Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/20243 |
Resumo: | Tropical forests have great importance in maintaining biodiversity, yet suffer from illegal logging and deforestation. On the other hand, sustainable forest management practices contribute to the rational use of forest resources. In this way, it is salutary that new technologies and methods are used to follow these activities. This study aimed to identify and delimit gaps in tropical forest from LiDAR data with the use of different density of returns. The study area is located in Fazenda Cauaxi, munipality of Paragominas-PA, in which the forest management activity is carried out. Forest data were obtained from forest inventory from 22 plots of 20 x 500m, and trees with DBH greater than or equal to 35cm were measured. LiDAR data were obtained on a flight that covered an area of 1.216ha composed of 20 scenes consisting of a cloud of points. A minimum gap area of 34m² was defined, based on the canopy rays measured in the forest inventory. The cloud of points was processed in the FUSION/LDV and a segmented raster was obtained in gap areas for each of the density of returns tested (37, 28, 18, 9, 4 and 1ppm², corresponding to the treatments of that study). The areas were grouped into three size classes, Class 1, Class 2 and Class 3 (34 – 149, 150 – 399 and greater than or equal 400m², respectively). The roads were identified by the spatial distribution pattern in the area with the aid of the DTM. The statistical analysis as oerformed in the R and the Kruskal-Wallis test was used to evaluated if there was a difference between the evaluated treatments, which did not show any significance at the 0.05 level, so the densities did not differ. Due to the identification of roads, the areas were reclassified into Small gaps, Large gaps and Roads. The number of areas in small gaps varied between treatments from 80.7 to 87.4% of total gaps, which is expected for areas smaller than 150m², in relation to the area, the variation was from 50.4 to 62,3%. By accounting for areas of gaps and roads, the Roads had the greatest coverage in the study area, varying between treatments from 68.3 to 55.5%. It was possible to infer that some gaps were opened by the activity of selective extraction of wood due to the spatial arrangement of the areas. Rare gaps were not identified, areas greater than 400m². Therefore, working with the reduction of the density of points did not affect the identification and delimitation of gaps in tropical forest. LiDAR technology has proven to be an effective tool for monitoring forest canopy disturbances. Thus, it can cover its application to the monitoring of the activity of forest management, deforestation and illegal logging in the Amazon. |
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2021-01-07T10:12:20Z2021-01-07T10:12:20Z2019-01-30http://repositorio.ufsm.br/handle/1/20243Tropical forests have great importance in maintaining biodiversity, yet suffer from illegal logging and deforestation. On the other hand, sustainable forest management practices contribute to the rational use of forest resources. In this way, it is salutary that new technologies and methods are used to follow these activities. This study aimed to identify and delimit gaps in tropical forest from LiDAR data with the use of different density of returns. The study area is located in Fazenda Cauaxi, munipality of Paragominas-PA, in which the forest management activity is carried out. Forest data were obtained from forest inventory from 22 plots of 20 x 500m, and trees with DBH greater than or equal to 35cm were measured. LiDAR data were obtained on a flight that covered an area of 1.216ha composed of 20 scenes consisting of a cloud of points. A minimum gap area of 34m² was defined, based on the canopy rays measured in the forest inventory. The cloud of points was processed in the FUSION/LDV and a segmented raster was obtained in gap areas for each of the density of returns tested (37, 28, 18, 9, 4 and 1ppm², corresponding to the treatments of that study). The areas were grouped into three size classes, Class 1, Class 2 and Class 3 (34 – 149, 150 – 399 and greater than or equal 400m², respectively). The roads were identified by the spatial distribution pattern in the area with the aid of the DTM. The statistical analysis as oerformed in the R and the Kruskal-Wallis test was used to evaluated if there was a difference between the evaluated treatments, which did not show any significance at the 0.05 level, so the densities did not differ. Due to the identification of roads, the areas were reclassified into Small gaps, Large gaps and Roads. The number of areas in small gaps varied between treatments from 80.7 to 87.4% of total gaps, which is expected for areas smaller than 150m², in relation to the area, the variation was from 50.4 to 62,3%. By accounting for areas of gaps and roads, the Roads had the greatest coverage in the study area, varying between treatments from 68.3 to 55.5%. It was possible to infer that some gaps were opened by the activity of selective extraction of wood due to the spatial arrangement of the areas. Rare gaps were not identified, areas greater