Detection of logging infrastructure in the state of rondônia using remotely sensed data

Pinagé, ER; Matricardi, EAT

Detection of logging infrastructure in the state of rondônia using remotely sensed data

Pinagé, ER

Matricardi, EAT

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Publication Type:: Journal Article
Citation:: Floresta e Ambiente, 2015, 22 (3), pp. 377 - 390
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Pinagé, ER https://orcid.org/0000-0003-3714-8175	en_US
dc.contributor.author	Matricardi, EAT	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Floresta e Ambiente, 2015, 22 (3), pp. 377 - 390	en_US
dc.identifier.issn	1415-0980	en_US
dc.identifier.uri	http://hdl.handle.net/10453/85300
dc.description.abstract	Logging lands (forest roads and log decks) are an underlying issue during selective logging activities, but they are responsible for most impacts on the forest. This study aimed to apply and assess the performance of five geoprocessing techniques on remotely sensed data using three different spatial resolutions to detect logging lands under forest management at the Jamari National Forest, state of Rondônia, Brazil. The research results showed that Normalized Difference Vegetation Index (NDVI) and Principal Components Analysis (PCA) presented the best overall accuracy using spatial resolutions of 5 and 10 meters, and 30 meters, respectively. Generally, the overall accuracy and Kappa statistics for the selectively logged forest classifications were not good (39.2% or lower, and 0.38 or lower, respectively). The low performance of the geoprocessing techniques is related to the subtle changes on the forest canopy cover under selective logging activities.	en_US
dc.relation.ispartof	Floresta e Ambiente	en_US
dc.relation.isbasedon	10.1590/2179-8087.064013	en_US
dc.title	Detection of logging infrastructure in the state of rondônia using remotely sensed data	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	22	en_US
utslib.for	0705 Forestry Sciences	en_US
utslib.for	0909 Geomatic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

Logging lands (forest roads and log decks) are an underlying issue during selective logging activities, but they are responsible for most impacts on the forest. This study aimed to apply and assess the performance of five geoprocessing techniques on remotely sensed data using three different spatial resolutions to detect logging lands under forest management at the Jamari National Forest, state of Rondônia, Brazil. The research results showed that Normalized Difference Vegetation Index (NDVI) and Principal Components Analysis (PCA) presented the best overall accuracy using spatial resolutions of 5 and 10 meters, and 30 meters, respectively. Generally, the overall accuracy and Kappa statistics for the selectively logged forest classifications were not good (39.2% or lower, and 0.38 or lower, respectively). The low performance of the geoprocessing techniques is related to the subtle changes on the forest canopy cover under selective logging activities.

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http://hdl.handle.net/10453/85300