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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]

Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows / Pramaditya Wicaksono in Geocarto international, vol 37 n° 27 ([20/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 27 [20/12/2022] . - pp 15161 - 15186 Titre : Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows Type de document : Article/Communication Auteurs : Pramaditya Wicaksono, Auteur ; Amanda Maishella, Auteur ; Wahyu Lazuardi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 15161 - 15186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] carte thématique [Termes IGN] classification par arbre de décision [Termes IGN] classification pixellaire [Termes IGN] correction d'image [Termes IGN] filtrage du bruit [Termes IGN] herbier marin [Termes IGN] image PlanetScope [Termes IGN] Indonésie Résumé : (auteur) Seagrass percent cover is a crucial and influential component of the biophysical characteristics of seagrass beds and is a key parameter for monitoring seagrass conditions. Therefore, the availability of seagrass percent cover maps greatly assists in sustainable coastal ecosystem management. This research aimed to assess the consistency of PlanetScope imagery for seagrass percent cover mapping using two study areas, namely Parang Island and Labuan Bajo, Indonesia. Assessing the consistency of the PlanetScope imagery performance in seagrass percent cover mapping helps understand the effects of variations in the image quality on its performance in monitoring changes in seagrass cover. Percent cover maps were derived using object-based image analysis (image segmentation and random forest) and pixel-based random forest algorithm. Accuracy assessment and consistency analysis were conducted on the basis of the following three approaches: overall accuracy consistency, agreement percentage and consistent pixel locations. Results show that PlanetScope images can fairly consistently map seagrass percent cover for a specific area across different dates. However, these images produced different levels of accuracy when used for mapping in seagrass meadows with various characteristics and benthic cover complexities. The mapping accuracy (OA–overall accuracy) and consistency (AP–agreement percentage) in patchy seagrass meadows (Parang Island, mean OA 18.4%–38.6%, AP 44.1%–70.3%) are different from those in continuous seagrass meadows (Labuan Bajo, OA 43.0%–56.2%, and AP 41.8%–55.8%). Moreover, PlanetScope images are consistent when used for mapping seagrasses with low and high percent covers but strive to obtain good consistency for medium percent cover due to the combination of seagrass and non-seagrass in a pixel. Furthermore, images with relatively similar image acquisition conditions (i.e., winds, aerosol optical depth, signal-to-noise ratio, and sunglint intensity) produce better consistency. The OA is related to the image acquisition conditions, whilst the AP is related to variation in these conditions. Nevertheless, PlanetScope is still the best high spatial resolution image that provides daily acquisition and is highly beneficial for various applications in tropical areas with persistent cloud coverage. Numéro de notice : A2022-932 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2096122 Date de publication en ligne : 06/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2096122 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102668 [article]

A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples / Ali Jamali in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 115 (December 2022) . - n° 103095 Titre : A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples Type de document : Article/Communication Auteurs : Ali Jamali, Auteur ; Masoud Mahdianpari, Auteur ; fariba Mohammadimanesh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103095 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] carte thématique [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] réseau antagoniste génératif [Termes IGN] zone humide Résumé : (auteur) Wetlands have long been recognized among the most critical ecosystems globally, yet their numbers quickly diminish due to human activities and climate change. Thus, large-scale wetland monitoring is essential to provide efficient spatial and temporal insights for resource management and conservation plans. However, the main challenge is the lack of enough reference data for accurate large-scale wetland mapping. As such, the main objective of this study was to investigate the efficient deep-learning models for generating high-resolution and temporally rich training datasets for wetland mapping. The Sentinel-1 and Sentinel-2 satellites from the European Copernicus program deliver radar and optical data at a high temporal and spatial resolution. These Earth observations provide a unique source of information for more precise wetland mapping from space. The second objective was to investigate the efficiency of vision transformers for complex landscape mapping. As such, we proposed a 3D Generative Adversarial Network (3D GAN) to best achieve these two objectives of synthesizing training data and a Vision Transformer model for large-scale wetland classification. The proposed approach was tested in three different study areas of Saint John, Sussex, and Fredericton, New Brunswick, Canada. The results showed the ability of the 3D GAN to stimulate and increase the number of training data and, as a result, increase the accuracy of wetland classification. The quantitative results also demonstrated the capability of jointly using data augmentation, 3D GAN, and Vision Transformer models with overall accuracy, average accuracy, and Kappa index of 75.61%, 73.4%, and 71.87%, respectively, using a disjoint data sampling strategy. Therefore, the proposed deep learning method opens a new window for large-scale remote sensing wetland classification. Numéro de notice : A2022-828 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103095 Date de publication en ligne : 08/11/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103095 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102012 [article]

Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands / Katrin Krzepek in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol 90 n° 6 (December 2022)  

