Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion

Alexakis Dimitrios, Tapoglou Evdokia, Vozinaki Anthi-Eirini, Tsanis Ioannis

Simple record


URIhttp://purl.tuc.gr/dl/dias/1E698660-5AF5-4525-BDFD-8F3DA28C0CD7-
Identifierhttps://doi.org/10.3390/rs11091106-
Identifierhttps://www.mdpi.com/2072-4292/11/9/1106-
Languageen-
Extent21 pagesen
TitleIntegrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosionen
CreatorAlexakis Dimitriosen
CreatorΑλεξακης Δημητριοςel
CreatorTapoglou Evdokiaen
CreatorΤαπογλου Ευδοκιαel
CreatorVozinaki Anthi-Eirinien
CreatorΒοζινακη Ανθη-Ειρηνηel
CreatorTsanis Ioannisen
CreatorΤσανης Ιωαννηςel
PublisherMDPIen
Content SummarySoil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-06-15-
Date of Publication2019-
SubjectANNen
SubjectField spectroscopyen
SubjectGWRen
SubjectLandsat 8en
SubjectOLSRen
SubjectRemote sensingen
SubjectRUSLEen
SubjectSentinel-2en
SubjectSoil erosionen
Bibliographic CitationD.D. Alexakis, E. Tapoglou, A.K. Vozinaki and I.K. Tsanis "Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion," Remote Sens., vol. 11, no. 9, May 2019. doi: 10.3390/rs11091106en

Available Files

Services

Statistics