Kamal Jain

Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data

The cropping pattern (rotation) of a region depends on the soil, water availability, economic conditions and climatic factors. Remote sensing is one of the effective tools that can provide precise and up-to-date information on the performance of agricultural systems. Four seasons data from the Indian Remote Sensing Satellite (IRS)-P6 Advanced Wide Field Sensor (AWiFS) were used for the generation of the cropping pattern of Uttar Pradesh by geographic information system (GIS)-aided integration of digitally classified crop and land use inventories of the kharif, rabi and zaid crop seasons. Twelve different cropping patterns were delineated and mapped in the Indo-Gangetic plain of Uttar Pradesh. The forests covered about 6.32% of the total geographical area. The net cropped area was 20 282 159.46 ha (84.18% of the total geographical area) and the non-agricultural area observed was 3 437 376.00 ha (14.26% of the total geographical area). Rice was the single most dominant crop of the state, occupying about 32.94% of the total geographical area during the kharif season. Maize/jowar was the second major cereal crop, accounting for 13.77% of the total geographical area of the state. The major crops grown during the rabi season were wheat and pulses/oilseed, covering areas of 7 979 267.71 ha (33.12%) and 5 974 742.58 ha (24.80%), respectively. Rice-wheat, sugarcane and rice-pulses were the major cropping patterns, occupying about 3 958 739.85 ha (16.43%), 3 609 939.74 ha (14.98%) and 2 511 298.24 ha (10.42%), respectively. The areas under pulses/oilseed were significantly higher in the rabi season. Sugarcane-wheat and pulses shared an almost equal area (6.49%). The maize/jowar-wheat cropping pattern occupied 6.14% of the total geographical area of the state. Single cropping patterns (i.e. rice-fallow, fallow-pulses, fallow-wheat, maize-fallow and sugarcane-fallow) were minor, occupying 6.08, 2.94, 4.06, 2.69 and 2.51%, respectively. Waste land, including gulley, salt-affected, waterlogged and rock land, accounted for 3.80% of the total geographical area. The results of this study indicate that temporal IRS-P6 (AWiFS) data are very useful for studying spatial cropping patterns. The values of the Multiple Cropping Index (MCI) and the Cultivated Land Utilization Index (CLUI) show that the study area has a high cropping intensity.

Mapping of Lineaments and Knowledge Base Preparation using Geomatics Techniques for part of the Godavari and Tapi Basins, India: A Case Study

Earth consists of hard rock layers where water is restricted to secondary permeability, and thus to fractures and the weather zones. Structural geology studies, geologic lineaments and their pattern information are essential for better planning and execution of projects to avoid any natural hazards. Satellite images, aerial photographs and digital elevation models will give lineament information. Recent advances in digital image processing allow such lineament extraction to be accomplished in semi-automatic to fully automatic approaches. The accuracy of extracting lineaments depends strongly on the spatial resolution of the imagery, higher resolution imagery result in a higher quality of lineament map. In this paper, an attempt has been made for Mapping of lineaments and knowledge base preparation using geomatics techniques for part of the Godavari and Tapi Basins, India. A methodology for lineament extraction and the design of a knowledge-based lineament identification system has been proposed for geological aspects of any developmental activity. This methodology might potentially be adopted for the identification of several features of geological or anthropogenic origin. The study results of lineaments and the rose diagrams of the extracted lineaments can be applied to structural geology studies and their applications such as oreforming systems, mineral exploration, petroleum, nuclear energy facility sittings and water resource investigations, groundwater studies and also for finding suitable sites for dams and reservoirs. General Terms GIS, Knowledgebase.

Sub pixel level arrangement of spatial dependences to improve classification accuracy

The colors in universe have sharp boundaries everybody is aware of specifically wherever a color starts and wherever it ends and that any color communicates the details about the targets in the scene in a much better way and that this detailed information can be used to further polish the interpretation of an imaging system. In this paper, the proposed subpixel level arrangements of spatial dependences provide super resolved landuse landcover information using the output of soft classified fractional values. The output of soft classifier satisfies the constraint of non-negativity and sum to 1 instead of whatever their “natural” total is of fractional abundance within the pixels. This phenomenon is also discussed while defining mixed pixels, the pixels at boundary contain both the colors in a proportion so that the pixel appears the color different from either of two. This paper main goal is to extract the information from mixed pixels and subpixel analysis with the subpixel level arrangements of spatial dependences to get the super resolved information.

Modelling of Gangotri glacier thickness and volume using an artificial neural network

The volume of glaciers in a glacierized basin is an important characteristic for the existence of the glaciers and their evolution. Knowledge of glacier volume motivates scientific interest for two main reasons. First, the volumes of individual glaciers are monitored to estimate future water and sea level rises. Second, glaciers in the Indian Himalayas have been recognized as important water storage systems for municipal, industrial, and hydroelectric power generation purposes. Therefore, estimation of glacier volume is desired to estimate sea level rise accurately. The problem of deriving volume and glacier ice thickness is solved by developing an artificial neural network (ANN) approach that requires glacier boundaries, central branch lines, width-wise lines, digital elevation model (DEM), and slope information. Two geomorphic assumptions were taken in this investigation after testing, and strong relationships were found between elevation values of the frontal ice-denuded area of the Gangotri glacier and ice thickness derived from an ANN.

