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		<issn>2178-8634</issn>
		<label>lattes: 4721908858063120 1 MarianoFoscMore:2014:MaLaUs</label>
		<citationkey>MarianoFoscMore:2014:MaLaUs</citationkey>
		<title>Mapping land use classes by analyzing MODIS LST time-series / Mapeamento de classes de uso do solo por meio de análise de séries temporais de dados MODIS-LST</title>
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		<year>2014</year>
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		<author>Mariano, Denis Araujo,</author>
		<author>Foschiera, William,</author>
		<author>Moreira, Maurício Alves,</author>
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		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<e-mailaddress>marcelo.pazos@inpe.br</e-mailaddress>
		<conferencename>Seminário de Atualização em Sensoriamento Remoto e Sistemas de Informações Geográficas Aplicados à Engenharia Florestal, 11 (SenGeF).</conferencename>
		<conferencelocation>Curitiba</conferencelocation>
		<date>14-16 out. 2014</date>
		<publisher>IEP</publisher>
		<publisheraddress>Curitiba</publisheraddress>
		<pages>561-568</pages>
		<booktitle>Anais</booktitle>
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		<keywords>Land surface temperature, MODIS, time-series, Python, agriculture, thermal.</keywords>
		<abstract>The current paper presents a method to discriminate land use classes (LCCs) by analysing Land Surface Temperature (LST) time-series derived from the Moderate Resolution Imaging Spectro radiometer (MODIS). We used Terra and Aqua LST daytime and night time data (M_D11A2) with 8-day temporal and 1km spatial resolution. The physical basis behind the method is the heat transfer between soil, plant and atmosphere over time. There are two approaches, inter-daily and intra-daily LST variation. We tested daytime and day-night difference time-series, being the latter more efficient on discriminating classes. Regarding the satellites, Aqua proves on being more efficient due the passage hour for daytime. In sense, the couple Aqua/Difference yielded better results. However, the performance is strongly dependent upon the targets' acreage due to the high thermal mixing effect. Despite the limitations, this approach shows potential on being coupled to traditional vegetation indices (VI) based methods for furthering the biophysical meaning and relationships between vegetation and the electromagnetic spectrum. It also brings new findings about vegetation thermal behaviour throughout the time.</abstract>
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		<language>en</language>
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