Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/110738
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dc.contributor.authorCharrua, Alberto Bento-
dc.contributor.authorPadmanaban, Rajchandar-
dc.contributor.authorCabral, Pedro-
dc.contributor.authorBandeira, Salomão-
dc.contributor.authorRomeiras, Maria M.-
dc.date.accessioned2021-01-25T23:35:33Z-
dc.date.available2021-01-25T23:35:33Z-
dc.date.issued2021-01-02-
dc.identifier.issn2072-4292-
dc.identifier.otherPURE: 27693932-
dc.identifier.otherPURE UUID: 027ffd73-0900-4905-a1e4-c6fcbc2c16c0-
dc.identifier.otherScopus: 85099247952-
dc.identifier.otherORCID: /0000-0001-8622-6008/work/87549644-
dc.identifier.otherWOS: 000611554400001-
dc.identifier.urihttp://hdl.handle.net/10362/110738-
dc.description.abstractThe Central Region of Mozambique (Sofala Province) bordering on the active cyclone area of the southwestern Indian Ocean has been particularly affected by climate hazards. The Cyclone Idai, which hit the region in March 2019 with strong winds causing extensive flooding and a massive loss of life, was the strongest recorded tropical cyclone in the Southern Hemisphere. The aim of this study was to use pre-and post-cyclone Idai Landsat satellite images to analyze temporal changes in Land Use and Land Cover (LULC) across the Sofala Province. Specifically, we aimed—(i) to quantify and map the changes in LULC between 2012 and 2019; (ii) to investigate the correlation between the distance to Idai’s trajectory and the degree of vegetation damage, and (iii) to determine the damage caused by Idai on different LULC. We used Landsat 7 and 8 images (with 30 m resolution) taken during the month of April for the 8-year period. The April Average Normalized Difference Vegetation Index (NDVI) over the aforementioned period (2012–2018, pre-cyclone) was compared with the values of April 2019 (post-cyclone). The results showed a decreasing trend of the productivity (NDVI 0.5 to 0.8) and an abrupt decrease after the cyclone. The most devastated land use classes were dense vegetation (decreased by 59%), followed by wetland vegetation (−57%) and shrub land (−56%). The least damaged areas were barren land (−23%), barren vegetation (−27%), and grassland and dambos (−27%). The Northeastern, Central and Southern regions of Sofala were the most devastated areas. The Pearson Correlation Coefficient between the relative vegetation change activity after Idai (NDVI%) and the distance to Idai’s trajectory was 0.95 (R-square 0.91), suggesting a strong positive linear correlation. Our study also indicated that the LULC type (vegetation physiognomy) might have influenced the degree of LULC damage. This study provides new insights for the management and conservation of natural habitats threatened by climate hazards and human factors and might accelerate ongoing recovery processes in the Sofala Province.en
dc.format.extent17-
dc.language.isoeng-
dc.rightsopenAccess-
dc.subjectCyclone Idai-
dc.subjectLand use and land cover-
dc.subjectRemote sensing-
dc.subjectVegetation damage-
dc.subjectVegetation index-
dc.subjectEarth and Planetary Sciences(all)-
dc.subjectSDG 1 - No Poverty-
dc.subjectSDG 3 - Good Health and Well-being-
dc.subjectSDG 10 - Reduced Inequalities-
dc.subjectSDG 15 - Life on Land-
dc.subjectSDG 13 - Climate Action-
dc.titleImpacts of the tropical cyclone idai in Mozambique-
dc.typearticle-
degois.publication.firstPage1-
degois.publication.issue2-
degois.publication.lastPage17-
degois.publication.titleRemote Sensing-
degois.publication.volume13-
dc.peerreviewedyes-
dc.identifier.doihttps://doi.org/10.3390/rs13020201-
dc.description.versionpublishersversion-
dc.description.versionpublished-
dc.title.subtitleA multi-temporal landsat satellite imagery analysis-
dc.contributor.institutionNOVA School of Business and Economics (NOVA SBE)-
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School-
dc.contributor.institutionNOVA Information Management School (NOVA IMS)-
Aparece nas colecções:NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)
Home collection (NSBE)

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