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Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine / Daniel Marc G. dela Torre in Geo-spatial Information Science, vol 24 n° 4 (October 2021)
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Titre : Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine Type de document : Article/Communication Auteurs : Daniel Marc G. dela Torre, Auteur ; Jay Gao, Auteur ; Cate Macinnis-Ng, Auteur ; Yan Shi, Auteur Année de publication : 2021 Article en page(s) : pp 695 - 710 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] Google Earth Engine
[Termes IGN] image Sentinel-MSI
[Termes IGN] Oryza (genre)
[Termes IGN] phénologie
[Termes IGN] rizièreRésumé : (auteur) Paddy rice agriculture is practiced in both rain-fed and irrigated ecosystems in the Philippines. However, small farms are prevalent in the region, and current satellite-based mapping techniques do not distinguish between the two ecosystems at farm scales. This study developed an approach to rapidly map irrigated and rain-fed paddy rice in Iloilo, Philippines at 10 m resolutions using Google Earth Engine. This approach used an ensemble of classifiers based on time-series vegetation indices to produce dry and wet seasonal maps for the entire province. Results showed a predominance of rain-fed rice areas in both seasons, with irrigated rice making up only one-fourth of the total rice area. The overall accuracy was achieved at 68% for the dry season and 75% for the wet season based on ground-acquired points and very high-resolution imagery. The two types of paddies were classified at accuracies up to 87%. Furthermore, the land cover maps showed a strong agreement with the municipal statistics. The resultant maps complement current official statistics and demonstrate the prowess of phenology-based mapping to create paddy inventories in a timely manner to inform food security and agricultural policies. Numéro de notice : A2021-969 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/10095020.2021.1984183 En ligne : https://doi.org/10.1080/10095020.2021.1984183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100385
in Geo-spatial Information Science > vol 24 n° 4 (October 2021) . - pp 695 - 710[article]Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
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Titre : Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning Type de document : Article/Communication Auteurs : Xin Jiang, Auteur ; Shijing Liang, Auteur ; Xinyue He, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] cartographie des risques
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] Google Earth Engine
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation d'image
[Termes IGN] superpixel
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally. Numéro de notice : A2021-560 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.05.019 Date de publication en ligne : 09/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.05.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98118
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 36 - 50[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021081 SL Revue Centre de documentation Revues en salle Disponible 081-2021083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)
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Titre : Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 Type de document : Article/Communication Auteurs : Dimitris Poursanidis, Auteur ; Dimosthenis Traganos, Auteur ; Luisa Teixeira, Auteur ; Aurélie Shapiro, Auteur ; Lara Muaves, Auteur Année de publication : 2021 Article en page(s) : pp 275 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] écosystème
[Termes IGN] Google Earth Engine
[Termes IGN] habitat (nature)
[Termes IGN] image Sentinel-MSI
[Termes IGN] Mozambique
[Termes IGN] récif corallien
[Termes IGN] réserve naturelle
[Termes IGN] surveillance écologiqueRésumé : (auteur) The lack of detailed spatial information on coastal resources, notably shallow water coral reefs and associated benthic habitats, impedes our ability to protect and manage them in the face of global climate change and anthropogenic impacts. Here, we develop a semi-automated workflow in the cloud that uses freely available Sentinel-2 data from the European Space Agency (ESA) Copernicus programme to derive information on near-shore coral reef habitats in the Quirimbas National Park (QNP), a recently declared biosphere reserve in northern Mozambique. We use an end-to-end cloud-based framework within the Google Earth Engine cloud geospatial platform to process imagery from raw pixels to cloud-free composites which are corrected for glint and surface artefacts, water column and derived estimated depth and then classified into four benthic habitats. Using independent training and validation data, we apply three supervised classification algorithms: random forests (RF), support vector machine (SVM) and classification and regression trees (CART). Our results show that random forests are the most accurate supervised algorithm with over 82% overall accuracy. We mapped over 105 000 ha of shallow water habitat inside the protected area, of which 18% are dominated by coral and hardbottom; 27.5% are seagrass and submerged aquatic vegetation and another 23.4% are soft and sandy substrates, and the remaining area is optically deep water. We employ satellite-derived bathymetry to assess slope, bathymetric position, rugosity and underwater topography of these habitats. Finally, a spectral unmixing model provides further sub-pixel–level information of habitats with the potential to monitor changes over time. This effort provides the first, consistent and repeatable and also scalable coastal information system for an east African tropical marine protected area, which hosts shallow-water ecosystems which are of great significance to local communities and building resilience towards climate change. Numéro de notice : A2021-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/rse2.187 Date de publication en ligne : 29/11/2020 En ligne : https://doi.org/10.1002/rse2.187 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98679
in Remote sensing in ecology and conservation > vol 7 n° 2 (June 2021) . - pp 275 - 291[article]Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)
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Titre : Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine Type de document : Article/Communication Auteurs : Tongxi Hu, Auteur ; Elizabeth Myers Toman, Auteur ; Gang Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 250 - 261 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bassin hydrographique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification bayesienne
