Demonstration of lightweight gamma spectrometry systems in urban environments

Urban areas present highly complex radiation environments; with small scale features resulting from different construction materials, topographic effects and potential anthropogenic inputs from past industrial activity or other sources. Mapping of the radiation fields in urban areas allows a detailed assessment of exposure pathways for the people who live and work there, as well as locating discrete sources of activity that may warrant removal to mitigate dose to the general public. These areas also present access difficulties for radiometric mapping using vehicles or aircraft. A lightweight portable gamma spectrometry system has been used to survey sites in the vicinity of Glasgow to demonstrate the possibilities of radiometric mapping of urban areas, and to investigate the complex radiometric features such areas present. Variations in natural activity due to construction materials have been described, the presence of 137Cs used to identify relatively undisturbed ground, and a previously unknown NORM feature identified. The effect of topographic enclosure on measurements of activity concentration has been quantified. The portable system is compared with the outputs that might be expected from larger vehicular or airborne systems. For large areas airborne surveys are the most cost effective approach, but provide limited spatial resolution, vehicular surveys can provide sparse exploratory data rapidly or detailed mapping of open areas where off-road access is possible. Backpack systems are ideally suited to detailed surveys of small areas, especially where vehicular access is difficult

Optimal land cover mapping and change analysis in northeastern oregon using landsat imagery.

Abstract The necessity for the development of repeatable, efficient, and accurate monitoring of land cover change is paramount to successful management of our planet’s natural resources. This study evaluated a number of remote sensing methods for classifying land cover and land cover change throughout a two-county area in northeastern Oregon (1986 to 2011). In the past three decades, this region has seen significant changes in forest management that have affected land use and land cover. This study employed an accuracy assessment-based empirical approach to test the optimality of a number of advanced digital image processing techniques that have recently emerged in the field of remote sensing. The accuracies are assessed using traditional error matrices, calculated using reference data obtained in the field. We found that, for single-time land cover classification, Bayes pixel-based classification using samples created with scale and shape segmentation parameters of 8 and 0.3, respectively, resulted in the highest overall accuracy. For land cover change detection, using Landsat-5 TM band 7 with a change threshold of 1.75 standard deviations resulted in the highest accuracy for forest harvesting and regeneration mapping

Minimizing the residual topography effect on interferograms to improve DInSAR results: estimating land subsidence in Port-Said City, Egypt

The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007–2010) was estimated to be −28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise

Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼  200 km) to 1∕8° ( ∼  12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼  50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis

Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data

Spectral filtering was compared with traditional mean spatial filters to assess their ability to identify and remove striped artefacts in digital elevation data. The techniques were applied to two datasets: a 100 m contour derived digital elevation model (DEM) of southern Norway and a 2 m LiDAR DSM of the Lake District, UK. Both datasets contained diagonal data artefacts that were found to propagate into subsequent terrain analysis. Spectral filtering used fast Fourier transformation (FFT) frequency data to identify these data artefacts in both datasets. These were removed from the data by applying a cut filter, prior to the inverse transform. Spectral filtering showed considerable advantages over mean spatial filters, when both the absolute and spatial distribution of elevation changes made were examined. Elevation changes from the spectral filtering were restricted to frequencies removed by the cut filter, were small in magnitude and consequently avoided any global smoothing. Spectral filtering was found to avoid the smoothing of kernel based data editing, and provided a more informative measure of data artefacts present in the FFT frequency domain. Artefacts were found to be heterogeneous through the surfaces, a result of their strong correlations with spatially autocorrelated variables: landcover and landsurface geometry. Spectral filtering performed better on the 100 m DEM, where signal and artefact were clearly distinguishable in the frequency data. Spectrally filtered digital elevation datasets were found to provide a superior and more precise representation of the landsurface and be a more appropriate dataset for any subsequent geomorphological applications

Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications

Soil surface roughness, as investigated in this study, is decomposed in a dual scale process. Therefore, we investigated photogrammetrically acquired roughness information over different agricultural fields in the size of 6-22 m(2) and decomposed them into a dual scale process by using geostatistical techniques. For the characterization of soil surface roughness, we calculated two different roughness indices (the RMS height s and the autocorrelation length l) differing significantly for each scale. While we could relate the small scale roughness pattern clearly to the seedbed rows, the larger second scale pattern could be related to the appearance of wheel tracks of the tillage machine used. As a result, major progress was made in the understanding of the different scales in soil surface roughness characterization and its quantification possibilities

Earth Resources Laboratory research and technology

The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed

TaLAM: Mapping Land Cover in Lowlands and Uplands with Satellite Imagery

End-of-Project ReportThe Towards Land Cover Accounting and Monitoring (TaLAM) project is part of Ireland’s response to creating a national land cover mapping programme. Its aims are to demonstrate how the new digital map of Ireland, Prime2, from Ordnance Survey Ireland (OSI), can be combined with satellite imagery to produce land cover maps