Public [article]  inPFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science > vol 90 n° 6 (December 2022) . - pp 561 - 575 Titre : Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands Type de document : Article/Communication Auteurs : Katrin Krzepek, Auteur ; Jacob Schmidt, Auteur ; Dorota Iwaszczuk, Auteur Année de publication : 2022 Article en page(s) : pp 561 - 575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage non-dirigé [Termes IGN] aquifère [Termes IGN] Bade-Wurtemberg (Allemagne) [Termes IGN] bande C [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] fusion d'images [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Normalized Difference Water Index [Termes IGN] puits de carbone [Termes IGN] seuillage d'image [Termes IGN] théorie de Dempster-Shafer [Termes IGN] tourbière Résumé : (auteur) Peatlands as natural carbon sinks have a major impact on the climate balance and should therefore be monitored and protected. The hydrology of the peatland serves as an indicator of the carbon storage capacity. Hence, we investigate the question how suitable different remote sensing data are for monitoring the size of open water surface and the water table depth (WTD) of a peatland ecosystem. Furthermore, we examine the potential of combining remote sensing data for this purpose. We use C-band synthetic aperture radar (SAR) data from Sentinel-1 and multi-spectral data from Sentinel-2. The radar backscatter σ0, the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) are calculated and used for consideration of the WTD and the lake size. For the measurement of the lake size, we implement and investigate the methods: random forest, adaptive thresholding and an analysis according to the Dempster–Shafer theory. Correlations between WTD and the remote sensing data σ0 as well as NDWI are investigated. When looking at the individual data sets the results of our case study show that the VH polarized σ0 data produces the clearest delineation of the peatland lake. However the adaptive thresholding of the weighted fusion image of σ0-VH, σ0-VV and MNDWI, and the random forest algorithm with all three data sets as input proves to be the most suitable for determining the lake area. The correlation coefficients between σ0/NDWI and WTD vary greatly and lie in ranges of low to moderate correlation. Numéro de notice : A2022-942 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41064-022-00216-w Date de publication en ligne : 06/09/2022 En ligne : https://doi.org/10.1007/s41064-022-00216-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102876 [article]

Urban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis / Das Subhasis in Geocarto international, vol 37 n° 25 ([01/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 25 [01/12/2022] . - pp 7800 - 7818 Titre : Urban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis Type de document : Article/Communication Auteurs : Das Subhasis, Auteur ; Partha Pratim Adhikary, Auteur ; Pravat Kumar Shit, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7800 - 7818 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse du paysage [Termes IGN] analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] Calcutta [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] Inde [Termes IGN] occupation du sol [Termes IGN] QGIS [Termes IGN] régression multiple [Termes IGN] service écosystémique [Termes IGN] zone humide [Termes IGN] zone urbaine Résumé : (auteur) Dynamics of ecosystem service value (ESV) of various wetlands has been assessed by researchers globally. But the impact of spatio-temporal variation of landscape metrics on ESV in the lower Gangetic plains has not been examined, fully. The present work has established linkages between landscape metrics and ESV in Kolkata urban agglomeration using support vector machine and multivariate regression analysis. Result indicates that wetland area has been reduced by 5.26%, 13.67% and 9.03% during the periods 1990–2000, 2000–2010 and 2010–2020, respectively and the ESV contributed by wetlands has been decreased by $131428, $323674 and $184649, respectively during the same period at an annual rate of 0.85%. Number of patches, mean patch area and edge density are the main determinants of wetland fragmentation and decreased by 44.12%, 10.23% and 8.65%, respectively during the last three decades. A wetland restoration strategy based on dynamic restoration, reactive restoration and wetland creation for the study area has been formulated, which can guide for sustainable management of wetland resources in Kolkata urban agglomeration. Numéro de notice : A2022-930 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1985174 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1080/10106049.2021.1985174 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102665 [article]

Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? / Arthur Sanguet in Global ecology and conservation, vol 39 (November 2022)  

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A fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation / Xiaoguo Guan in Survey review, vol 54 n° 387 (November 2022)  

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Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)  

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Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series / Maximilian Lange in Remote sensing of environment, vol 277 (August 2022)  

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Multiscale assimilation of Sentinel and Landsat data for soil moisture and Leaf Area Index predictions using an ensemble-Kalman-filter-based assimilation approach in a heterogeneous ecosystem / Nicola Montaldo in Remote sensing, vol 14 n° 14 (July-2 2022)  

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Heat wave-induced augmentation of surface urban heat islands strongly regulated by rural background / Shiqi Miao in Sustainable Cities and Society, vol 82 (July 2022)  

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How can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? / Katja Kuhwald in Remote sensing in ecology and conservation, vol 8 n° 3 (June 2022)  

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A voxel-based method for the three-dimensional modelling of heathland from lidar point clouds: first results / N. Homainejad in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)  

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Estimation and testing of linkages between forest structure and rainfall interception characteristics of a Robinia pseudoacacia plantation on China’s Loess Plateau / Changkun Ma in Journal of Forestry Research, vol 33 n° 2 (April 2022)  

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The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands / Aaron Judah in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)  

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