[Termes IGN] détection de changement
[Termes IGN] estimation bayesienne
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] précision infrapixellaire
[Termes IGN] série temporelleRésumé : (auteur) Large fractions of human-altered lands are working landscapes where people and nature interact to balance social, economic, and ecological needs. Achieving these sustainability goals requires tracking human footprints and landscape disturbance at fine scales over time—an effort facilitated by remote sensing but still under development. Here, we report a satellite time-series analysis approach to detecting fine-scale human disturbances in an Ohio watershed dominated by forests and pastures but with diverse small-scale industrial activities such as hydraulic fracturing (HF) and surface mining. We leveraged Google Earth Engine to stack decades of Landsat images and explored the effectiveness of a fuzzy change detection algorithm called the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) to capture fine-scale disturbances. BEAST is an ensemble method, capable of estimating changepoints probabilistically and identifying sub-pixel disturbances. We found the algorithm can successfully capture the patterns and timings of small-scale disturbances, such as grazing, agriculture management, coal mining, HF, and right-of-ways for gas and power lines, many of which were not captured in the annual land cover maps from Cropland Data Layers—one of the most widely used classification-based land dynamics products in the US. For example, BEAST could detect the initial HF wellpad construction within 60 days of the registered drilling dates on 88.2% of the sites. The wellpad footprints were small, disturbing only 0.24% of the watershed in area, which was dwarfed by other activities (e.g., right-of-ways of utility transmission lines). Together, these known activities have disturbed 9.7% of the watershed from the year 2000 to 2017 with evergeen forests being the most affected land cover. This study provides empirical evidence on the effectiveness and reliability of BEAST for changepoint detection as well as its capability to detect disturbances from satellite images at sub-pixel levels and also documents the value of Google Earth Engine and satellite time-series imaging for monitoring human activities in complex working landscapes. Numéro de notice : A2021-415 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.008 Date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97746
in ISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 250 - 261[article]Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)
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Titre : Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine Type de document : Article/Communication Auteurs : Dong Liang, Auteur ; Huadong Guo, Auteur ; Lu Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112318 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] albedo
[Termes IGN] Antarctique
[Termes IGN] calotte glaciaire
[Termes IGN] changement climatique
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] distribution spatiale
[Termes IGN] fonte des glaces
[Termes IGN] Google Earth Engine
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] montée du niveau de la mer
[Termes IGN] série temporelleRésumé : (auteur) The Antarctic ice sheet is an important mass of glacier ice. It is particularly sensitive to climate change, and the flow of Antarctica's inland glaciers into the sea, accelerated by collapsing ice shelves, threatens global sea level rise. The amount of snowmelt on the surface of the ice sheet is an important metric for accurately assessing surface material loss and albedo change, which affect the stability of the ice sheet. This study proposes a framework for quickly extracting time-series freeze-thaw information at the continental scale and 40 m resolution by taking advantage of the huge amount of synthetic aperture radar (SAR) data acquired by Sentinel-1 satellites over the Antarctic, available for rapid processing on Google Earth Engine. Co-orbit normalization is used in the proposed framework to establish a unified standard of judgement by reducing the variations in the backscattering coefficient introduced by observation geometry, terrain fluctuations, and melt conditions between images acquired at different times. We implemented the framework to produce a massive dataset of both monthly freeze-thaw information over the Antarctic and higher temporal resolution freeze-thaw information for the Larsen C ice shelf from 2015 to 2019, with overall accuracies of 93% verified by a manual visual interpretation method and 84% evaluated from automatic weather station temperatures. Due to its effectiveness and robustness, the framework can be used to analyse the spatiotemporal distribution of snowmelt, the change in melt area, and anomalous melt events in Antarctica, especially those in Larsen C caused by foehn wind. Numéro de notice : A2021-477 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112318 Date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112318 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97117
in Remote sensing of environment > Vol 256 (April 2020) . - n° 112318[article]Urban heat island formation in greater Cairo: Spatio-temporal analysis of daytime and nighttime land surface temperatures along the urban–rural gradient / Darshana Athukorala in Remote sensing, vol 13 n° 7 (April-1 2021)
PermalinkAutomated detection of individual Juniper tree location and forest cover changes using Google Earth Engine / Sudeera Wickramarathna in Annals of forest research, vol 64 n° 1 (2021)
PermalinkMapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine / Aparna R. Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkCombining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine / Thuan Sarzynski in Remote sensing, vol 12 n° 7 (April 2020)
PermalinkNational scale identification and characterization of braided rivers in New Zealand using Google Earth Engine / Alexis Jean (2020)
PermalinkRegional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n° 1 (January 2020)
PermalinkPermalinkA mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform / Bangqian Chen in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
PermalinkMapping vegetation species succession in a mountainous grassland ecosystem using Landsat, ASTER MI, and Sentinel-2 data / Efosa Gbenga Adagbasa in Plos one, vol 17 n° 1 (January 2022)
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