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Cliff change detection using siamese KPCONV deep network on 3D point clouds / Iris de Gelis in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-3-2022 (2022 edition) . - pp 649 - 656 Titre : Cliff change detection using siamese KPCONV deep network on 3D point clouds Type de document : Article/Communication Auteurs : Iris de Gelis, Auteur ; Zoé Bessin, Auteur ; Pauline Letortu, Auteur ; Marion Jaud, Auteur ; C. Delacourt, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 649 - 656 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] érosion côtière [Termes IGN] falaise [Termes IGN] semis de points [Termes IGN] surveillance géologique Mots-clés libres : KPConv = Kernel Point Convolution Résumé : (auteur) Mainly depending on their lithology, coastal cliffs are prone to changes due to erosion. This erosion could increase due to climate change leading to potential threats for coastal users, assets, or infrastructure. Thus, it is important to be able to understand and characterize cliff face changes at fine scale. Usually, monitoring is conducted thanks to distance computation and manual analysis of each cliff face over 3D point clouds to be able to study 3D dynamics of cliffs. This is time consuming and inclined to each one judgment in particular when dealing with 3D point clouds data. Indeed, 3D point clouds characteristics (sparsity, impossibility of working on a classical top view representation, volume of data, …) make their processing harder than 2D images. Last decades, an increase of performance of machine learning methods for earth observation purposes has been performed. To the best of our knowledge, deep learning has never been used for 3D change detection and categorization in coastal cliffs. Lately, Siamese KPConv brings successful results for change detection and categorization into 3D point clouds in urban area. Although the case study is different by its more random characteristics and its complex geometry, we demonstrate here that this method also allows to extract and categorize changes on coastal cliff face. Results over the study area of Petit Ailly cliffs in Varengeville-sur-Mer (France) are very promising qualitatively as well as quantitatively: erosion is retrieved with an intersection over union score of 83.86 %. Numéro de notice : A2022-444 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-3-2022-649-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-3-2022-649-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100779 [article]

Influence of determinant factors towards soil erosion using ordinary least squared regression in GIS domain / Imran Ahmad in Applied geomatics, vol 14 n° 1 (March 2022)  

Public [article]  inApplied geomatics > vol 14 n° 1 (March 2022) . - pp 57 - 63 Titre : Influence of determinant factors towards soil erosion using ordinary least squared regression in GIS domain Type de document : Article/Communication Auteurs : Imran Ahmad, Auteur ; Jahier Abbas Shaaikh, Auteur ; Assefa Fenta, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 57 - 63 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] autocorrélation spatiale [Termes IGN] érosion [Termes IGN] Ethiopie [Termes IGN] méthode des moindres carrés [Termes IGN] modèle de régression [Termes IGN] modèle RUSLE [Termes IGN] régression [Termes IGN] système d'information géographique Résumé : (auteur) An attempt has been made in this study to analyze the hotspots of soil erosion in Denbie Woreda, Ethiopia. A total of five determinant factors were multiplied (using raster calculator in GIS domain) and integrated to obtain the soil erosion hotspots of the area. These five determinant factors were used as exploratory variables to understand the spatial behavior of soil loss rates. The ordinary least squared (OLS) regression model diagnostics showed that the R-squared and adjusted R-squared values of the explanatory variables are 0.71 and 0.75 respectively. Variance inflation factor (VIF) values of the OLS range between 1.03 and 1.47 indicating the absence of multicollinearity among explanatory variables. Koenker (BP) statistic is statistically insignificant (p > 0.005) indicating that relationships modeled are consistent. The OLS regression model was subject to different tests to confirm its reliability. Spatial autocorrelation tool (Global Moran’s I) result showed that the residuals exhibit a Gaussian spatial pattern. This study proved that all the determinant factors of Revised Universal Soil Loss Equation played a significant role towards demarcation of soil loss rates. Numéro de notice : A2022-217 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s12518-021-00409-9 Date de publication en ligne : 27/11/2021 En ligne : https://doi.org/10.1007/s12518-021-00409-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100087 [article]

