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		<identifier>8JMKD3MGP6W34M/494DLGL</identifier>
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		<isbn>978-65-89159-04-9</isbn>
		<citationkey>RodriguesSouzScafLass:2023:MaMaSt</citationkey>
		<title>Mangrove mapping strategies using Google Earth Engine and Landsat-8 and Sentinel-2 imagery data</title>
		<format>Internet</format>
		<year>2023</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>2</numberoffiles>
		<size>1316 KiB</size>
		<author>Rodrigues, Flávio Henrique,</author>
		<author>Souza Filho, Carlos Roberto de,</author>
		<author>Scafutto, Rebecca Del’Papa Moreira,</author>
		<author>Lassalle, Guillaume,</author>
		<affiliation>Universidade Estadual de Campinas (UNICAMP)</affiliation>
		<affiliation>Universidade Estadual de Campinas (UNICAMP)</affiliation>
		<affiliation>Universidade Estadual de Campinas (UNICAMP)</affiliation>
		<affiliation>Universidade Estadual de Campinas (UNICAMP)</affiliation>
		<electronicmailaddress>rodrigues.ambiental@gmail.com</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<editor>Sanches, Ieda DelArco,</editor>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 20 (SBSR)</conferencename>
		<conferencelocation>Florianópolis</conferencelocation>
		<date>02-05 abril 2023</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>e156061</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>full paper</tertiarytype>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<keywords>Mangrove mapping, Google Earth Engine.</keywords>
		<abstract>Vegetation indices based on remote sensing data have been widely used for mangrove monitoring. Nowadays, the availability of cloud-based platforms allows the processing of large datasets of orbital imagery with moderate spatial and spectral resolutions such as the computation of numerous vegetation spectral indices to map coastal vegetated wetlands. This study presents the performance of the Mangrove Vegetation Index (MVI) and image classification algorithms, embedded in the Google Earth Engine, applied to Landsat-8 and Sentinel-2 data, to map tracts of mangroves in Aracaju (Sergipe, Brazil). Results reveal that the Cobweb clustering algorithm applied to MVIderived from Landsat-8 data favors reliable and practical mangrove mapping, considering the broad diversity of vegetation conditions in this habitat.</abstract>
		<area>SRE</area>
		<type>Floresta e outros tipos de vegetação</type>
		<language>en</language>
		<targetfile>156061.pdf</targetfile>
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