A hierarchical model for estimating methane emission from wetlands using MODIS data and ARIMA modeling

A three-step hierarchical Semi Automated Empirical Methane Emission Model (SEMEM) has been used to estimate methane emission from wetlands and waterlogged areas in India using Moderate Resolution Imagine Spectroradiometer (MODIS) sensor data onboard Terra satellite. Wetland Surface Temperature (WST), methane emission fluxes and wetland extent have been incorporated as parameters in order to model the methane emission. Analysis of monthly MODIS data covering the whole of India from November 2004 to April 2006 was carried out and monthly methane emissions have been estimated. Interpolation techniques were adopted to fill the data gaps due to cloudy conditions during the monsoon period. AutoRegressive Integrated Moving Average (ARIMA) model has been fitted to estimate the emitted methane for the months of May 2006 to August 2006 using SPSS software.     

An approach to aid INS using time-differenced GPS carrier phase (TDCP) measurements

This paper presents a simple and effective approach that incorporates single-frequency, L1 time-differenced GPS carrier phase (TDCP) measurements without the need of ambiguity resolution techniques and the complexity to accommodate the delayed-state terms. Static trial results are included to illustrate the stochastic characteristics and effectiveness of the TDCP measurements in controlling position error growth. The formulation of the TDCP observation model is also described in a 17-state tightly-coupled GPS/INS iterative, extended Kalman filter (IEKF) approach. Preliminary land vehicle trial results are also presented to illustrate the effectiveness of the TDCP which provides sub-meter positional accuracies when operating for more than 10 min.     

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.     

Use of laser range and height texture cues for building identification

Airborne LiDAR has found application in an increasing number of mapping and Geo-data acquisition tasks. Apart from terrain information generation, applications such as automatic detection and modeling of objects like buildings or vegetation for the generation of 3-D city models have been explored. Besides the height itself, height texture defined by local variations of the height is a significant parameter for object recognition. The paper explores the potential of the analysis of height texture as a cue for the automatic detection of objects in LiDAR datasets. A number of texture measures were computed. Based on their definition and computation these measures were used as bands in a classification algorithm, and objects like buildings, single trees, and roads could be recognized.     

Water Harvesting Structure Positioning by Using Geo-Visualization Concept and Prioritization of Mini-Watersheds Through Morphometric Analysis in the Lower Tapi Basin

Geo-visualization concept has been used for positioning water harvesting structures in Varekhadi watershed consisting of 26 mini watersheds, falling in Lower Tapi Basin (LTB), Surat district, Gujarat state. For prioritization of the mini watersheds, morphometric analysis was utilized by using the linear parameters such as bifurcation ratio (R_b), drainage density (D_d), stream frequency (F_u), texture ratio (T), length of overland flow (L_o) and the shape parameter such as form factor (R_f), shape factor (B_s), elongation ratio (R_e), compactness constant (C_c) and circularity ratio (R_c). The different prioritization ranks were assigned after evaluation of the compound factor. 3 Dimensional (3D) Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM) and DEM from topo contour were analyzed in ArcScene 9.1 and the fly tool was utilized for the Geo-visualization of Varekhadi mini watersheds as per the priority ranks. Combining this with soil map and slope map, the best feasibility of positioning check dams in mini-watershed no. 1, 5 and 24 has been proposed, after validation of the sites.     

A Hierarchical Hybrid SVM Method for Classification of Remotely Sensed Data

The focus of this work is on developing a new hierarchical hybrid Support Vector Machine (SVM) method to address the problems of classification of multi or hyper spectral remotely sensed images and provide a working technique that increases the classification accuracy while lowering the computational cost and complexity of the process. The paper presents issues in analyzing large multi/hyper spectral image data sets for dimensionality reduction, coping with intra pixel spectral variations, and selection of a flexible classifier with robust learning process. Experiments conducted revealed that a computationally cheap algorithm that uses Hamming distance between the pixel vectors of different bands to eliminate redundant bands was quite effective in helping reduce the dimensionality. The paper also presents the concept of extended mathematical morphological profiles for segregating the input pixel vectors into pure or mixed categories which will enable further computational cost reductions. The proposed method’s overall classification accuracy is tested with IRS data sets and the Airborne Visible Infrared Imaging Spectroradiometer Indian Pines hyperspectral benchmark data set and presented.     