Monitoring coastal vulnerability by using DEMs based on UAV spatial data / Antonio Minervino Amodio in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 3 (March 2022) . - n° 155 Titre : Monitoring coastal vulnerability by using DEMs based on UAV spatial data Type de document : Article/Communication Auteurs : Antonio Minervino Amodio, Auteur ; Gianluigi Di Paola, Auteur ; Carmen Maria Rosskopf, Auteur Année de publication : 2022 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Adriatique, mer [Termes IGN] détection de changement [Termes IGN] érosion côtière [Termes IGN] géoréférencement [Termes IGN] image captée par drone [Termes IGN] Italie [Termes IGN] littoral méditerranéen [Termes IGN] modèle numérique de surface [Termes IGN] orthophotographie [Termes IGN] point d'appui [Termes IGN] structure-from-motion [Termes IGN] surveillance du littoral [Termes IGN] trait de côte [Termes IGN] vulnérabilité Résumé : (auteur) The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic changes of the beach system and related implications for coastal vulnerability assessment. UAV surveys were performed during the summers of 2019 and 2020 along a beach stretch affected by erosion, located along the central Adriatic coast. Acquired high-resolution aerial photos were used to generate large-scale DEMs as well as orthophotos of the beach using the Structure from Motion (SfM) image processing tool. Comparison of the generated 2019 and 2020 DEMs highlighted significant morphological changes and a sediment volume loss of about 780 m3 within a surface area of about 4400 m2. Based on 20 m spaced beach profiles derived from the DEMs, a coastal vulnerability assessment was performed using the CVA approach that highlighted some significant variations in the CVA index between 2019 and 2020. Results evidence that UAV surveys provide high-resolution topographic data, suitable for specific beach monitoring activities and the updating of some parameters that enter in the CVA model contributing to its correct application. Numéro de notice : A2022-185 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11030155 Date de publication en ligne : 22/02/2022 En ligne : https://doi.org/10.3390/ijgi11030155 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99895 [article]

Assessment and mapping soil water erosion using RUSLE approach and GIS tools: Case of Oued el-Hai watershed, Aurès West, Northeastern of Algeria / Aida Bensekhria in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 84 Titre : Assessment and mapping soil water erosion using RUSLE approach and GIS tools: Case of Oued el-Hai watershed, Aurès West, Northeastern of Algeria Type de document : Article/Communication Auteurs : Aida Bensekhria, Auteur ; Rabah Bouhata, Auteur Année de publication : 2022 Article en page(s) : n° 84 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Algérie [Termes IGN] Aurès, massif des [Termes IGN] bassin hydrographique [Termes IGN] carte thématique [Termes IGN] conservation des ressources naturelles [Termes IGN] érosion hydrique [Termes IGN] modèle RUSLE [Termes IGN] outil d'aide à la décision [Termes IGN] système d'information géographique Résumé : (auteur) The problem of soil water erosion is one of the primary causes of agro-pedological heritage degradation. The combined effect of natural factors and inappropriate human actions has weakened the soil, which seriously threatens the region’s fertile lands and soils, which can ultimately lead to an irreversible situation of desertification. This study focuses on analysis and mapping of the vulnerability to erosion in Oued el-Hai watershed, Algeria, based on a technical methodology that combines the universal soil loss equation (USLE) with the geographic information system (GIS) tools. The results are organized into three main classes of different rate values, from one area to another, depending on the influence of different factors that control the erosion process. The highest loss rate value is greater than 30 t·ha−1·yr−1 and covers 23.2% of the total area, mainly located in the mountainous areas with steep slopes. However, the minimum potential erosion rate value is mainly located on the plain, with an average of 10 t·ha−1·yr−1 covering 45.2% of the total area of the watershed. The estimate of potential water erosion has given alarming results. The total area of the watershed could lose a rate of 16.69 t·ha−1·yr−1 (on average) each year. The method and results described in this article are valuable for understanding the soil erosion risk and are useful for managing and planning land use that will avoid land degradation. Hence, the results of this study are considered an important document which constitutes a decision support tool in terms of the management and protection of natural resources. Numéro de notice : A2022-119 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020084 Date de publication en ligne : 24/01/2022 En ligne : https://doi.org/10.3390/ijgi11020084 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99650 [article]

