To guarantee food security and job creation of small scale farmers to commercial farmers, unproductive farms in the South 24 PGS, West Bengal need land reform program to be restructured and evaluated for agricultural productivity. This study established a potential role of remote sensing and GIS for identification and mapping of salinity zone and spatial planning of agricultural land over the Basanti and Gosaba Islands(808.314sq. km) of South 24 PGS. District of West Bengal. The primary data i.e. soil pH, Electrical Conductivity (EC) and Sodium Absorption ratio (SAR) were obtained from soil samples of various GCP (Ground Control Points) locations collected at 50 mts. intervals by handheld GPS from 0–100 cm depths. The secondary information is acquired from the remotely sensed satellite data (LANDSAT ETM+) in different time scale and digital elevation model. The collected field samples were tested in the laboratory and were validated with Remote Sensing based digital indices analysisover the temporal satellite data to assess the potential changes due to over salinization.Soil physical properties such as texture, structure, depth and drainage condition is stored as attributes in a geographical soil database and linked with the soil map units. The thematic maps are integrated with climatic and terrain conditions of the area to produce land capability maps for paddy. Finally, The weighted overlay analysis was performed to assign theweights according to the importance of parameters taken into account for salineareaidentification and mapping to segregate higher, moderate, lower salinity zonesover the study area.
The possibilities of remote sensing techniques in the field of the Earth surface monitoring and protection specifically for the problems caused by petroleum contaminations, for the mapping of insufficiently plugged and abandoned old oil wells and for the analysis of onshore oil seeps are described. Explained is the methodology for analyzing and detection of potential hydrocarbon contaminations using the Earth observation in the area of interest in Slovakia (Korňa) and in Czech Republic (Nesyt), mainly building and calibrating the spectral library for oil seeps. The acquisition of the in-situ field data (ASD, Cropscan spectroradiometers) for this purpose, the successful building and verification of hydrocarbon spectral library, the application of hydrocarbon indexes and use of shift in red-edge part of electromagnetic spectra, the spectral analysis of input data are clarified in the paper. Described is approach which could innovate the routine methods for investigating the occurrence of hydrocarbons and can assist during the mapping and locating the potential oil seep sites. Important outcome is the successful establishment of a spectral library (database with calibration data) suitable for further application in data classification for identifying the occurrence of hydrocarbons.
Image processing techniques (band rationing, color composite, Principal Component Analyses) are widely used by many researchers to describe various mines and minerals. The primary aim of this study is to use remote sensing data to identify iron deposits and gossans located in Kaman, Kırşehir region in the central part of Anatolia, Turkey. Capability of image processing techniques is proved to be highly useful to detect iron and gossan zones. Landsat ETM+ was used to create remote sensing images with the purpose of enhancing iron and gossan detection by applying ArcMap image processing techniques. The methods used for mapping iron and gossan area are 3/1 band rationing, 3/5 : 1/3 : 5/7 color composite, third PC and PC4 : PC3 : PC2 as RG B which obtained result from Standard Principal Component Analysis and third PC which obtained result from Developed Selected Principal Component Analyses (Crosta Technique), respectively. Iron-rich or gossan zones were mapped through classification technique applied to obtained images. Iron and gossan content maps were designed as final products. These data were confirmed by field observations. It was observed that iron rich and gossan zones could be detected through remote sensing techniques to a great extent. This study shows that remote sensing techniques offer significant advantages to detect iron rich and gossan zones. It is necessary to confirm the iron deposites and gossan zones that have been detected for the time being through field observations.
The paper presents the capability of applying selected modern remote sensing methods based on commonly available high spatial resolution MODIS images to fog and low layer clouds detection. Single spectral channel images, differential images and selected color compositions are analyzed for distinguishing the areas of the phenomena occurrence. Their internal structure and fog/cloud particles properties are assessed using brightness temperature and reflectance diagrams.