than 400m². Therefore, working with the reduction of the density of points did not affect the identification and delimitation of gaps in tropical forest. LiDAR technology has proven to be an effective tool for monitoring forest canopy disturbances. Thus, it can cover its application to the monitoring of the activity of forest management, deforestation and illegal logging in the Amazon.As florestas tropicais possuem grande importância na manutenção da biodiversidade, contudo sofrem com a exploração ilegal de madeira e o desmatamento. Por outro lado, práticas de manejo florestal sustentável contribuem para o uso racional dos recursos florestais. Dessa forma, é salutar que se empregue novas tecnologias e métodos para o acompanhamento dessas atividades. Este estudo objetivou identificar e delimitar clareiras em floresta tropical a partir de dados LiDAR com o emprego de diferentes densidades de retornos. A área de estudo se localiza na Fazenda Cauaxi, município de Paragominas-PA, na qual é desempenhada a atividade de manejo florestal. Foram obtidos dados de inventário florestal a partir de 22 parcelas de 20 x 500m, sendo medidas as árvores com DAP maior ou igual a 35cm. Os dados LiDAR foram obtidos em um voo que cobriu uma área de 1.216ha composta por 20 cenas constituídas de uma nuvem de pontos. Foi definida uma área mínima de clareira de 34m², baseada nos raios das copas medidas no inventário florestal. A nuvem de pontos foi processada no FUSION/LDV e obteve-se um raster segmentado em áreas de clareiras para cada uma das densidades de retornos testadas (37, 28, 18, 9, 4 e 1ppm², correspondendo aos tratamentos desse estudo). As áreas foram agrupadas em três classes de tamanhos, Classe 1, Classe 2 e Classe 3 (34 – 149, 150 – 399 e maior ou igual a 400m², respectivamente). As estradas foram identificadas pelo padrão da distribuição espacial na área com o auxílio do DTM. A análise estatística foi realizada no R utilizando-se o teste de Kruskal-Wallis para avaliar se houve diferença entre os tratamentos avaliados, o qual não acusou significância ao nível de 0,05, portanto as densidades não diferiram entre si. Devido à identificação das estradas, as áreas foram reclassificadas em Clareiras pequenas, Clareiras grandes e Estradas. O número de áreas em Clareiras pequenas variou entre os tratamentos de 80,7 a 87,4% do total de clareiras, o que é esperado para áreas menores que 150m², em relação à área, a variação foi de 50,4 a 62,3%. Contabilizando áreas de clareiras e estradas, a classe Estrada foi a que teve maior cobertura na área de estudo, variando entre os tratamentos de 68,3 a 55,5%. Foi possível inferir que determinadas clareiras foram abertas pela atividade de extração seletiva de madeira devido ao arranjo espacial das áreas. Não foram identificadas clareiras raras, áreas superiores a 400m². Portanto, trabalhar com a redução da densidade de pontos não prejudicou a identificação e delimitação de clareiras em floresta tropical. A tecnologia LiDAR se mostrou uma eficiente ferramenta para monitorar distúrbios no dossel florestal. Assim, podendo abranger sua aplicação ao monitoramento da atividade de manejo florestal, do desmatamento e exploração ilegal de madeira na Amazônia.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em Engenharia FlorestalUFSMBrasilRecursos Florestais e Engenharia FlorestalAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAbertura em dossel florestalEscaneamento laserFloresta tropicalDensidade de pontosForest canopy openningLaser scanningTropical forestPoint densityCNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTALDados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta AmazônicaSensor LiDAR data in the identification and characterization of gaps and roads in the Amazon forestinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisPereira, Rudiney Soareshttp://lattes.cnpq.br/9479801378014588Silva, Emanuel Araújohttp://lattes.cnpq.br/2765651276275384Hendges, Elvis Rabuskehttp://lattes.cnpq.br/5292160200165795http://lattes.cnpq.br/8279714190173249Favarin, José Augusto Spiazzi500200000003600a7274a7d-8dcd-466b-9a0d-c2b629e2ca88884d76ec-bf5a-4fa9-924d-b82833ea7a23e6d1c135-94cf-4a16-b760-f232683b96900c0a0148-f05d-43ce-aba0-d3a38ca2d07breponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGEF_2019_FAVARIN_JOSE.pdfDIS_PPGEF_2019_FAVARIN_JOSE.pdfDissertação de Mestradoapplication/pdf3653980http://repositorio.ufsm.br/bitstream/1/20243/1/DIS_PPGEF_2019_FAVARIN_JOSE.pdfa50e1bf52595176e570d0c7b99c67090MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica |
dc.title.alternative.eng.fl_str_mv |
Sensor LiDAR data in the identification and characterization of gaps and roads in the Amazon forest |
title |
Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica |
spellingShingle |
Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica Favarin, José Augusto Spiazzi Abertura em dossel florestal Escaneamento laser Floresta tropical Densidade de pontos Forest canopy openning Laser scanning Tropical forest Point density CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
title_short |
Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica |
title_full |
Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica |
