Urban cadastral mapping using very high resolution remote sensing data

With growing urban expanses, one of the pre-requisites for effective governance is Urban Information Systems (UIS) with content down to individual properties (and individuals). The basic input i.e., a map, in UIS should show individual property boundaries showing the plan outline of all structures existing within, at a scale of 1:1000 and larger with sub-metre to centimeters planimetric and geometric accuracy. With very high resolution remote sensing data of the order of 1m available in hand, it is possible to prepare maps with high resolution spatial content. The present exercise demonstrates a method of preparing a geometrically and planimetrically accurate urban cadastral map on very large scale for a small area of about 5 sq km. IKONOS merged data with 1m resolution is used for the purpose. Mapping was done in conjunction with on-site measurements and sketches. Guides are used to maintain shape symmetry and accuracy of buildings and other features. Working out cost of mapping per unit area is another objective in the present exercise. For want of fully or semi-automatic methods of information extraction from very high resolution remote sensing data, it is imperative that mapping should be carried out in conjunction with some on-site measurements wherever necessary.     

Generation and validation of DEM using SAR interferometry and differential GPS supported by multispectral optical data

Digital elevation model (DEM) and the derived terrain parameters e.g. contour, slope, aspect, drainage pattern, etc are required for natural resources management, infrastructure planning and disaster management. The present paper aims at generating DEM from ERS tandem pair using interferometric technique supported by differential GPS measurements (DGPS) and multispectral optical data. Validation of DEM has been carried out by DGPS measurements. Ground Control Points (GCP) established by DGPS measurements have been used to georeference the IRS-1D optical data that has finally been co-registered with SAR amplitude image. Optical data, co-registered with ERS - I SAR data has helped in locating the GCP’s and check points, precisely, for refinement of DEM and its validation.     

Mapping of suspended sediments using site specific seasonal algorithms

This article reports a preliminary work in which two site specific seasonal algorithms have been proposed for estimating the suspended sediments concentration (SSC) from the digital numbers recorded on Indian Remote sensing Satellite, IRS-P4 Ocean Colour Monitor (OCM) sensor. For estimation of SSC, the proposed algorithms utilize dark pixel deduction atmospheric correction technique. The computations are performed with respect to north east monsoon phase situations of Palk Strait coastal stretch. The algorithms performance was satisfactory during the north east monsoon period. Although the results obtained cannot be generalized, we suggest that the authority of proposed algorithms can be extended to other seasons with the addition of more temporal experimental validation data sets and with numeric constants adjusted to present existing conditions. (As this area was severely affected by Tsunami, it may have dissimilar conditions at present).     

A study on the polarimetric properties of various features using SIR-C data

Spaceborne Imaging Radar (SIR-C) data acquired over Gujarat, India in 1994 were processed and analysed using differnet techniques applicable to polarimetric SAR data such as polarization signatures, polarization index, decomposition of the signal and polarization phase difference and limited groundtruth data. It has been observed that multi-frequency polarimetric data enhances the potential of retrieving geo-physical parameters. The polarization signatures are found to vary with the nature of the target. Target decomposition of the returned signal will be useful for the classification of various features. Polarization Phase Difference (PPD) gives good information about the vegetation parameters.     

A data-driven approach to local gravity field modelling using spherical radial basis functions

We propose a methodology for local gravity field modelling from gravity data using spherical radial basis functions. The methodology comprises two steps: in step 1, gravity data (gravity anomalies and/or gravity disturbances) are used to estimate the disturbing potential using least-squares techniques. The latter is represented as a linear combination of spherical radial basis functions (SRBFs). A data-adaptive strategy is used to select the optimal number, location, and depths of the SRBFs using generalized cross validation. Variance component estimation is used to determine the optimal regularization parameter and to properly weight the different data sets. In the second step, the gravimetric height anomalies are combined with observed differences between global positioning system (GPS) ellipsoidal heights and normal heights. The data combination is written as the solution of a Cauchy boundary-value problem for the Laplace equation. This allows removal of the non-uniqueness of the problem of local gravity field modelling from terrestrial gravity data. At the same time, existing systematic distortions in the gravimetric and geometric height anomalies are also absorbed into the combination. The approach is used to compute a height reference surface for the Netherlands. The solution is compared with NLGEO2004, the official Dutch height reference surface, which has been computed using the same data but a Stokes-based approach with kernel modification and a geometric six-parameter “corrector surface” to fit the gravimetric solution to the GPS-levelling points. A direct comparison of both height reference surfaces shows an RMS difference of 0.6 cm; the maximum difference is 2.1 cm. A test at independent GPS-levelling control points, confirms that our solution is in no way inferior to NLGEO2004.     