Soil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. Kashiwar in Natural Hazards, vol 110 n° 2 (January 2022)  

Public [article]  inNatural Hazards > vol 110 n° 2 (January 2022) . - pp 937 - 959 Titre : Soil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS Type de document : Article/Communication Auteurs : Sumedh R. Kashiwar, Auteur ; Manik Chandra Kundu, Auteur ; Usha R. Dongarwar, Auteur Année de publication : 2022 Article en page(s) : pp 937 - 959 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte d'occupation du sol [Termes IGN] carte thématique [Termes IGN] dégradation des sols [Termes IGN] érosion [Termes IGN] érosion hydrique [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] Inde [Termes IGN] modèle numérique de surface [Termes IGN] modèle RUSLE [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] précipitation [Termes IGN] rive [Termes IGN] système d'information géographique Résumé : (auteur) The agricultural land of the whole world is deteriorating due to the loss of top fertile soil reducing agricultural productivity and groundwater availability. Mainly, natural conditions and human manipulations have made soils extremely prone to soil erosion. Therefore, information on soil erosion status is of paramount importance to the policymakers for land conservation planning in a limited time. Spatial information systems like GIS and RS are known for their efficiencies. With that prospect, the GIS-based RUSLE model is used in this study to assess the soil erosion losses from Bhandara regions of Maharashtra, India. The study area comes under Wainganga sub-river basin, a portion of the Godavari River basin. We have prepared the required five potential parameters (R*K*LS*C*P) of RUSLE model on pixel-to-pixel basis. We have prepared the R factor map from monthly rainfall data of Indian Meteorological Department (IMD) and K factor map by digital the soil series map of NBSS & LUP, Govt. of India. We have used the digital elevation model data (DEM) of Cartosat-1 for LS-factor map, Landsat 8 and Sentinel-2A satellite dataset to generate LULC and NDVI map to obtain C and P factors. The results and satellite data were validated using Google Earth Pro and field observations. The results showed significant soil erosion from the river banks and wastelands near water bodies, with the soil loss values ranging between 20 and 40 t ha−1 yr−1. The land under reserved forest was very slight erosion-prone soil with soil loss of  Numéro de notice : A2022-180 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s11069-021-04974-5 Date de publication en ligne : 16/08/2021 En ligne : https://doi.org/10.1007/s11069-021-04974-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99856 [article]

Historical shoreline analysis and field monitoring at Ennore coastal stretch along the Southeast coast of India / M. Dhananjayan in Marine geodesy, vol 45 n° 1 (January 2022)  

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GIS to identify exposed shoreline sectors to wave impacts: case of El Tarf coast / Abdeldjalil Goumrasa in Applied geomatics, vol 13 n° 4 (December 2021)  

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OBIA-based extraction of artificial terrace damages in the Loess plateau of China from UAV photogrammetry / Xuan Fang in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)  

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Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine / Yongjing Mao in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

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Evaluation of watershed soil erosion hazard using combination weight and GIS: a case study from eroded soil in Southern China / Shifa Chen in Natural Hazards, vol 109 n° 2 (November 2021)  

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Seven decades of coastal change at Barter Island, Alaska: Exploring the importance of waves and temperature on erosion of coastal permafrost bluffs / Ann E. Gibbs in Remote sensing, vol 13 n° 21 (November-1 2021)  

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Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques / Peter G. Chirico in Journal of maps, vol 17 n° 4 (October 2021)  

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GIS models for vulnerability of coastal erosion assessment in a tropical protected area / Luís Russo Vieira in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

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A hybrid data model for dynamic GIS: application to marine geomorphological dynamics / Younes Hamdani in International journal of geographical information science IJGIS, vol 35 n° 8 (August 2021)  

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DEM- and GIS-based analysis of soil erosion depth using machine learning / Kieu Anh Nguyen in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)  

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