Wavelength-dependent spatial variation in the reflectance of 'homogeneous' ground calibration targets (Paper presented at XIX ISPRS Congress, 16-22 July, 2000, Amsterdam, The Netherlands)

Remotely sensed data are most useful if calibrated to spectral reflectance of known features. One simple method of calibration is regression of remote data on the reflectance of several ground targets as measured in the field, the so called empirical line method (ELM). The ideal situation would be one where a range of ground targets representing all the features of interest in the remote image were available for ground measurements (Lawless et al., 1998). The identification of suitable ground targets is constrained by several limitations, such as their size (to minimise edge effects), their absolute reflectance (to represent spectral characteristics of the image) and their effective spatial variability (to extract reflectance characteristics representative of the target). The size of a ground target is dependent on the spatial resolution of the image that must be calibrated (Justice & Townshend, 1981) and the number of observations needed to represent features in the image has been suggested to depend upon the spatial resolution of the remotely sensed image (Justice & Townshend, 1981) and on the spatial variability of the ground target (Harlan et al., 1979; Curran & Williamson, 1986). Although ground targets used for calibration should be spectrally “bland” and spatially uniform by definition (Clark et al., 1999), it is sometimes very difficult to find such places available for calibrating remotely sensed images. When surfaces that apparently satisfy these conditions are available in suitable size, their sampling needs to be designed to optimise representation of the whole surface and available resources (e.g., effort and time). Surfaces that look spatially uniform by eye may actually contain spatial variation, and this spatial variation may depends on wavelength (Atkinson & Emery, 1999). Such variability can be detected using geostatistics, which is concerned with issues such as spatial correlation and analyses of spatial data. Geostatistical tools have been used in a variety of studies and the variogram has been applied in remote sensing and ecology to design optimal sampling strategies for variables sampled in space (Atkinson, 1991; Rossi et al., 1992) and time (Salvatori et al., 1999). This study investigates the spatial variability of potentially suitable ground calibration targets (GCT) using a geostatistical approach, which gives results that can be used to design optimal sampling strategies for such surfaces. The targets were selected from an area where an Itres Instruments Compact Airborne Spectral Imager (casi) with ground resolution of about 1.5 metres was flown at the same time as ground data were acquired

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery.We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool formapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale

Interactions between Silica Particles in the Presence of Multivalent Coions

Forces between charged silica particles in solutions of multivalent coions are measured with colloidal probe technique based on atomic force microscopy. The concentration of 1:z electrolytes is systematically varied to understand the behavior of electrostatic interactions and double-layer properties in these systems. Although the coions are multivalent the Derjaguin, Landau, Verwey, and Overbeek (DLVO) theory perfectly describes the measured force profiles. The diffuse-layer potentials and regulation properties are extracted from the forces profiles by using the DLVO theory. The dependencies of the diffuse-layer potential and regulation parameter shift to lower concentration with increasing coion valence when plotted as a function of concentration of 1:z salt. Interestingly, these profiles collapse to a master curve if plotted as a function of monovalent counterion concentration

O naravi i zadaći teologije

The coastal zone is under considerable pressure from development and is subject to change. Consequently, shoreline monitoring has grown in importance. Remotely sensed imagery from satellite sensors has been used as an alternative to conventional methods, such as those based on the interpretation of aerial photography and ground-based surveying, for monitoring shoreline position. However, the accuracy of shoreline mapping from satellite sensor imagery has been limited because of the relatively coarse spatial resolution (>10 m) of the sensors commonly used. Because of major practical and financial constraints, very fine spatial resolution (<5 m) data are often impractical for mapping large stretches of shoreline, so refinement of image analysis methods are needed to extract the desired subpixel-scale information from relatively coarse spatial resolution imagery. In this paper, the potential to map the shoreline at a subpixel scale from a soft classification of relatively coarse spatial resolution satellite sensor imagery was evaluated. Unlike conventional approaches, the methods used allowed the shoreline to be mapped within image pixels and have the potential to yield an accurate and realistic prediction of shoreline location. The approach involved the use of a soft image classification to estimate the subpixel-scale thematic composition of image pixels, which were then located geographically through postclassification analysis. Specifically, a contouring and geostatistical method based on a two-point histogram was used to position geographically the shoreline within image pixels. The approach was applied to differently shaped shoreline extracts in imagery at two spatial resolutions. The most accurate prediction of the shoreline position from images with 16- and 32-m spatial resolutions were typically for a simple linear stretch of coast for which the smallest root mean square error values were 1.20 m. The shoreline predictions satisfied the map accuracy standards specified for large-scale maps

Žrtvama palim za Hrvatsku

Remote sensing, geographical information systems (GIS) and spatial analysis provide important tools that are as yet under-exploited in the fight against disease. As the use of such tools becomes more accepted and prevalent in epidemiological studies, so our understanding of the mechanisms of disease systems has the potential to increase. This paper introduces a range of techniques used in remote sensing, GIS and spatial analysis that are relevant to epidemiology. Possible future directions for the application of remote sensing, GIS and spatial analysis are also suggested. <br/

