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		<doi>10.1117/1.JRS.16.034518</doi>
		<issn>1931-3195</issn>
		<citationkey>UeharaKörtSoarQuev:2022:TiMeAp</citationkey>
		<title>Time-series metrics applied to land use and land cover mapping with focus on landslide detection</title>
		<year>2022</year>
		<month>July</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Uehara, Tatiana Dias Tardelli,</author>
		<author>Körting, Thales Sehn,</author>
		<author>Soares, Anderson dos Reis,</author>
		<author>Quevedo, Renata Pacheco,</author>
		<orcid>0000-0003-1861-8848</orcid>
		<orcid>0000-0002-0876-0501</orcid>
		<orcid>0000-0001-6513-2192</orcid>
		<orcid>0000-0002-7528-9166</orcid>
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		<group>SER-SRE-DIPGR-INPE-MCTI-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Cognizant Technology Solutions</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>tatiana.tardelli@gmail.com</electronicmailaddress>
		<electronicmailaddress>contato.tsk@gmail.com</electronicmailaddress>
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		<electronicmailaddress>renatapquevedo@gmail.com</electronicmailaddress>
		<journal>Journal of Applied Remote Sensing</journal>
		<volume>16</volume>
		<number>3</number>
		<pages>e034518</pages>
		<secondarymark>A2_GEOGRAFIA B1_GEOCIÊNCIAS B1_CIÊNCIAS_AGRÁRIAS_I B1_BIODIVERSIDADE B5_CIÊNCIAS_AMBIENTAIS</secondarymark>
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		<keywords>mass movements, image time series, landslide inventory, random forest, machine learning, remote sensing.</keywords>
		<abstract>Landslides are a recurring phenomenon in Brazil and have caused many socioeconomic losses and casualties. To monitor them, land use and land cover (LULC) and landslide inventory maps are essential to identifying high susceptibility areas. In this sense, the main aim of this study is to produce LULC classification focused on landslide detection via semi-automatic methods, using data mining techniques with remote sensing time-series imagery. For that, different indices, such as the normalized difference vegetation index, the normalized difference built-up index (NDBI), and the soil adjusted vegetation index were extracted from Sentinel-2 imagery. Basic, polar, and fractal metrics were extracted from the time series. From the Shuttle Radar Topography Mission digital elevation model, six geomorphometric features were extracted. Then, classification was performed with random forest with four different approaches: mono-temporal, bi-temporal, metrical, and all. In every approach, the NDBI index or metric derived from it presented the highest importance, and the slope was ranked among the six first predictors. The all approach showed the highest overall accuracy (OA) (88.96%), followed by metrical (87.90%), bi-temporal (82.59%), and mono-temporal (74.95%). Briefly, the metrical approach presented the most beneficial result, presenting high OA and low levels of commission and omission errors.</abstract>
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		<language>en</language>
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