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Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) / Mirza Razi Imam Baig in Annals of GIS, vol 26 n° 4 (October 2020)
[article]
Titre : Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) Type de document : Article/Communication Auteurs : Mirza Razi Imam Baig, Auteur ; Ishita Afreen Ahmad, Auteur ; Mohammad Tayyab, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 361 - 376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Andhra Pradesh (Inde ; état)
[Termes IGN] détection de changement
[Termes IGN] érosion côtière
[Termes IGN] géomorphologie locale
[Termes IGN] image Landsat
[Termes IGN] pondération
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] surveillance du littoral
[Termes IGN] trait de côteRésumé : (auteur) Coastline or Shoreline calculation is one of the important factors in the finding of coastal accretion and erosion and the study of coastal morphodynamic. Coastal erosion is a tentative hazard for communities especially in coastal areas as it is extremely susceptible to increasing coastal disasters. The study has been conducted along the coast of Vishakhapatnam district, Andhra Pradesh, India with the help of multi-temporal satellite images of 1991 2001, 2011 and 2018. The continuing coastal erosion and accretion rates have been calculated using the Digital Shoreline Analysis System (DSAS). Linear regression rate (LRR), End Point Rate (EPR) and Weighted Linear Regression (WLR) are used for calculating shoreline change rate. Based on calculations the district shoreline has been classified into five categories as high and low erosion, no change and high and low accretion. Out of 135 km, high erosion occupied 5.8 km of coast followed by moderate or low erosion 46.2 km. Almost 34.7 km coastal length showed little or no change. Moderate accretion is found along 30.5 km whereas high accretion trend found around 17.8 km. The outcome of shows that erosion is prevailing in Vishakhapatnam taluk, Ankapalli taluk, Yellamanchili taluk whereas most of the Bhemunipatnam coast is accreting. Natural and manmade activities and phenomena influence the coastal areas in terms of erosion and accretion. The study could be used for further planning and development and also for disaster management authority in the decision-making process in the study area. Numéro de notice : A2020-801 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1815839 Date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1080/19475683.2020.1815839 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96724
in Annals of GIS > vol 26 n° 4 (October 2020) . - pp 361 - 376[article]Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])
[article]
Titre : Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands Type de document : Article/Communication Auteurs : Bappa Das, Auteur ; Rabi N. Sahoo, Auteur ; Sourabh Pargal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1415 - 1432 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] blé (céréale)
[Termes IGN] canopée
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] réflectance spectrale
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] séparateur à vaste marge
[Termes IGN] spectroradiomètreRésumé : (auteur) Successful retrieval of leaf area index (LAI) from hyperspectral remote sensing relies on the proper selection of indices or multivariate models. The objectives of the research work were to identify best vegetation index and multivariate model based on canopy reflectance and LAI measured at different growth stages of wheat. Comparison of existing indices revealed optimized soil-adjusted vegetation index (OSAVI) as the best index based on R2 of calibration, validation and root mean square error of validation. Proposed ratio index (RI; R670, R845) and normalized difference index (NDI; R670, R845) provided comparable performance with the existing vegetation indices (R2 = 0.65 and 0.62 for RI and NDI, respectively, during validation). Among the multivariate models, partial least squares regression (PLSR) model with Hyperion band configuration performed the best during validation (R2 = 0.80 and RMSE = 0.58 m2 m−2). Our results manifested the opportunities for developing biophysical products based on satellite sensors. Numéro de notice : A2020-607 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581271 Date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95967
in Geocarto international > vol 35 n° 13 [01/10/2020] . - pp 1415 - 1432[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020101 RAB Revue Centre de documentation En réserve L003 Disponible Spatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris / Xu Ge in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)
[article]
Titre : Spatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris Type de document : Article/Communication Auteurs : Xu Ge, Auteur ; Dasaraden Mauree, Auteur ; Roberto Castello, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] espace vert
[Termes IGN] Genève
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-8
[Termes IGN] Normalized Difference Built-up Index
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] Paris (75)
[Termes IGN] surface imperméable
[Termes IGN] température au sol
[Termes IGN] variation saisonnière
[Termes IGN] zone urbaineRésumé : (auteur) Currently, more than half of the world’s population lives in cities, which leads to major changes in land use and land surface temperature (LST). The associated urban heat island (UHI) effects have multiple impacts on energy consumption and human health. A better understanding of how different land covers affect LST is necessary for mitigating adverse impacts, and supporting urban planning and public health management. This study explores a distance-based, a grid-based and a point-based analysis to investigate the influence of impervious surfaces, green area and waterbodies on LST, from large (distance and grid based analysis with 400 m grids) to smaller (point based analysis with 30 m grids) scale in the two mid-latitude cities of Paris and Geneva. The results at large scale confirm that the highest LST was observed in the city centers. A significantly positive correlation was observed between LST and impervious surface density. An anticorrelation between LST and green area density was observed in Paris. The spatial lag model was used to explore the spatial correlation among LST, NDBI, NDVI and MNDWI on a smaller scale. Inverse correlations between LST and NDVI and MNDWI, respectively, were observed. We conclude that waterbodies display the greatest mitigation on LST and UHI effects both on the large and smaller scale. Green areas play an important role in cooling effects on the smaller scale. An increase of evenly distributed green area and waterbodies in urban areas is suggested to lower LST and mitigate UHI effects. Numéro de notice : A2020-666 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9100593 Date de publication en ligne : 10/10/2020 En ligne : https://doi.org/10.3390/ijgi9100593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96143
in ISPRS International journal of geo-information > vol 9 n° 10 (October 2020) . - 24 p.[article]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]Comparison of tree-based classification algorithms in mapping burned forest areas / Dilek Kucuk Matci in Geodetski vestnik, vol 64 n° 3 (September - November 2020)
[article]
Titre : Comparison of tree-based classification algorithms in mapping burned forest areas Type de document : Article/Communication Auteurs : Dilek Kucuk Matci, Auteur ; Resul Comert, Auteur ; Ugur Avdan, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image orientée objet
[Termes IGN] bassin méditerranéen
[Termes IGN] carte forestière
[Termes IGN] classification dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-8
[Termes IGN] incendie de forêt
[Termes IGN] matrice de confusion
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] Rotation Forest classification
[Termes IGN] Turquie
[Termes IGN] zone sinistréeRésumé : (auteur) In this study, we compared the performance of tree-based classification algorithms – Random Forest (RF), Rotation Forest (RotF), J48, The Alternating Decision Tree (ADTree), Forest by Penalising Attributes (Forest PA), Logical Analysis of Data Algorithm (LADTree) and Functional Trees (FT) – for mapping burned forest areas within the Mediterranean region in Turkey. Object-based image analysis (OBIA) was performed to pan-sharpened the Landsat 8 images. Four different burned areas, namely Kumluca, Adrasan, Anamur, and Alanya, were used as study areas. Kumluca, Anamur, and Alanya regions were used as training areas, and Adrasan region was used as the test area. Obtained results were evaluated with confusion matrix and statistically significant analysis. According to the results, FT and RotF produced more accurate results than other algorithms. Also, the results obtained with these algorithms are statistically significant. Numéro de notice : A2020-626 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.03.348-360 Date de publication en ligne : 23/08/2020 En ligne : https://doi.org/10.15292/geodetski-vestnik.2020.03.348-360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96087
in Geodetski vestnik > vol 64 n° 3 (September - November 2020) . - 13 p.[article]Réservation
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