Image Based Surface Temperature Extraction and Trend Detection in an Urban Area of West Bengal, India

Rapid urbanization and change of landuse/landcover results in changes of the thermal spectrum of a city even in small cities like English Bazaar Municipality (EBM) of Malda district. Monitoring the spatio-temporal surface temperature patterns is important, therefore, the present paper attempts to extract spatio-temporal surface temperature from thermal band of Landsat imageries and tries to validate it with factor based Land Surface Temperature (LST) models constructed based on six proxy temperature variables for selected time periods (1991, 2010 and 2014). Seasonal variation of temperature is also analyzed from the LST models over different time phases. Landsat TIRS based LST shows that in winter season, the minimum and maximum LST have raised up 2.32°C and 3.09°C in last 25 years. In pre monsoon season, the increase is much higher (2.80°C and 6.74°C) than in the winter period during the same time frame. In post monsoon season, exceptional situation happened due to high moisture availability caused by previous monsoon rainfall spell. Trend analysis revealed that the LST has been rising over time. Expansion and intensification of built up land as well as changing thermal properties of the urban heartland and rimland strongly control LST. Factor based surface temperature models have been prepared for the same period of times as done in case of LST modeling. In all seasons and selected time phases, correlation coefficient values between the extracted spatial LST model and factor based surface temperature model varies from 0.575 to 0.713 and these values are significant at 99% confidence level. So, thinking over ecological growth of urban is highly required for making the environment ambient for living

Impacts of Stone Mining and Crushing on Stream Characters and Vegetation Health of Dwarka River Basin of Jharkhand and West Bengal, Eastern India

Dwarka River basin (3882.71 km2) of Eastern India in the Chotonagpur Plateau and Gangetic Plain is highly affected by stone mining and crushing generated dust. In the middle catchment of this basin, there are 239 stone mines and 982 stone crushing units. These produce approximately 258120 tons of dust every year and this dust enters into the river and coats the leaves of plants. On the one hand, this is aggrading in the stream bed, increasing sediment load, decreasing water quality, specifically increasing total dissolved solid, pH, water colour, and it also degrades the vegetation quality. Vegetation quality is also degraded as indicated by decreasing of NDVI values (maximum NDVI in 1990 was 0.70 and in 2016 it was 0.48). Considering all these issues, the present paper intends to identify dust vulnerable zones based on six major driving parameters and the impact of the dust on river morphology, water quality and vegetation quality in different vulnerable zones. Weighted linear combination method (in Arc Gis environment) is used for compositing the selected parameters and deriving vulnerable zones. Weight to the each parameter is assigned based on analytic hierarchy process, a semi quantitative method. According to the results, 579.64 km2 (14.93%) of the catchment area is very highly vulnerable: Here 581 rivers have a length of 713 km and these riversare prone to high dust deposition, increased sediment load and water quality deterioration

Impacts of stone mining and crushing on stream characters and vegetation health of Dwarka river basin of Jharkhand and West Bengal, Eastern India

Dwarka River basin (3882.71 km2) of Eastern India in the Chotonagpur Plateau and Gangetic Plain is highly affected by stone mining and crushing generated dust. In the middle catchment of this basin, there are 239 stone mines and 982 stone crushing units. These produce approximately 258120 tons of dust every year and this dust enters into the river and coats the leaves of plants. On the one hand, this is aggrading in the stream bed, increasing sediment load, decreasing water quality, specifically increasing total dissolved solid, pH, water colour, and it also degrades the vegetation quality. Vegetation quality is also degraded as indicated by decreasing of NDVI values (maximum NDVI in 1990 was 0.70 and in 2016 it was 0.48). Considering all these issues, the present paper intends to identify dust vulnerable zones based on six major driving parameters and the impact of the dust on river morphology, water quality and vegetation quality in different vulnerable zones. Weighted linear combination method (in Arc Gis environment) is used for compositing the selected parameters and deriving vulnerable zones. Weight to the each parameter is assigned based on analytic hierarchy process, a semi quantitative method. According to the results, 579.64 km2 (14.93%) of the catchment area is very highly vulnerable: Here 581 rivers have a length of 713 km and these riversare prone to high dust deposition, increased sediment load and water quality deterioration

