Over the last two decades, Mozambique has experienced tremendous tropical cyclonic activities causing many flooding activities accompanied by disastrous human casualties. Studies that integrate remote sensing, elevati...Over the last two decades, Mozambique has experienced tremendous tropical cyclonic activities causing many flooding activities accompanied by disastrous human casualties. Studies that integrate remote sensing, elevation data and coupled with demographic analysis in Mozambique are very limited. This study seeks to fill the void by employing satellite data to map inundation caused by Tropical Cyclones in Mozambique. In pursuit of this objective, Sentinel-2 satellite data was obtained from the United States Geological Survey (USGS)’s Earth Explorer free Online Data Services imagery website covering the months of March 20, 2019, March 25, 2019, and April 16, 2019 for two cities, Maputo and Beira in Mozambique. The images were geometrically corrected to remove, haze, scan lines and speckles, and then referenced to Mozambique ground-based Geographic: Lat/Lon coordinate system and WGS 84 Datum. Data from twelve spectral bands of Sentinel-2 satellite, covering the visible and near infrared sections of the electromagnetic spectrum, were further used in the analysis. In addition, Normalized Difference Water Index (NDWI) within the study area was computed using the green and near infrared bands to highlight water bodies of Sentinel-2 detectors. To project and model the population of Mozambique and see the impact of cyclones on the country, demographic data covering 1980 to 2017 was obtained from the World Bank website. The Exponential Smoothing (ETS) method was adopted to forecast the population of Mozambique. Results from NDWI analysis showed that the NDWI is higher for flood areas and lower for non-flooded ones. The ETS algorithm results indicate that the population of Mozambique would nearly double by 2047. Human population along the coastal zone in the country is also on the rise exponentially. The paper concludes by outlining policy recommendations in the form of uniform distribution of economic activities across the country and prohibition of inland migration to the coastal areas where tropical cyclonic activities are very high.展开更多
Louisiana is endowed with forest resources. Forest wastes generated after thinning, land clearing, and logging operations, such as wood debris, tree trimmings, barks, sawdust, wood chips, and black liquor, among other...Louisiana is endowed with forest resources. Forest wastes generated after thinning, land clearing, and logging operations, such as wood debris, tree trimmings, barks, sawdust, wood chips, and black liquor, among others, can serve as potential fuels for energy production in Louisiana. This paper aims to evaluate the potential annual volumes of forest wastes established on detailed and existing data on the forest structure in the rural-urban interface of Louisiana. It also demonstrates the state’s prospects of utilizing forest wastes to produce bio-oils. The data specific to the study was deduced from secondary data sources to obtain the annual average total residue production in Louisiana and estimate the number of logging residues available for procurement for bioenergy production. The total biomass production per year was modeled versus years by polynomial regression curve fitting using Microsoft Excel. Results of the model show that the cumulative annual total biomass production for 2025 and 2030 in Louisiana is projected to be 80000000 Bone Dry Ton (BDT) and 16000000 (BDT) respectively. The findings of the study depict that Louisiana has a massive biomass supply from forest wastes for bioenergy production. Thus, the potential for Louisiana to become an influential player in the production of bio-based products from forest residues is evident. The author recommends that future research can use Geographic Information Systems (GIS) to create maps displaying the potential locations and utilization centers of forest wastes for bioenergy production in the state.展开更多
Rising greenhouse gas emissions are causing climate change, and the world’s focus has shifted to the need to reduce our reliance on fossil fuels. There has been a rise in the published literature on the utilization o...Rising greenhouse gas emissions are causing climate change, and the world’s focus has shifted to the need to reduce our reliance on fossil fuels. There has been a rise in the published literature on the utilization of crops for bioenergy production in Louisiana. However, very few scholarly documents have used Geographic Information Systems (GIS) to map the distribution of potential bioenergy crops in Louisiana. This study seeks to fill the void by evaluating the potential of bioenergy crops in Louisiana for energy production using GIS. Given this objective, the agricultural census data for 1999, 2009, 2019, and 2020 obtained from the U.S. Department of Agriculture were used in the analysis. The quantities of various crops produced in the state were loaded into an attribute table and joined to a shapefile using ArcGIS software. The symbology tool’s graduated option was used to create five maps representing each of the bioenergy crops in Louisiana. The findings of the GIS analysis show that some of the parishes, such as Franklin produced the most bushels of corn (13,795,416), Iberia produced the most tons of sugarcane (1,697,980), East Carroll produced the most bushels of soybean (8,237,991), Tensas harvested the most bales of cotton (80,898) and Avoyelles produced the most bushels of sorghum (630,694). The abundance and availability of crops as raw materials for energy production will translate into lower prices in terms of energy use, making bioenergy crops a promising alternative to fossil fuels. In addition, gasoline price data from 1993-2022 was obtained from U.S. Energy Information Administration. A regression model for the average annual gasoline price over the years was constructed. The results show that the average annual gasoline price variation with respect to years is statistically significant (p 0.05). This suggests that gasoline prices will generally rise despite a price drop over the years. The paper concludes by outlining policy recommendations in the form of assessing the availability and viability of other crop types, such as wheat, oats, and rice, for energy production in the state.展开更多
