Search results for "REMOTE"
showing 10 items of 1455 documents
Exploring the validity of the long term data record V4 database for land surface monitoring
2015
The last (and final) version of the Long Term Data Record (LTDR) — Version 4 — has been released recently by NASA. This database includes daily information for all AVHRR (Advanced Very High Resolution Radiometer) channels, as well as ancillary data, since July 1981 up to present. This database is the longest available record of remotely sensed data useful for land surface monitoring, since it allows the estimation of vegetation indices at daily resolution, as well as the daily estimation of land surface temperature (LST). Here, we analyze the fitness of this database for land surface monitoring. To that end, we first estimated NDVI (Normalized Difference Vegetation Index), LST, as well as e…
Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications
2009
Interest in remote sensing (RS) of solar-induced chlorophyll fluorescence (F) by terrestrial vegetation is motivated by the link of F to photosynthetic efficiency which could be exploited for large scale monitoring of plant status and functioning. Today, passive RS of F is feasible with different prototypes and commercial ground-based, airborne, and even spaceborne instruments under certain conditions. This interest is generating an increasing number of research projects linking F and RS, such as the development of new F remote retrieval techniques, the understanding of the link between the F signal and vegetation physiology and the feasibility of a satellite mission specifically designed f…
Hyperspectral techniques and GIS for archaeological investigation
2004
Aerial photos, both in colour and in black and white, have always been very important tools in archaeological surveys. Sensors, called hyperspectral, were available on the market for some years: they are able to expand the research beyond the visible area of the electromagnetic spectrum as far as the thermal infrared too. The use of these sensors, at first restricted to the applications in the traditional fields of Remote Sensing (such as, for instance, Botany, Agronomy, Geology, Hydrology), was spreading, in recent years, to some sectors, such as archaeological surveys, which were unexplored before. The presence of structures and hollows in the top subsurface is likely to cause variations …
An integrated program of geophysical survey, coring, and test excavations to study a 4th millennium bc-cal ditch at Alt del Punxó (Muro de L’alcoi, A…
2008
The potentially long and interesting archaeological sequence revealed by systematic survey at the site of Alt Del Punxó (Muro de l’Alcoi, Alacant) was the basis for initiating a study of the locality’s subsurface structures using new methods of remote sensing. Geophysical survey (magnetometry and tomography) and systematic augering revealed buried structures, including monumental earthworks, and guided subsequent excavations to collect more detailed data about the nature and age of these prehistoric features. The information recovered, including new radiocarbon dates, offers a new perspective on the organization of prehistoric populations in this region of south-central Valencia since the b…
Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta v…
2018
Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll conc…
Automated detection and classification of synoptic scale fronts from atmospheric data grids
2021
<p>Automatic determination of fronts from atmospheric data is an important task for weather prediction as well as for research of synoptic scale phenomena. We developed a deep neural network to detect and classify fronts from multi-level ERA5 reanalysis data. Model training and prediction is evaluated using two different regions covering Europe and North America with data from two weather services. Due to a label deformation step performed during training we are able to directly generate frontal lines with no further thinning during post processing. Our network compares well against the weather service labels with a Critical Success Index higher than 66.9% and a Object Detecti…
A Review of Kernel Methods in Remote Sensing Data Analysis
2011
Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastruc…
Cloud screening with combined MERIS and AATSR images
2009
This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more ac…
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
2021
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…
Polarization calibration techniques for the new-generation VLBI
2020
The calibration and analysis of polarization observations in Very Long Baseline Interferometry (VLBI) requires the use of specific algorithms that suffer from several limitations, closely related to assumptions in the data properties that may not hold in observations taken with new-generation VLBI equipment. Nowadays, the instantaneous bandwidth achievable with VLBI backends can be as high as several GHz, covering several radio bands simultaneously. In addition, the sensitivity of VLBI observations with state-of-the-art equipment may reach dynamic ranges of tens of thousands, both in total intensity and in polarization. In this paper, we discuss the impact of the limitations of common VLBI …