Wheat Monitoring by Using Kernel Based Possibilistic <Emphasis Type="Italic">c</Emphasis>-Means Classifier: Bi-sensor Temporal Multi-spectral Data

The existence of mixed pixels in the satellite images has always been an area of concern. Adding to the challenge is an occurrence of non-linearity between the classes, which is generally overlooked. The study makes an attempt to solve the two frequently occurring problems by kernel based fuzzy approach. This research work deals with Possibilistic c-Means (PCM) classifier with local, global, spectral angle and hyper tangent kernels for wheat crop (Triticum aestivum) identification in Haridwar, Uttarakhand, India. The multi-temporal vegetation index data of Formosat-2 have been used which covers the whole phenology of wheat crop. The additional sensor Landsat-8 OLI imagery has been filled the crucial gap of Formosat-2 temporal datasets. Nine types of proposed kernels based PCM classifier have been applied on three temporal datasets (four, five and six date combinations) to classify two classes early sown and late sown wheat crop. These test results have been concluded that at optimized weighted constant KMOD and polynomial kernel was found effective to separate wheat crop. The five and six date combination were sufficient to discriminate early sown and late sown wheat crop.     

Earthquake-induced built-up damage identification using IRS-P6 data: a comparative study using fuzzy-based classifiers

The 8 October 2005 earthquake caused widespread destruction in both the state of Jammu and Kashmir of India and Northern Pakistan. Due to poor accessibility in the hazardous and difficult mountainous terrain, a proper and comprehensive ground-based survey was not possible. However, with the help of remote sensing data and its analysis techniques, it is feasible to undertake both earthquake-related damage identification and assessment. This study attempts to document and identify built-up damaged (BD) areas using spectral indices taking temporal multispectral images from IRS-P6 LISS-IV. Five spectral indices have been used to identify BD areas using supervised possibilistic c-means (PCM) and noise cluster (NC) classifiers, to analyse the satellite data. The result indicates that Class Based Sensor Independent (CBSI) based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices provide the best results for identifying BD areas, while Simple Ratio (SR) index gives the best results for built-up undamaged area identification. Further, it observed that PCM classifier performed better in comparison to NC classifier.     

G-WADI—the first decade

The G-WADI network by UNESCO promotes the global capacity for management of water resources in arid and semi-arid areas. The primary aim has been to build a comprehensive global network to promote regional and international cooperation so as to increase knowledge and improve management practices through the sharing of information. The G-WADI objectives and achievements of the past 10 years are reviewed. A number of key initiatives have been implemented––the formation of five regional networks, the creation of a central G-WADI web site, promotion of near-real-time rainfall distribution software enhanced by the inclusion of satellite based precipitation estimations, as well as workshop and web-based activities on chemical and isotopic tracers and on rain water harvesting. Two workshops on surface and on groundwater modeling, supported by publications have been held in India and China. The Asian G-WADI network remains very active, but activities in the other three regions are developing (Africa, Arab Region, Latin America and the Caribbean).     

On the Use of HF Surface Wave Radar in Congested Waters: Influence of Masking Effect on Detection of Small Ships

In this paper, we assess the capability of a high-frequency surface wave radar (HFSWR) to detect a small fast boat moving behind a ship, the dimensions of which are comparable to the wavelength. We show that, in the HF-band, the scattered field in the shadow region of the large ship is significant enough to induce strong coupling between the two vessels. This results in fluctuations in the radar cross section (RCS) values of the small boat of about 12 dB at 10 MHz, for instance. We also introduce a complete simulation tool to account for the environment and, thus, to be able to simulate real scenes. We have validated these results through anechoic chamber measurements, with two different masking vessels and three different masked ships. The measurements have shown both the low-attenuation results and the RCS fluctuations     

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm,India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d^?1 for ET_SEBAL, i.e. ET_SEBAL having good accuracy with respect to observed ET_Lysimeter. Results were also validated using R-RMSE test, which also proved that ET_SEBAL data are having good accuracy with respect to observed ET_Lysimeter as R-RMSE of crop-growing period is 0.19 mm d^?1. MAE (0.19), NRMSE (0.21) and r² (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET_24 h) estimation. The temporal study of the ET_24 h values analysed has revealed that the highest ET_24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.     

Phylogenetic analysis and biological characteristic tests of marine bacteria isolated from Southern Ocean (Indian sector) water

Fifty-seven bacteria were isolated from Southern Ocean(Indian sector) water samples which were collected from different latitude and longitude of the ocean. All the isolates were able to grow at 4°C, 20°C, 37°C and tolerable Na Cl concentration up to 13.5%(w/v). 29 out of 57 isolates were identified using 16 S rDNA amplification and the sequences were submitted to National Center for Biotechnology Information(NCBI). All the isolates were classified by using Ribosomal Database Project(RDP) and found that isolates belongs to Proteobacteria and Bacteriodes. The average G+C content was 56.4%. The isolates were screened for the presence of extracellular enzymes, viz. amylase, catalase, urease, esterase, lipase and protease. The disc diffusion method is used to screen antibiotic production by the isolates against four pathogenic bacteria, viz. Salmonella typhimurium(NCIM 2501),Staphylococcus aureus(NCIM 2122), Bacillus subtilis(NCIM 2193), and Pseudomonas aeruginosa(NCIM 2036).Nine out of 29 were found to be antibiotic producer.     

Combining the FCM Classifier with Various Kernels to Handle Non-linearity of Class Boundaries

This article presents the use of kernel functions in fuzzy classifiers for an efficient land use/land cover mapping. It focuses on handling mixed pixels obtained from a remote sensing image by considering non-linearity between class boundaries. It uses kernel functions combined with the conventional fuzzy c-means (FCM) classifier. Kernel-based fuzzy c-mean classifiers were applied to classify AWiFS and LISS-III images from Resourcesat-1 and Resourcesat-2 satellites. Optimal kernels were obtained from eight single kernel functions. Fractional images generated from high resolution LISS-IV image were used as reference data. Classification accuracy of the FCM classifier increased with 12.93%. Improvement in overall accuracy shows that non-linearity in the dataset was handled adequately. The inverse multiquadratic kernel and the Gaussian kernel with the Euclidean norm were identified as optimal kernels. The study showed that overall classification accuracy of the FCM classifier improved if kernel functions were included.