Remote sensing based forest canopy opening and their spatial representation

The use of remote sensing in natural resource management is an easily accessible input for obtaining detailed information on the ground and landscape. There is a wide range of procedures to analyze the forest canopy through satellite images. The purpose of this work is to obtain a map of forest open...

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Autores principales:	Tania Fernández Vargas, Irma Trejo Vázquez, Raúl Aguirre Gómez
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	remote sensing canopy opening vegetation indexes tasseled cap Forestry SD1-669.5
Acceso en línea:	https://doaj.org/article/29545831bbd54976b5f29103f87b7c52
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id	oai:doaj.org-article:29545831bbd54976b5f29103f87b7c52
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spelling	oai:doaj.org-article:29545831bbd54976b5f29103f87b7c522021-11-17T14:21:59ZRemote sensing based forest canopy opening and their spatial representation2158-01032158-071510.1080/21580103.2021.2002198https://doaj.org/article/29545831bbd54976b5f29103f87b7c522021-11-01T00:00:00Zhttp://dx.doi.org/10.1080/21580103.2021.2002198https://doaj.org/toc/2158-0103https://doaj.org/toc/2158-0715The use of remote sensing in natural resource management is an easily accessible input for obtaining detailed information on the ground and landscape. There is a wide range of procedures to analyze the forest canopy through satellite images. The purpose of this work is to obtain a map of forest opening with remote sensing by relating several vegetation indices, Kauth-Thomas transformation and texture filters, to a Landsat 8OLI image. A factor analysis was made to evaluate the contribution of these variable to identify the opening of the forest cover, yielding a σ2 = 76%. The results show that the Modified Soil Adjusted Vegetation Index (MSAVI), Soil Adjusted Vegetation Index (SAVI), and brightness factor have the best correlation (0.225–0.216 component coefficient). The resulting model was reclassified into five categories of forest opening and associated with land use data from the National Institute of Statistics and Geography (INEGI-México). Thus, 95% of human settlements have a canopy opening between medium and very high, the crops areas 72%, and the low deciduous forest with secondary shrub vegetation 100% of the opening. Coniferous and mixed forests have a low to very low canopy opening 46% and 55%, respectively of their surface. The forests with secondary vegetation, both shrub and arboreal, present greater openness than the same forests in the primary state. Verification of the spatial representation data of canopy opening was made by comparing 94 hemispheric photographs with 94 sites located in open areas obtaining an r = 0.57. This work offers a simple and straightforward methodology, easily replicable in different types of vegetation using free satellite imagery. Hence, it is a helpful tool for decision-makers when considering the general status of conservation of forest systems and their spatial distribution.Tania Fernández VargasIrma Trejo VázquezRaúl Aguirre GómezTaylor & Francis Grouparticleremote sensingcanopy openingvegetation indexestasseled capForestrySD1-669.5ENForest Science and Technology, Vol 0, Iss 0, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	remote sensing canopy opening vegetation indexes tasseled cap Forestry SD1-669.5
spellingShingle	remote sensing canopy opening vegetation indexes tasseled cap Forestry SD1-669.5 Tania Fernández Vargas Irma Trejo Vázquez Raúl Aguirre Gómez Remote sensing based forest canopy opening and their spatial representation
description	The use of remote sensing in natural resource management is an easily accessible input for obtaining detailed information on the ground and landscape. There is a wide range of procedures to analyze the forest canopy through satellite images. The purpose of this work is to obtain a map of forest opening with remote sensing by relating several vegetation indices, Kauth-Thomas transformation and texture filters, to a Landsat 8OLI image. A factor analysis was made to evaluate the contribution of these variable to identify the opening of the forest cover, yielding a σ2 = 76%. The results show that the Modified Soil Adjusted Vegetation Index (MSAVI), Soil Adjusted Vegetation Index (SAVI), and brightness factor have the best correlation (0.225–0.216 component coefficient). The resulting model was reclassified into five categories of forest opening and associated with land use data from the National Institute of Statistics and Geography (INEGI-México). Thus, 95% of human settlements have a canopy opening between medium and very high, the crops areas 72%, and the low deciduous forest with secondary shrub vegetation 100% of the opening. Coniferous and mixed forests have a low to very low canopy opening 46% and 55%, respectively of their surface. The forests with secondary vegetation, both shrub and arboreal, present greater openness than the same forests in the primary state. Verification of the spatial representation data of canopy opening was made by comparing 94 hemispheric photographs with 94 sites located in open areas obtaining an r = 0.57. This work offers a simple and straightforward methodology, easily replicable in different types of vegetation using free satellite imagery. Hence, it is a helpful tool for decision-makers when considering the general status of conservation of forest systems and their spatial distribution.
format	article
author	Tania Fernández Vargas Irma Trejo Vázquez Raúl Aguirre Gómez
author_facet	Tania Fernández Vargas Irma Trejo Vázquez Raúl Aguirre Gómez
author_sort	Tania Fernández Vargas
title	Remote sensing based forest canopy opening and their spatial representation
title_short	Remote sensing based forest canopy opening and their spatial representation
title_full	Remote sensing based forest canopy opening and their spatial representation
title_fullStr	Remote sensing based forest canopy opening and their spatial representation
title_full_unstemmed	Remote sensing based forest canopy opening and their spatial representation
title_sort	remote sensing based forest canopy opening and their spatial representation
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/29545831bbd54976b5f29103f87b7c52
work_keys_str_mv	AT taniafernandezvargas remotesensingbasedforestcanopyopeningandtheirspatialrepresentation AT irmatrejovazquez remotesensingbasedforestcanopyopeningandtheirspatialrepresentation AT raulaguirregomez remotesensingbasedforestcanopyopeningandtheirspatialrepresentation
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Remote sensing based forest canopy opening and their spatial representation

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