2,525 research outputs found
Linking remote sensing, land cover and disease
No full textLand cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover: the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field
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)
No full textRemotely 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
Atmospheric correction of multiple flightline hyperspectral data (CASI-3)
No full textMultiple parallel airborne flightlines are often necessary to provide the areal coverage desired, but standardisation to a common unit such as reflectance can be difficult, particularly where errors in the transform may be propagated sequentially through adjacent flightlines. This paper proposes a method for transformation to reflectance for such multiple flightlines based on an additional orthogonal flightline. The method is demonstrated for the CASI-2 data provided by the Environment Agency as part of the NCAVEO experiment. The uncorrected and corrected data were used to estimate NDVI using various wavebands and the NDVI estimates used to predict LAI. In all cases, the corrected data produced a slightly larger correlation than the uncorrected data
Empirical correction of multiple flightline hyperspectral aerial image mosaics
No full textAerial survey provides the user with great flexibility in terms of the geometry of sensing and the timing of measurements, but mosaicking individual aerial images to produce an extensive coverage remains a problem. Empirical methods based on normalising individual images to a common standard are used widely to create visually acceptable mosaics. However, the effect of these methods on quantitative estimation of land surface properties is unknown. An existing method for atmospherically correcting an aerial image mosaic involves fitting a regression model using pixels from the overlapping edges of adjacent flightlines. Here, we demonstrate a new method of atmospherically correcting an aerial image mosaic, based on use of an additional orthogonal flightline. The two methods were compared by using the two image mosaics to calculate vegetation indices (NDVI, SAVI, ARVI), which were then used to predict leaf area index, which was known in detail from ground survey. The second method was found to have lower uncertainty for all three vegetation indices tested. ARVI was found to be the most robust of the three when applied across multiple flightlines, regardless of the method of atmospheric correction
The combined effect of spatial resolution and measurement uncertainty on the accuracy of empirical atmospheric correction
No full textThe combined effect of positional uncertainty of field data and pixel size on the accuracy of the empirical line method is examined and quantified. Positional uncertainty reduces accuracy, although this effect decreases as sample size
and pixel size increases. For a pre–defined accuracy requirement, this information is used to specify the sample size required for a given pixel size
Resolving the support when combining remotely sensed and field data: the case of atmospheric correction of airborne remotely sensed imagery using the Empirical Line method
No full textValidation of the MODIS reflectance product under UK conditions
No full textSurface reflectance obtained from remote sensing data is the main input to almost all remote sensing applications. The availability and special characteristics of MODIS products have led to their use worldwide. Validation of the MODIS reflectance product is then crucial to provide information on uncertainty in the reflectance data, and in other MODIS products and in the applied surface-atmosphere models. Airborne CASI and SPOT data, collected during the NCAVEO 2006 Field Campaign, were applied to validate daily MODIS reflectance data over a site in southern England. The difference in the view geometry of at-nadir CASI and SPOT data and off-nadir MODIS data was dealt with using a semi-empirical bidirectional reflectance distribution function (BRDF) model. The validation results showed that for our particular study site, the absolute errors in the MODIS reflectance product were too large to allow the albedo data to be used directly in climate models. The errors were mainly related to the uncertainties in the MODIS atmospheric variables, the BRDF model, and undetected clouds and cloud shadows. More generally, the study highlights the extreme difficulty of achieving pixel-level validation of coarse spatial resolution satellite sensor data in an environment in which the atmosphere is constantly changing, and in which the landscape is characterised by high space-time heterogeneit
A per-pixel, non-stationary mixed model for empirical line atmospheric correction in remote sensing
No full textAtmospheric correction is a key stage in the processing of remotely sensed data. The empirical line method (ELM) is used widely to correct at-sensor radiance or DN to at-surface reflectance. It is based on a simple linear relationship between those two variables. Effective application of the model requires that it is estimated in a precise and unbiased fashion. The usual approach is to use ordinary least squares (OLS) regression to model the relationship between the average reflectance and radiance for a small number (3 to 8) of ground targets (GTs) and then to apply the regression on a per-pixel basis to the image. This leads to a mismatch between the scale at which the model is estimated and the scale at which the model is applied. Further, this approach wastes information and can lead to inconsistent estimators. These problems are addressed in the new approach presented here. The model was estimated on a per-pixel rather than per-GT basis. This yielded consistent, precise estimators for the ELM, but placed stronger requirements on the modeling. Specifically spatial autocorrelation and non-constant variance (heteroskedasticity) in the model residuals needed to be addressed. This was undertaken using the linear mixed model (LMM), which is a model-based expression of the geostatistical method. Of particular interest is the use of a non-stationary LMM to address the heteroskedasticity.The approach taken in this paper is of significance for a broader set of remote sensing applications. Regression and geostatistics are often applied, based typically on a stationary model. This paper shows how heteroskedasticity can be assessed and modeled using the non-stationary LMM. Heteroskedasticity is present in other remote sensing applications hence the non-stationary modeling approach, demonstrated here, is likely to be beneficial
Interviews with Philip Johnston, John Claw, E.J. Balbos, and Martin Link. Pt. 1
No full textDoris Paul interviewing Philip Johnston, John Claw, E.J. Balbos, and Martin Link, for her book "The Navajo code talkers" (1973). Phillip Johnston was the initiator of the code talker program
Interviews with Philip Johnston, John Claw, E.J. Balbos, and Martin Link
No full textDoris Paul interviewing Philip Johnston, John Claw, E.J. Balbos, and Martin Link, for her book "The Navajo code talkers" (1973). Phillip Johnston was the initiator of the code talker program
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