There has been significant research in recent decades on Land use Land cover (LULC) changes and their influence on biodiversity but little to no research on its impact on air quality. This research seeks to demonstrat...There has been significant research in recent decades on Land use Land cover (LULC) changes and their influence on biodiversity but little to no research on its impact on air quality. This research seeks to demonstrate how geospatial technologies such as geographic information system (GIS) and remote sensing can be used to assess the effects of LULC changes on particulate matter emissions and their impact on air quality in the East Baton Rouge area. In pursuit of these objectives, this study uses LANDSAT imageries from the past 30 years specifically Landsat Thematic Mapper (TM C2L2) and Landsat 8 Operational Land Imager/Thermal Infrared (OLI/TIRS C2L2) covering 1991, 2001, 2011 and 2021 were collected, processed, and analyzed for the LULC change analysis using QGIS software. Additionally, Sentinel 5P and the Air quality index from the U.S. Environmental Protection Agency (EPA) were used to assess the air quality trend over the years to establish the correlation between LULC and air quality. Results showed an increasing trend in air quality over the past 3 decades with concentrations of CO, NO<sub>2</sub>, and PM2.5 abruptly falling however, urbanization and the population expanded throughout the time. The paper concludes by outlining a policy recommendation in the form of encouraging Louisiana residents to use alternative renewable energies rather than the over-dependence on coal-fired electric generating plants that have an impact on the environment.展开更多
This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 mill...This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software w<span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands </span><i><span style="font-family:Verdana;">i.e</span></i></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 46.1314</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">, and, 18.3437</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 30.9693</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> respectively. Results of Kumasi also show a higher range of temperatures from 32.6986</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 19.1077<span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span></span><span style="font-family:Verdana;">C</span><span style="font-family:Verdana;"> during the dry season. In the wet season, temperatures ranged from 26.4142</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to </span><span style="font-family:Verdana;">-</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">0</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.898728</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.</span></span></span></span>展开更多
Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values ...Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values at specific stations is key for environmental and agricultural decision making. This research developed Autoregressive Integrated Moving Average (ARIMA) models for selected stations with Integrated component and Autoregressive Moving Average (ARMA) for selected stations without Integrated component at Louisiana State. The ARIMA module is represented as ARIMA(p, d, q)(P,D,Q). The selected lag order for the Autoregressive (AR) component is represented with p and P for seasonal AR component, while the integrated form (number of times data were differenced) is d and D for seasonal differencing, and the Moving Average (MA) lag order is q and Q for seasonal MA component. Data from 1950 to 2020 were employed in this research. Results of the analysis indicated that Baton Rouge (ARIMA (0,1,1) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Abbeville (ARMA (0,0,1) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Monroe Regional (ARMA (0,0,1) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), New Orleans Airport (ARMA (1,0,0) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Alexandria (ARMA (1,0,1) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Logansport (ARIMA (0,1,2) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), New Orleans Audubon (ARMA (1,0,0) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Lake Charles Airport (ARMA (2,0,2) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">) are the best ARIMA models for predicting precipitation in Louisiana. The models were used to predict the average monthly rainfall at each station. The highest precipitation observed in Louisiana was recorded in 1991. The Precipitation in Louisiana fluctuated over the years but has adopted a decreasing trend from the year 2000 to 2020. It was recommended that the government, researchers, and individuals take note of these models to make future plans to help increase the production of agricultural commodities and prevent destructions caused by excessive precipitation.展开更多
