2016
DOI: 10.21120/le/10/3-4/13
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Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories

Abstract: The remote sensing techniques provide a great possibility to analyze the environmental processes in local or global scale. Landsat images with their 30 m resolution are suitable among others for land cover mapping and change monitoring. In this study three spectral indices (NDVI, NDWI, MNDWI) were investigated from the aspect of land cover types: water body (W); plough land (PL); forest (F); vineyard (V); grassland (GL) and built-up areas (BU) using Landsat-7 ETM+ data. The range, the dissimilarities and the c… Show more

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Cited by 118 publications
(56 citation statements)
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“…Water bodies should first be removed from the images to avoid noise as it would reduce unmixing errors caused by low-albedo impervious surfaces [44]. Existing methods of extracting water include principal component analysis (PCA) [45], spectral indices [46], and (un-)supervised classification [44]. Due to its simplification and accuracy, the modified normalized difference water index (MNDWI) [46], a widely used spectral index, was adopted to obtain and mask water body from the remote sensing data.…”
Section: Linear Spectral Mixture Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…Water bodies should first be removed from the images to avoid noise as it would reduce unmixing errors caused by low-albedo impervious surfaces [44]. Existing methods of extracting water include principal component analysis (PCA) [45], spectral indices [46], and (un-)supervised classification [44]. Due to its simplification and accuracy, the modified normalized difference water index (MNDWI) [46], a widely used spectral index, was adopted to obtain and mask water body from the remote sensing data.…”
Section: Linear Spectral Mixture Analysismentioning
confidence: 99%
“…Existing methods of extracting water include principal component analysis (PCA) [45], spectral indices [46], and (un-)supervised classification [44]. Due to its simplification and accuracy, the modified normalized difference water index (MNDWI) [46], a widely used spectral index, was adopted to obtain and mask water body from the remote sensing data. Then, we adopted brightness normalization to decrease intra-class variability [18,19,30], and the minimum noise fraction (MNF) was adopted to separate the noise in the data and improve the quality of the endmember selection [18].…”
Section: Linear Spectral Mixture Analysismentioning
confidence: 99%
“…We are witnessing the increasing relevance of remote sensing in all areas of life. The first applications aimed at the analysis of land use and land cover (LULC), and then, parallel with the wider palette of satellite and aerial images, the detection of changes became the focus of research (Szabó, S. et al 2016;Gulácsi, A. and Kovács, F. 2018). The geometric resolution of images has improved from 80-100 m to about 1 m over the last 30-40 years; furthermore, there are images (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…A döntésifa-módszer hatékonyságának (térképpontosságának) növelése érdekében további kettő, 2000. április 30-án és július 3-án készült Landsat 7-es műholdfelvételt, illetve SRTM magassági adatokat vontuk be az elemzésbe. Ezen távérzékelt adatokon kívül további információkat tartalmazó rétegeket vettünk be az elemzésbe, melyeket gyakran használnak a tájértékelési és a felszínborítással kapcsolatos elemzések során (Hussein et al 2017, Szabó et al 2016, Szilassi et al 2017 A modell építéséhez három alaplépést ismétel az algoritmus: 1. ellenőrzi, hogy az ágon lévő összes eset egy osztályba tartozik-e, ha igen akkor az egy végződés (levél) lesz és megkapja az osztály nevét, ha nem, akkor az adatok szétválasztása tovább folyik. 2. minden attribútumra az információ és információnyereség kiszámítása; 3. a számítások alapján a legjobb attribútum kiválasztása a felosztáshoz és a felosztás elvégzése.…”
Section: Adatokunclassified