LEADER 04463nam 2201081z- 450 001 9910637782703321 005 20231214133155.0 010 $a3-0365-5677-X 035 $a(CKB)5470000001631710 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94495 035 $a(EXLCZ)995470000001631710 100 $a20202212d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMineralogical Approaches to Archaeological Materials$eTechnological and Social Insights 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (238 p.) 311 $a3-0365-5678-8 330 $aArchaeometry is based on the necessary interdisciplinary relationship between diverse branches of the natural and social sciences. This relationship is essential in archaeology, since, from physical materials (objects), scholars have to face questions that go beyond the limits of the tangible and pertain instead to abstract and social concerns. Currently, archaeometric studies are fundamental to the accurate classification and characterization of archaeological materials, providing relevant data, among other aspects, about their production, function and social meaning. In this book, we present a set of papers that show the potential of mineralogical studies (e.g. petrography, mineral geochemistry, X-ray Diffraction) and multiproxy approaches to characterize the composition of a wide diversity of archaeological materials such as ceramics, terracotta, tiles, metals, glazes, glass and mortars related to several periods (Bronze Age, Roman, Middle Age, Modern period). In this sense, this book can be of interest for specialized researchers who seek specific case studies and are mainly concerned with certain kinds of materials, but also for those students, researchers and professionals who look for a practical overview of the chief methods that can be followed in the study of material culture. 517 $aMineralogical Approaches to Archaeological Materials 606 $aBiography & True Stories$2bicssc 606 $aArchaeology$2bicssc 610 $acarreaux de pavement 610 $amedieval pottery 610 $aarchaeometry 610 $amineralogical analysis 610 $aplumbiferous glaze 610 $asilicoaluminate engobe 610 $areddish paste 610 $aancient mortars 610 $aanalytical characterization 610 $aSorrento Peninsula 610 $aglass production 610 $aSpain 610 $a16th century 610 $aµPIXE 610 $aglass kiln 610 $aproduction remains 610 $aobjects 610 $aItaly 610 $amilitary equipment 610 $abronze 610 $apXRF 610 $amuseum collections 610 $anon-destructive analysis 610 $aRoman mortars 610 $aaqueduct 610 $amicroanalysis 610 $ared pozzolan 610 $aSabatini Volcanic District 610 $acopper minerals 610 $amicro-XRF 610 $apetrographic analysis 610 $arock fragment 610 $apottery 610 $aceramics 610 $aEarly Bronze Age 610 $aThrace 610 $aAlmohad period 610 $aAl-Andalus 610 $alead glazes 610 $atin glazes 610 $aSEM-EDS 610 $adefensive structure 610 $astone masonry bedding mortar 610 $arammed earth 610 $aair lime 610 $aarchitectural heritage 610 $aarchitectural terracottas 610 $aproduction technology 610 $aAlba Fucens 610 $atechnological choices 610 $apetrography 610 $aSEM-EDX 610 $aWDXRF 610 $aPXRD 610 $aheat transfer properties 610 $afracture strength 615 7$aBiography & True Stories 615 7$aArchaeology 700 $aSantacreu$b Daniel$4edt$01285498 702 $aCarvajal López$b José Cristóbal$4edt 702 $aDurán Benito$b Adrián$4edt 702 $aSantacreu$b Daniel$4oth 702 $aCarvajal López$b José Cristóbal$4oth 702 $aDurán Benito$b Adrián$4oth 906 $aBOOK 912 $a9910637782703321 996 $aMineralogical Approaches to Archaeological Materials$93019609 997 $aUNINA LEADER 09469nam 2200409 n 450 001 9910765758803321 005 20230324214741.0 010 $a9783038978855 010 $a303897885X 024 8 $a10.3390/books978-3-03897-885-5 035 $a(CKB)5400000000000030 035 $a(NjHacI)995400000000000030 035 $a(ScCtBLL)ed3dc6a4-b69b-4ae1-bcf8-e6192f8c498a 035 $a(OCoLC)1105805546 035 $a(EXLCZ)995400000000000030 100 $a20230324d2019 uu 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGoogle Earth Engine Applications /$fLalit Kumar, Onisimo Mutanga 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 online resource (420 pages) 327 $aAbout the Special Issue Editors . ix -- Onisimo Mutanga and Lalit Kumar Google Earth Engine Applications Reprinted from: Remote Sens. 2019, 11, 591, doi:10.3390/rs11050591 . 1 -- Lalit Kumar and Onisimo Mutanga Google Earth Engine Applications Since Inception: Usage, Trends, and Potential Reprinted from: Remote Sens. 2018, 10, 1509, doi:10.3390/rs10101509 . 5 -- Manuel Campos-Taberner,Alvaro ´ Moreno-Mart´?nez, Francisco Javier Garc´?a-Haro, Gustau Camps-Valls, Nathaniel P. Robinson, Jens Kattge, and Steven W. Running Global Estimation of Biophysical Variables from Google Earth Engine Platform Reprinted from: Remote Sens. 2018, 10, 1167, doi:10.3390/rs10081167 . 20 -- Ate Poortinga, Nicholas Clinton, David Saah1, Peter Cutter, Farrukh Chishtie, Kel N.Markert, Eric R. Anderson, Austin Troy, Mark Fenn, Lan Huong Tran, Brian Bean,Quyen Nguyen, Biplov Bhandari, Gary Johnson and Peeranan Towashiraporn An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale Reprinted from: Remote Sens. 2018, 10, 760, doi:10.3390/rs10050760 . 