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Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia / Lifan Ji in European journal of remote sensing, vol 56 n° 1 (2023)  

Public [article]  inEuropean journal of remote sensing > vol 56 n° 1 (2023) . - pp 1 - 16 Titre : Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia Type de document : Article/Communication Auteurs : Lifan Ji, Auteur ; Yihao Shao, Auteur ; Jianjun Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] changement climatique [Termes IGN] Egypte [Termes IGN] gestion de l'eau [Termes IGN] humidité du sol [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] réseau neuronal artificiel [Termes IGN] stress hydrique [Termes IGN] Tunisie [Termes IGN] zone semi-aride Résumé : (auteur) This study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data. Numéro de notice : A2023-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2022.2157335 Date de publication en ligne : 06/01/2023 En ligne : https://doi.org/10.1080/22797254.2022.2157335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102430 [article]

A machine learning method for Arctic lakes detection in the permafrost areas of Siberia / Piotr Janiec in European journal of remote sensing, vol 56 n° 1 (2023)  

Public [article]  inEuropean journal of remote sensing > vol 56 n° 1 (2023) . - n° 2163923 Titre : A machine learning method for Arctic lakes detection in the permafrost areas of Siberia Type de document : Article/Communication Auteurs : Piotr Janiec, Auteur ; Jakub Nowosad, Auteur ; Sbigniew Zwoliński, Auteur Année de publication : 2023 Article en page(s) : n° 2163923 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] Arctique [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Landsat-8 [Termes IGN] lac glaciaire [Termes IGN] MERIT [Termes IGN] modèle numérique de surface [Termes IGN] pergélisol [Termes IGN] Short Waves InfraRed [Termes IGN] Sibérie Résumé : (auteur) Thermokarst lakes are the main components of the vast Arctic and subarctic landscapes. These lakes can serve as geoindicators of permafrost degradation; therefore, proper lake distribution assessment methods are necessary. In this study, we compared four machine learning methods to improve existing lake detection systems. The northern part of Yakutia was selected as the study area owing to its complex environment. We used data from Landsat 8 and spectral indices to take into account the spectral characteristics of the lakes, and MERIT DEM data to take into account the topography. The lowest accuracy was found for the classification and regression trees (CART) method (overall accuracy = 81%). On the other hand, the random forests (RF) classification provided the best results (overall accuracy = 92%), and only this classification coped well in all problematic areas, such as shaded and humid areas, near steep slopes, burn scars, and rivers. The altitude and bands SWIR1 (Short wave infrared 1), SWIR2 (Short wave infrared 2), and Green were the most important. Spectral indices did not have significant impact on the classification results in the specific conditions of the thermokarst lakes environment. 17,700 lakes were identified with the total area of 271.43 km2. Numéro de notice : A2023-218 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2022.2163923 Date de publication en ligne : 19/01/2023 En ligne : https://doi.org/10.1080/22797254.2022.2163923 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103156 [article]

UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) / Martin Štroner in European journal of remote sensing, vol 56 n° 1 (2023)  

Public [article]  inEuropean journal of remote sensing > vol 56 n° 1 (2023) . - n° 2179942 Titre : UAV DTM acquisition in a forested area – comparison of low-cost photogrammetry (DJI Zenmuse P1) and LiDAR solutions (DJI Zenmuse L1) Type de document : Article/Communication Auteurs : Martin Štroner, Auteur ; Rudolf Urban, Auteur ; Thomas Křemen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2179942 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] densité de la végétation [Termes IGN] données lidar [Termes IGN] image captée par drone [Termes IGN] modèle numérique de terrain [Termes IGN] rugosité du sol [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) In this paper, we evaluated the results in terms of accuracy and coverage of the LiDAR-UAV system DJI Zenmuse L1 and Digital Aerial Photogrammetric system (DAP – UAV) DJI Zenmuse P1 in a forested area under leaf-off conditions on three sites with varying terrain ruggedness/tree type combinations. Detailed reference clouds were obtained using terrestrial scanning by Leica P40. Our results show that branches pose no problem to the accuracy of LiDAR-UAV and DAP-UAV derived terrain clouds. Elevation accuracies for photogrammetric data were even better than for LiDAR data – as low as 0.015 m on all sites. However, the LiDAR system provided better coverage, with almost full coverage at all sites, while the DAP-UAV coverage declined with the increasing density of branches (being worst in the young forest). In the very dense young forest (Site 1), the coverage by photogrammetrically extracted terrain cloud using high calculation quality and no filtering achieved 80.7% coverage, while LiDAR-UAV reached almost 100% coverage. The importance of the use of multiple (or last) returns when using LiDAR-UAV systems was demonstrated by the fact that on the site with the densest vegetation, only 11% of the ground points were represented by first returns. Numéro de notice : A2023-219 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/22797254.2023.2179942 Date de publication en ligne : 01/03/2023 En ligne : https://doi.org/10.1080/22797254.2023.2179942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103161 [article]