Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica

Detalhes bibliográficos
Autor(a) principal: Favarin, José Augusto Spiazzi
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Manancial - Repositório Digital da 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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spelling Dados 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 forestAbertura em dossel florestalEscaneamento laserFloresta tropicalDensidade de pontosForest canopy openningLaser scanningTropical forestPoint densityCNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTALTropical 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESAs 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.Universidade Federal de Santa MariaBrasilRecursos Florestais e Engenharia FlorestalUFSMPrograma de Pós-Graduação em Engenharia FlorestalCentro de Ciências RuraisPereira, Rudiney Soareshttp://lattes.cnpq.br/9479801378014588Silva, Emanuel Araújohttp://lattes.cnpq.br/2765651276275384Hendges, Elvis Rabuskehttp://lattes.cnpq.br/5292160200165795Favarin, José Augusto Spiazzi2021-01-07T10:12:20Z2021-01-07T10:12:20Z2019-01-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/20243porAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2021-01-08T06:01:29Zoai:repositorio.ufsm.br:1/20243Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2021-01-08T06:01:29Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false
dc.title.none.fl_str_mv Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica
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.none.fl_str_mv Pereira, Rudiney Soares
http://lattes.cnpq.br/9479801378014588
Silva, Emanuel Araújo
http://lattes.cnpq.br/2765651276275384
Hendges, Elvis Rabuske
http://lattes.cnpq.br/5292160200165795
dc.contributor.author.fl_str_mv Favarin, José Augusto Spiazzi
dc.subject.por.fl_str_mv 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
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
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.none.fl_str_mv 2019-01-30
2021-01-07T10:12:20Z
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.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.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
Brasil
Recursos Florestais e Engenharia Florestal
UFSM
Programa de Pós-Graduação em Engenharia Florestal
Centro de Ciências Rurais
publisher.none.fl_str_mv Universidade Federal de Santa Maria
Brasil
Recursos Florestais e Engenharia Florestal
UFSM
Programa de Pós-Graduação em Engenharia Florestal
Centro de Ciências Rurais
dc.source.none.fl_str_mv reponame:Manancial - Repositório Digital da UFSM
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Manancial - Repositório Digital da UFSM
collection Manancial - Repositório Digital da UFSM
repository.name.fl_str_mv Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)
repository.mail.fl_str_mv atendimento.sib@ufsm.br||tedebc@gmail.com
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