Quantitative assessment of desertification in an arid oasis using remote sensing data and spectral index techniques

Lamqadem, AA; Saber, H; Pradhan, B

Quantitative assessment of desertification in an arid oasis using remote sensing data and spectral index techniques

Lamqadem, AA Saber, H Pradhan, B

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Publication Type:: Journal Article
Citation:: Remote Sensing, 2018, 10 (12)
Issue Date:: 2018-12-01

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Field	Value	Language
dc.contributor.author	Lamqadem, AA	en_US
dc.contributor.author	Saber, H	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.issued	2018-12-01	en_US
dc.identifier.citation	Remote Sensing, 2018, 10 (12)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130803
dc.description.abstract	© 2018 by the authors. Desertification is an environmental problem worldwide. Remote sensing data and technique offer substantial information for mapping and assessment of desertification. Desertification is one of the most serious forms of environmental threat in Morocco, especially in the oases in the south-eastern part of the country. This study aims to map the degree of desertification in middle Draa Valley in 2017 using a Sentinel-2 MSI (multispectral instrument) image. Firstly, three indices, namely, tasselled cap brightness (TCB), greenness (TCG) and wetness (TCW) were extracted using the tasselled cap transformation method. Secondly, other indices, such as normalized difference vegetation index (NDVI) and albedo, were retrieved. Thirdly, a linear regression analysis was performed on NDVI-albedo, TCG-TCB and TCW-TCB combinations. Results showed a higher correlation between TCW and TCB (r = -0.812) than with that of the NDVI-albedo (r = -0.50). On the basis of this analysis, a desertification degree index was developed using the TCW-TCB feature space classification. A map of desertification grades was elaborated and divided into five classes, namely, nondesertification, low, moderate, severe and extreme levels. Results indicated that only 6.20% of the study area falls under the nondesertification grade, whereas 26.92% and 32.85% fall under the severe and extreme grades, respectively. The employed method was useful for the quantitative assessment of desertification with an overall accuracy of 93.07%. This method is simple, robust, powerful, and easy to use for the management and protection of the fragile arid and semiarid lands.	en_US
dc.relation.ispartof	Remote Sensing	en_US
dc.relation.isbasedon	10.3390/rs10121862	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Quantitative assessment of desertification in an arid oasis using remote sensing data and spectral index techniques	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	10	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0502 Environmental Science and Management	en_US
utslib.for	0203 Classical Physics	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 Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

© 2018 by the authors. Desertification is an environmental problem worldwide. Remote sensing data and technique offer substantial information for mapping and assessment of desertification. Desertification is one of the most serious forms of environmental threat in Morocco, especially in the oases in the south-eastern part of the country. This study aims to map the degree of desertification in middle Draa Valley in 2017 using a Sentinel-2 MSI (multispectral instrument) image. Firstly, three indices, namely, tasselled cap brightness (TCB), greenness (TCG) and wetness (TCW) were extracted using the tasselled cap transformation method. Secondly, other indices, such as normalized difference vegetation index (NDVI) and albedo, were retrieved. Thirdly, a linear regression analysis was performed on NDVI-albedo, TCG-TCB and TCW-TCB combinations. Results showed a higher correlation between TCW and TCB (r = -0.812) than with that of the NDVI-albedo (r = -0.50). On the basis of this analysis, a desertification degree index was developed using the TCW-TCB feature space classification. A map of desertification grades was elaborated and divided into five classes, namely, nondesertification, low, moderate, severe and extreme levels. Results indicated that only 6.20% of the study area falls under the nondesertification grade, whereas 26.92% and 32.85% fall under the severe and extreme grades, respectively. The employed method was useful for the quantitative assessment of desertification with an overall accuracy of 93.07%. This method is simple, robust, powerful, and easy to use for the management and protection of the fragile arid and semiarid lands.

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http://hdl.handle.net/10453/130803