Analysis of seasonal variation in the Niger River interior delta using satellite data∗

The Niger River is one of the most important sources of water supply for human consumption and agriculture in Western Africa. Two Landsat‐5 Multispectral Scanner (MSS) images, corresponding to the dry and wet seasons, over a selected area of the Niger River interior delta were classified to produce a land cover/land use map that reflects the geo‐hydrological units of this area. To classify the satellite data, training statistics were generated using a clustering algorithm with parameter values that maximize the separability among spectral classes. Both dry and wet season images are required to obtain an accurate classification for evaluation of hydrological parameters. The spatial resolution of the MSS proved to be adequate for this kind of work, since all the major cover types and geographic features were correctly recognized.     

Comparing the fractality of European urban neighbourhoods: do national contexts matter?

The objective of this paper is to show that morphological similarities between built-up urban surfaces are greater across borders than within cities in Europe: living, architectural and planning trends are international. The spatial arrangement of built-up areas is analysed here by means of fractal indices using a set of 97 town sections selected from 18 European urban agglomerations. The fractal dimension is estimated by correlation techniques. Results confirm that morphological similarities are higher across countries/cities than within. Moreover, two types of fractal laws are considered: one uses the basic fractal scaling law; the other introduces a prefactor a that is often called a “form factor” in the fractal literature. Differences in the results obtained by both laws are explained empirically as well as theoretically, and suggestions are made for further measurements.     

Elevation change around Dome A region of Antarctica from EnviSat satellite radar altimetry during 2002–2012

The expected responses of ice sheets to climate warming are growth in the thickness of the inland ice areas and thinning near the margins. In recent decades, researchers have identified glacier acceleration along Antarctic ice sheet coastal margins. However, the study of ice sheet interiors where seasonal accumulation eventually balances ice wastage at the lower elevation is poorly understood. In this paper, the ice sheet elevation change around Dome A region is analyzed from 2002 to 2012 using two million elevation change measurements from EnviSat satellite radar altimeter data covering an area of about 7000 km². A declining trend of 0.572 ± 1.31 mm/year which means that the Dome A region was in balance during the last decade can be captured. In addition, two obvious changes in accumulation which divide elevation change time series into three independent equilibration stages are also extracted. In order to explain this phenomenon, two speculations related to snowfall and firn compaction are proposed in this paper.     

CART and IDC – based classification of irrigated agricultural fields using multi-source satellite data

To understand water productivity of crops cultivated in the Eastern Province of Saudi Arabia, this study was conducted to generate a reliable crop type map using a multi-temporal satellite data (ASTER, Landsat-8 and MODIS) and crop phenology. Classification And Regression Tree (CART) and ISO-DATA Cluster (IDC) classification techniques were utilized for the identification of crops. The Ideal Crop Spectral Curves were generated and utilized for the formulation of CART decision rules. For IDC, the stacked images of the phenology-integrated Normalized Difference Vegetation Index were utilized for the classification. The overall accuracy of the classified maps of CART was 76, 77 and 81% for ASTER, MODIS and Landsat-8, respectively. For IDC, the accuracy was determined at 67, 63 and 60% for ASTER, MODIS and Landsat-8, respectively. The developed decision rules can be efficiently used for mapping of crop types for the same agro-climatic region of the study area.     

Computation of satellite clock–ephemeris corrections using a priori knowledge for satellite-based augmentation system

GPS Solutions - The dual-frequency multi-constellation (DFMC) satellite-based augmentation system (SBAS) should be able to provide a vertical protection level in the range of...     

