Forest cover assessment using remote-sensing techniques in Crete Island, Greece

Remote-sensing satellite images provided rapid and continuous spectral and spatial information of the land surface in the Sougia River catchment by identifying the major changes that have taken place over 20 years (1995–2015). Vegetation indices (VIs) of normalized difference vegetation index (NDVI)...

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Autores principales:	Elhag Mohamed, Boteva Silevna, Al-Amri Nassir
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	change detection enhanced vegetation index leaf area index normalized difference vegetation index soil degradation Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/63299da9d5a647199f72d18deb05951a
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spelling	oai:doaj.org-article:63299da9d5a647199f72d18deb05951a2021-12-05T14:10:48ZForest cover assessment using remote-sensing techniques in Crete Island, Greece2391-544710.1515/geo-2020-0235https://doaj.org/article/63299da9d5a647199f72d18deb05951a2021-03-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0235https://doaj.org/toc/2391-5447Remote-sensing satellite images provided rapid and continuous spectral and spatial information of the land surface in the Sougia River catchment by identifying the major changes that have taken place over 20 years (1995–2015). Vegetation indices (VIs) of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index were derived for monitoring and mapping variations in vegetation cover. The quantified decrease in NDVI was found to be 4% between 1995 and 2005, and further decreased by 77.1% between 2005 and 2015; it declined back to almost the initial status of 1995. EVI results were inconsistent suggesting that seasonal crops influence the temporal distribution of vegetation cover. The temporal variations in the VIs were important input parameters for the modelling and management of the catchment’s hydrological behaviour. Image classification found that the 4- and the 6-class classifications between 1995 and 2005 were unstable and produced, respectively, a 13.8% and 16.2% total change between classes. Meanwhile, the 8-, 10- and the 12-class showed an almost horizontal line with a minor fluctuation of less than 0.05%. The results of the post-classification change detection analysis indicated a land degradation in terms of natural vegetation losses with sparser or even with no natural vegetation cover.Elhag MohamedBoteva SilevnaAl-Amri NassirDe Gruyterarticlechange detectionenhanced vegetation indexleaf area indexnormalized difference vegetation indexsoil degradationGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 345-358 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	change detection enhanced vegetation index leaf area index normalized difference vegetation index soil degradation Geology QE1-996.5
spellingShingle	change detection enhanced vegetation index leaf area index normalized difference vegetation index soil degradation Geology QE1-996.5 Elhag Mohamed Boteva Silevna Al-Amri Nassir Forest cover assessment using remote-sensing techniques in Crete Island, Greece
description	Remote-sensing satellite images provided rapid and continuous spectral and spatial information of the land surface in the Sougia River catchment by identifying the major changes that have taken place over 20 years (1995–2015). Vegetation indices (VIs) of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index were derived for monitoring and mapping variations in vegetation cover. The quantified decrease in NDVI was found to be 4% between 1995 and 2005, and further decreased by 77.1% between 2005 and 2015; it declined back to almost the initial status of 1995. EVI results were inconsistent suggesting that seasonal crops influence the temporal distribution of vegetation cover. The temporal variations in the VIs were important input parameters for the modelling and management of the catchment’s hydrological behaviour. Image classification found that the 4- and the 6-class classifications between 1995 and 2005 were unstable and produced, respectively, a 13.8% and 16.2% total change between classes. Meanwhile, the 8-, 10- and the 12-class showed an almost horizontal line with a minor fluctuation of less than 0.05%. The results of the post-classification change detection analysis indicated a land degradation in terms of natural vegetation losses with sparser or even with no natural vegetation cover.
format	article
author	Elhag Mohamed Boteva Silevna Al-Amri Nassir
author_facet	Elhag Mohamed Boteva Silevna Al-Amri Nassir
author_sort	Elhag Mohamed
title	Forest cover assessment using remote-sensing techniques in Crete Island, Greece
title_short	Forest cover assessment using remote-sensing techniques in Crete Island, Greece
title_full	Forest cover assessment using remote-sensing techniques in Crete Island, Greece
title_fullStr	Forest cover assessment using remote-sensing techniques in Crete Island, Greece
title_full_unstemmed	Forest cover assessment using remote-sensing techniques in Crete Island, Greece
title_sort	forest cover assessment using remote-sensing techniques in crete island, greece
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/63299da9d5a647199f72d18deb05951a
work_keys_str_mv	AT elhagmohamed forestcoverassessmentusingremotesensingtechniquesincreteislandgreece AT botevasilevna forestcoverassessmentusingremotesensingtechniquesincreteislandgreece AT alamrinassir forestcoverassessmentusingremotesensingtechniquesincreteislandgreece
_version_	1718371725101498368

Forest cover assessment using remote-sensing techniques in Crete Island, Greece

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