Alfonso Vitti

A GIS decision support system for regional forest management to assess biomass availability for renewable energy production

Currently, the use of a mix of renewable and traditional energy sources is deemed to help in solving increasing energy demands and environmental issues, thus making it particularly important to assess the availability of renewable energy sources. In a heavily forested region, such as the Italian Alps, one of the main renewable energy sources is woody biomass. A reliable evaluation of biomass availability must take into account the local management of forest resources and the ability to reach forest areas, which is related to existing road networks, and the characteristics and morphology of the terrain. We have developed a new methodology to estimate forest biomass availability for energy production in the Alpine area and to support management decisions, combining the morphological features of the mountain landscape with the current capabilities of forest technology. The approach has been implemented in a tool for forest biomass evaluation based on the Free and Open Source Software for Geospatial (FOSS4G) framework and to refine the current estimates made by the local government. The methodology was tested on the forests of Trentino province (Italy), providing an accurate evaluation of biomass availability, which can be effectively used to identify possible locations for biomass power plants and to suggest new forest management guidelines. The methodology, combining GRASS, PostgreSQL and PostGIS, can be applied to a wide area and can also be executed as a new GRASS module. Being open source it is already available for testing and development.

Development and Application of 2D and 3D GRASS Modules for Simulation of Thermally Driven Slope Winds

The ability to manage and process fully three-dimensional information has only recently been made available for a few Geographical Information Systems (GIS). An example of integrated and complementary use of 2D and 3D GRASS modules for the evaluation and representation of thermally induced slope winds over complex terrain is presented. The analytic solution provided by Prandtl (1942) to evaluate wind velocity and (potential) temperature anomaly induced by either diurnal heating or nocturnal cooling on a constant angle slope is adopted to evaluate wind and temperature profiles at any point over both idealised and real complex terrain. As these quantities depend on the slope angle of the ground and on the distance from the slope surface suitable procedures are introduced to determine the coordinate n of a point in the 3D volume measured along the direction locally normal to the terrain surface. A new GRASS module has been developed to evaluate this quantity and to generate a 3D raster file where each cell is assigned the value of the cell on the surface belonging to the normal vector. The application of the algorithm implemented in

Sigseg: a tool for the detection of position and velocity discontinuities in geodetic time-series

The detection of discontinuities in geodetic data is an ever more important topic due to both the influence of discontinuities on the results of models and analyses, and to the very meaning of discontinuities in physical phenomena. We consider and describe a mathematical model, originally formulated for the approximation of images by smooth functions, in one dimension (1D). The model had been designed to smooth the data while preserving and detecting its discontinuities following a variational approach. A second and more complex model for the approximation of images by functions with smooth first derivatives is also available. This second model had been designed to smooth the data while preserving and detecting the discontinuities of the data, but also those of the first derivative. Such interesting features suggest the chance to apply this second model to 1D geodetic data, in particular for the detection of discontinuities and velocity changes in position time-series rather than for signal smoothing. The sigseg (signal segmentation) program implements the two variational models in 1D and is presented with applications to geodetic data. The essential mathematical elements are sketched, and some details on the numerical implementation are given.

Reconstruction of River Boundaries at Sub-Pixel Resolution: Estimation and Spatial Allocation of Water Fractions

Boundary pixels of rivers are subject to a spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super-resolution mapping (SRM) are conducted in two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for the normalized difference water index (OBA-NDWI) is proposed for identifying the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) algorithm and interpolation-based algorithms are also applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2 = 0.9, RMSE = 7% for eight-band WorldView-3 (WV-3) image and R2 = 0.87, RMSE = 9% for GeoEye image). The spectral bands of WV-3 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas by about 10% with respect to conventional hard classification.

