The COVID-19 pandemic has affected the entire world and has had a devastating impact on both lives and livelihoods in India. The only way to defeat the rapid spread of COVID-19, is to shut down socio-economic activities and to maintain minimal human interaction with the implementation of a lockdown. Such lockdowns have manifested in a pollution curtailment in almost all spheres of the planet, including in marine pollution. Quantifying this decrease in pollution levels enables the scientific community to assess the contribution of anthropogenic (especially non-essential) activities to global/regional pollution levels. This paper aims to study the impact of the stringent lockdown period (phase 1 and 2) on coastal water quality along the Chennai coast of India, by analyzing suspended matter concentration (SPM), a key element of water quality and diffuse attenuation coefficient, Kd(490), using LANDSAT-8 Operational Land Imager (OLI) data. LANDSAT-8/OLI, L1TP scenes were subjected to radiometric calibration and atmospheric correction to derive surface reflectance values from raw digital numbers using ACOLITE software and a brief insight has been given for the Dark Spectrum Fitting algorithm used in ACOLITE. SPM concentration decreased by 15.48 and 37.50% in the Chennai and Ennore ports, respectively, due to minimal vessel movement and cargo handling. The stringent lockdown led to the operation of fewer thermal plant units, thus less fly ash was emanated, resulting in a 28.05% reduction in SPM levels over Ennore creek. As industrial and commercial activities subsided, the city’s water bodies became clearer than they were just a fortnight prior to the lockdown, with a reduction of 22.26% of SPM in Adyar and 33.97% in Cooum riverine estuaries. Decrease in Kd(490) showed a positive relationship with SPM and thus improved coastal water quality because of the reduction of SPM during this period. The variations in PM2.5 and PM10 concentrations were studied using National Air Quality Monitoring Programme (NAMP) data and reduced levels in particulate matter concentration (PM2.5 and PM10) for the Adyar residential area (24.38 and 28.43%) and for the Nungampakkam commercial area (36.09 and 67.18%) were observed. A significant reduction in PM2.5 concentration (45.63%) was observed in the Ennore-Manali Industrial region.
ABSTRACT:Primary Productivity is the ultimate source of energy for all organisms in an ecosystem. It is associated with the food production and the global carbon cycle. Sensors on remote platforms (satellites) are capable of estimating the Chlorophyll-a concentration in surface waters by measurement of spectral changes of the upwelling light. From these data, which connected with other remotely sensed data, it is possible to use algorithms to estimate the primary production. In this paper, an initial attempt is made to estimate the Primary Productivity along the east coast of India. Vertically Generalized Productivity Model (VGPM) which is a depth (euphotic depth) integrated model is used for the estimation. The common input variables or geophysical parameters used for the model are chlorophyll-a concentration (chl-a), vertically diffuse attenuation coefficient (Kd-490), Photosynthetically Available Radiation (PAR), and Sea Surface Temperature (SST). The chlorophyll-a and Kd-490 parameters were estimated using Oceansat-2 OCM data whereas PAR and SST were taken from MODIS-aqua data. Oceansat-2 Ocean Colour Monitor (OCM) data for the year 2013 is used in the analysis to compute the primary productivity using the weekly (8-day) data products of all the parameters as mentioned above. These products were inter compared with the MODIS Weekly (8-day) Primary Productivity products which were estimated at a global scale using the modified Vertically Generalized Productivity Model (VGPM) with which uses the exponential function of Sea surface temperature (SST).
Ocean colour remote sensing is one of the conventional methods in satellite oceanography used to study the biological response of the upper ocean to the tropical cyclones. This paper aims to study the impact of the Very Severe Cyclonic storm PHAILIN, and its consequence on the surface chlorophyll-a concentration distribution in the Bay of Bengal using Oceansat-2 Ocean Colour Monitor (OCM). The impact of this cyclone on ocean primary productivity has been studied using MODIS-A data. Sea surface temperature (SST) plays an important role in the generation of primary productivity along with the other oceanographic parameters; SST patterns in the Bay of Bengal during the cyclone period were studied. From the analysis, it is observed that the chlorophyll-a concentration has increased from 1.08 (before) to 7.06 mg/m 3 after the cyclone with an SST drop of ~3˚C (29.19˚C to 26˚C). The primary productivity has increased from 410.0506 to 779.9814 mg/C/m 2 /day after the cyclone. In addition to the above analysis, an attempt has also been made to study the impact of cyclone intensity on the chlorophyll concentration. The study shows that the comparison between cyclone intensity (CI) and chlorophyll concentration shows a positive relationship.
<p><strong>Abstract.</strong> Now-a-days, collecting accurate and meaningful information about the urban localities/environment with the maximum efficiency in terms of cost and time has become more relevant for urban, rural and city level development planning and administration. This work presents a technical procedure for automatic extraction of building information and characterization of different urban building types within the Andhra Pradesh Capital Region Development Authority (APCRDA) jurisdiction areas using UAVs. The methodology consists of a number of sequential processes of acquisition and generation of high resolution Orthomosaic images, creation of 3D point cloud data, and image classification algorithm for feature extraction using exclusively the geometric coordinates. The main parameters of the urban structures/buildings assessed in this work are site area of the building, built-up area, and building dimensions, building setbacks and building height. Different geometric and appropriate metrics were automatically extracted for each of the elements, defining the urban typology. In this study, residential and commercial buildings were considered for the analysis and the measurements from Drone were validated with respective approved plans and manual inspections and showed positive results with threshold parameters like setbacks and height as per building bye-laws of Andhra Pradesh Government Order (G.O) 119. Based on the results, measurements from Drone are used for the buildings occupancy permissions following the State government building rules. This automated system would replace physical inspections and manual reports and significantly reduce costs and improve efficiency. As an important component in this pilot study, visualisation of the building information were represented / displayed on a web application in an interactive mode. This added value of UAV technology with an automated system in comparison with traditional ways provides geospatial information and can also be considered as an essential Earth Observation indicator which has the potential to lead to next generation Urban Information Services and in the Smart cities development. The considerable potential use of these indicators in urban planning and development offers an opportunity in appropriate decision making in day to day urban planning measures.</p>
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