteurs : Q.Q. Wang, Auteur ; Q.H. Huang, Auteur ; N. He, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 485 - 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] auscultation d'ouvrage
[Termes IGN] barrage
[Termes IGN] déformation d'édifice
[Termes IGN] érosion hydrique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] série temporelle
[Termes IGN] Soudan
[Termes IGN] surveillance d'ouvrageRésumé : (auteur) Dam is an important part of engineering structure, in the process of dam construction, the dam monitoring is crucial since water erosion and time-dependent motion may cause deformation. Traditional monitoring methods are time-consuming and labour-intensive. However, Interferometric Synthetic Aperture Radar (InSAR) can provide precise and spatially dense information on slow deformations. This research investigated the longest earth-rock-fill dam in Sudan to determine the spatial and temporal deformations Sentinel-1A descending SAR images were further used to analyse the issues mentioned above. The results suggested that the dam existed the maximum displacement with a value up to 190 mm on the dam crest. Besides, the selected sections along the riverbed of the dam were analysed and the RMSE was approximately 2 mm/year. The results were in good agreement with the in situ measurements, indicating the advancement of time series InSAR in dam deformation monitoring. Numéro de notice : A2020-686 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2019.1643529 Date de publication en ligne : 17/07/2019 En ligne : https://doi.org/10.1080/00396265.2019.1643529 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96217
in Survey review > vol 52 n° 375 (November 2020) . - pp 485 - 496[article]Soil erosion assessment using RUSLE model and its validation by FR probability model / Amiya Gayen in Geocarto international, vol 35 n° 15 ([01/11/2020])
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Titre : Soil erosion assessment using RUSLE model and its validation by FR probability model Type de document : Article/Communication Auteurs : Amiya Gayen, Auteur ; Sunil Saha, Auteur ; Hamid Reza Pourghasemi, Auteur Année de publication : 2020 Article en page(s) : pp 1750 - 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] érosion
[Termes IGN] érosion hydrique
[Termes IGN] fréquence
[Termes IGN] Inde
[Termes IGN] modèle RUSLE
[Termes IGN] modèle stochastique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] pente
[Termes IGN] surveillance géologique
[Termes IGN] utilisation du solRésumé : (auteur) The objective of the current study is to estimate the annual average soil loss through RUSLE model and furthermore assess the soil erosion risk and its distribution using frequency ratio (FR) probability algorithm. At first, soil erosion risk zones were identified using FR model by the consideration 14 soil erosion conditioning factors such as land use (LU/LC), slope, slope aspect, normalized difference vegetation index (NDVI), altitude, plan curvature, stream power index, distance from river, road, and lineament, soil types, rainfall erosivity, slope length and lineament density. Secondly, the spatial pattern of annual average soil loss rates was estimated using RUSLE model with consideration of five factors such as, rainfall erosivity (R), cover management (C), slope length (LS), soil erodability (K), and conservation practice factors (P). In order to map soil erosion susceptibility by the FR model, dataset divided randomly into parts 70/30 percent for training and validation purposes, respectively. Based on the FR value, the susceptibility map was reclassified into five different critical erosion probability zones. Among this, the severe and high erosion zones occupy 13.69% and 16.26%, respectively, of the total area, where as low and very low susceptibility zones together constitute 32.98% of the River Basin. The assessed high amount of average annual soil erosion (more than 100 t/ha/year) is occupied 9.55% of the total study area. It is conclude that high soil erosion susceptibility and yearly average soil loss were performed in this study area. Therefore, the produced soil erosion susceptibility maps and annual average soil erosion map can be very useful for primary land use planning and soil erosion hazard mitigation purpose for prioritizing areas. Numéro de notice : A2020-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581272 Date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96134
in Geocarto international > vol 35 n° 15 [01/11/2020] . - pp 1750 - 1768[article]Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) / Mirza Razi Imam Baig in Annals of GIS, vol 26 n° 4 (October 2020)
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Titre : Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) Type de document : Article/Communication Auteurs : Mirza Razi Imam Baig, Auteur ; Ishita Afreen Ahmad, Auteur ; Mohammad Tayyab, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 361 - 376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Andhra Pradesh (Inde ; état)
[Termes IGN] détection de changement
[Termes IGN] érosion côtière
[Termes IGN] géomorphologie locale
[Termes IGN] image Landsat
[Termes IGN] pondération
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) Coastline or Shoreline calculation is one of the important factors in the finding of coastal accretion and erosion and the study of coastal morphodynamic. Coastal erosion is a tentative hazard for communities especially in coastal areas as it is extremely susceptible to increasing coastal disasters. The study has been conducted along the coast of Vishakhapatnam district, Andhra Pradesh, India with the help of multi-temporal satellite images of 1991 2001, 2011 and 2018. The continuing coastal erosion and accretion rates have been calculated using the Digital Shoreline Analysis System (DSAS). Linear regression rate (LRR), End Point Rate (EPR) and Weighted Linear Regression (WLR) are used for calculating shoreline change rate. Based on calculations the district shoreline has been classified into five categories as high and low erosion, no change and high and low accretion. Out of 135 km, high erosion occupied 5.8 km of coast followed by moderate or low erosion 46.2 km. Almost 34.7 km coastal length showed little or no change. Moderate accretion is found along 30.5 km whereas high accretion trend found around 17.8 km. The outcome of shows that erosion is prevailing in Vishakhapatnam taluk, Ankapalli taluk, Yellamanchili taluk whereas most of the Bhemunipatnam coast is accreting. Natural and manmade activities and phenomena influence the coastal areas in terms of erosion and accretion. The study could be used for further planning and development and also for disaster management authority in the decision-making process in the study area. Numéro de notice : A2020-801 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1815839 Date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1080/19475683.2020.1815839 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96724
in Annals of GIS > vol 26 n° 4 (October 2020) . - pp 361 - 376[article]Atmospheric pathways and distance range analysis of castanea pollen transport in Southern Spain / Rocio López-Orozco in Forests, vol 11 n° 10 (October 2020)PermalinkBoreal peatland forests: ditch network maintenance effort and water protection in a forest rotation framework / Jenny Miettinen in Canadian Journal of Forest Research, vol 50 n° 10 (October 2020)PermalinkUse of visible and near-infrared reflectance spectroscopy models to determine soil erodibility factor (K) in an ecologically restored watershed / Qinghu Jiang in Remote sensing, vol 12 n° 18 (September-2 2020)PermalinkArctic tsunamis threaten coastal landscapes and communities – survey of Karrat Isfjord 2017 tsunami effects in Nuugaatsiaq, western Greenland / Mateusz C. Strzelecki in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)PermalinkComparative study of different models for soil erosion and sediment yield in Pairi watershed, Chhattisgarh, India / Tarun Kumar in Geocarto international, vol 35 n° 11 ([01/08/2020])PermalinkModeling soil erosion after mechanized logging operations on steep terrain in the Northern Black Forest, Germany / Julian Haas in European Journal of Forest Research, vol 139 n°4 (August 2020)PermalinkLong time-series remote sensing analysis of the periodic cycle evolution of the inlets and ebb-tidal delta of Xincun Lagoon, Hainan Island, China / Huaguo Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)PermalinkCoastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) / Yawo Konko in Advances in Remote Sensing, vol 9 n° 2 (June 2020)PermalinkData-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India / Subrata Mondal in Geocarto international, Vol 35 n° 8 ([01/06/2020])PermalinkHydrogeology of the western Po plain (Piedmont, NW Italy) / Domenico Antonio De Luca in Journal of maps, vol 16 n° 2 ([01/06/2020])Permalink