Analysis of building deformation in landslide area using multisensor PSInSAR™ technique

Buildings are sensitive to movements caused by ground deformation. The mapping both of spatial and temporal distribution, and of the degree of building damages represents a useful tool in order to understand the landslide evolution, magnitude and stress distribution. The high spatial resolution of space-borne SAR interferometry can be used to monitor displacements related to building deformations. In particular, PSInSAR technique is used to map and monitor ground deformation with millimeter accuracy. The usefulness of the above mentioned methods was evaluated in San Fratello municipality (Sicily, Italy), which was historically affected by landslides: the most recent one occurred on 14th February 2010. PSInSAR data collected by ERS 1/2, ENVISAT, RADARSAT-1 were used to study the building deformation velocities before the 2010 landslide. The X-band sensors COSMO-SkyMed and TerraSAR-X were used in order to monitor the building deformation after this event. During 2013, after accurate field inspection on buildings and structures, damage assessment map of San Fratello were created and then compared to the building deformation velocity maps. The most interesting results were obtained by the comparison between the building deformation velocity map obtained through COSMO-SkyMed and the damage assessment map. This approach can be profitably used by local and Civil Protection Authorities to manage the post-event phase and evaluate the residual risks.     

Analysis of geographical disparities in temporal trends of health outcomes using space–time joinpoint regression

Analyzing temporal trends in health outcomes can provide a more comprehensive picture of the burden of a disease like cancer and generate new insights about the impact of various interventions. In the United States such an analysis is increasingly conducted using joinpoint regression outside a spatial framework, which overlooks the existence of significant variation among U.S. counties and states with regard to the incidence of cancer. This paper presents several innovative ways to account for space in joinpoint regression: (1) prior filtering of noise in the data by binomial kriging and use of the kriging variance as measure of reliability in weighted least-square regression, (2) detection of significant boundaries between adjacent counties based on tests of parallelism of time trends and confidence intervals of annual percent change of rates, and (3) creation of spatially compact groups of counties with similar temporal trends through the application of hierarchical cluster analysis to the results of boundary analysis. The approach is illustrated using time series of proportions of prostate cancer late-stage cases diagnosed yearly in every county of Florida since 1980s. The annual percent change (APC) in late-stage diagnosis and the onset years for significant declines vary greatly across Florida. Most counties with non-significant average APC are located in the north-western part of Florida, known as the Panhandle, which is more rural than other parts of Florida. The number of significant boundaries peaked in the early 1990s when prostate-specific antigen (PSA) test became widely available, a temporal trend that suggests the existence of geographical disparities in the implementation and/or impact of the new screening procedure, in particular as it began available.     

Delineation and classification of rural–urban fringe using geospatial technique and onboard DMSP–Operational Linescan System

This study aims to analyse the processes and patterns of peri-urbanization using diurnal earth observation data-sets from onboard DMSP–Operational Linescan System. In this study, multiple correlation, simple and conditional linear regression are used to find out the degree of relationship and spatial behavioural pattern of the factors responsible for the urbanization. All the factors are standardized using the Analytical Hierarchy Process (AHP) coupled fuzzy membership functions. AHP is used to derive the weighting of the factors to produce the urbanity index. In total three functional zones – urban, rural and urban shadow are generated based on factor standardization and spatial contiguity index. Urban fringe is sharing ≥ 60% of Urbanity Index followed by rural fringe (39.50–60% of urbanity index) and urban shadow <39.50% of urbanity index. Shape index indicates that the city is going through unplanned development following cross to star shape growth.     

On the influence of the ground track on the gravity field recovery from high–low satellite-to-satellite tracking missions: CHAMP monthly gravity field recovery using the energy balance approach revisited