A Web-based Survey on Digital Elevation Models

Abstract In many earth and environmental science applications, DEMs serve as inputs for detailed spatial analyses, such as the determination of the extent of hydrographic networks, and the classification of terrain for suitability assessments such applications, appreciating the spatial accuracy of the DEM and its variability as a function of location can be critical. Accuracy is very important for any application and user should aware the important of DEM errors and accuracy. Present study, a survey on DEMs investigates how much importance DEM users giving for quality assessment and error analysis. The information provided in this document is based on survey responses received since last 6 months. DEM users from various countries, organizations and industries participated in the survey via the World Wide Web (WWW).

Colorimetry-based edge preservation approach for color image enhancement

Abstract. “Subpixel-based downsampling” is an approach that can implicitly enhance perceptible image resolution of a downsampled image by managing subpixel-level representation preferably with individual pixel. A subpixel-level representation for color image sample at edge region and color image representation is focused with the problem of directional filtration based on horizontal and vertical orientations using colorimetric color space with the help of saturation and desaturation pixels. A diagonal tracing algorithm and an edge preserving approach with colorimetric color space were used for color image enhancement. Since, there exist high variations at the edge regions, it could not be considered as constant or zero, and when these variations are random the need to compensate these to minimum value and then process for image representation. Finally, the results of the proposed method show much better image information as compared with traditional direct pixel-based methods with increased luminance and chrominance resolutions.

A New Super Resolution Mapping Algorithm by Combining Pixel and Subpixel-Level Spatial Dependences With Colorimetry

Super resolution mapping is a continuously growing area of remote sensing. Satellite images coupled with a very high spectral resolution, and are suitable for detection and classification of surfaces and different elements in the observed image. The main problem with high resolution data for these applications is the (relatively) low spatial resolution, which can vary from a few to tens of meters. In the case of classification purposes, the major problem caused by low spatial resolution is related to subpixels, i.e., pixels in the image where more than one land cover class is within the same pixel. In such a case, the pixel cannot be considered as belonging to just one class, and the assignment of the pixel to a single class will inevitably lead to a loss of information, no matter what class is chosen. A new super resolution mapping (SRM) algorithm by combining pixel and subpixel-level spatial dependences with colorimetry is proposed in this paper. The pixel-level dependence is measured by the spatial attraction model with either surrounding or quadrant neighborhood, while the subpixel-level dependence is characterized by either the mean filter or the exponential weighting function. Both pixel-level and subpixel-level dependences are then fused as the weighted dependence for quickly obtaining the optimal spatial distribution of subpixels by employing the colorimetric algorithm. Synthetic imagery and a QuickBird image are tested for validation of the proposed method. The results demonstrate that the proposed method can achieve results with greater accuracy than two traditional subpixel mapping (SPM) methods and the mixed spatial attraction model method. Meanwhile, the proposed method needs considerably less computation time than the conventional mixed spatial attraction model method, and hence it provides a new solution to subpixel land cover mapping.

Pumped Storage Hydropower Plants Environmental Impacts using Geomatics Techniques: An Overview

Pumped Storage Hydropower plants are generally developed to improve the peak power scenario of any country in the world and also in India. These types of projects involve construction of upper, lower reservoirs and the supporting infrastructure includes cement concrete mixing plant, quarters for working staff, service roads and disposal ground, which in turn demanded a huge amount of forest and agricultural land. In this paper an attempt has been made to give an overview of Pumped Storage Hydropower plants environmental impacts using geomatics techniques. Landsat data and Advanced Spaceborne Thermal Emission and Reflection Radiometers (ASTER) Global Digital Elevation Model (GDEM) of 30 m resolution data have been used for processing, interpretation and analysis of various parameters. The overall environmental Impacts of pumped storage hydropower plants depending on the selection of site, shape and size of reservoir, operational regime, mitigating measures, can be limited, but must be evaluated case by case with detailed surveys including social and political aspects.

Accuracy improvement of ASTER stereo satellite generated DEM using texture filter

The grid DEM (digital elevation model) generation can be from any of a number of sources: for instance, analogue to digital conversion of contour maps followed by application of the TIN model, or direct elevation point modelling via digital photogrammetry applied to airborne images or satellite images. Currently, apart from the deployment of point-clouds from LiDAR data acquisition, the generally favoured approach refers to applications of digital photogrammetry. One of the most important steps in such deployment is the stereo matching process for conjugation point (pixel) establishment: very difficult in modelling any homogenous areas like water cover or forest canopied areas due to the lack of distinct spatial features. As a result, application of automated procedures is sure to generate erroneous elevation values. In this paper, we present and apply a method for improving the quality of stereo DEMs generated via utilization of an entropy texture filter. The filter was applied for extraction of homogenous areas before stereo matching so that a statistical texture filter could then be applied for removing anomalous evaluation values prior to interpolation and accuracy assessment via deployment of a spatial correlation technique. For exemplification, we used a stereo pair of ASTER 1B images.

Use of OBIA for extraction of cadastral parcels

With the increased availability of High Resolution Satellite Imagery (HRSI), the Object based Image Analysis (OBIA) has now become an indispensable tool for analysis and modeling in Remote Sensing technology. The OBIA basically consist of image segmentation, object attribution and classification. The OBIA analysis has better results than the traditional pixel based analysis because the OBIA analysis is not only based on the spectral signature of the pixels but also on the statistical, geometric and topographical feature of the objects. The high resolution imagery of the study area is taken up and the analysis of segmentation, object attribution and classification was done. The resulting layer was exported as a vector layer in .shp format. The same was exported to QGIS and the .shp file clearly indicates the shape of the individual land parcels. The extracted parcels were comparable with the original cadastral vector layer of parcels. Thus OBIA can be treated as an automated procedure to extract the cadastral land parcels from high resolution imagery.