Spectral remote sensing is a very popular method in atmospheric monitoring. The paper presents an approach that involves mid-infrared spectral measurements of combustion processes. The dominant feature in this spectral range is CO2 radiation, which is used to determine the maximum temperature of nonluminous flames. Efforts are also made to determine the temperature profile of hot CO2, but they are limited to the laboratory conditions. The paper presents an analysis of the radiation spectrum of a non-uniform-temperature gas environment using a radiative transfer equation. Particularly important are the presented experimental measurements of various stages of the combustion process. They allow for a qualitative description of the physical phenomena involved in the process and therefore permit diagnostics. The next step is determination of a non-uniform-temperature profile based on the spectral radiation intensity with the 8 m optical path length.
Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis.
Classification techniques have been widely used in different remote sensing applications and correct classification of mixed pixels is a tedious task. Traditional approaches adopt various statistical parameters, however does not facilitate effective visualisation. Data mining tools are proving very helpful in the classification process. We propose a visual mining based frame work for accuracy assessment of classification techniques using open source tools such as WEKA and PREFUSE. These tools in integration can provide an efficient approach for getting information about improvements in the classification accuracy and helps in refining training data set. We have illustrated framework for investigating the effects of various resampling methods on classification accuracy and found that bilinear (BL) is best suited for preserving radiometric characteristics. We have also investigated the optimal number of folds required for effective analysis of LISS-IV images.
Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.
Priority wise channelization of resources is the key to successful environmental management, especially when funds are limited. The study in hand has successfully developed an algorithmic criterion to compare hazardous effects of Municipal Solid Waste (MSW) dumping sites quantitatively. It is a Multi Criteria Analysis (MCA) that has made use of the scaling function to normalize the data values, Analytical Hierarchy Process (AHP) for assigning weights to input parameters showing their relevant importance, and Weighted Linear Combination (WLC) for aggregating the normalized scores. Input parameters have been divided into three classes namely Resident’s Concerns, Groundwater Vulnerability and Surface Facilities. Remote Sensing data and GIS analysis were used to prepare most of the input data. To elaborate the idea, four dumpsites have been chosen as case study, namely Old-FSD, New-FSD, Saggian and Mahmood Booti. The comparison has been made first at class levels and then class scores have been aggregated into environmental normalized index for environmental impact ranking. The hierarchy of goodness found for the selected sites is New-FSD > Old-FSD > Mahmood Booti > Saggian with comparative scores of goodness to environment as 36.67, 28.43, 21.26 and 13.63 respectively. Flexibility of proposed model to adjust any number of classes and parameters in one class will be very helpful for developing world where availability of data is the biggest hurdle in research based environmental sustainability planning. The model can be run even without purchasing satellite data and GIS software, with little inaccuracy, using imagery and measurement tools provided by Google Earth.
A high-temperature piezo-resistive nano-crystalline diamond strain sensor and wireless powering are presented in this paper. High-temperature sensors and electronic devices are required in harsh environments where the use of conventional electronic circuits is impractical or impossible. Piezo-resistive sensors based on nano-crystalline diamond layers were successfully designed, fabricated and tested. The fabricated sensors are able to operate at temperatures of up to 250°C with a reasonable sensitivity. The basic principles and applicability of wireless powering using the near magnetic field are also presented. The system is intended mainly for circuits demanding energy consumption, such as resistive sensors or devices that consist of discrete components. The paper is focused on the practical aspect and implementation of the wireless powering. The presented equations enable to fit the frequency to the optimal range and to maximize the energy and voltage transfer with respect to the coils’ properties, expected load and given geometry. The developed system uses both high-temperature active devices based on CMOS-SOI technology and strain sensors which can be wirelessly powered from a distance of up to several centimetres with the power consumption reaching hundreds of milliwatts at 200°C. The theoretical calculations are based on the general circuit theory and were performed in the software package Maple. The results were simulated in the Spice software and verified on a real sample of the measuring probe.