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In the present paper, a methodology has been developed for the mapping of snow cover in Beas basin, Indian Himalaya using AWiFS (IRS-P6) satellite data. The complexities in the mapping of snow cover in the study area are snow under vegetation, contaminated snow and patchy snow. To overcome these problems, field measurements using spectroradiometer were carried out and reflectance/snow indices trend were studied. By evaluation and validation of different topographic correction models, it was observed that, the normalized difference snow index (NDSI) values remain constant with the variations in slope and aspect and thus NDSI can take care of topography effects. Different snow cover mapping methods using snow indices are compared to find the suitable mapping technique. The proposed methodology for snow cover mapping uses the NDSI (estimated using planetary reflectance), NIR band reflectance and forest/vegetation cover information. The satellite estimated snow or non-snow pixel information using proposed methodology was validated with the snow cover information collected at three observatory locations and it was found that the algorithm classify all the sample points correctly, once that pixel is cloud free. The snow cover distribution was estimated using one year (2004-05) cloud free satellite data and good correlation was observed between increase/decrease areal extent of seasonal snow cover and ground observed fresh snowfall and standing snow data. [ABSTRACT FROM AUTHOR]

Mapping of wetlands is carried out using remotely sensed data globally from various sensors data and techniques. Present paper describes a knowledge-based classifier to identify the wetlands in Gujarat state using Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data. Spectral knowledge of BLUE, RED and NIR bands in conjunction with Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) have been used to define the thresholding ranges for various wetland classes. A comparative study of the performance of this classifier in comparison to two traditional classifiers namely Minimum distance to mean, and Maximum Likelihood classifiers was carried out for a small area. Results of the classification indicates better performance of knowledge based classifier in terms of overall accuracy as well as kappa statistic which are 84% and 0.79 respectively. The results indicate the feasibility of the development of a generalized knowledge based classifier for automated extraction of wetlands at regional scale using coarse spatial resolution data. [ABSTRACT FROM AUTHOR]

In this paper we present the results of our analyses of multidiurnal low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) thermal data for coal fire-related thermal anomaly detection. Results are presented for data of the Jharia coal mining region of India. We combine three relatively new approaches: first, we use low-resolution MODIS data for coal fire area analyses, which has only been undertaken by a few authors. Second, we analyse data from four different times of day (morning, afternoon, evening and predawn) and for three different bands (MODIS bands 20, 32 and a ratio thereof); and third, we use an unbiased automated algorithm for thermal anomaly extraction of local thermal anomalies. The MODIS data analysed stem from the years 2001 and 2005. In 2001, MODIS data were only been available from the platform TERRA as morning and evening data (around 1030 and 2200 h). In 2005, MODIS data were available from this platform as well as from the platform AQUA as afternoon and predawn data (around 0130 and 0200 h). Our analyses indicate that MODIS multidiurnal data, and especially bands 20, 32 and ratio bands thereof, have a high potential for the detection of coal fire zones and coal fire hot spot zones, as well as for regular thermal monitoring activities. However, the data are not suitable for a quantitative coal fire analysis concerning fire outline, fire temperature or fire classification into surface and subsurface fires. We used higher-resolution ASTER and LANDSAT data from 2005 and 2002 for general orientation and later comparison of thermal anomaly extraction results. We also used high-resolution Quickbird data for the characterization of individual anomalous thermal clusters. Comparisons demonstrate that even low-resolution thermal sensors such as MODIS can support coal fire detection and zonation into warm and very hot zones. [ABSTRACT FROM AUTHOR]

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001–2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 km × 30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r = 0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product. [ABSTRACT FROM AUTHOR]

Abstract: Dust storms are common during the summer season over the Indo-Gangetic basin and are considered to be a major health hazard to millions of people living in the basin. In countries like India, there is no early warning made for dust outbreaks and as a result the day-to-day life is affected by these dust events. In this paper, efforts have been made to utilize multi sensor data to study the characteristics of dust storms. Moderate Resolution Imaging Spectroradiometer and Multiangle Imaging SpectroRadiometer images clearly show dust storm events over the Indo-Gangetic basin. The Total Ozone Mapping Spectroradiometer (TOMS) Aerosol Index and the Advanced Microwave Sounding Unit (AMSU) data over the western end of the Indo-Gangetic basin have been analyzed. These data show characteristic behavior of brightness temperature and Aerosol Index due to dust, which change significantly as dust migrates towards east over Kanpur. The TOMS Aerosol Index and the AMSU brightness temperature (T b) show a characteristic anti-correlation, which confirms the presence of a dust storm over Indo-Gangetic basin. [Copyright &y& Elsevier]