title_fullStr |
Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica |
title_full_unstemmed |
Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica |
title_sort |
Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica |
author |
Favarin, José Augusto Spiazzi |
author_facet |
Favarin, José Augusto Spiazzi |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Pereira, Rudiney Soares |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/9479801378014588 |
dc.contributor.referee1.fl_str_mv |
Silva, Emanuel Araújo |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/2765651276275384 |
dc.contributor.referee2.fl_str_mv |
Hendges, Elvis Rabuske |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/5292160200165795 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8279714190173249 |
dc.contributor.author.fl_str_mv |
Favarin, José Augusto Spiazzi |
contributor_str_mv |
Pereira, Rudiney Soares Silva, Emanuel Araújo Hendges, Elvis Rabuske |
dc.subject.por.fl_str_mv |
Abertura em dossel florestal Escaneamento laser Floresta tropical Densidade de pontos |
topic |
Abertura em dossel florestal Escaneamento laser Floresta tropical Densidade de pontos Forest canopy openning Laser scanning Tropical forest Point density CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
dc.subject.eng.fl_str_mv |
Forest canopy openning Laser scanning Tropical forest Point density |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
description |
Tropical forests have great importance in maintaining biodiversity, yet suffer from illegal logging and deforestation. On the other hand, sustainable forest management practices contribute to the rational use of forest resources. In this way, it is salutary that new technologies and methods are used to follow these activities. This study aimed to identify and delimit gaps in tropical forest from LiDAR data with the use of different density of returns. The study area is located in Fazenda Cauaxi, munipality of Paragominas-PA, in which the forest management activity is carried out. Forest data were obtained from forest inventory from 22 plots of 20 x 500m, and trees with DBH greater than or equal to 35cm were measured. LiDAR data were obtained on a flight that covered an area of 1.216ha composed of 20 scenes consisting of a cloud of points. A minimum gap area of 34m² was defined, based on the canopy rays measured in the forest inventory. The cloud of points was processed in the FUSION/LDV and a segmented raster was obtained in gap areas for each of the density of returns tested (37, 28, 18, 9, 4 and 1ppm², corresponding to the treatments of that study). The areas were grouped into three size classes, Class 1, Class 2 and Class 3 (34 – 149, 150 – 399 and greater than or equal 400m², respectively). The roads were identified by the spatial distribution pattern in the area with the aid of the DTM. The statistical analysis as oerformed in the R and the Kruskal-Wallis test was used to evaluated if there was a difference between the evaluated treatments, which did not show any significance at the 0.05 level, so the densities did not differ. Due to the identification of roads, the areas were reclassified into Small gaps, Large gaps and Roads. The number of areas in small gaps varied between treatments from 80.7 to 87.4% of total gaps, which is expected for areas smaller than 150m², in relation to the area, the variation was from 50.4 to 62,3%. By accounting for areas of gaps and roads, the Roads had the greatest coverage in the study area, varying between treatments from 68.3 to 55.5%. It was possible to infer that some gaps were opened by the activity of selective extraction of wood due to the spatial arrangement of the areas. Rare gaps were not identified, areas greater than 400m². Therefore, working with the reduction of the density of points did not affect the identification and delimitation of gaps in tropical forest. LiDAR technology has proven to be an effective tool for monitoring forest canopy disturbances. Thus, it can cover its application to the monitoring of the activity of forest management, deforestation and illegal logging in the Amazon. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019-01-30 |
dc.date.accessioned.fl_str_mv |
2021-01-07T10:12:20Z |
dc.date.available.fl_str_mv |
2021-01-07T10:12:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/20243 |
url |
http://repositorio.ufsm.br/handle/1/20243 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
500200000003 |
dc.relation.confidence.fl_str_mv |
600 |
dc.relation.authority.fl_str_mv |
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dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Florestal |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Recursos Florestais e Engenharia Florestal |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
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UFSM |
institution |
UFSM |
reponame_str |
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collection |
Biblioteca Digital de Teses e Dissertações do UFSM |
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MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
_version_ |
1801485129104752640 |