1,721,417 research outputs found
Downscaling in remote sensing
No full textDownscaling has an important role to play in remote sensing. It allows prediction at a finer spatial resolution than that of the input imagery, based on either (i) assumptions or prior knowledge about the character of the target spatial variation coupled with spatial optimisation, (ii) spatial prediction through interpolation or (iii) direct information on the relation between spatial resolutions in the form of a regression model. Two classes of goal can be distinguished based on whether continua are predicted (through downscaling or area-to-point prediction) or categories are predicted (super-resolution mapping), in both cases from continuous input data. This paper reviews a range of techniques for both goals, focusing on area-to-point kriging and downscaling cokriging in the former case and spatial optimisation techniques and multiple point geostatistics in the latter case. Several issues are discussed including the information content of training data, including training images, the need for model-based uncertainty information to accompany downscaling predictions, and the fundamental limits on the representativeness of downscaling predictions. The paper ends with a look towards the grand challenge of downscaling in the context of time-series image stacks. The challenge here is to use all the available information to produce a downscaled series of images that is coherent between images and, thus, which helps to distinguish real changes (signal) from noise
Super-resolution target mapping from soft classified remotely sensed imagery
No full textA simple, efficient algorithm is presented for sub-pixel target mapping from remotely-sensed images. Following an initial random allocation of “soft” pixel proportions to “hard” sub-pixel binary classes, the algorithm works in a series of iterations, each of which contains three stages. For each pixel, for all sub-pixel locations, a distance-weighted function of neighboring sub-pixels is computed. Then, for each pixel, the sub-pixel representing the target class with the minimum value of the function, and the sub-pixel representing the background with the maximum value of the function are found. Third, these two sub-pixels are swapped if the swap results in an increase in spatial correlation between sub-pixels. The new algorithm predicted accurately when applied to simple simulated and real images. It represents an accessible tool that can be coded and applied readily by remote sensing investigators
Geoinformatics and water-erosion processes
No full textGeomorphologists have commonly published conclusions about soil erosion and water movement based on experimental data obtained at the catchment scale. The underlying assumptions were that there exists little spatial variation in conditions at the hillslope scale (the fundamental unit) and that the catchments are representative of other catchments in the same region. These assumptions are unlikely to be tenable in practice. Indeed, we suggest that there is substantial spatial variation in geomorphological properties even at small distances when observed at fine spatial resolution and that modern geoinformatics approaches can be used to quantify and characterize this variation. This introduction reviews the ten papers that comprise this Special Issue on Studying Water-Erosion Processes with Geoinformatics, drawn from across the geomorphological sciences. The water erosion processes studied in these papers include sediment transport, fluvial processes, slope denudation, landsliding, bank erosion and bank line migration. The findings suggest that innovative measurement and modeling approaches such as GPS measurements, geostatistics, image processing techniques, and physically-based models deliver new data with which to study water erosion processes. These findings involve domains that are associated with fundamental aspects of geomorphology. Hence, there are strong grounds for claiming that geoinformatics can contribute to greater understanding of water erosion processes through characterization of space–time dynamics. We suggest that geomorphologists need to use more geoinformatics to collect more data relating to the outcomes of water erosion processes, to seek out and apply innovative processing methods and, finally, model the data to provide greater understanding of processes and to forecast and explore future scenarios
Modelling the effect of urbanization on the transmission of an infectious disease
No full textThis paper models the impact of urbanization on infectious disease transmission by integrating a CA land use development model, population projection matrix model and CA epidemic model in S-Plus. The innovative feature of this model lies in both its explicit treatment of spatial land use development, demographic changes, infectious disease transmission and their combination in a dynamic, stochastic model. Heuristically-defined transition rules in cellular automata (CA) were used to capture the processes of both land use development with urban sprawl and infectious disease transmission. A population surface model and dwelling distribution surface were used to bridge the gap between urbanization and infectious disease transmission. A case study is presented involving modelling influenza transmission in Southampton, a dynamically evolving city in the UK. The simulation results for Southampton over a 30-year period show that the pattern of the average number of infection cases per day can depend on land use and demographic changes. The modelling framework presents a useful tool that may be of use in planning application
Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study
No full textSuper-resolution mapping is a relatively new field in remote sensing whereby classification is undertaken at a finer spatial resolution than that of the input remotely sensed multiple-waveband imagery. A variety of different methods for super-resolution mapping have been proposed, including spatial pixel-swapping, spatial simulated annealing, Hopfield neural networks, feed-forward back-propagation neural networks and geostatistical methods. The accuracy of all of these new approaches has been tested, but the tests have tended to focus on the new technique (i.e. with little benchmarking against other techniques) and have used different measures of accuracy. There is, therefore, a need for greater inter-comparison between the various methods available, and a super-resolution inter-comparison study would be a welcome step towards this goal. This paper describes some of the issues that should be considered in the design of such a study
A comparison of small-area population estimation techniques using built-area and height data, Riyadh, Saudi Arabia
No full textSmall-area population estimation is important for many applications. This paper explores the usefulness of Landsat ETM + data, remotely sensed height data, census population, and dwelling unit data to provide small-area population estimates. Riyadh, Saudi Arabia was selected as a suitable area to test a set of methods for population downscaling. Two broad approaches were applied: 1) statistical modeling and 2) areal interpolation. With regard to statistical modeling, regression through the origin was used to model the relationship between density of dwelling units and built area proportion at the block level and the coefficients were used to downscale the density of dwelling units to the parcel level. Areal interpolation with ancillary data (dasymetric mapping) used the block and parcel levels as the source and target zones, respectively. The population distribution was then estimated based on the average population per dwelling unit. Eight models were developed and tested. A conventional regression model, using only built area as a covariate, was used as a benchmark and compared with the more sophisticated models. Remotely sensed height data were used to: 1) create number of floors; 2) classify the built area into different categories; and 3) increase the user’s accuracy of the built area. It was found that remotely sensed height data were useful to explain the variation in the dependent variable across the selected study area. Dasymetric mapping was applied in order to provide a comparison, while acknowledging that the method uses population data not available in the regression approac
Exploring the impact of climate and land use changes on streamflow trends in a monsoon catchment
No full textFlooding appears to be increasing in Kelantan, Malaysia, in terms of frequency as well as magnitude. This is likely to be due to changes in precipitation, but may also be contributed to by land use change. The Mann–Kendallnon-parametric method was used to test for trends in streamflow and precipitation at the 90% significance level.Several significant trends in streamflow were found for the upstream (River Galas) and downstream (River Kelantan) sub-catchments for all variables (annual, seasonal and monthly time-series). In particular, streamflow increased in all seasons in the upstream sub-catchment, but increased in the wet season and decreased in the dry season downstream. Several trends were also observed for precipitation. Precipitation trends were increasing in the wet season and decreasing in the dry season for both upstream and downstream sub-catchments. Analysis of land use change revealed that most changes occurred through conversion of forest to agricultural land (i.e. rubber and oil palm), predominantly in the upstream sub-catchment. The analysis suggests a clear association between streamflow change and precipitation change, but also reveals that land use change may be an important contributing factor, particularly in the upstream sub-catchment
Enhancing spectral unmixing by considering the point spread function effect
No full textThe point spread function (PSF) effect exists ubiquitously in real remotely sensed data and such that the observed pixel signal is not only determined by the land cover within its own spatial coverage but also by that within neighboring pixels. The PSF, thus, imposes a fundamental limit on the amount of information captured in remotely sensed images and it introduces great uncertainty in the widely applied, inverse goal of spectral unmixing. Until now, spectral unmixing has erroneously been performed by assuming that the pixel signal is affected only by the land cover within the pixel, that is, ignoring the PSF. In this paper, a new method is proposed to account for the PSF effect within spectral unmixingto produce more accurate predictions of land cover proportions. Based on the mechanism of the PSF effect, the mathematical relation between the coarse proportion and sub-pixel proportions in a local window was deduced. Area-to-point kriging (ATPK) was then proposed to find a solution for the inverse prediction problem of estimating the sub-pixel proportions from the original coarse proportions. The sub-pixel proportions were finally upscaled using an ideal square wave response to produce the enhanced proportions. The effectiveness of the proposed method was demonstrated using two datasets. The proposed method has great potential for wide application since spectral unmixing is an extremely common approach in remote sensing.</p
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