Debashis Mitra

Optical and Radiative Properties of Aerosols over Two Locations in the North-West Part of India during Premonsoon Season

The present study examines the aerosol characteristics over two locations in the northwest region of India (Dehradun and Patiala) during premonsoon season of 2013. The average mass concentrations of particulates (PM10; PM2.5; PM1) were found to be , , and µgm−3 and , , and µgm−3 over Dehradun and Patiala, respectively. The average aerosol optical depth () is observed to be over Dehradun and over Patiala. Angstrom exponent and fine mode fraction show higher values over Dehradun as compared to Patiala. The average mass concentration of black carbon was found to be ngm−3 and ngm−3 over Dehradun and Patiala, respectively. The diurnal pattern of BC is mainly controlled by boundary layer dynamics and local anthropogenic activities over both the stations. The average single scattering albedo () exhibited low value over Patiala () in comparison to Dehradun (), suggesting the abundance of absorbing type aerosols over Patiala. The average atmospheric aerosol radiative forcing is

Performance testing of selected automated coastline detection techniques applied on multispectral satellite imageries

Coastline detection has been of major interest for environmentalists and many methods have been introduced to detect coastline automatically. Remote Sensing techniques are the most promising ones to deliver a satisfactory result in this regard. In our study, the objective was to retrieve performance level of certain image processing techniques vigorously used for the purpose to delineate coastline automatically and they were tested against two images acquired almost on the same period by LISS III and LANDSAT ETM+ sensors. The algorithms used in the study are Water Index, NDVI, Complex Band Ratio, ISODATA, Thresholding, ISH Transfirmation techniques. Accuracy of the shoreline detection by classifying the image in land and water has been tried to be estimated in three ways, firstly with comparison to the visually interpreted high resolution google earth image, secondly field collected GCP data of reference points of classes and thirdly the raw image itself. But problem in temporal disparity caused the constraint doing accuracy assessment from the first two reference data and maps along the coast. As a whole although four techniques among six, show satisfactory results namely density slicing, ISODATA classification, Water Index and ISH transformation technique, in the case of LISS-III and ETM+, Water Index (with kappa value being 0.95 for LISS-III and 0.97 for ETM+) and Intensity-Hue-Saturation transformation techniques give better performance. Sensor to sensor variation might have introduced certain differences in shoreline detection in images of same season with similar tidal influence.

Mangrove Classification in Sundarban using High Resolution Multi-Spectral Remote Sensing Data and GIS

Mangrove is one of the most magnificent ecosystems of the coastal regions from ecological as well as biodiversity point of view. Mangrove pose real challenge to mapping due to their inaccessibility for field survey in most of the places. Multispectral optical remote sensing offers a tangible solution to this problem. In this study, the remote sensing and GIS were used to delineate and map the mangrove distribution at species level. As distribution of different mangrove species depends on the physico chemical conditions of the substrate, especially the soil salinity, the distribution of threeAvicennis species could be possible using soil salinity gradient. From the present observations, it is revealed that most of the species occupy a zone to which it is best adapted; however, there are overlapping occurrences of different species with varying ecological optima along salinity gradients.

Analyzing the Influence of Urban Growth on Thermal Environment Through Demographic, Environmental, and Physical Parameters in Bangladesh

Urban growth is the most evident aspect of anthropogenic impact on the earth system, replacing the natural physical characteristics of earth’s surface and thus influencing the thermal environment. The resulting thermal environment impact is especially observed in developing countries like Bangladesh. In this study, we assess, evaluate, and explore the growth of urban areas over Bangladesh for summer and winter seasons of 2003–2013 using Landsat-7 ETM+. We integrate the expected urban growth scenarios with the thermal environment through demographic, environmental, and physical datasets and also predict urban growth. We delineated urban areas over Bangladesh using Impervious Surface Area (ISA) with 90% accuracy and observed a 128% increase in urban areas during the 10 years. We used multivariate technique with satellite-derived land surface temperature, Surface Urban Heat Island Intensity (SUHII), Albedo and artificial heat flux in identifying the urban hotspots in various cities over Bangladesh. The results indicate an increase in urban areas in the first 5 years (2003–2008) by over 100% and in the next 5 years (2008–2013) by 200% mainly due to lack of urban planning policies. Our results indicate an enormous increase of 167% in Urban Heat Island Effect Ratio (UHIER) during the period. We also used advanced statistical analysis to assess the relationship between selected demographic (population), environmental (PM2.5, PM10, relative humidity, and air temperature) and physical parameters (Urbanization Index and Urban Density Cluster) and identified parameters which are most influencing to the thermal environment. Our results suggest the significant increase in UHIER by 2018 over major cities in Bangladesh. To reduce the influence of urban growth on thermal environment, we recommend mitigation measures useful for urban planners and decision makers to ensure safety and public health in Bangladesh.

