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Identification of wet areas in forest using remote sensing data

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dc.contributor.authorIvanovs, J.
dc.contributor.authorLupikis, A.
dc.date.accessioned2018-09-04T11:17:39Z
dc.date.available2018-09-04T11:17:39Z
dc.date.issued2018
dc.descriptionArticleeng
dc.description.abstractAim of this study is to evaluate different remote sensing indices to detect spatial distribution of wet soils using GIS based algorithms. Ar ea of this study represents different soil types on various quaternary deposits as well as different forest types. We analyzed 25 sites with the area of 1 km 2 each in central and western part of Latvia. Data about soil characteristics like thickness of pea t layer and presence of reductimorphic colors in soil was collected during field surveys in 228 random points within study sites. ANOVA test for comparing means of different soil wetness classes and binary logistic regression analysis for evaluating the ac curacy of different remote sensing indices to model spatial distribution of wet areas are used for analysis. Main conclusion of this study is that for different quaternary deposits and soil texture classes different algorithms for soil wetness prediction s hould be used. Data layers for predicting soil wetness in this study are various modifications and resolutions of digital elevation model like depressions, slope and SAGA wetness index as well as Sentinel - 2 multispectral satellite imagery. Accuracy of soil wetness classification of soils on moraine, fluvial and eolian sediments exceeds 94%, whereas on the clayey sediments it is close to 80%.eng
dc.identifier.issn1406-894X
dc.identifier.publicationAgronomy Research, 2018, vol. 16, no. 5, pp. 2049-2055eng
dc.identifier.urihttp://hdl.handle.net/10492/4452
dc.identifier.urihttp://dx.doi.org/10.15159/ar.18.192
dc.rights.holderCopyright 2009 by Estonian University of Life Sciences, Latvia University of Agriculture, Aleksandras Stulginskis University, Lithuanian Research Centre for Agriculture and Forestry. No part of this publication may be reproduced or transmitted in any form, or by any means, electronic or mechanical, incl. photocopying, electronic recording, or otherwise without the prior written permission from the Estonian University of Life Sciences, Latvia University of Agriculture, Aleksandras Stulginskis University, Lithuanian Research Centre for Agriculture and Forestryeng
dc.subjectDEMeng
dc.subjectsatellite imageryeng
dc.subjectquaternary depositseng
dc.subjectarticleseng
dc.titleIdentification of wet areas in forest using remote sensing dataeng
dc.typeArticleeng

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2018, Vol. 16, No. 5