37 -- Yu Hsin Tsai, Douglas Stow, Hsiang Ling Chen, Rebecca Lewison, Li An and Lei Shi Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine Reprinted from: Remote Sens. 2018, 10, 927, doi:10.3390/rs10060927 . 50 -- Nathaniel P. Robinson, Brady W. Allred, Matthew O. Jones, Alvaro Moreno, John S. Kimball, David E. Naugle, Tyler A. Erickson and Andrew D. Richardson A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States Reprinted from: Remote Sens. 2017, 9, 863, doi:10.3390/rs9080863 . 64 -- Ran Goldblatt, Alexis Rivera Ballesteros and Jennifer Burney High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertao? Reprinted from: Remote Sens. 2017, 9, 1336, doi:10.3390/rs9121336 78 -- Leandro Parente and Laerte Ferreira Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016 Reprinted from: Remote Sens. 2018, 10, 606, doi:10.3390/rs10040606 . 104 -- Dimosthenis Traganos, Bharat Aggarwal, Dimitris Poursanidis, Konstantinos Topouzelis, Nektarios Chrysoulakis and Peter Reinartz Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas Reprinted from: Remote Sens. 2018, 10, 1227, doi:10.3390/rs10081227 . 118 -- Jacky Lee, Jeffrey A. Cardille and Michael T. Coe BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine Reprinted from: Remote Sens. 2018, 10, 1455, doi:10.3390/rs10091455 . 132 -- Roberta Ravanelli, Andrea Nascetti, Raffaella Valeria Cirigliano, Clarissa Di Rico, Giovanni Leuzzi, Paolo Monti and Mattia Crespi Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems Reprinted from: Remote Sens. 2018, 10, 1488, doi:10.3390/rs10091488 . 153 -- Mingzhu He, John S. Kimball, Marco P. Maneta, Bruce D. Maxwell, Alvaro Moreno, Santiago Beguer´?a and Xiaocui Wu Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data Reprinted from: Remote Sens. 2018, 10, 372, doi:10.3390/rs10030372 . 174 -- Masoud Mahdianpari, Bahram Salehi, Fariba Mohammadimanesh, Saeid Homayouni and Eric Gill The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform Reprinted from: Remote Sens. 2019, 11, 43, doi:10.3390/rs11010043 195 -- Rosa Aguilar, Raul Zurita-Milla, Emma Izquierdo-Verdiguier and Rolf A. de By A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems Reprinted from: Remote Sens. 2018, 10, 729, doi:10.3390/rs10050729 . 222 -- Jun Xiong, Prasad S. Thenkabail, James C. Tilton, Murali K. Gumma, Pardhasaradhi Teluguntla, Adam Oliphant, Russell G. Congalton, Kamini Yadav and Noel Gorelick Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine Reprinted from: Remote Sens. 2016, .9, 1065, doi:10.3390/rs9101065 240 -- Eric A. Sproles, Ryan L. Crumley, Anne W. Nolin, Eugene Mar and Juan Ignacio Lopez Moreno SnowCloudHydro-A New Framework for Forecasting Streamflow in Snowy, Data-Scarce Regions Reprinted from: Remote Sens. 2018, 10, 1276, doi:10.3390/rs10081276 . 267 -- Cheng-Chien Liu, Ming-Chang Shieh, Ming-Syun Ke and Kung-Hwa Wang Flood Prevention and Emergency Response System Powered by Google Earth Engine Reprinted from: Remote Sens. 2018, 10, 1283, doi:10.3390/rs10081283 . 282 -- Nazmus Sazib, Iliana Mladenova and John Bolten Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data Reprinted from: Remote Sens. 2018, 10, 1265, doi:10.3390/rs10081265 . 302 -- Gonzalo Mateo-Garc´?a, Luis Gomez-Chova, ´ Julia Amoros-L ´ opez, ´ Jordi Munoz-Mar´?, ? Gustau Camps-Valls Multitemporal Cloud Masking in the Google Earth Engine Reprinted from: Remote Sens. 2018, 10, 1079, doi:10.3390/rs10071079 . 325 -- Kel N. Markert, Calla M. Schmidt, Robert E. Griffin, Africa I. Flores, Ate Poortinga, David S. Saah, Rebekke E. Muench, Nicholas E. Clinton, Farrukh Chishtie, Kritsana Kityuttachai, Paradis Someth, Eric R. Anderson, Aekkapol Aekakkararungroj and David J. Ganz Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing Reprinted from: Remote Sens. 2018, 10, 909, doi:10.3390/rs10060909 . 343 -- Felipe de Lucia Lobo, Pedro Walfir M. Souza-Filho, Evlyn M´arcia Le ?ao de Moraes Novo, Felipe Menino Carlos and Claudio Clemente Faria Barbosa Mapping Mining Areas in the Brazilian Amazon Using MSI/Sentinel-2 Imagery (2017) Reprinted from: Remote Sens. 2018, 10, 1178, doi:10.3390/rs10081178 . 362 -- Dimosthenis Traganos, Dimitris Poursanidis, Bharat Aggarwal, Nektarios Chrysoulakis and Peter Reinartz Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2 Reprinted from: Remote Sens. 2018, 10, 859, doi:10.3390/rs10060859 . 376 -- Sean A. Parks, Lisa M. Holsinger, Morgan A. Voss, Rachel A. Loehman and Nathaniel P. Robinson Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential Reprinted from: Remote Sens. 2018, 10, 879, doi:10.3390/rs10060879 . 394. 330 $aIn a rapidly changing world, there is an ever-increasing need to monitor the Earth's resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth's surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales. 606 $aGoogle Earth 615 0$aGoogle Earth. 676 $a910.285 700 $aKumar$b Lalit$01369316 702 $aMutanga$b Onisimo 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910765758803321 996 $aGoogle Earth Engine Applications$93395461 997 $aUNINA