A Practical Quality Index for the Applied Helmert Transformation

AbstractThe Helmert transformation is used in different procedures in geodesy, photogrammetry, and in general in geomatics. The assessment of the accuracy of the transformation, and in particular of the transformed coordinates, is often critical. In this paper, a new overall quality index describing the accuracy of the transformed points is proposed. This index was derived from the spectral radius of the variance-covariance matrix of the transformed coordinates by writing an expression that represents an upper bound of this quantity. The proposed index is fast to compute and expresses the extent of the uncertainty propagation in a clear and synthetic way. The paper presents the rationale behind the choice of the index and the algebraic steps for its computation. Finally, two examples, one using synthetic data and one involving the coregistration of a point cloud acquired with a laser scanner, are presented.

Sub-pixel mapping of water boundaries using pixel swapping algorithm (case study: Tagliamento River, Italy)

Taking the advantages of remotely sensed data for mapping and monitoring of water boundaries is of particular importance in many different management and conservation activities. Imagery data are classified using automatic techniques to produce maps entering the water bodies’ analysis chain in several and different points. Very commonly, medium or coarse spatial resolution imagery is used in studies of large water bodies. Data of this kind is affected by the presence of mixed pixels leading to very outstanding problems, in particular when dealing with boundary pixels. A considerable amount of uncertainty inescapably occurs when conventional hard classifiers (e.g., maximum likelihood) are applied on mixed pixels. In this study, Linear Spectral Mixture Model (LSMM) is used to estimate the proportion of water in boundary pixels. Firstly by applying an unsupervised clustering, the water body is identified approximately and a buffer area considered ensuring the selection of entire boundary pixels. Then LSMM is applied on this buffer region to estimate the fractional maps. However, resultant output of LSMM does not provide a sub-pixel map corresponding to water abundances. To tackle with this problem, Pixel Swapping (PS) algorithm is used to allocate sub-pixels within mixed pixels in such a way to maximize the spatial proximity of sub-pixels and pixels in the neighborhood. The water area of two segments of Tagliamento River (Italy) are mapped in sub-pixel resolution (10m) using a 30m Landsat image. To evaluate the proficiency of the proposed approach for sub-pixel boundary mapping, the image is also classified using a conventional hard classifier. A high resolution image of the same area is also classified and used as a reference for accuracy assessment. According to the results, sub-pixel map shows in average about 8 percent higher overall accuracy than hard classification and fits very well in the boundaries with the reference map.

Bayesian and Variational Methods for Discontinuity Detection: Theory Overview and Performance Comparison

Discontinuity detection is of great relevance at different stages of the processing and analysis of geodetic time-series of data. This paper is essentially a review of two possible methods. The first method follows a stochastic approach and exploits the Bayesian theory to compute the posterior distributions of the discontinuity parameters. The epoch and the amplitude of the discontinuity are then selected as maximum a posteriori (MAP). The second method follows a variational approach based on the Mumford and Shah functional to segment the time-series and to detect the discontinuities. Whereas the original formulation was developed in a continuous form, discrete approaches are also available presenting some interesting connections with robust regressions. Both the methods have been applied to identify the occurrence of cycle-slips in GNSS phase measurements. Simulated and real data have been processed to compare the performance and to evaluate pros and cons of the two approaches. Results clearly show that both the methods can successfully identify cycle-slips.

A novel approach for bathymetry of shallow rivers based on spectral magnitude and shape predictors using stepwise regression

Spatial heterogeneities of substrate type, water-surface roughness and also inherent optical properties (IOPs) of the water column can pose substantial challenges to optical remote sensing of fluvial bathymetry. Development of robust techniques with respect to the optical complexities of riverine environments is then central to produce accurate bathymetry maps over large spatial extents. The empirical (regression-based) techniques (e.g., Lyzenga’s model) have widely been applied for estimation of bathymetry from optical imagery in inland/coastal waters. The models in the literature are built upon only magnitude-related predictors derived from spectral radiances/reflectances at different bands. However, optically complicating factors such as variations in bottom type and water column constituents can change not only the magnitude but also the shape of water-leaving spectra. This research incorporates spectral derivatives as shaperelated predictors in order to enhance the description of spectra through the regression-based depth retrieval. A stepwise regression is utilized to select the optimal predictors among all the possible Lyzenga (i.e., magnitude-related) and derivative (i.e., shape-related) predictors. Radiative transfer simulations are used to examine the bathymetry models in optically-complex shallow rivers by considering variable bottom-types and IOPs. The methods are also applied to a WorldView-3 image of the Sarca River located in Italian Alps and resultant bathymetry estimates are assessed using insitu measurements. The results indicate the effectiveness of spectral derivatives in improving the accuracies of depth retrievals particularly for optically-complex waters.