Near real-time high-resolution airborne camera,AEROCam, for precision agriculture

Precision agriculture often relies on high-resolution imagery to delineate the variability within a field. Airborne Environmental Research Observational Camera (AEROCam) was designed to meet the needs of agriculture producers, ranchers, and researchers, who require high-resolution imagery in a near real-time environment for rapid decision support. AEROCam was developed and operated through a unique collaboration between several departments at the University of North Dakota, including the Upper Midwest Aerospace Consortium (UMAC), the School of Engineering and Mines, and flight operations at the John D. Odegard School of Aerospace Sciences. AEROCam consists of a Redlake MS4100 area-scan multi-spectral digital camera that features a 1920 × 1080 CCD array (7.4-μm detector) with 8-bit quantization. When operated at ～2 km above ground level, multispectral images with four bands in the visible and near infrared have a ground sample distance of 1 m with a horizontal extent of just over 1.6 km. Depending on the applications, flying at different altitudes can adjust the spatial resolution from 0.25 to 2 m. Rigorous spectral and radiometric calibrations allow AEROCam to be used in a variety of applications, qualitative and quantitative. Equipped with an inertial measurement unit (IMU) system, the images acquired can be geo-referenced automatically and delivered to end users near real time through our Digital Northern Great Plains system (DNGP). The images are also available to zone mapping application for precision farming (ZoneMAP), an online decision support tool for creating management zones from remote sensing imagery and data from other sources. Operational since 2004, AEROCam has flown over 250 sorties and delivered over 150,000 images to the users in the Northern Great Plains region, resulting in numerous applications in precision agriculture and resource management.     

Spatial-Correlation Based Persistent Scatterer Interferometric Study for Ground Deformation

Interferometric Synthetic Aperture Radar (InSAR), nowadays, is a precise technique for monitoring and detecting ground deformation at a millimetric level over large areas using multi-temporal SAR images. Persistent Scatterer Interferometric SAR (PSInSAR), an advanced version of InSAR, is an effective tool for measuring ground deformation using temporally stable reference points or persistent scatterers. We have applied both PSInSAR and Small Baseline Subset (SBAS) methods, based on the spatial correlation of interferometric phase, to estimate the ground deformation and time-series analysis. In this study, we select Las Vegas, Nevada, USA as our test area to detect the ground deformation along satellite line-of-sight (LOS) during November 1992–September 2000 using 44 C-band SAR images of the European Remote Sensing (ERS-1 and ERS-2) satellites. We observe the ground displacement rate of Las Vegas is in the range of ?19 to 8 mm/year in the same period. We also cross-compare PSInSAR and SBAS using mean LOS velocity and time-series. The comparison shows a correlation coefficient of 0.9467 in the case of mean LOS velocity. Along this study, we validate the ground deformation results from the satellite with the ground water depth of Las Vegas using time-series analysis, and the InSAR measurements show similar patterns with ground water data.     

Ionospheric and receiver DCB-constrained multi-GNSS single-frequency PPP integrated with MEMS inertial measurements

Single-frequency precise point positioning (SF-PPP) is a potential precise positioning technique due to the advantages of the high accuracy in positioning after convergence and the low cost in operation. However, there are still challenges limiting its applications at present, such as the long convergence time, the low reliability, and the poor satellite availability and continuity in kinematic applications. In recent years, the achievements in the dual-frequency PPP have confirmed that its performance can be significantly enhanced by employing the slant ionospheric delay and receiver differential code bias (DCB) constraint model, and the multi-constellation Global Navigation Satellite Systems (GNSS) data. Accordingly, we introduce the slant ionospheric delay and receiver DCB constraint model, and the multi-GNSS data in SF-PPP modular together. In order to further overcome the drawbacks of SF-PPP in terms of reliability, continuity, and accuracy in the signal easily blocking environments, the inertial measurements are also adopted in this paper. Finally, we form a new approach to tightly integrate the multi-GNSS single-frequency observations and inertial measurements together to ameliorate the performance of the ionospheric delay and receiver DCB-constrained SF-PPP. In such model, the inter-system bias between each two GNSS systems, the inter-frequency bias between each two GLONASS frequencies, the hardware errors of the inertial sensors, the slant ionospheric delays of each user-satellite pair, and the receiver DCB are estimated together with other parameters in a unique Kalman filter. To demonstrate its performance, the multi-GNSS and low-cost inertial data from a land-borne experiment are analyzed. The results indicate that visible positioning improvements in terms of accuracy, continuity, and reliability can be achieved in both open-sky and complex conditions while using the proposed model in this study compared to the conventional GPS SF-PPP.