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 correlation of spectral indices were examined. In BU -GL -F categories similar NDVI values were calculated, but the other land cover types differed significantly. The water related indices (NDWI, MNDWI) were more effective (especially the MNDWI) to enhance water features, but the values of other categories ranged from narrower interval. Weak correlation were found among the indices due to the differences caused by the water land cover class. Statistically, most land cover types differed from each other, but in several cases similarities can be found when delineating vegetation with various water content. MNDWI was found as the most effective in highlighting water bodies.

Mapping and Characterizing Spatiotemporal Dynamics of Impervious Surfaces Using Landsat Images: A Case Study of Xuzhou, East China from 1995 to 2018

“…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.…”

“…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].…”

Identification of roofing materials with Discriminant Function Analysis and Random Forest classifiers on pan-sharpened WorldView-2 imagery – a comparison

“…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.…”

A CORINE felszínborítási térkép automatikus előállításának lehetősége döntésifa-osztályozó segítségével

“…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.…”