The COVID-19 pandemic posed a serious threat to life on the entire planet, necessitating the imposition of a lockdown mechanism that restricted people’s movements to stop the disease’s spread. This period experience...The COVID-19 pandemic posed a serious threat to life on the entire planet, necessitating the imposition of a lockdown mechanism that restricted people’s movements to stop the disease’s spread. This period experienced a decline in air pollution emissions and some environmental changes, offering a rare opportunity to understand the effects of fewer human activities on the earth’s temperature. Hence, this study compares the changes in Land Surface Temperature (LST) that were observed prior to the pandemic (March & April 2019) and during the pandemic lockdown (March & April 2020) of three parishes in Louisiana. The data for this study was acquired using Landsat 8 Thermal Infrared Sensor (TIRS) Level 2, Collection 2, Tier 2 from the Google Earth Engine Catalog. For better visualization, the images that were derived had a cloud cover of less than 10%. Also, images for the three study areas were processed and categorized into four main classes: water, vegetation, built-up areas, and bare lands using a Random Forest Supervised Classification Algorithm. To improve the accuracy of the image classifications, three Normalized Difference Indices namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-Up Index (NDBI) were employed using the Near Infrared (NIR), Red, Green and SWIR bands for the calculations. After, these images were processed in Google Earth Engine to generate the LST products gridded at 30 m with a higher spatial resolution of 100 m according to the pre-pandemic (2019) and lockdown (2020) periods for the three study areas. Results of this study showed a decrease in LST values of the land cover classes from 2019 to 2020, with LST values in East Baton Parish decreasing from 44°C to 38°C, 42°C to 38°C in Lafayette Parish, and 43°C to 38°C in Orleans Parish. The variations in the LST values therefore indicate the impact of fewer anthropogenic factors on the earth’s temperature which requires regulatory and mitigative measures to continually reduce LST and control microclimate, especially in urban areas.展开更多
The impacts of climate change are being felt in Louisiana, in the form of changing weather patterns that have resulted in changes in floods, hurricanes, tornadoes frequencies of occurrence, and magnitudes, among other...The impacts of climate change are being felt in Louisiana, in the form of changing weather patterns that have resulted in changes in floods, hurricanes, tornadoes frequencies of occurrence, and magnitudes, among others resulting in, flooding. The variabilities in rainfall in a drainage basin affect water availability and sustainability. This study analyzed the precipitation data of Southeastern Louisiana, United States, for the period 1990 to 2020. Data used in the study was from, Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations. These stations were selected because the differences between each of their highest and lowest average annual rainfall data were greater than 20 inches. To investigate climate patterns and trends for the given weather stations in Southeastern Louisiana, precipitation data were analyzed on annual time scales using data collected from the World Bank Group Climate Change Knowledge Portal for Development Practitioners and Policy Makers and the Applied Climate Information System (ACIS) of the National Weather Service Prediction Center. The data were further aggregated using annual average blocks of 4 years, and linear and polynomial regression was performed to establish trends. The highest and lowest average annual rainfall data for Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations were, 75 and 48, 71 and 44, 73.5 and 52.7, 75 and 46.4, 72 and 41.3, 94 and 55.3, Ponchatoula, and 78.6 and 44, respectively. Plaquemine recorded the highest average annual average rainfall while New Orleans, Audubon station recorded the lowest. The projection of the precipitation in 2030 has been carried out to inform scientists and stakeholders about the approximate quantity of rainfall expected and enable them to make their expected impacts on agriculture, economy, etc. The precipitation for 2030 was predicted by extrapolating models for the weather stations. The data used for the modeling was selected based on the data entries most representative. Hence, the coefficient of correlation and the number of data entries were both considered. Extrapolating results for 2030 precipitation in Donaldsonville, Galliano, Gonzales, Morgan, New Orleans, Audubon, and Plaquemine were found to be within the ranges, (85.6 - 86.7), (75.55 - 76.60), (89.7 - 90.67), (99.9 - 100.5), (71.68 - 72.66), and (107.7 - 108.8) inches, respectively. Hence, the average annual precipitations in areas covered by these stations except for Plaquemine station are expected to significantly increase. A restively low increase in average precipitation is expected for Plaquemine station. The increase could impact agriculture negatively or positively depending on the crop’s soil moisture tolerance.展开更多
文摘Over the last two decades, Mozambique has experienced tremendous tropical cyclonic activities causing many flooding activities accompanied by disastrous human casualties. Studies that integrate remote sensing, elevation data and coupled with demographic analysis in Mozambique are very limited. This study seeks to fill the void by employing satellite data to map inundation caused by Tropical Cyclones in Mozambique. In pursuit of this objective, Sentinel-2 satellite data was obtained from the United States Geological Survey (USGS)’s Earth Explorer free Online Data Services imagery website covering the months of March 20, 2019, March 25, 2019, and April 16, 2019 for two cities, Maputo and Beira in Mozambique. The images were geometrically corrected to remove, haze, scan lines and speckles, and then referenced to Mozambique ground-based Geographic: Lat/Lon coordinate system and WGS 84 Datum. Data from twelve spectral bands of Sentinel-2 satellite, covering the visible and near infrared sections of the electromagnetic spectrum, were further used in the analysis. In addition, Normalized Difference Water Index (NDWI) within the study area was computed using the green and near infrared bands to highlight water bodies of Sentinel-2 detectors. To project and model the population of Mozambique and see the impact of cyclones on the country, demographic data covering 1980 to 2017 was obtained from the World Bank website. The Exponential Smoothing (ETS) method was adopted to forecast the population of Mozambique. Results from NDWI analysis showed that the NDWI is higher for flood areas and lower for non-flooded ones. The ETS algorithm results indicate that the population of Mozambique would nearly double by 2047. Human population along the coastal zone in the country is also on the rise exponentially. The paper concludes by outlining policy recommendations in the form of uniform distribution of economic activities across the country and prohibition of inland migration to the coastal areas where tropical cyclonic activities are very high.
文摘Louisiana is endowed with forest resources. Forest wastes generated after thinning, land clearing, and logging operations, such as wood debris, tree trimmings, barks, sawdust, wood chips, and black liquor, among others, can serve as potential fuels for energy production in Louisiana. This paper aims to evaluate the potential annual volumes of forest wastes established on detailed and existing data on the forest structure in the rural-urban interface of Louisiana. It also demonstrates the state’s prospects of utilizing forest wastes to produce bio-oils. The data specific to the study was deduced from secondary data sources to obtain the annual average total residue production in Louisiana and estimate the number of logging residues available for procurement for bioenergy production. The total biomass production per year was modeled versus years by polynomial regression curve fitting using Microsoft Excel. Results of the model show that the cumulative annual total biomass production for 2025 and 2030 in Louisiana is projected to be 80000000 Bone Dry Ton (BDT) and 16000000 (BDT) respectively. The findings of the study depict that Louisiana has a massive biomass supply from forest wastes for bioenergy production. Thus, the potential for Louisiana to become an influential player in the production of bio-based products from forest residues is evident. The author recommends that future research can use Geographic Information Systems (GIS) to create maps displaying the potential locations and utilization centers of forest wastes for bioenergy production in the state.
文摘Rising greenhouse gas emissions are causing climate change, and the world’s focus has shifted to the need to reduce our reliance on fossil fuels. There has been a rise in the published literature on the utilization of crops for bioenergy production in Louisiana. However, very few scholarly documents have used Geographic Information Systems (GIS) to map the distribution of potential bioenergy crops in Louisiana. This study seeks to fill the void by evaluating the potential of bioenergy crops in Louisiana for energy production using GIS. Given this objective, the agricultural census data for 1999, 2009, 2019, and 2020 obtained from the U.S. Department of Agriculture were used in the analysis. The quantities of various crops produced in the state were loaded into an attribute table and joined to a shapefile using ArcGIS software. The symbology tool’s graduated option was used to create five maps representing each of the bioenergy crops in Louisiana. The findings of the GIS analysis show that some of the parishes, such as Franklin produced the most bushels of corn (13,795,416), Iberia produced the most tons of sugarcane (1,697,980), East Carroll produced the most bushels of soybean (8,237,991), Tensas harvested the most bales of cotton (80,898) and Avoyelles produced the most bushels of sorghum (630,694). The abundance and availability of crops as raw materials for energy production will translate into lower prices in terms of energy use, making bioenergy crops a promising alternative to fossil fuels. In addition, gasoline price data from 1993-2022 was obtained from U.S. Energy Information Administration. A regression model for the average annual gasoline price over the years was constructed. The results show that the average annual gasoline price variation with respect to years is statistically significant (p 0.05). This suggests that gasoline prices will generally rise despite a price drop over the years. The paper concludes by outlining policy recommendations in the form of assessing the availability and viability of other crop types, such as wheat, oats, and rice, for energy production in the state.
