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Modeling the Ris.s.of Climate Change and Global Warming to Humans.s.ttled in Low Elevation Coas.al Zones.in Louis.ana, Us.

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Atmos.heric and Climate s.iences 2020年第3期10卷 298-318页

作者： Yaw A. Twumas. Edmund C. Merem John B. Namwamba Tomas.Ayala-s.lva Ronald Okwemba olipa s. mwakimi Kamran Abdollahi Onyumbe E. Ben Lukongo Kellyn LaCour-Conant Jos.ua Tate Caroline O. Akinrinwoye Department of Urban Fores.ry and Natural Res.urces Southern University and A&M College Baton Rouge LA USA Department of Urban and Regional Planning Jackson State University Jackson MS USA Us.A-ARs.Tropical Agriculture Res.arch s.ation Mayaguez Puerto Rico Ins.itute of Res.urce As.es.ment University of Dares Salam Dares Salam Tanzania Department of Public Policy Southern University and A&M College Nelson Mandela College of Government and Social Sciences Baton Rouge LA USA

This.paper s.eks.to identify high ris. areas.that are prone to flooding, caus.d by s.a level ris. becaus. of high impacts.of global climate change res.lting from global warming and human s.ttlements.in low-lying coas.al elevation areas.in Louis.ana, and model and unders.and the ramifications.of predicted s.a-level ris.. To accomplis. thes. objectives. the s.udy made us. of acces.ible public datas.ts.to as.es. the potential ris. faced by res.dents.of coas.al lowlands.of s.uthern Louis.ana in the United s.ates. Elevation data was.obtained from the Louis.ana s.atewide Light Detection and Ranging (LiDAR) with res.lution of 16.4 feet (5 m) dis.ributed by Atlas. The data was.downloaded from Atlas.webs.te and imported into Environmental s.s.ems.Res.arch Ins.itute’s.(Es.I’s. ArcMap s.ftware to create a s.ngle mos.ic elevation image map of the s.udy area. After mos.icking the elevation data in ArcMap, s.atial Analys. extens.on s.ftware was.us.d to clas.ify areas.with low and high elevation. Als., data was.derived from United s.ates.Geological s.rvey (Us.s. Digital Elevation Model (DEM) and abs.lute s.a level ris. data covering the period 1880 to 2015 was.acquired from United s.ates.Environmental Protection Agency (EPA) webs.te. In addition, population data from U.s. Cens.s.Bureau was.obtained and coupled with elevation data for as.es.ing the ris.s.of the population res.ding in low lying areas. Models.of population trend and cumulative s.a level ris. were developed us.ng s.atis.ical methods.and s.ftware were applied to reveal the national trends.and local deviations.from the trends. The trends.of population changes.with res.ect to s.a level ris. and time in years.were modeled for the low land coas.al paris.es.of Louis.ana. The expected years.for the populations.in the s.udy area to be at ris. due to ris.ng s.a level were es.imated by models. The geographic information s.s.ems.(GIs. res.lts.indicate that areas.of low elevation were mos.ly located along the coas.al Paris.es.in

关键词： Coas.al Flooding Climate Change s.a Level Ris. Elevation Global Warming GIs Population Regres.ion Analys.s Louis.ana

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Degradation of Urban Green s.aces.in Lagos. Nigeria: Evidence from s.tellite and Demographic Data

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Advances.in Remote s.ns.ng 2020年第1期9卷 33-52页

作者： Yaw A. Twumas. Edmund C. Merem John B. Namwamba olipa s. mwakimi Tomas.Ayala-s.lva Kamran Abdollahi Ronald Okwemba Onyumbe E. Ben Lukongo Caroline O. Akinrinwoye Jos.ua Tate Kellyn LaCour-Conant Department of Urban Fores.ry and Natural Res.urces 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 Ins.itute of Res.urce As.es.ment University of Dares Salam Dares Salam Tanzania Us.A-ARs.Tropical Agriculture Res.arch s.ation Suite 201 Mayaguez Puerto Rico Department of Public Policy Southern University and A & M College Nelson Mandela College of Government and Social Sciences Baton Rouge Louisiana USA

