619 research outputs found
Flood Mud Index (FMI): A Rapid and Effective Tool for Mapping Muddy Areas After Floods—The Valencia Case
No full textMapping flooded areas immediately after heavy rainfall is particularly challenging when sediment-laden floodwaters dominate the landscape. Traditional indices, such as the Normalized Difference Water Index (NDWI), are designed to detect water-covered areas but fail to identify muddy zones with high turbidity, which are common during extreme flood events. These muddy floodwaters often blend spectrally with surrounding land, leading to significant misclassifications. This study introduces the Flood Mud Index (FMI), a novel spectral index specifically developed to detect debris-laden flooded areas using only the red and blue bands. Landsat 8 imagery was utilized to validate the FMI, and its performance was evaluated through confusion matrices. The index achieved an overall accuracy of 97.86%, outperforming existing indices and demonstrating exceptional precision in delineating muddy floodplains. By relying solely on red and blue bands, the FMI is applicable to any platform equipped with RGB sensors, offering versatility for flood monitoring. Its compatibility with low-cost drones makes it especially valuable for rapid post-flood assessments, enabling immediate data collection even in scenarios with persistent cloud cover. The FMI addresses a critical gap in flood mapping, providing an effective tool for emergency response and management in sediment-rich environments
Comparison of different interpolation methods for DEM production
No full textSpatial interpolation, or the estimation of the variables at
unobserved locations in geographic space based on the values
at observed locations, is fundamental in all geophysical
sciences, first of all for the construction of digital elevation
model (DEM). Several methods are available in literature for
spatial interpolation and the choice of the most suitable of
them for building DEM, depends on many factors,
particularly on the distribution of the sampled points,
therefore, on the morphology of the area to be mapped.
This paper aims to choose the most appropriate interpolators
for DEM production, by comparing different methods usually
available in GIS software. For the purpose of developing the
best performing model and comparing interpolators, a set of
elevation data collected by digital vector map is used. The
accuracy of interpolation methods is tested by analyzing 4
statistic parameters, which are achieved by cross-validation
leave-one-out. Particularly, minimum, maximum, mean and
root mean square error (RMSE) are calculated for each
interpolation method considering the residual in each
sampling point between measured and interpolated value
Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach
No full textIn recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications
Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery
No full textThe monitoring of burned areas can easily be performed using satellite multispectral images: several indices are available in the literature for highlighting the differences between healthy vegetation areas and burned areas, in consideration of their different signatures. However, these indices may have limitations determined, for example, by the presence of clouds or water bodies that produce false alarms. To avoid these inaccuracies and optimize the results, this work proposes a new index for detecting burned areas named Normalized Burn Ratio Plus (NBR+), based on the involvement of Sentinel-2 bands. The efficiency of this index is verified by comparing it with five other existing indices, all applied on an area with a surface of about 500 km2 and covering the north-eastern part of Sicily (Italy). To achieve this aim, both a uni-temporal approach (single date image) and a bi-temporal approach (two date images) are adopted. The maximum likelihood classifier (MLC) is applied to each resulting index map to define the threshold separating burned pixels from non-burned ones. To evaluate the efficiency of the indices, confusion matrices are constructed and compared with each other. The NBR+ shows excellent results, especially because it excludes a large part of the areas incorrectly classified as burned by other indices, despite being clouds or water bodies
On the Accuracy of Geoid Heights Derived from Discrete GNSS/Levelling Data Using Kriging Interpolation
No full textLocal geoid models presenting higher resolution than global ones are generally derived by a combination of different datasets, integrating individual pure astrogeodetic, gravimetric and GNSS/levelling solutions. To define local geoid, different interpolators may be applied starting from dataset of geoid height values. It is well known that the accuracy of the resulting models depends not only by interpolation method, but also by points numerosity and distribution. This article aims to analyse the performance of Kriging approaches in dependence of the density of the dataset. The experiments are carried out on geoid heights extracted in random way from an already existing local geoid model: different subsets are organized containing an increasing number of points in the same area and each of them is submitted to Kriging interpolations (Universal Kriging and Ordinary Kriging). The resulting models are compared with the original one and residuals are calculated to evaluate the accuracy in dependence of point density. The results demonstrate the efficiency of the Kriging methods, highlighting the possibility to achieve higher accuracy (a few centimetres) using a point density of 1 point/100 sqkm, in absence of gravity anomalies. Ordinary Kriging provides better results than Universal Kriging but the undulations between the resulting models are minimal (a few millimetres) when a high number of points is involved. Furthermore, the results highlight the limit of the leave one out Cross validation since it supplies higher residuals than direct comparison for both Universal Kriging and Ordinary Kriging, when few points are used
GIS applications to support coastal erosion analysis: the case study of the Domitian littoral