文摘There has been significant research in recent decades on Land use Land cover (LULC) changes and their influence on biodiversity but little to no research on its impact on air quality. This research seeks to demonstrate how geospatial technologies such as geographic information system (GIS) and remote sensing can be used to assess the effects of LULC changes on particulate matter emissions and their impact on air quality in the East Baton Rouge area. In pursuit of these objectives, this study uses LANDSAT imageries from the past 30 years specifically Landsat Thematic Mapper (TM C2L2) and Landsat 8 Operational Land Imager/Thermal Infrared (OLI/TIRS C2L2) covering 1991, 2001, 2011 and 2021 were collected, processed, and analyzed for the LULC change analysis using QGIS software. Additionally, Sentinel 5P and the Air quality index from the U.S. Environmental Protection Agency (EPA) were used to assess the air quality trend over the years to establish the correlation between LULC and air quality. Results showed an increasing trend in air quality over the past 3 decades with concentrations of CO, NO<sub>2</sub>, and PM2.5 abruptly falling however, urbanization and the population expanded throughout the time. The paper concludes by outlining a policy recommendation in the form of encouraging Louisiana residents to use alternative renewable energies rather than the over-dependence on coal-fired electric generating plants that have an impact on the environment.
文摘This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software w<span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands </span><i><span style="font-family:Verdana;">i.e</span></i></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 46.1314</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">, and, 18.3437</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 30.9693</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> respectively. Results of Kumasi also show a higher range of temperatures from 32.6986</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to 19.1077<span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span></span><span style="font-family:Verdana;">C</span><span style="font-family:Verdana;"> during the dry season. In the wet season, temperatures ranged from 26.4142</span><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;"> to </span><span style="font-family:Verdana;">-</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">0</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">.898728</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;"><span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">o</span></span></span>C</span><span style="font-family:Verdana;">. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.</span></span></span></span>
文摘Precipitation is very important for both the environment and its inhabitants. Agricultural activities mostly depend on precipitation and its availability. Therefore, the ability to predict future precipitation values at specific stations is key for environmental and agricultural decision making. This research developed Autoregressive Integrated Moving Average (ARIMA) models for selected stations with Integrated component and Autoregressive Moving Average (ARMA) for selected stations without Integrated component at Louisiana State. The ARIMA module is represented as ARIMA(p, d, q)(P,D,Q). The selected lag order for the Autoregressive (AR) component is represented with p and P for seasonal AR component, while the integrated form (number of times data were differenced) is d and D for seasonal differencing, and the Moving Average (MA) lag order is q and Q for seasonal MA component. Data from 1950 to 2020 were employed in this research. Results of the analysis indicated that Baton Rouge (ARIMA (0,1,1) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Abbeville (ARMA (0,0,1) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Monroe Regional (ARMA (0,0,1) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), New Orleans Airport (ARMA (1,0,0) (0,0,2)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Alexandria (ARMA (1,0,1) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Logansport (ARIMA (0,1,2) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), New Orleans Audubon (ARMA (1,0,0) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">), Lake Charles Airport (ARMA (2,0,2) (0,0,0)</span><sub><span style="font-family:Verdana;">12</span></sub><span style="font-family:Verdana;">) are the best ARIMA models for predicting precipitation in Louisiana. The models were used to predict the average monthly rainfall at each station. The highest precipitation observed in Louisiana was recorded in 1991. The Precipitation in Louisiana fluctuated over the years but has adopted a decreasing trend from the year 2000 to 2020. It was recommended that the government, researchers, and individuals take note of these models to make future plans to help increase the production of agricultural commodities and prevent destructions caused by excessive precipitation.