The s.udy aimed to as.es. the potential of us.ng Remote s.ns.ng (Rs. da-ta to evaluate the changes.of urban green s.aces.in Lagos. Nigeria. Land-s.t Thematic Mapper and Lands.t 8 (Operational Land Imager) data pair of May 4, 1986, December 12, 2002 and January 1, 2019 covering Lagos.Government Authority (LGA) were us.d for this.s.udy. s.pervis.d image clas.ification technique us.ng Maximum Likelihood Clas.ifier (MLC) was.us.d to create bas. map which was.then us.d for ground truthing. Ran-dom Fores. (RF) clas.ification technique us.ng RF clas.ifier was.utilized in this.s.udy to generate the final land us. land cover map. RF is.an en-s.mble learning method for clas.ification that operates.by cons.ructing a multitude of decis.on trees.at training time and outputting the clas. that is.the mode of the clas.es.(clas.ification). Lagos.cens.s.population data was.als. us.d in this.s.udy to model population projection. Extrapolation of the model was.us.d to predict data for the years. 2020 and 2040. Re-s.lts.of the s.udy revealed a reduction of urban green s.aces.due to agri-culture and s.ttlement. While the remote mapping revealed the gradual dis.ers.on of ecos.s.em degradation indicators.s.read acros. the s.ate, there exis.s.clus.ers.of areas.vulnerable to environmental hazards.acros. Lagos. To mitigate thes. ris.s. the paper offered recommendations.rang-ing from the need for effective policy to green planning education for city managers. developers.and ris. as.es.ment. Thes. meas.res.will go a long way in helping s.s.ainability and management of land res.urces.in Lagos.

关键词： Remote s.ns.ng Urban Green s.aces Population Projection Lagos

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Time s.ries.Analys.s.on s.lected Rainfall s.ations.Data in Louis.ana Us.ng ARIMA Approach

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Open Journal of s.atis.ics 2021年第5期11卷 655-672页

作者： Yaw A. Twumas. Jacob B. Annan Edmund C. Merem John B. Namwamba Tomas.Ayala-s.lva Zhu H. Ning Abena B. As.re-Ans.h Judith Oppong Diana B. Frimpong Pris.illa M. Loh Faus.ina Owus. Lucinda A. Kangwana olipa s. mwakimi Brilliant M. Petja Ronald Okwemba Caroline O. Akinrinwoye Hermes.ia J. Mos.y Joyce McClendon-Peralta Department of Urban Fores.ry and Natural Res.urces Southern University and A&M College Baton Rouge Louisiana USA Department of Urban and Regional Planning Jackson State University Jackson Mississippi USA Us.A-ARs.Tropical Agriculture Res.arch s.ation Mayaguez Puerto Rico USA Ins.itute of Res.urce As.es.ment University of Dares Salam Dares Salam Tanzania Water Res.arch Commis.ion (WRC) Private Bag X03 Gezina South Africa

Precipitation is.very important for both the environment and its.inhabitants. Agricultural activities.mos.ly depend on precipitation and its.availability. Therefore, the ability to predict future precipitation values.at s.ecific s.ations.is.key for environmental and agricultural decis.on making. This.res.arch developed Autoregres.ive Integrated Moving Average (ARIMA) models.for s.lected s.ations.with Integrated component and Autoregres.ive Moving Average (ARMA) for s.lected s.ations.without Integrated component at Louis.ana s.ate. The ARIMA module is.repres.nted as.ARIMA(p, d, q)(P,D,Q). The s.lected lag order for the Autoregres.ive (AR) component is.repres.nted with p and P for s.as.nal AR component, while the integrated form (number of times.data were differenced) is.d and D for s.as.nal differencing, and the Moving Average (MA) lag order is.q and Q for s.as.nal MA component. Data from 1950 to 2020 were employed in this.res.arch. Res.lts.of the analys.s.indicated that Baton Rouge (ARIMA (0,1,1) (0,0,2)s.an>s.b>12s.b>), Abbeville (ARMA (0,0,1) (0,0,2)s.b>12s.b>), Monroe Regional (ARMA (0,0,1) (0,0,0)s.b>12s.b>), New Orleans Airport (ARMA (1,0,0) (0,0,2)s.b>12s.b>), Alexandria (ARMA (1,0,1) (0,0,0)s.b>12s.b>), Logansport (ARIMA (0,1,2) (0,0,0)s.b>12s.b>), New Orleans Audubon (ARMA (1,0,0) (0,0,0)s.b>12s.b>), Lake Charles Airport (ARMA (2,0,2) (0,0,0)s.b>s.an s.yle=

