Centre dedocumentation
scientifique

Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([01/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [01/07/2021] . - pp 1217 - 1235 Titre : Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers Type de document : Article/Communication Auteurs : Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1217 - 1235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification bayesienne [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification pixellaire [Termes IGN] fond marin [Termes IGN] Google Earth [Termes IGN] habitat d'espèce [Termes IGN] image Quickbird [Termes IGN] Malaisie [Termes IGN] précision infrapixellaire [Termes IGN] récif corallien Résumé : (auteur) This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping. Numéro de notice : A2021-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1637466 date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1637466 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97803 [article]

Groundwater vulnerability assessment of the chalk aquifer in the northern part of France / Lahcen Zouhri in Geocarto international, vol 36 n° 11 ([01/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [01/07/2021] . - pp 1193 - 1216 Titre : Groundwater vulnerability assessment of the chalk aquifer in the northern part of France Type de document : Article/Communication Auteurs : Lahcen Zouhri, Auteur ; Romain Armand, Auteur Année de publication : 2021 Article en page(s) : pp 1193 - 1216 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de sensibilité [Termes IGN] aquifère [Termes IGN] ArcGIS [Termes IGN] carte hydrogéologique [Termes IGN] craie [Termes IGN] eau souterraine [Termes IGN] Hauts-de-France (région 2016) [Termes IGN] Oise (60) [Termes IGN] utilisation du sol [Termes IGN] vulnérabilité Résumé : (auteur) This study explores the groundwater vulnerability of the chalk aquifer (northern part of France) using a well-known overlay and index DRASTIC method for intrinsic scenario and using land use (LU) parameter as additional factor. Different sources have allowed to compile data necessary to map the vulnerability of the aquifer under study, which used to generate the seven parameters of DRASTIC, namely: groundwater Depth, groundwater Recharge, lithology, Soil media, Topography, Impact of the vadose zone and hydraulic Conductivity. Applying the model in ArcGIS 10.2 platform leads to identify three classes of vulnerability: low, medium and high vulnerability. The highest DRASTIC indexes appear in areas where the groundwater depth is low and in more permeable unsaturated zones. The LU has a little effect on the distribution of vulnerability classes: this distribution is marked by the low vulnerability 44% against 6.5 of high vulnerability. Numéro de notice : A2021-434 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1637465 date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1637465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97801 [article]

Forest cover mapping and Pinus species classification using very high-resolution satellite images and random forest / Laura Alonso-Martinez in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2-2021 (July 2021)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2-2021 (July 2021) . - pp 203 - 210 Titre : Forest cover mapping and Pinus species classification using very high-resolution satellite images and random forest Type de document : Article/Communication Auteurs : Laura Alonso-Martinez, Auteur ; J. Picos, Auteur ; Julia Armesto, Auteur Année de publication : 2021 Article en page(s) : pp 203 - 210 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte de la végétation [Termes IGN] classification par forêts aléatoires [Termes IGN] couvert forestier [Termes IGN] Espagne [Termes IGN] Eucalyptus (genre) [Termes IGN] image multibande [Termes IGN] image Worldview [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Pinus pinaster [Termes IGN] Pinus radiata [Termes IGN] Pinus sylvestris [Termes IGN] ressources forestières [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Advances in remote sensing technologies are generating new perspectives concerning the role of and methods used for National Forestry Inventories (NFIs). The increase in computation capabilities over the last several decades and the development of new statistical techniques have allowed for the automation of forest resource map generation through image analysis techniques and machine learning algorithms. The availability of large-scale data and the high temporal resolution that satellite platforms provide mean that it is possible to obtain updated information about forest resources at the stand level, thus increasing the quality of the spatial information. However, photointerpretation of satellite and aerial images is still the most common way that remote sensing information is used for NFIs or forest management purposes. This study describes a methodology that automatically maps the main forest covers in Galicia (Eucalyptus spp., conifers and broadleaves) using Worldview-2 and the random forest classifier. Furthermore, the method also evaluates the separate mapping of the three most abundant Pinus tree species in Galicia (Pinus pinaster, Pinus radiata and Pinus sylvestris). According to the results, Worldview-2 multispectral images allow for the efficient differentiation between the main forest classes that are present in Galicia with a very high degree of accuracy (91%) and ample spatial detail. Pinus species can also be efficiently differentiated (83%). Numéro de notice : A2021-493 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-3-2021-203-2021 date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-3-2021-203-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97958 [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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 250 - 261 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 temporelle Résumé : (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 [article]

Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change / Debojyoti Chakraborty in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 2 (June 2021) . - Article 26 Titre : Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change Type de document : Article/Communication Auteurs : Debojyoti Chakraborty, Auteur ; Norbert Móricz, Auteur ; Ervin Rasztovits, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : Article 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] carte forestière [Termes IGN] changement climatique [Termes IGN] conservation des ressources forestières [Termes IGN] espèce végétale [Termes IGN] Europe (géographie politique) [Termes IGN] Fagus sylvatica [Termes IGN] Larix decidua [Termes IGN] outil d'aide à la décision [Termes IGN] peuplement forestier [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Quercus pedunculata [Termes IGN] Quercus sessiliflora [Termes IGN] vulnérabilité [Vedettes matières IGN] Ecologie forestière Résumé : (Auteur) We developed a dataset of the potential distribution of seven ecologically and economically important tree species of Europe in terms of their climatic suitability with an ensemble approach while accounting for uncertainty due to model algorithms. The dataset was documented following the ODMAP protocol to ensure reproducibility. Our maps are input data in a decision support tool “SusSelect” which predicts the vulnerability of forest trees in climate change and recommends adapted planting material. Numéro de notice : A2021-329 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01029-4 date de publication en ligne : 22/03/2021 En ligne : https://doi.org/10.1007/s13595-021-01029-4 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97490 [article]

Resolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Permalink

Walking through the forests of the future: using data-driven virtual reality to visualize forests under climate change / Jiawei Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)  

Permalink

Evaluation of light pollution in global protected areas from 1992 to 2018 / Haowei Mu in Remote sensing, vol 13 n° 9 (May 2021)  

Permalink

What is the difference between augmented reality and 2D navigation electronic maps in pedestrian wayfinding? / Weihua Dong in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)  

Permalink

Decision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping / Jiadi Yin in Remote sensing, vol 13 n° 8 (April-2 2021)  

Permalink

Detecting archaeological features with airborne laser scanning in the alpine tundra of Sápmi, Northern Finland / Oula Seitsonen in Remote sensing, vol 13 n° 8 (April-2 2021)  

Permalink

Electrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed / H.S. Virupaksha in Geocarto international, vol 36 n° 8 ([15/04/2021])  

Permalink

A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Permalink

DEM resolution influences on peak flow prediction: a comparison of two different based DEMs through various rescaling techniques / Ali H. Ahmed Suliman in Geocarto international, vol 36 n° 7 ([01/04/2021])  

Permalink

Geovisualization of COVID-19: State of the art and opportunities / Yu Lan in Cartographica, vol 56 n° 1 (Spring 2021)  

Permalink