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Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 10 [01/08/2020] . - pp 1067 - 1087 Titre : Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method Type de document : Article/Communication Auteurs : Vijendra Singh Bramhe, Auteur ; Sanjay Kumar Ghosh, Auteur ; Pradeep Kumar Garg, Auteur Année de publication : 2020 Article en page(s) : pp 1067 - 1087 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] analyse texturale [Termes descripteurs IGN] bati [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] matrice de co-occurence [Termes descripteurs IGN] niveau de gris (image) [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] texture d'image Résumé : (auteur) Information of built-up area is essential for various applications, such as sustainable development or urban planning. Built-up area extraction using optical data is challenging due to spectral confusion between built-up and other classes (bare land or river sand, etc.). Here an automated approach has been proposed to generate built-up maps using spectral-textural features and feature selection techniques. Eight Grey-Level Co-Occurrence Matrix based texture features are extracted using Landsat-8 Operational Land Imager bands and combined with multispectral data. The most informative features are selected from combined spectral-textural dataset using feature selection techniques. Further, Support Vector Machine (SVM) classifiers are trained on labelled samples using optimal features and results are compared with Back Propagation-Neural Network (BP-NN) and k-Nearest Neighbour (k-NN). The results show that inclusion of textural features and applying feature selection methods increases the highest overall accuracy of Linear-SVM, RBF-SVM, BP-NN, and k-NN by 9.20%, 9.09%, 8.42%, and 7.39%, respectively. Numéro de notice : A2020-425 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1566406 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566406 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95489 [article]

A water identification method basing on grayscale Landsat 8 OLI images / Zhitian Deng in Geocarto international, vol 35 n° 7 ([15/05/2020])  

Public [article]  inGeocarto international > vol 35 n° 7 [15/05/2020] . - pp 700 - 710 Titre : A water identification method basing on grayscale Landsat 8 OLI images Type de document : Article/Communication Auteurs : Zhitian Deng, Auteur ; Yonghua Sun, Auteur ; Ke Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 700 - 710 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Chine [Termes descripteurs IGN] correction atmosphérique [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] eau de surface [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] Kappa de Cohen [Termes descripteurs IGN] niveau de gris (image) [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] ressources en eau Résumé : (auteur) Accurate identification of water boundaries is of great significance to water resources surveys. Most water indexes have been designed for different districts and cannot be universally utilized in different regions and, in addition, they rely on atmospheric correction. A new water index, None-Radiation-Calibration Water Index (NRCWI), was constructed by Landsat OLI Band 3 (Green), Band 5 (NIR), and Band 6 (SWIR1), and was compared to the previous method, Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI). We evaluated the accuracy of four water index methods for classifying water in 30-m resolution Landsat 8 OLI imagery from the Bohai Sea Rim in China, which takes in a broad assortment of features including sea and coastline, lakes, rivers, man-made water features, and mountains (shadow water). The following outcomes were obtained: 1. The overall accuracy of NRCWI was 95.23%, which is higher than NDWI, MNDWI, AWEI; 2. The leakage error of NRCWI was 5.48%, the misclassification error was 6.15%, and it implies that the error of NRCWI was effected decrease; 3. NRCWI had the highest kappa coefficient in lakes, rivers, man-made waters, mountains, and other ground features, which means that the method can reach a high accuracy in case 2 water which is principally situated in the near shore, estuary and so on; 4. In the applicability study, the kappa values of NRCWI were 89.99% (OLI), 87.36% (ETM+), 87.33% (TM), and 81.20% (Sentinel-2 MSI). Overall, the NRCWI method performed the best, with the highest accuracy and the lowest leakage error, which may be useful in OLI, ETM+, and TM imagery. Numéro de notice : A2020-272 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1552324 date de publication en ligne : 14/06/2019 En ligne : https://doi.org/10.1080/10106049.2018.1552324 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95056 [article]

An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (2020)  

Public [article]  inSilva fennica > vol 54 n° 2 (2020) Titre : An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data Type de document : Article/Communication Auteurs : Olga Grigorieva, Auteur ; Olga Brovkina, Auteur ; Alisher Saidov, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Betula (genre) [Termes descripteurs IGN] carte forestière [Termes descripteurs IGN] classification [Termes descripteurs IGN] erreur de classification [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] phénologie [Termes descripteurs IGN] Pinus (genre) [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] République Tchèque [Termes descripteurs IGN] Russie [Termes descripteurs IGN] signature spectrale [Termes descripteurs IGN] variation saisonnière Résumé : (auteur) his study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectral–temporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time. Numéro de notice : A2020-324 Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10143 date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.14214/sf.10143 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95198 [article]

Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database / Collin Homer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 184 - 199 Titre : Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database Type de document : Article/Communication Auteurs : Collin Homer, Auteur ; Jon Dewitz, Auteur ; Suming Jin, Auteur Année de publication : 2020 Article en page(s) : pp 184 - 199 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Advanced Very High Resolution Radiometer [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] cultures [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] forêt [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] Medium Resolution Imaging Spectrometer [Termes descripteurs IGN] surveillance de la végétation [Termes descripteurs IGN] zone humide Résumé : (auteur) The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001 to 2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error (SE) was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001 and 2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this period. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001–2006 at twice the rate of 2011–2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database. Numéro de notice : A2020-121 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.019 date de publication en ligne : 03/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.019 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94746 [article]

GIS-based modeling for selection of dam sites in the Kurdistan region, Iraq / Arsalan Ahmed Othman in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 34 p. Titre : GIS-based modeling for selection of dam sites in the Kurdistan region, Iraq Type de document : Article/Communication Auteurs : Arsalan Ahmed Othman, Auteur ; Ahmed F. Al-Maamar, Auteur ; Diary Ali Mohammed Amin Al-Manmi, Auteur Année de publication : 2020 Article en page(s) : 34 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] barrage [Termes descripteurs IGN] capacité de stockage [Termes descripteurs IGN] construction [Termes descripteurs IGN] gestion de l'eau [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] Iraq [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] processus d'analyse hiérarchisée floue [Termes descripteurs IGN] régression géographiquement pondérée Résumé : (auteur) Iraq, a country in the Middle East, has suffered severe drought events in the past two decades due to a significant decrease in annual precipitation. Water storage by building dams can mitigate drought impacts and assure water supply. This study was designed to identify suitable sites to build new dams within the Al-Khabur River Basin (KhRB). Both the fuzzy analytic hierarchy process (AHP) and the weighted sum method (WSM) were used and compared to select suitable dam sites. A total of 14 layers were used as input dataset (i.e., lithology, tectonic zones, distance to active faults, distance to lineaments, soil type, land cover, hypsometry, slope gradient, average precipitation, stream width, Curve Number Grid, distance to major roads, distance to towns and cities, and distance to villages). Landsat-8/Operational Land Imager (OLI) and QuickBird optical images were used in the study. Three types of accuracies were tested: overall, suitable pixels by number, and suitable pixels by weight. Based on these criteria, we determined that 11 sites are suitable for locating dams for runoff harvesting. Results were compared to the location of 21 preselected dams proposed by the Ministry of Agricultural and Water Resources (MAWR). Three of these dam sites coincide with those proposed by the MAWR. The overall accuracies of the 11 dams ranged between 76.2% and 91.8%. The two most suitable dam sites are located in the center of the study area, with favorable geology, adequate storage capacity, and in close proximity to the population centers. Of the two selection methods, the AHP method performed better as its overall accuracy is greater than that of the WSM. We argue that when stream discharge data are not available, use of high spatial resolution QuickBird imageries to determine stream width for discharge estimation is acceptable and can be used for preliminary dam site selection. The study offers a valuable and relatively inexpensive tool to decision-makers for eliminating sites having severe limitations (less suitable sites) and focusing on those with the least restriction (more suitable sites) for dam construction. Numéro de notice : A2020-265 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040244 date de publication en ligne : 15/04/2020 En ligne : https://doi.org/10.3390/ijgi9040244 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95028 [article]

How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)  

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Sea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 2020)  

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A novel fire index-based burned area change detection approach using Landsat-8 OLI data / Sicong Liu in European journal of remote sensing, vol 53 n°1 (2020)  

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Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

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Remote Sensing Applications for Agriculture and Crop Modelling / Piero Toscano (2020)  

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Utilisation des SIG et de la télédétection pour la cartographie de la susceptibilité aux mouvements d'instabilité de versant dans l'Ouest montagneux de la Côte d'Ivoire / Boyossoro Hélène Kouadio in Revue Française de Photogrammétrie et de Télédétection, n° 221 (novembre 2019)

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Residences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])  

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Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes / Meng Zhang in Geocarto international, vol 34 n° 10 ([15/07/2019])  

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A novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery / Tingting Wu in Advances in space research, vol 64 n°1 (1 July 2019)  

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Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover / Rei Sonobe in Geocarto international, vol 34 n° 8 ([15/06/2019])  

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