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		<isbn>978-85-17-00088-1</isbn>
		<label>59529</label>
		<citationkey>PadilhaSchiLies:2017:AvInDe</citationkey>
		<title>Avaliação das informações derivadas de uma varredura LASER terrestre em uma unidade amostral de reflorestamento de Pinus taeda</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>1747 KiB</size>
		<author>Padilha, Alan Schreiner,</author>
		<author>Schimalski, Marcos Benedito,</author>
		<author>Liesenberg, Veraldo,</author>
		<electronicmailaddress>alanschreinerpadilha@gmail.com</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>5936-5943</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Terrestrial laser scanning (TLS) provides measurements with a millimeter-level of details from a certain object. Such measurements over a forest stand allow precise estimates of important forest inventory attributes such as the diameter at the breast height (DBH) and tree height. We selected a common Pinus taeda stand with 17 years old destinated to pulp and paper production. Our objective was to explore different data processing steps, registering and point editing using FARO Scene and CloudCompare software. After, routines were written in Python to detect the spatial position of each tree in the forest stand. We also perform several tests changing the square dimensions to select individual trees as well as to remove duplicated points aiming to obtain the DBH. We validate the TLS measurements with continuous forest inventory data. Our results show that the best parameters were found for a square size of 43 by 43 cm with a Pearson coefficient of 0,86 and a Coefficient of determination of 0,74. The point density can still be reduced until the factor of 0.025 in which there is not any significant difference (&#945;=5%).</abstract>
		<area>SRE</area>
		<type>LIDAR: sensores e aplicações</type>
		<language>pt</language>
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