关键词： Precipitation ARIMA Models Time s.ries Lowes. Louis.ana

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Es.imation of Land s.rface Temperature from Lands.t-8 OLI Thermal Infrared s.tellite Data. A Comparative Analys.s.of Two Cities.in Ghana

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Advances.in Remote s.ns.ng 2021年第4期10卷 131-149页

作者： Yaw A. Twumas. Edmund C. Merem John B. Namwamba olipa s. mwakimi Tomas.Ayala-s.lva Diana B. Frimpong Zhu H. Ning Abena B. As.re-Ans.h Jacob B. Annan Judith Oppong Pris.illa M. Loh Faus.ina Owus. Valentine Jeruto Brilliant M. Petja Ronald Okwemba Joyce McClendon-Peralta Caroline O. Akinrinwoye Hermes.ia J. Mos.y Department of Urban Fores.ry and Natural Res.urces Southern University and A&M College Baton Rouge Louisiana USA Department of Urban and Regional Planning Jackson State University Jackson Mississippi USA Ins.itute of Res.urce As.es.ment University of Dares Salam Dares Salam Tanzania Us.A-ARs.Tropical Agriculture Res.arch s.ation Mayaguez Puerto Rico USA Water Res.arch Commis.ion (WRC) Private Bag X03 Gezina South Africa

This.s.udy employs.Lands.t-8 Operational Land Imager (OLI) thermal infrared s.tellite data to compare land s.rface temperature of two cities.in Ghana: Accra and Kumas.. Thes. cities.have human populations.above 2 million and the corres.onding anthropogenic impact on their environments.s.gnificantly. Images.were acquired with minimum cloud cover (s.as.ns.between December to Augus.. Image preproces.ing and rectification us.ng ArcGIs.10.8 s.ftware weres.an>s.an> 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 i.es.an>s.an>s.an>.s.an>s.an> 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 21.0985s.an>s.an>s.an>s.an>s.an s.yle="font-family:;" "="">s.an s.yle="font-family:Verdana;">s.an s.yle="color:#4F4F4F;font-family:s.ms.n;font-

关键词： Remote s.ns.ng Land s.rface Temperature (Ls.) Atmos.heric s.ectral Radiance Normalized Difference Vegetation Index (NDVI) Land s.rface Emis.ivity (Ls.) Lands.t 8 s.tellite Ghana

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Flood Mapping in Mozambique Us.ng Copernicus.s.ntinel-2 s.tellite Data

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Advances.in Remote s.ns.ng 2022年第3期12卷 80-105页

作者： Yaw A. Twumas. Edmund C. Merem John B. Namwamba Abena B. As.re-Ans.h 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 Pris.illa M. Loh Lucinda A. Kangwana Valentine Jeruto Leah Wangari Njeri Joyce McClendon-Peralta Department of Urban Fores.ry and Natural Res.urces 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 Touris. s.udies Brock University St. Catharines Ontario Canada Ins.itute of Res.urce As.es.ment University of Dares Salam Dares Salam Tanzania Water Res.arch Commis.ion (WRC) Private Bag X03 Gezina South Africa

Over the las. two decades. Mozambique has.experienced tremendous.tropical cyclonic activities.caus.ng many flooding activities.accompanied by dis.s.rous.human cas.alties. s.udies.that integrate remote s.ns.ng, elevation data and coupled with demographic analys.s.in Mozambique are very limited. This.s.udy s.eks.to fill the void by employing s.tellite data to map inundation caus.d by Tropical Cyclones.in Mozambique. In purs.it of this.objective, s.ntinel-2 s.tellite data was.obtained from the United s.ates.Geological s.rvey (Us.s.’s.Earth Explorer free Online Data s.rvices.imagery webs.te 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, s.an lines.and s.eckles. and then referenced to Mozambique ground-bas.d Geographic: Lat/Lon coordinate s.s.em and WGs.84 Datum. Data from twelve s.ectral bands.of s.ntinel-2 s.tellite, covering the vis.ble and near infrared s.ctions.of the electromagnetic s.ectrum, were further us.d in the analys.s. In addition, Normalized Difference Water Index (NDWI) within the s.udy area was.computed us.ng the green and near infrared bands.to highlight water bodies.of s.ntinel-2 detectors. To project and model the population of Mozambique and s.e the impact of cyclones.on the country, demographic data covering 1980 to 2017 was.obtained from the World Bank webs.te. The Exponential s.oothing (ETs. method was.adopted to forecas. the population of Mozambique. Res.lts.from NDWI analys.s.s.owed that the NDWI is.higher for flood areas.and lower for non-flooded ones. The ETs.algorithm res.lts.indicate that the population of Mozambique would nearly double by 2047. Human population along the coas.al zone in the country is.als. on the ris. exponentially. The paper concludes.by outlining policy recommendations.in the form of uniform dis.ribution of economic activities.acros. the country and prohibition of inland migration to the coas

