Methodology for bare soil detection and discrimination by Landsat TM image

Dematte, JA; Huete, A; Ferreira, LG; Nanni, MR; Fiorio, PR

Methodology for bare soil detection and discrimination by Landsat TM image

Dematte, JA Huete, A

Ferreira, LG Nanni, MR Fiorio, PR

Permalink

Publisher:: Bentham Science Publishers
Publication Type:: Journal Article
Citation:: The Open Remote Sensing Journal, 2009, 2 (1), pp. 24 - 35
Issue Date:: 2009-01

Closed Access

	Filename	Description	Size
	2010001585OK.pdf		4.36 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Dematte, JA	en_US
dc.contributor.author	Huete, A https://orcid.org/0000-0003-2809-2376	en_US
dc.contributor.author	Ferreira, LG	en_US
dc.contributor.author	Nanni, MR	en_US
dc.contributor.author	Fiorio, PR	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	The Open Remote Sensing Journal, 2009, 2 (1), pp. 24 - 35	en_US
dc.identifier.issn	1875-4139	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14718
dc.description.abstract	The objective of this work was to develop and test a remote sensing technique to determine bare soils with pixel information from satellite images. The methodology was tested and improved on a 2,805 km2 area located in the state of São Paulo, Brazil. The pixel data from a Landsat-5/TM image was transformed into reflectances. 294 pixels were evaluated by five factors simultaneously and included the following: color composition image; vegetation index; soil brightness information (soil line concept), and a comparison between spectral curve of the pixel with spectral patterns of soils. A validation procedure was based on the discriminate analysis for the real soil related with each pixel. For this, a soil map was overlaid onto the image, and the pixels were related to its respective soil class. Soil brightness variations were readily observed in the spectral curves and in red-NIR features and corresponded to differences in texture and particle size as well in iron and organic matter content. Although qualitative, the observation of color composition was useful for pixel identification. The soil line concept was very useful as it presented a high R2 coefficient (0.90). Comparison between ground level soil spectral curves with satellite information could assist on the evaluation of the real format of the curves. Discriminate analysis indicated a 99.3% correct classification of the soils. Field work validation indicated 90% significance. The present method could help researchers acquire valuable information (i.e., soil attributes quantification), when soil data must be acquired from satellite images.	en_US
dc.publisher	Bentham Science Publishers	en_US
dc.relation.ispartof	The Open Remote Sensing Journal	en_US
dc.title	Methodology for bare soil detection and discrimination by Landsat TM image	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	2	en_US
utslib.for	040699 Physical Geography and Environmental Geoscience not elsewhere classified	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/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	2	en_US

Abstract:

The objective of this work was to develop and test a remote sensing technique to determine bare soils with pixel information from satellite images. The methodology was tested and improved on a 2,805 km2 area located in the state of São Paulo, Brazil. The pixel data from a Landsat-5/TM image was transformed into reflectances. 294 pixels were evaluated by five factors simultaneously and included the following: color composition image; vegetation index; soil brightness information (soil line concept), and a comparison between spectral curve of the pixel with spectral patterns of soils. A validation procedure was based on the discriminate analysis for the real soil related with each pixel. For this, a soil map was overlaid onto the image, and the pixels were related to its respective soil class. Soil brightness variations were readily observed in the spectral curves and in red-NIR features and corresponded to differences in texture and particle size as well in iron and organic matter content. Although qualitative, the observation of color composition was useful for pixel identification. The soil line concept was very useful as it presented a high R2 coefficient (0.90). Comparison between ground level soil spectral curves with satellite information could assist on the evaluation of the real format of the curves. Discriminate analysis indicated a 99.3% correct classification of the soils. Field work validation indicated 90% significance. The present method could help researchers acquire valuable information (i.e., soil attributes quantification), when soil data must be acquired from satellite images.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/14718