Landslide Risk Assessment in Darjeeling Hills Using Multi-criteria Decision Support System: A Bayesian Network Approach

Landslide is one of the most recurrent and hazardous events of hilly slopes all over the world and particularly in the hilly regions of Himalayas. Darjeeling district of northern West Bengal, being the most important hill station in terms of tourism and trance-boundary strategic location, experiences landslide very often which causes intermittent loss of tourism revenues and is a problem for national security. In order to assess the landslide risk and accordingly prepare a landslide-risk zonation for the Darjeeling district, factors like slope, drainage density, rainfall soil depth, land use/land cover and geology have been considered. The factors responsible for landslide and their interdependency have been critically evaluated. In the present study, Bayesian network model has been implemented which is a probabilistic statistical graphical model that represents a set of variables and their conditional dependencies. Bayesian network was applied to assess the influences of the factors, and accordingly weightage and ranking of the contributing factors for landslide have been calculated. Finally, using multi-criteria decision support system (MCDSS) in GIS environment, landslide-risk zonation of the Darjeeling district has been prepared. Validation has been done taking into account 25 historical landslide locations, and more than 92% accuracy has been achieved. Rangli Rangliot is the most landslide-susceptible block of Darjeeling district. Kalimpong I, Kalimpong II, Mirik, Jorebunglow Sukhiapokhri and Bijanbari also come under the ambit of the highly susceptible areas.

Relative Radiometric Normalisation - performance testing of selected techniques and impact analysis on vegetation and water bodies

Abstract In this paper, six image-based Relative Radiometric Normalization (RRN) techniques were applied to normalize the bi-temporal Landsat 5 TM data-set. RRN techniques do not require any atmospheric and ground information at the time of image acquisition. The target image for the year 2009 was normalized in such a way that it resembled the atmospheric and sensor conditions similar to those under which the reference image of the same season for the year 1990 was acquired. Among the selected methods applied, it was found that the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) method performed better, based on the error statistic. The IR-MAD technique was found to be advantageous as it identified a large set of true time-invariant pixels automatically from the change background using iterative canonical component analysis. The technique also stretches the values of Normalized Difference Vegetation Index and Normalized Difference Water Index and may help to distinguish different vegetation and water bodies better.

Variability of aerosol optical depth and aerosol radiative forcing over Northwest Himalayan region

In recent years, the aerosol loading in India is increasing that has significant impact on the weather/climatic conditions. The present study discusses the analysis of temporal (monthly and seasonal) variation of aerosol optical depth(AOD) by the ground based observations from sun photometer and estimate the aerosol radiative forcing and heating rate over selected station Dehradun in North western Himalayas, India during 2015. The in-situ measurements data illustrate that the maximum seasonal average AOD observed during summer season AOD at 500nm ≈ 0.59±0.27 with an average angstrom exponent, α ≈0.86 while minimum during winter season AOD at 500nm ≈ 0.33±0.10 with angstrom exponent, α ≈1.18. The MODIS and MISR derived AOD was also compared with the ground measured values and are good to be in good agreement. Analysis of air mass back trajectories using HYSPLIT model reveal that the transportation of desert dust during summer months. The Optical Properties of Aerosols and clouds (OPAC) model was used to compute the aerosol optical properties like single scattering albedo (SSA), Angstrom coefficient (α) and Asymmetry(g) parameter for each day of measurement and they are incorporated in a Discrete Ordinate Radiative Transfer model, i.e Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) to estimate the direct short-wave (0.25 to 4 μm) Aerosol Radiative forcing at the Surface (SUR), the top-of-atmosphere (TOA) and Atmosphere (ATM). The maximum Aerosol Radiative Forcing (ARF) was observed during summer months at SUR ≈ -56.42 w/m2, at TOA ≈-21.62 w/m2 whereas in ATM ≈+34.79 w/m2 with corresponding to heating rate 1.24°C/day with in lower atmosphere.