Information Loss-Guided Multi-Resolution Image Fusion

Spatial downscaling is an ill-posed, inverse problem, and information loss (IL) inevitably exists in the predictions produced by any downscaling technique. The recently popularized area-to-point kriging (ATPK)-based downscaling approach can account for the size of support and the point spread function (PSF) of the sensor, and moreover, it has the appealing advantage of the perfect coherence property. In this article, based on the advantages of ATPK and the conceptualization of IL, an IL-guided image fusion (ILGIF) approach is proposed. ILGIF uses the fine spatial resolution images acquired in other wavelengths to predict the IL in ATPK predictions based on the geographically weighted regression (GWR) model, which accounts for the spatial variation in land cover. ILGIF inherits all the advantages of ATPK, and its prediction has perfect coherence with the original coarse spatial resolution data which can be demonstrated mathematically. ILGIF was validated using two data sets and was shown in each case to predict downscaled images more accurately than the compared benchmark methods

Evaluating the impact of declining tsetse fly (Glossina pallidipes) habitat in the Zambezi valley of Zimbabwe

Tsetse flies transmit trypanosomes that cause Human African Trypanosomiasis (HAT) in humans and African Animal Trypanosomiasis (AAT) in animals. Understanding historical trends in the spatial distribution of tsetse fly habitat is necessary for planning vector control measures. The objectives of this study were (i) to test for evidence of any trends in suitable tsetse fly habitat and (ii) to test whether there is an association between trypanosomiasis detected from livestock sampled in dip tanks and local tsetse habitat in the project area. Results indicate a significant decreasing trend in the amount of suitable habitat. There is no significant correlation between trypanosomiasis prevalence rates in cattle and distance from patches of suitable tsetse habitat. The observed low trypanosomiasis prevalence and the lack of dependence on suitable tsetse fly habitat can be explained by the observed decreases in suitable tsetse habitat, which themselves are due to expansion of settlement and agriculture in North Western Zimbabwe. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group

Quantifying the Effect of Registration Error on Spatio-Temporal Fusion

It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resolution, especially for monitoring at global scales. Among the widely used global monitoring satellite sensors, Landsat data have a coarse temporal resolution, but fine spatial resolution, while moderate resolution imaging spectroradiometer (MODIS) data have fine temporal resolution, but coarse spatial resolution. One solution to this problem is to blend the two types of data using spatio-temporal fusion, creating images with both fine temporal and fine spatial resolution. However, reliable geometric registration of images acquired by different sensors is a prerequisite of spatio-temporal fusion. Due to the potentially large differences between the spatial resolutions of the images to be fused, the geometric registration process always contains some degree of uncertainty. This article analyzes quantitatively the influence of geometric registration error on spatio-temporal fusion. The relationship between registration error and the accuracy of fusion was investigated under the influence of different temporal distances between images, different spatial patterns within the images and using different methods (i.e., spatial and temporal adaptive reflectance fusion model (STARFM), and Fit-FC; two typical spatio-temporal fusion methods). The results show that registration error has a significant impact on the accuracy of spatio-temporal fusion; as the registration error increased, the accuracy decreased monotonically. The effect of registration error in a heterogeneous region was greater than that in a homogeneous region. Moreover, the accuracy of fusion was not dependent on the temporal distance between images to be fused, but rather on their statistical correlation. Finally, the Fit-FC method was found to be more accurate than the STARFM method, under all registration error scenarios. © 2008-2012 IEEE

Using hierarchical information-theoretic criteria to optimize subsampling of extensive datasets

This paper addresses the challenge of subsampling large datasets, aiming to generate a smaller dataset that retains a significant portion of the original information. To achieve this objective, we present a subsampling algorithm that integrates hierarchical data partitioning with a specialized tool tailored to identify the most informative observations within a dataset for a specified underlying linear model, not necessarily first-order, relating responses and inputs. The hierarchical data partitioning procedure systematically and incrementally aggregates information from smaller-sized samples into new samples. Simultaneously, our selection tool employs Semidefinite Programming for numerical optimization to maximize the information content of the chosen observations. We validate the effectiveness of our algorithm through extensive testing, using both benchmark and real-world datasets. The real-world dataset is related to the physicochemical characterization of white variants of Portuguese Vinho Verde. Our results are highly promising, demonstrating the algorithm's capability to efficiently identify and select the most informative observations while keeping computational requirements at a manageable level

A new multi-resolution based method for estimating local surface roughness from point clouds

From some empirical and theoretical research on the digital elevation model (DEM) accuracy obtained for different source data densities, it can be observed that when the same degree of data reduction is applied to a whole area, the rate of change in the DEM error is statistically greater in local areas where the surface is rougher. Based on this observation, it is possible to characterize surface roughness or complexity from the differences between two digital elevation models (DEMs) built using point clouds that represent the same terrain surface but are of different spatial resolutions (or data spacings). Following this logic, a new approach for estimating surface roughness is proposed in this article. Numerical experiments are used to test the effectiveness of the approach. The study datasets considered in this article consist of four elevation point clouds obtained from terrestrial laser scanning (TLS) and airborne light detection and ranging (LiDAR). These types of topographical data are now used widely in Earth science and related disciplines. The method proposed was found to be an effective means of quantifying local terrain surface roughness. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS