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Fusion of Landsat 8 OLI and PlanetScope Images for Urban Forest Management in Baton Rouge, Louisiana

Journal of Geographic Information System 2022年第5期14卷 444-461页

作者： Yaw Adu Twumasi Abena Boatemaa Asare-Ansah Edmund Chukwudi Merem Priscilla Mawuena Loh John Bosco Namwamba Zhu Hua Ning Harriet Boatemaa Yeboah Matilda Anokye Rechael Naa Dedei Armah Caroline Yeboaa Apraku Julia Atayi Diana Botchway Frimpong Ronald Okwemba Judith Oppong Lucinda A. Kangwana Janeth Mjema leah wangari njeri Joyce McClendon-Peralta Valentine Jeruto Department of Urban Forestry and Natural Resources Southern University and A&M College Baton Rouge USA Department of Urban and Regional Planning Jackson State University Jackson USA Department of Geography and Tourism Studies Brock University Catharines Canada

In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral imagery in order to assist policymakers in the effective planning and management of urban forest ecosystem in Baton Rouge. To accomplish these objectives, Landsat 8 and PlanetScope satellite images were acquired from United States Geological Survey (USGS) Earth Explorer and Planet websites with pixel resolution of 30m and 3m respectively. The reference images (observed Landsat 8 and PlanetScope imagery) were acquired on 06/08/2020 and 11/19/2020. The image processing was performed in ArcMap and used 6-5-4 band combination for Landsat 8 to visually inspect healthy vegetation and the green spaces. The near-infrared (NIR) panchromatic band for PlanetScope was merged with Landsat 8 image using the Create Pan-Sharpened raster tool in ArcMap and applied the Intensity-Hue-Saturation (IHS) method. In addition, location of urban forestry parks in the study area was picked using the handheld GPS and recorded in an excel sheet. This sheet was converted into Excel (.csv) file and imported into ESRI ArcMap to identify the spatial distribution of the green spaces in East Baton Rouge parish. Results show fused images have better contrast and improve visualization of spatial features than non-fused images. For example, roads, trees, buildings appear sharper, easily discernible, and less pixelated compared to the Landsat 8 image in the fused image. The paper concludes by outlining policy recommendations in the form of sequential measurement of urban forest over time to help track changes and allows for better informed policy and decision making with respect to urban forest management.

关键词： Remote Sensing Image Fusion Multispectral Images Urban Forest Landsat 8 Operational Land Imager (OLI) PlanetScope Baton Rouge

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Flood Mapping in Mozambique Using Copernicus Sentinel-2 Satellite Data

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Advances in Remote Sensing 2022年第3期12卷 80-105页

作者： Yaw A. Twumasi Edmund C. Merem John B. Namwamba Abena B. Asare-Ansah Jacob B. Annan Zhu H. Ning Rechael N. D. Armah Caroline Y. Apraku Harriet B. Yeboah Julia Atayi Matilda Anokye Diana B. Frimpong Ronald Okwemba Olipa S. Mwakimi Judith Oppong Brilliant M. Petja Janeth Mjema Priscilla M. Loh Lucinda A. Kangwana Valentine Jeruto leah wangari njeri Joyce McClendon-Peralta Department of Urban Forestry and Natural Resources Southern University and A & M College 102 Fisher Hall Baton Rouge Louisiana USA Department of Urban and Regional Planning Jackson State University 101 Capitol Center Jackson Mississippi USA Department of Geography and Tourism Studies Brock University St. Catharines Ontario Canada Institute of Resource Assessment University of Dares Salam Dares Salam Tanzania Water Research Commission (WRC) Private Bag X03 Gezina South Africa

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 coas

关键词： Tropical Cyclones Floods Remote Sensing NDWI Exponential Smoothing (ETS) Digital Elevation Model (DEM) Sentinel-2 Satellite Mozambique

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Bioenergy Crops as a Promising Alternative to Fossil Fuels in Louisiana: A Geographic Information System (GIS) Perspective

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Journal of Sustainable Bioenergy Systems 2022年第4期12卷 57-81页

作者： Yaw A. Twumasi Zhu H. Ning John B. Namwamba Abena B. Asare-Ansah Edmund C. Merem Harriet B. Yeboah Judith Oppong Matilda Anokye Diana B. Frimpong Priscilla M. Loh Julia Atayi Rechael N. D. Armah Caroline Y. Apraku Opeyemi I. Oladigbolu Cynthia C. Ogbu leah W. njeri Joyce McClendon-Peralta Department of Urban Forestry and Natural Resources Southern University and A&M College Baton Rouge USA Department of Urban and Regional Planning Jackson State University Jackson USA Department of Geography and Tourism Studies Brock University St. Catharines Canada

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

关键词： Bioenergy Crops Biomass Fossil Fuel Gasoline Geographic Information Sys-tem (GIS) Regression Analysis Louisiana

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