Privacy markets in the Apps and IoT age

@TechReport{UCAM-CL-TR-925, author = {Pal, Ranjan and Crowcroft, Jon and Kumar, Abhishek and Hui, Pan and Haddadi, Hamed and De, Swades and Ng, Irene and Tarkoma, Sasu and Mortier, Richard}, title = {{Privacy markets in the Apps and IoT age}}, year = 2018, month = sep, url = {https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-925.pdf}, institution = {University of Cambridge, Computer Laboratory}, number = {UCAM-CL-TR-925} }In the era of the mobile apps and IoT, huge quantities of data about individuals and their activities offer a wave of opportunities for economic and societal value creation. However, the current personal data ecosystem is fragmented and inefficient. On one hand, end-users are not able to control access (either technologically, by policy, or psychologically) to their personal data which results in issues related to privacy, personal data ownership, transparency, and value distribution. On the other hand, this puts the burden of managing and protecting user data on apps and ad-driven entities (e.g., an ad-network) at a cost of trust and regulatory accountability. In such a context, data holders (e.g., apps) may take advantage of the individuals’ inability to fully comprehend and anticipate the potential uses of their private information with detrimental effects for aggregate social welfare. In this paper, we investigate the problem of the existence and design of efficient ecosystems (modeled as markets in this paper) that aim to achieve a maximum social welfare state among competing data holders by preserving the heterogeneous privacy preservation constraints up to certain compromise levels, induced by their clients, and at the same time satisfying requirements of agencies (e.g., advertisers) that collect and trade client data for the purpose of targeted advertising, assuming the potential practical inevitability of some amount inappropriate data leakage on behalf of the data holders. Using concepts from supply-function economics, we propose the first mathematically rigorous and provably optimal privacy market design paradigm that always results in unique equilibrium (i.e, stable) market states that can be either economically efficient or inefficient, depending on whether privacy trading markets are monopolistic or oligopolistic in nature. Subsequently, we characterize in closed form, the efficiency gap (if any) at market equilibrium

Preference-Based Privacy Markets

Correction IEEE ACCESS Volume9 Pages14179-14180 Article Numbere abe4038 DOI10.1109/ACCESS.2021.3051825 Published 2021In the modern era of the mobile apps (the era of surveillance capitalism - as termed by Shoshana Zuboff) huge quantities of surveillance data about consumers and their activities offer a wave of opportunities for economic and societal value creation. ln-app advertising - a multi-billion dollar industry, is an essential part of the current digital ecosystem driven by free mobile applications, where the ecosystem entities usually comprise consumer apps, their clients (consumers), ad-networks, and advertisers. Sensitive consumer information is often being sold downstream in this ecosystem without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)?. In view of (a) the behavioral assumption that humans are 'compromising' beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.Peer reviewe

Impact of dam on inundation regime of flood plain wetland of punarbhaba river basin of barind tract of Indo-Bangladesh

Present study raises a serious issue of wetland loss and transformation due to damming and water diversion. At present study, it is noticed that overall rainfall trend (−0.006) of the study period (1978–2015) remains unchanged but riparian wetland area is attenuated after damming both pre monsoon (March to May) and post monsoon season (October to December). Total wetland area in pre- and post- monsoon seasons is respectively reduced from 42.2 km2 to 27.87 km2, and from 277.85 km2 to 220.90  km2 in post dam period. Transformation of frequently inundated wetland area into sparsely inundated wetland is mainly triggered by flow modification due to installation of Komardanga dam and Barrage over Punarbhaba and its major tributary Tangon river. Sparsely inundated seasonal wetland area is rapidly reclaimed for agricultural practice. This extreme issue will invite instability in socio-ecological setup of the neighbouring region

Monitoring dual-season hydrological dynamics of seasonally flooded wetlands in the lower reach of Mayurakshi River, Eastern India

The seasonally flooded wetlands are often neglected due to their ephemeral and erratic appearance and small size, however, their hydro-ecological importance, socio-economic values are equivalent to the permanent wetlands. Using dual-season Landsat multi-spectral imagery, this study highlights a comprehensive monitoring of the hydrological dynamics of wetlands in the lower reach of the Mayurakshi River at a 14-year temporal resolution with the seasonal mode of 1987–2014 time frames. Our results demonstrate the seasonal and periodic hydrological variability of water presence frequency (WPF) in six wetland complexes defined for this study. The Hijal, Ghambira and Maldah wetland complexes are highly affected by the change of WPF, while Dwarka complex is relatively stable. The assessment of WPF change analysis showed that the method is proficient in identifying stable and physically vulnerable wetland patches and absolute loss of wetland inundation frequency resulting from various anthropogenic causes like regulation of river, dry farming practices and other integrated developmental works. The outcome of our study provides a robust basis for the fundamental hydrological and ecological studies and helpful for the conservation and management of seasonally flooded wetland resources in the tropical monsoon climate