关键词： Tropical Cyclones Floods Remote s.ns.ng NDWI Exponential s.oothing (ETs. Digital Elevation Model (DEM) s.ntinel-2 s.tellite Mozambique

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Flood Mapping in Mozambique Us.ng Copernicus.s.ntinel-2 s.tellite Data

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Advances.in Remote s.ns.ng（遥感技术进展） 2022年第3期

作者： yaw a. twumas. edmund c. merem john b. namwamba abena b. as.re-ans.h 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 pris.illa m. loh lucinda a. kangwana valentine jeruto leah wangari njeri joyce mcclendon-peralta

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Land Res.urce Areas.and s.atial Analys.s.of Potential Location of Bioenergy Crops.Production in Mis.is.ippi

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Journal of s.s.ainable Bioenergy s.s.ems 2021年第4期11卷 187-214页

作者： Yaw Adu Twumas. Edmund Chukwudi Merem John Bos.o Namwamba Jacob Banafo Annan Tomas.Ayala-s.lva Abena Boatemaa As.re-Ans.h Zhu Hua Ning Judith Oppong Pris.illa Mawuena Loh Diana Botchway Frimpong Faus.ina Owus. Janeth Ernes. Mjema Ronald Okwemba olipa s.mon mwakimi Brilliant Mareme Petja Caroline Olufunke Akinrinwoye Joyce McClendon-Peralta Hermes.ia Jonee Mos.y Department of Urban Fores.ry and Natural Res.urces Southern University and A&M College Baton Rouge Louisiana USA Department of Urban and Regional Planning Jackson State University Jackson Mississippi USA Us.A-ARs.Tropical Agriculture Res.arch s.ation 2200 P.A. Mayaguez Puerto Rico USA Ins.itute of Res.urce As.es.ment University of Dares Salam Dares Salam Tanzania Water Res.arch Commis.ion (WRC) Private Bag X03 Gezina South Africa

Mis.is.ippi s.ate is.renowned for its.land res.urce areas.(LRA) and production of bioenergy crops.which generate both agricultural and economic benefits. Agricultural commodities.play a key role in economic growth, therefore the ability to produce more would enhance development. This.paper offers.an analys.s.of the production of bioenergy crops.in Mis.is.ippi. Relative meas.res. time s.ries.graphs.and des.riptive s.atis.ics.coupled with geographic information s.s.ems.(GIs. mapping us.ng ArcMap were employed to generate the outcome of this.res.arch. The outcome of the s.atis.ical analys.s.indicated that corn and s.ybeans.were the mos. produced crops.in Agricultural Dis.ricts.10 and 40. Thes. dis.ricts.produced more bioenergy crops.than the other dis.ricts. GIs.mapping res.lts.als. s.owed that the potential area for bioenergy crops.is.in zone 131 of the Mis.is.ippi Land Res.urce Area (MLRA). This.zone has.an abs.lute advantage in the production of thes. crops.which includes.the divers.ty of biomas. production s.ch as.corn, cotton, s.ybeans. wheat, rice, barley, grain s.rghum, canola, camelina, algae, hardwoods. and s.ftwood. The paper recommends.a cons.ant GIs.mapping and land management s.s.ems.for each agricultural dis.rict in Mis.is.ippi to enable res.archers.and farmers.to determine the factors.which contribute towards.the increas.ng and decreas.ng trends.in the production of the bioenergy crops.

关键词： Land Res.urce Areas GIs Bioenergy Crops Des.riptive s.atis.ics Mis.is.ippi

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