In this paper, the influence of the ground track coverage on the quality of a monthly gravity field solution is investigated for the scenario of a high–low satellite- to-satellite tracking mission. Data from the CHAllenging Minisatellite Payload (champ) mission collected in the period April 2002 to February 2004 has been used to recover the gravity field to degree and order 70 on a monthly basis. The quality is primarily restricted by the accuracy of the instruments. Besides, champ passed through a 31/2 repeat mode three times during the period of interest resulting in an insufficient spatial sampling and a degraded solution. Contrary to the rule of thumb by Colombo (The global mapping of gravity with two satellites, Publications on Geodesy, vol 7(3), Netherlands Geodetic Commission, The Netherlands, 263 pp, 1984), see also Wagner (J Geod 80(2): 94–103, 2006), we found that the monthly solutions themselves could be recovered to about degree 30, not 15. In order to improve the monthly gravity solutions, two strategies have been developed: the restriction to a low degree, and the densification of the sampling by the introduction of additional sensitive measurements from contemporaneous satellite missions. The latter method is tested by combining the champ measurements with data from the Gravity Recovery And Climate Experiment (grace). Note that the two grace satellites are considered independent here, i.e. no use is made of the K-band ranging data. This way, we are able to almost entirely remove the influence of the ground track leaving the accuracy of the instruments as the primary restriction on the quality of a monthly solution. These findings are especially interesting for the upcoming swarm-mission since it will consist of a similar configuration as the combined champ and (grace) missions.     

Landuse/land cover mapping and change detection using satellite data — a case study of dehlon block, district Ludhiana, Punjab

The land use information collected for Dehlon block of Ludhiana district, Punjab from the analysis of the IRS-1B LISS-II data for the year 1993 and IRS PAN data for the year 1997 and SOI topographical maps for 1964 revealed a large change in the area of different land use categories during the period from 1964 to 1997. The agricultural land covering an area of about 94.14 per cent in 1964 reduced to 90.26 per cent in 1997. while the area under rural settlements increased from 312 ha in 1964 to 1162 ha in 1997. An extra area of about 169 ha under waste land was added during the period under study making total waste land area to about 400 ha in 1997. However, the block lacks the forest cover of the required limit. Considerable change in living environment was observed in the block. Number of persons per unit settlement area (ha) being 213.3 in 1964 reduced to 97.1 in 1991; it indicate that the living standard of the people of the block has improved with the changed cropping pattern and increased agricultural production during the period from 1964 to 1991.     

How fast is a cow? Cross‐Scale Analysis of Movement Data

Data representing the trajectories of moving point objects are becoming increasingly ubiquitous in GIScience, and are the focus of much methodological research aimed at extracting patterns and meaning describing the underlying phenomena. However, current research within GIScience in this area has largely ignored issues related to scale and granularity – in other words how much are the patterns that we see a function of the size of the looking glass that we apply? In this article we investigate the implications of varying the temporal scale at which three movement parameters, speed, sinuosity and turning angle are derived, and explore the relationship between this temporal scale and uncertainty in the individual data points making up a trajectory. A very rich dataset, representing the movement of 10 cows over some two days every 0.25 s is investigated. Our cross‐scale analysis shows firstly, that movement parameters for all 10 cows are broadly similar over a range of scales when the data are segmented to remove quasi‐static subtrajectories. However, by exploring realistic values of GPS uncertainty using Monte Carlo Simulation, it becomes apparent that fine scale measurement of all movement parameters is masked by uncertainties, and that we can only make meaningful statements about movement when we take these uncertainties into account.     

On the Importance of Time for GIS View Measures and Their Use in Hedonic Property Models: Does Being Temporally Explicit Matter?

Progress in GIScience has advanced the ability to represent and analyze view characteristics. GIS‐derived view measures requiring digital elevation surface models are used in hedonic property models to quantify the amenity value of view for parcel sales transactions. Ideally models should represent surface elevations that are temporally synchronized with parcel sale dates. Temporal synchronization for studies spanning multiple years may require significant effort. Few studies have undertaken this effort, leading us to investigate in this research the need to be temporally explicit. We evaluate two competing surface model approaches based on: (1) a single year 2000 LiDAR surface product; and (2) annual‐specific surface products for 1995–2002. Two competing view measures based on the different surface approaches are constructed for 561 parcel transactions during 1995–2002 in a coastal North Carolina county and are input into hedonic regression models. Results showed that being temporally explicit did matter in terms of finding significantly different view measures but did not matter in terms of finding significantly different effects of view on parcel sales prices. Despite mixed results for our case study, we advise that future research involving GIS‐based view measurement should consider the spatial and temporal contexts of study area development patterns when evaluating the need to be temporally explicit.