<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>marte2.sid.inpe.br 802</site>
		<holdercode>{isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}</holdercode>
		<identifier>8JMKD3MGP6W34M/3PSMFLL</identifier>
		<repository>sid.inpe.br/marte2/2017/10.27.16.21.18</repository>
		<lastupdate>2017:10.27.16.21.18 dpi.inpe.br/marte2/2013/05.17.15.03.06 banon</lastupdate>
		<metadatarepository>sid.inpe.br/marte2/2017/10.27.16.21.19</metadatarepository>
		<metadatalastupdate>2018:06.06.03.12.13 dpi.inpe.br/marte2/2013/05.17.15.03.06 administrator {D 2017}</metadatalastupdate>
		<isbn>978-85-17-00088-1</isbn>
		<label>61629</label>
		<citationkey>Bourscheidt:2017:CoPlGo</citationkey>
		<title>Análise de tendência da temperatura de superfície a partir de imagens Landsat 5: contribuições da plataforma Google Earth Engine</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>1382 KiB</size>
		<author>Bourscheidt, Vandoir,</author>
		<electronicmailaddress>vandoir@ufscar.br</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>7401-7407</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Different studies have analyzed the impacts of land use change on the temperature and how it may important on the modeled climate change scenarios. This paper presents a specific analysis of the surface temperature, verifying how it has changed in a wide spatial scale and over more than two decades. To do this, we have used the computational power of Google Earth Engine to create a linear regression model over the entire Landsat 5 collection, resulting in specific images with constant, slope and residuals values for every pixel. Data was filtered for different environmental conditions (cloud, shadow and other artifacts on images) and for temporal effects as well, removing seasonal variations. Thermal band was processed to land surface temperature applying specific and well known procedures, including atmospheric correction based on Landsat SR (Surface Reflectance) product. On the surface temperature and land use perspective, the results show a significant dependence of surface temperature on the land use, as expected. The angular coefficient (slope) obtained from linear regression indicate that positive trends are predominant (more than 60%) over of the studied area. For the more significant cases (pixels), positive trends over the 28 years indicate an increment in the surface temperature of more than 2,8°C. These findings may be an instrument to base more studies on land use and surface temperature influences on the radiation/energy balance.</abstract>
		<area>SRE</area>
		<type>Análise de séries temporais de imagens de satélite</type>
		<language>pt</language>
		<targetfile>61629.pdf</targetfile>
		<usergroup>banon</usergroup>
		<visibility>shown</visibility>
		<mirrorrepository>urlib.net/www/2011/03.29.20.55</mirrorrepository>
		<nexthigherunit>8JMKD3MGP6W34M/3PMFNUS</nexthigherunit>
		<citingitemlist>sid.inpe.br/marte2/2017/09.25.14.55 5</citingitemlist>
		<hostcollection>dpi.inpe.br/marte2/2013/05.17.15.03.06</hostcollection>
		<username>banon</username>
		<lasthostcollection>dpi.inpe.br/marte2/2013/05.17.15.03.06</lasthostcollection>
		<url>http://marte2.sid.inpe.br/rep-/sid.inpe.br/marte2/2017/10.27.16.21.18</url>
	</metadata>
</metadatalist>