Grain size mapping in shallow rivers using spectral information: a lab spectroradiometry perspective

Every individual attribute of a riverine environment defines the overall spectral signature to be observed by an optical sensor. The spectral characteristic of riverbed is influenced not only by the type but also the roughness of substrates. Motivated by this assumption, potential of optical imagery for mapping grain size of shallow rivers (< 1 m deep) is examined in this research. The previous studies concerned with grain size mapping are all built upon the texture analysis of exposed bed material using very high resolution (i.e. cm resolution) imagery. However, the application of texturebased techniques is limited to very low altitude sensors (e.g. UAVs) to ensure the sufficient spatial resolution. Moreover, these techniques are applicable only in the presence of exposed substrates along the river channel. To address these drawbacks, this study examines the effectiveness of spectral information to make distinction among grain sizes for submerged substrates. Spectroscopic experiments are performed in controlled condition of a hydraulic lab. The spectra are collected over a water flume in a range of water depths and bottoms with several grain sizes. A spectral convolution is performed to match the spectra to WorldView-2 spectral bands. The material type of substrates is considered the same for all the experiments with only variable roughness/size of grains. The spectra observed over dry beds revealed that the brightness/reflectance increases with the grain size across all the spectral bands. Based on this finding, the above-water spectra over a river channel are simulated considering different grain sizes in the bottom. A water column correction method is then used to retrieve the bottom reflectances. Then the inferred bottom reflectances are clustered to segregate among grain sizes. The results indicate high potential of the spectral approach for clustering grain sizes (overall accuracy of 92%) which opens up some horizons for mapping this valuable attribute of rivers using remotely sensed data.

Improving the Accuracies of Bathymetric Models Based on Multiple Regression for Calibration (Case Study: Sarca River, Italy)

The optical imagery has the potential for extraction of spatially and temporally explicit bathymetric information in inland/coastal waters. Lyzenga’s model and optimal band ratio analysis (OBRA) are main bathymetric models which both provide linear relations with water depths. The former model is sensitive and the latter is quite robust to substrate variability. The simple regression is the widely used approach for calibration of bathymetric models either Lyzenga’s model or OBRA model. In this research, a multiple regression is examined for empirical calibration of the models in order to take the advantage of all spectral channels of the imagery. This method is applied on both Lyzenga’s model and OBRA model for the bathymetry of a shallow Alpine river in Italy, using WorldView-2 (WV-2) and GeoEye images. Insitu depths are recorded using RTK GPS in two reaches. One-half of the data is used for calibration of models and the remaining half as independent check-points for accuracy assessment. In addition, radiative transfer model is used to simulate a set of spectra in a range of depths, substrate types, and water column properties. The simulated spectra are convolved to the sensors’ spectral bands for further bathymetric analysis. Investigating the simulated spectra, it is concluded that the multiple regression improves the robustness of the Lyzenga’s model with respect to the substrate variability. The improvements of multiple regression approach are much more pronounced for the Lyzenga’s model rather than the OBRA model. This is in line with findings from real imagery; for instance, the multiple regression applied for calibration of Lyzenga’s and OBRA models demonstrated, respectively, 22% and 9% higher determination coefficients (R2) as well as 3 cm and 1 cm better RMSEs compared to the simple regression using the WV-2 image.