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Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh / Mohammad Emran Hasan in Forests, vol 11 n° 9 (September 2020)
[article]
Titre : Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh Type de document : Article/Communication Auteurs : Mohammad Emran Hasan, Auteur ; Biswajit Nath, Auteur ; A.H.M. Raihan Sarker, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : N° 1016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] automate cellulaire
[Termes IGN] Bangladesh
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] couvert forestier
[Termes IGN] déboisement
[Termes IGN] dégradation de l'environnement
[Termes IGN] détection de changement
[Termes IGN] gestion forestière durable
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] mangrove
[Termes IGN] modèle de Markov
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] réserve forestière
[Termes IGN] réserve naturelle
[Termes IGN] santé des forêts
[Termes IGN] série temporelle
[Termes IGN] système d'information géographiqueRésumé : (auteur) Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF. Numéro de notice : A2020-752 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091016 Date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.3390/f11091016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96432
in Forests > vol 11 n° 9 (September 2020) . - N° 1016[article]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])
[article]
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 IGN] analyse spectrale
[Termes IGN] analyse texturale
[Termes IGN] bati
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] réseau neuronal artificiel
[Termes IGN] texture d'imageRésumé : (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 Affiliation des auteurs : non IGN 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
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1067 - 1087[article]A water identification method basing on grayscale Landsat 8 OLI images / Zhitian Deng in Geocarto international, vol 35 n° 7 ([15/05/2020])
[article]
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 IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] détection de contours
[Termes IGN] eau de surface
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] Kappa de Cohen
[Termes IGN] niveau de gris (image)
[Termes IGN] Normalized Difference Water Index
[Termes IGN] ressources en eauRésumé : (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 Affiliation des auteurs : non IGN 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
in Geocarto international > vol 35 n° 7 [15/05/2020] . - pp 700 - 710[article]Assessment of malaria hazard, vulnerability, and risks in Dire Dawa City Administration of eastern Ethiopia using GIS and remote sensing / Abdinasir Moha in Applied geomatics, vol 12 n° 1 (April 2020)
[article]
Titre : Assessment of malaria hazard, vulnerability, and risks in Dire Dawa City Administration of eastern Ethiopia using GIS and remote sensing Type de document : Article/Communication Auteurs : Abdinasir Moha, Auteur ; Molla Maru, Auteur ; Tebarek Lika, Auteur Année de publication : 2020 Article en page(s) : pp 15 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] ArcGIS
[Termes IGN] cartographie des risques
[Termes IGN] changement climatique
[Termes IGN] Ethiopie
[Termes IGN] image infrarouge
[Termes IGN] image Landsat-OLI
[Termes IGN] maladie parasitaire
[Termes IGN] risque sanitaire
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du solRésumé : (auteur) Malaria is a serious vector-borne disease affecting a greater proportion of the world’s population. Sub-Saharan Africa carries a disproportionately high share of the global malaria burden. Ethiopia is generally considered a low-to-moderate malaria transmission intensity country. However, the health sector in Ethiopia is greatly affected by climate change, which has profound consequences on the transmission cycles of vector-borne infectious diseases like malaria. The main objective of the study was to assess the spatial distribution of malaria hazard, vulnerability, and risk areas in Dire Dawa City Administration. GIS and remote-sensing in general and multi-criteria evaluation (MCE) in particular was used for assessing and mapping malaria hazard, risk, and vulnerable areas in Dire Dawa City Administration based on the data collected from various sources. The malaria hazard map of the study area labeled 0.6% of the region as low-hazard level, 79.7% moderate, 19.7% high, and 0.1% very low. Results of malaria vulnerability analysis reveal that about 23%, 73%, and 4% of the region was found to be vulnerable to malaria risk at very high, high, and low levels, respectively. The malaria risk map classifies 80% of the region as a moderate malaria-risk area and 20% as high malaria-risk area. This assessment advocates that the GIS and remote-sensing technology as tools can be used to provide timely information on malaria hazard, vulnerability, and risk areas for planning and taking measures at various levels ranging from early warning, monitoring, and control to prevention against malaria epidemics in a resource-efficient and cost-effective way. Numéro de notice : A2020-557 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00276-5 Date de publication en ligne : 17/07/2019 En ligne : https://doi.org/10.1007/s12518-019-00276-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95862
in Applied geomatics > vol 12 n° 1 (April 2020) . - pp 15 - 22[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)
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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 IGN] analyse diachronique
[Termes IGN] base de données d'occupation du sol
[Termes IGN] changement climatique
[Termes IGN] changement d'occupation du sol
[Termes IGN] cultures
[Termes IGN] détection de changement
[Termes IGN] Etats-Unis
[Termes IGN] forêt
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Envisat-MERIS
[Termes IGN] image Landsat-OLI
[Termes IGN] image NOAA-AVHRR
[Termes IGN] image Terra-MODIS
[Termes IGN] surveillance de la végétation
[Termes IGN] zone humideRésumé : (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 Affiliation des auteurs : non IGN 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
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 184 - 199[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)PermalinkHow 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)PermalinkAn original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)PermalinkSea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 2020)PermalinkA 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)PermalinkApplication of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)PermalinkDistribution spatiale et dynamique de la population de palmiers rôniers, Borassus aethiopum Mart., par approche de la télédétection et du Système d’Information Géographique (SIG) de la réserve de Lamto (Centre de la Côte d’Ivoire) / Kouakou Guy-Casimir Douffi (2020)PermalinkIdentification 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)PermalinkPermalinkUtilisation 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)PermalinkResidences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkCombining 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])PermalinkA 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)PermalinkEvaluating metrics derived from Landsat 8 OLI imagery to map crop cover / Rei Sonobe in Geocarto international, vol 34 n° 8 ([15/06/2019])PermalinkA new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation / Qing Wang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)PermalinkTree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 ([01/02/2019])PermalinkUrban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)PermalinkEffects of a large-scale late spring frost on a beech (Fagus sylvatica L.) dominated Mediterranean mountain forest derived from the spatio-temporal variations of NDVI / Angelo Nolè in Annals of Forest Science, vol 75 n° 3 (September 2018)PermalinkModeling of inland flood vulnerability zones through remote sensing and GIS techniques in the highland region of Papua New Guinea / Porejane Harley in Applied geomatics, vol 10 n° 2 (June 2018)PermalinkPermalink