文摘The COVID-19 pandemic posed a serious threat to life on the entire planet, necessitating the imposition of a lockdown mechanism that restricted people’s movements to stop the disease’s spread. This period experienced a decline in air pollution emissions and some environmental changes, offering a rare opportunity to understand the effects of fewer human activities on the earth’s temperature. Hence, this study compares the changes in Land Surface Temperature (LST) that were observed prior to the pandemic (March & April 2019) and during the pandemic lockdown (March & April 2020) of three parishes in Louisiana. The data for this study was acquired using Landsat 8 Thermal Infrared Sensor (TIRS) Level 2, Collection 2, Tier 2 from the Google Earth Engine Catalog. For better visualization, the images that were derived had a cloud cover of less than 10%. Also, images for the three study areas were processed and categorized into four main classes: water, vegetation, built-up areas, and bare lands using a Random Forest Supervised Classification Algorithm. To improve the accuracy of the image classifications, three Normalized Difference Indices namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-Up Index (NDBI) were employed using the Near Infrared (NIR), Red, Green and SWIR bands for the calculations. After, these images were processed in Google Earth Engine to generate the LST products gridded at 30 m with a higher spatial resolution of 100 m according to the pre-pandemic (2019) and lockdown (2020) periods for the three study areas. Results of this study showed a decrease in LST values of the land cover classes from 2019 to 2020, with LST values in East Baton Parish decreasing from 44°C to 38°C, 42°C to 38°C in Lafayette Parish, and 43°C to 38°C in Orleans Parish. The variations in the LST values therefore indicate the impact of fewer anthropogenic factors on the earth’s temperature which requires regulatory and mitigative measures to continually reduce LST and control microclimate, especially in urban areas.
文摘The impacts of climate change are being felt in Louisiana, in the form of changing weather patterns that have resulted in changes in floods, hurricanes, tornadoes frequencies of occurrence, and magnitudes, among others resulting in, flooding. The variabilities in rainfall in a drainage basin affect water availability and sustainability. This study analyzed the precipitation data of Southeastern Louisiana, United States, for the period 1990 to 2020. Data used in the study was from, Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations. These stations were selected because the differences between each of their highest and lowest average annual rainfall data were greater than 20 inches. To investigate climate patterns and trends for the given weather stations in Southeastern Louisiana, precipitation data were analyzed on annual time scales using data collected from the World Bank Group Climate Change Knowledge Portal for Development Practitioners and Policy Makers and the Applied Climate Information System (ACIS) of the National Weather Service Prediction Center. The data were further aggregated using annual average blocks of 4 years, and linear and polynomial regression was performed to establish trends. The highest and lowest average annual rainfall data for Donaldsonville, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, and Ponchatoula, Tangipahoa, weather stations were, 75 and 48, 71 and 44, 73.5 and 52.7, 75 and 46.4, 72 and 41.3, 94 and 55.3, Ponchatoula, and 78.6 and 44, respectively. Plaquemine recorded the highest average annual average rainfall while New Orleans, Audubon station recorded the lowest. The projection of the precipitation in 2030 has been carried out to inform scientists and stakeholders about the approximate quantity of rainfall expected and enable them to make their expected impacts on agriculture, economy, etc. The precipitation for 2030 was predicted by extrapolating models for the weather stations. The data used for the modeling was selected based on the data entries most representative. Hence, the coefficient of correlation and the number of data entries were both considered. Extrapolating results for 2030 precipitation in Donaldsonville, Galliano, Gonzales, Morgan, New Orleans, Audubon, and Plaquemine were found to be within the ranges, (85.6 - 86.7), (75.55 - 76.60), (89.7 - 90.67), (99.9 - 100.5), (71.68 - 72.66), and (107.7 - 108.8) inches, respectively. Hence, the average annual precipitations in areas covered by these stations except for Plaquemine station are expected to significantly increase. A restively low increase in average precipitation is expected for Plaquemine station. The increase could impact agriculture negatively or positively depending on the crop’s soil moisture tolerance.
