A fuzzy model integrating shoreline changes, NDVI and settlement influences for coastal zone human impact classification

Gonçalves, Rodrigo Mikosz; Saleem, Ashty; Queiroz, Heithor Alexandre de Araújo; Awange, Joseph

doi:10.1016/j.apgeog.2019.102093

Cited by 30 publications

(13 citation statements)

References 63 publications

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“…Automated data processing using the method of convolutional neural network (CNN) and color information was used by Kerkech et al (2018) for identifying infected areas of vegetation species. Gonçalves et al (2019) presented further steps in VIs applications by integrating NDVI with geomorphological classification and environmental GISanalysis. There are more reports on remote sensing image analysis using automated data processing presented in literature, for example Lemenkova (2016).…”

Section: Discussionmentioning

confidence: 99%

SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness

Lemenkova

2021

Contemporary Agriculture

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Summary The study presents a comparative analysis of eight Vegetation Indices (VIs) used to examine vegetation greenness over the northern coasts of Iceland. The geographical extent of the study area is set by the coordinates of the two fjords, Eyjafjörður and Skagafjörður, notable for their agricultural significance. Vegetation in Iceland is fragile due to the harsh climate, climate change, overgrazing and volcanic activity, which increase soil erosion. The study was conducted on a Landsat TM image using SAGA GIS as a technical tool for raster bands calculations. The NDVI dataset shows a range from -0.56 to 0.24, with 0 indicating ‘no vegetation’, and negative values – ‘other surfaces’ (e.g. rocks, open terrain). The DVI, compared to the NDVI, shows statistically non-normalized values ranging from -112 to 0, with extreme negative values while the coastal vegetation areas are badly distinguished from the water areas. The NRVI shows an extent from -0.24 to 0.48 with higher values for vegetation. The NRVI reduces topographic, solar and atmospheric effects and creates a normal data distribution. RVI shows a range in a dataset from 0.2 to 3.2 with vegetation in the river valleys clearly visible and depicted, while the water areas have values 0.8 to 1.0. The CTVI shows corrected TVI, in a data range -0.10 to 1.10, as the dataset of NDVI were negative. The TVI dataset ranges from 0.44 to 0.80 with the ice-covered areas and glaciers distinguishable and water values within a range from 0.60 to 0.64 and the vegetation from 0.60 to 0.44. The TTVI dataset ranges from 0.40 to 0.80 performing similarly to the TVI, but more refined with vegetation values 0.64 to 0.68. SAVI dataset ranges from -0.80 to 0.30 with minimized effects of soil on the vegetation through a constant soil adjustment factor added into the NDVI formula. The paper presents a comparison of eight VIs for Arctic vegetation monitoring. The overall behavior of SAGA GIS in calculation and mapping of the VIs is effective in terms of their use for vegetation mapping of the region.

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Section: Discussionmentioning

confidence: 99%

SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness

Lemenkova

2021

Contemporary Agriculture

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“…The Landsat group of satellites has been providing worldwide coverage for about three decades now. Landsat data has been used in a variety of applications, including land cover surveying [8]- [11] (soil, water, and vegetation) and land use (civilian and military) [5], [16], [18]. Images available through Landsat satellites are among the most commonly available data sets accessible by researchers with no more than ordinary access credentials.…”

Section: Data Used and Study Areamentioning

confidence: 99%

“…A variety of factors will contribute to the transformation of remotely sensed images into thematic maps remaining a critical issue [3]. Prominent among these factors are the complexity of the landscape being mapped, the particular selection of the remotely sensed data being processed and the suitability of the particular image processing and classification algorithms for an immediate purpose [5], [6].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

2022

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In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.

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“…Combined in mathematical equations, the VIs can give the numerical expression of the vegetation health. The most well-known VI is probably a normalized difference vegetation index (NDVI) often used in literature [17,48,49,34,35,19]. The VIs correlate with the leaf greenness and canopy density.…”

Section: Introductionmentioning

confidence: 99%

Distance-based vegetation indices computed by SAGA GIS: A comparison of the perpendicular and transformed soil adjusted approaches for the LANDSAT TM image

Lemenkova¹

2021

Poljoprivredna tehnika

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Landsat-TM of 2001 covering Iceland (15.5°W-21°W, 64.5°N-67°N) was processed using SAGA GIS for testing distance-based Vegetation Indices (VIs): four approaches of Perpendicular Vegetation Index (PVI) and two approaches of Transformed Soil Adjusted Vegetation Index TSAVI. The PVI of vegetation from the soil background line indicated healthiness as a leaf area index (LAI). The results showed that the reflectance for vegetation has a linear relation with soil background line. Four PVI models and two TSAVI shown coefficients of determination with LAI. The dataset demonstrate variations in the calculated coefficients. The mode in the histograms of the PVI based on four different algorithms show the difference:-7.1,-8.36, 2.78 and 7.0. The dataset for the two approaches of TSAVI: first case ranges in 4.4.-80.6 with a bell-shape mode of a histogram (8.09 to 23.29) for the first algorithm and an irregular shape for the second algorithm with several modes starting from 0.11 to 0.2 and decreasing to 0.26. SAGA GIS permits the calculation of PVI and TSAVI by computed NDVI based on the intersection of vegetation and soil background. Masking the NIR and R, a linear regression of grids was performed using an equation embedded in SAGA GIS. The advantages of the distance-based PVI and TSAVI consists in the adjusted position of pixels on the soil brightness line which refines it comparing to the slope-based VIs. The paper demonstrates SAGA GIS application in agricultural studies.

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A fuzzy model integrating shoreline changes, NDVI and settlement influences for coastal zone human impact classification

Cited by 30 publications

References 63 publications

SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness

SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

Distance-based vegetation indices computed by SAGA GIS: A comparison of the perpendicular and transformed soil adjusted approaches for the LANDSAT TM image

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