1.NDVI: DETECTION OF VEGETATION CHANGE USING REMOTE SENSING AND GIS-A STUDY ON BARISHAL CITY CORPORATION,
BANGLADESH
Abstract:
This article presents a method of analyzing satellite images to detect the change in the Land Cover
pattern of the city corporation of Barisal, Bangladesh, from 2002 to 2020 using the Normalized Difference
Vegetation Index (NDVI). The use of the Multi-Spectral Remotely Sensed data approach is widely known to find
the extent & to detect the change in Land Cover of an area of interest. NDVI, which uses specific band
combinations of remotely-sensed satellite data, assists in classifying Land Cover to detect the changes in
land resources over time. Remote Sensed Data from Landsat TM & Sentinel-2 images have been used to perform
the analysis. Different NDVI threshold values as required for classifying the Water bodies, Built-up areas,
Sparse Vegetation & Dense Vegetation, are found by analyzing pixel by pixel analysis of the respective
classes. NDVI map was prepared for the corresponding years; the highest and lowest of the pixel values for
the individual class was found by analyzing the pixel value of that class. NDVI is highly useful in finding
different surface characteristics of the visible area, which is helpful for policymakers to make decisions.
The Vegetation index helps find the changes in vegetation cover and allows the respective authority to
decide on environmental reservation and mitigation approaches. The empirical study finds a 32% decrease in
the Dense Vegetation from 2002 to 2020 in Barisal, Bangladesh, followed by a 40% increase in the Built-up
area throughout the concerned 18 years. Alongside that, Sparse Vegetation followed a rise of 130% from 2002
to 2020. Thus, the extent of significant water bodies remained unchanged from 2002 to 2020. The NDVI values
for the selected pixels of the respective classes were compared with the ground absolute values for accuracy
analysis. Accuracy analysis finds an overall 92.86% accuracy of the classification.
2.Geospatial Spreading of COVID-19 Cases Over Time and Relation with PM2.5 and Meteorological Parameters
in Dhaka City
Abstract:
Air pollution in Dhaka is a buzzword in recent times. Dhaka becomes the headlines of news frequently due
to its massively deteriorated air quality. Air pollution is a potential threat to city dwellers, and every
year, many people die of respiratory diseases. The increasing amount of personal vehicles, unfit public
transport, and some ongoing mega construction work significantly raise the air's particulate matter. The
particulate matter can act as a virus carrier. It can easily be deposited to the lung's alveolar zones and
affect the lung with the deadly Corona Virus. The study tried to find the correlation between particulate
matter and covid-19 virus infection in Dhaka city from 15 April to 15 June. We did a correlation with
particulate matter, and covid-19 daily reported cases. We tried to find the co-relation among particulate
matter and other meteorological parameters (Temperature, humidity, and precipitation) to better understand
the situation. We found a significant correlation among daily reported Covid-19 cases, temperature,
humidity, and precipitation. We observed a decreasing pattern of daily cases with temperature precipitation
and an increasing pattern with humidity. High precipitation and humidity decrease particulate matter in the
air causing less transmission of viruses. Our study didn't find any direct relation of PM 2.5 with daily
cases of Covid-19. As it was the onset of summer due to high humidity, the particle concentration remains
low compared to the winter season. That is why in Dhaka city, the Covid-19 virus's slow transmission was
observed in the initial phase.