Geodetic intersection on the ellipsoid

Through each of two known points on the ellipsoid a geodesic is passing in a known azimuth. We solve the problem of intersection of the two geodesics. The solution for the latitude is obtained as a closed formula for the sphere plus a small correction, of the order of the eccentricity of the ellipsoid, which is determined by numerical integration. The solution is iterative. Once the latitude is obtained, the longitude is determined without iteration.     

The ionospheric eclipse factor method (IEFM) and its application to determining the ionospheric delay for GPS

A new method for modeling the ionospheric delay using global positioning system (GPS) data is proposed, called the ionospheric eclipse factor method (IEFM). It is based on establishing a concept referred to as the ionospheric eclipse factor (IEF) λ of the ionospheric pierce point (IPP) and the IEF’s influence factor (IFF) . The IEF can be used to make a relatively precise distinction between ionospheric daytime and nighttime, whereas the IFF is advantageous for describing the IEF’s variations with day, month, season and year, associated with seasonal variations of total electron content (TEC) of the ionosphere. By combining λ and with the local time t of IPP, the IEFM has the ability to precisely distinguish between ionospheric daytime and nighttime, as well as efficiently combine them during different seasons or months over a year at the IPP. The IEFM-based ionospheric delay estimates are validated by combining an absolute positioning mode with several ionospheric delay correction models or algorithms, using GPS data at an international Global Navigation Satellite System (GNSS) service (IGS) station (WTZR). Our results indicate that the IEFM may further improve ionospheric delay modeling using GPS data.     

Spin rate estimation of sounding rockets using GPS wind-up

Carrier phase wind-up is a well-known effect that arises from the relative rotation between a transmitting and receiving antenna. In GPS measurements at L1 frequency, this effect translates into an error of 19.029 cm per full relative rotation of antennas. Since this effect is independent of the satellite elevation for pure rotation about the antenna boresight axis, it is usually absorbed by the clock estimation in navigation algorithms. Therefore, the impact of wind-up is usually neglected for applications that do not require accuracies to the cm level like RTK. However, in receiving platforms with high rotation rate, the accumulated wind-up value can be important and actually be larger than receiver noise or even ionospheric variations. Therefore, in such scenarios, the wind-up contribution can be isolated and used as a source of information to compute the spin rate of such platforms using an appropriate combination of GPS observables. This work shows some results of a coarse, yet simple, approach to monitor the rotation angle and spin-rate of spin stabilized sounding rockets flown by DLR.     

Using GPS multipath to measure soil moisture fluctuations: initial results

Measurements of soil moisture are important for studies of climate and weather forecasting, flood prediction, and aquifer recharge studies. Although soil moisture measurement networks exist, most are sparsely distributed and lack standardized instrumentation. Measurements of soil moisture from satellites have extremely large spatial footprints (40–60 km). A methodology is described here that uses existing networks of continuously-operating GPS receivers to measure soil moisture fluctuations. In this technique, incoming signals are reflected off and attenuated by the ground before reception by the GPS receiver. These multipath reflections directly affect signal-to-noise ratio (SNR) data routinely collected by GPS receivers, creating amplitude variations that are a function of ground reflectivity and therefore soil moisture content. After describing this technique, multipath reflection amplitudes at a GPS site in Tashkent, Uzbekistan are compared to estimates of soil moisture from the Noah land surface model. Although the GPS multipath amplitudes and the land surface model are uncalibrated, over the 70-day period studied, they both rise sharply following each rainfall event and slowly decrease over a period of ∼10 days.