No full textCoastal erosion is one of the problems that most afflicts coasts around the world, therefore, a thorough knowledge of this phenomenon and its evolution over time in each area is necessary to guide action planning. To reconstruct the morphological variations over time, at least as regards the position of the coastline, maps and remotely sensed images can be used and compared in GIS environment. The spatial analysis of the changes induced in an area requires the perfect overlay of all compared layers: we can reach this condition only carrying out georeferencing operations in accurate way and reporting all data to the same cartographic projection and datum. The aim of this work is to provide a method to measure the effects of coastal erosion and nourishment in terms of retreats and advances of the coastline and estimate the result accuracy. The attention is focused on Domitian coast (Italy), a prime example of areas that have been heavily affected by erosion in the past so as to require the construction of important defense works. Maps and remotely sensed images, in different cartographic datums, concerning this area and covering a time interval of 39 years (from 1974 to 2013), are analysed and harmonized using Quantum GIS software. The coastlines extracted from these layers are compared to define in quantitative way the coastline changes in the considered period. The experiments detect retreats and advancements reaching a few tens of meters; the results present different level of accuracy
GIS analysis for defining sea level rise effects on Sicily coasts for the end of the 21stcentury
No full textCoastal regions around the world are experiencing the increasing threat of sea level rise (SLR) due to climate change. Predictive models are needed to identify areas at risk of flooding and plan future activities aimed at avoiding, or at least limiting, damage to the natural environment, buildings, and people. Geographic Information Systems (GISs), through the processing of georeferenced data, allow not only to map the coastal areas that may be submerged, but also to carry out calculations and analysis to support further studies and insights. This paper examines the impact of SLR on the coasts of Sicily, Italy, using a Digital Elevation Model (DEM) of the study area (resolution: 20 m x 20 m) and GIS tools. The objective is to provide medium scale maps of the potential submerged zones for the end of the 21st century (2071-2100) as resulting from SLR values supplied by the Copernicus platform and identified as the IPCC (CMIP5) RCP4.5 scenario. The bathtub method available in literature is carried out using Quantum GIS (QGIS) software version 3.28. Attention is also focused on the limits and advantages of the adopted approach. The experiments demonstrate on the one hand the versatility of the GIS tools that allow the implementation of the bathtub method, on the other the seriousness of the SLR, given the considerable extension of the areas at risk of submersion as resulting from the maps produced for Sicily coasts
Integrating elevation and bathymetric data in GIS for a continuous 3D model of Ischia Island and surrounding seabed
No full textAutomation of pan-sharpening methods for pléiades images using GIS basic functions
No full textPan-sharpening methods allow the transfer of higher resolution panchromatic images to multispectral ones concerning the same scene. Different approaches are available in the literature, and only a part of these approaches is included in remote sensing software for automatic application. In addition, the quality of the results supplied by a specific method varies according to the characteristics of the scene; for consequence, different algorithms must be compared to find the best performing one. Nevertheless, pan-sharpening methods can be applied using GIS basic functions in the absence of specific pan-sharpening tools, but this operation is expensive and time-consuming. This paper aims to explain the approach implemented in Quantum GIS (QGIS) for automatic pan-sharpening of Pléiades images. The experiments are carried out on data concerning the Greek island named Lesbo. In total, 14 different pan-sharpening methods are applied to reduce pixel dimensions of the four multispectral bands from 2 m to 0.5 m. The automatic procedure involves basic functions already included in GIS software; it also permits the evaluation of the quality of the resulting images supplying the values of appropriate indices. The results demonstrate that the approach provides the user with the highest performing method every time, so the best possible fused products are obtained with minimal effort in a reduced timeframe
The importance of the coordinate transformation process in using heterogeneous data in coastal and marine geographic information system
No full textCoastal and Marine Geographic Information Systems (CMGISs) permit to collect, manage, and analyze a great amount of heterogeneous data concerning coastal, sea, and ocean environments, e.g., nautical charts, topographic maps, remotely sensed images. To integrate those heterogeneous layers in CMGIS, particular attention is necessary to ensure the perfect geo-localization of data, which is a basic requirement for the correct spatial analysis. In fact, the above-mentioned types of information sources are usually available in different cartographic projections, geodetic datum, and scale of representation. Therefore, automatic conversions supplied by Geographic Information System (GIS) software for layer overlay do not produce results with adequate positional accuracy. This paper aims to describe methodological aspects concerning different data integration in CMGIS in order to enhance its capability to handle topics of coastal and marine applications. Experiments are carried out to build a CMGIS of the Campania Region (Italy) harmonizing different data (maps and satellite images), which are heterogeneous for datum (World Geodetic System 1984 and European Datum 1950), projection (Mercator and Universal Transverse of Mercator), and scale of representation (large and medium scale). Results demonstrate that automatic conversion carried out by GIS software are insufficient to ensure levels of positional accuracy adequate for large scale representation. Therefore, additional operations such as those proposed in this work are necessary
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