Spatial Metrics to Analyse the Impact of Regional Factors on Pattern of Urbanisation in Gurgaon,India

In recent years, there has been lot of emphasis on the study of urban land use/ land cover changes to discover the growth pattern due to rapid urbanisation. This study presents spatial metrics and gradient analysis approach for quantifying and capturing changes in urban landscape using LISS III imagery of 1999, 2001 and 2004 of Gurgaon, India. A combination of spatial metrics i.e. percentage of landscape, mean patch size, number of patches, landscape shape index and largest patch index, available in Fragstats ver. 3.3, have been used to quantify the patterns of urban growth in different directions in terms of size, shape and complexity of development. The local built-up areas were quantified by the “moving window” technique. A gradient analysis has been carried out through sampling from a reference point to 8 km in 16 directions with a window size of 500 mts. Results of this study demonstrate the potential of spatial metrics and gradient modelling to quantify the impact of regional factors on the growth pattern of Gurgaon city.     

Transverse Mercator with an accuracy of a few nanometers

Implementations of two algorithms for the transverse Mercator projection are described; these achieve accuracies close to machine precision. One is based on the exact equations of Thompson and Lee and the other uses an extension of Krüger’s series for the mapping to higher order. The exact method provides an accuracy of 9 nm over the entire ellipsoid, while the errors in the series method are less than 5 nm within 3900 km of the central meridian. In each case, the meridian convergence and scale are also computed with similar accuracy. The speed of the series method is competitive with other less accurate algorithms and the exact method is about five times slower.     

Evaluating a thermal image sharpening model over a mixed agricultural landscape in India

Fine spatial resolution (e.g., <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (≥1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution-adjusted global model; the piecewise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of <2 K could, on average, be achieved only for 990–270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.     

Self-calibration Applied in Converting Simulation Surveying

In the field of converting simulation surveying and traditional close range photogrammetry, it has been developed so far to survey objects by commercial digital camera and this technique is applied widely in every part of production. In order to get three-dimensional information of objects, commercial digital camera must be examined. For a long time, digital camera has been examined by DLT. Then there must be a high-precision control field. For realizing surveying without control points, a method for self-calibration is proposed.     

Hyperspectral anomaly detection within the signal subspace

This letter describes the extension of signal subspace processing (SSP) to the arena of anomaly detection. In particular, we develop an SSP-based, local anomaly detector that exploits the rich information available in the multiple bands of a hyperspectral (HS) image. This SSP approach is based on signal processing considerations, and its entire formulation reduces to a straightforward (and intuitively pleasing) geometric and algebraic development. We extend the basic SSP concepts to the HS anomaly detection problem, develop an SSP HS anomaly detector, and evaluate this algorithm using multiple HS data files.     

Remote sensing study of granulitic terrain in parts of Gujarat and Rajasthan

The geology of northwestern part of Indian peninsula is considered to be important due to complete preservation of rocks from Archaean to Upper Proterozoic. Further, these rocks have served as ideal host of varieties of economic minerals. The present work is an attempt to study the structurally deformed granulitic terrain in parts of Gujarat and Rajasthan in light of remote sensing. The study area falls under Sirohi, Banas Kantha and Sabar Kantha districts of Rajasthan and Gujarat. Remote sensing technique is utilized for the understanding of structural geology and deciphering the shear pattern. The methods adopted in this study include generation of False Color Composite (FCC) of satellite data, interpretation of lineaments from FCC and study the drainage pattern, structural basin delineation, profiling, and field mapping. It is observed that the area has undergone extensive deformation. There are two major sets of lineaments interpreted in the granulitic terrain such as WNW-ESE and NE-SW directions. Majority of the WNW-ESE lineaments are brittle in nature and N-S, NE-SW trending lineaments are ductile in nature. Overall the study area bifurcated into seven structural basins comprises of basic granulites, calc granulites and pelitic granulites.     

Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India

Estimation and monitoring of crop evapotranspiration (ET_c) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ET_c by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ET_c was estimated 2.72 and 2.47 in mm day^?1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ET_c.     

Estimating surface ice velocity on Chhota Shigri glacier from satellite data using Particle Image Velocimetry (PIV) technique

Information about the surface ice velocity is one of the important parameters for Mass balance and Glacier dynamics. This study estimates the surface ice velocity of Chhota Shigri glacier using Landsat (TM/ETM+) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) temporal data-sets from a period of 2009 to 2016 and 2006 to 2007, respectively. A correlation based Particle Image Velocimetry (PIV) technique has been used for the estimation of surface ice velocity. This technique uses multiple window sizes in the same data-set. Four window sizes (low, medium, high, very high) are used for each image pair. Estimated results have been compared with the published data. The outcomes attained from the medium window size closely matches with the published results. The estimated mean surface ice velocities of medium window size are 24 and 28.5 myr^?1 for 2009/2010 and 2006/2007 images pair. Highest velocity is observed in middle part of the glacier while lowest in the accumulation zone of the glacier.     

Geomasking sensitive health data and privacy protection: an evaluation using an E911 database

Geomasking is used to provide privacy protection for individual address information while maintaining spatial resolution for mapping purposes. Donut geomasking and other random perturbation geomasking algorithms rely on the assumption of a homogeneously distributed population to calculate displacement distances, leading to possible under-protection of individuals when this condition is not met. Using household data from 2007, we evaluated the performance of donut geomasking in Orange County, North Carolina. We calculated the estimated k-anonymity for every household based on the assumption of uniform household distribution. We then determined the actual k-anonymity by revealing household locations contained in the county E911 database. Census block groups in mixed-use areas with high population distribution heterogeneity were the most likely to have privacy protection below selected criteria. For heterogeneous populations, we suggest tripling the minimum displacement area in the donut to protect privacy with a less than 1% error rate.     

Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment

The focus of soil erosion research in the Alps has been in two categories: (i) on-site measurements, which are rather small scale point measurements on selected plots often constrained to irrigation experiments or (ii) off-site quantification of sediment delivery at the outlet of the catchment. Results of both categories pointed towards the importance of an intact vegetation cover to prevent soil loss. With the recent availability of high-resolution satellites such as IKONOS and QuickBird options for detecting and monitoring vegetation parameters in heterogeneous terrain have increased. The aim of this study is to evaluate the usefulness of QuickBird derived vegetation parameters in soil erosion models for alpine sites by comparison to Cesium-137 (Cs-137) derived soil erosion estimates. The study site (67 km²) is located in the Central Swiss Alps (Urseren Valley) and is characterised by scarce forest cover and strong anthropogenic influences due to grassland farming for centuries. A fractional vegetation cover (FVC) map for grassland and detailed land-cover maps are available from linear spectral unmixing and supervised classification of QuickBird imagery. The maps were introduced to the Pan-European Soil Erosion Risk Assessment (PESERA) model as well as to the Universal Soil Loss Equation (USLE). Regarding the latter model, the FVC was indirectly incorporated by adapting the C factor. Both models show an increase in absolute soil erosion values when FVC is considered. In contrast to USLE and the Cs-137 soil erosion rates, PESERA estimates are low. For the USLE model also the spatial patterns improved and showed “hotspots” of high erosion of up to 16 t ha⁻¹ a⁻¹. In conclusion field measurements of Cs-137 confirmed the improvement of soil erosion estimates using the satellite-derived vegetation data.