Africa is a developing economy and as such, emphasis has been placed on the achievement of revolutionary goals that will place her on a similar rank as the developed economies. Pertaining to this objective, Heads of States and government all over Africa instigated the African Union (AU) Agenda 2063, which is a framework put in place to achieve a continental transformation over the next 40 years. The use of satellites has been proven to be a major influence on economic growth since it facilitates the exchange of information. Environmental hazards such as climate changes, pollution, and inefficient waste management can be classified as one of the drawbacks to achieving this economic growth we hope to accomplish. The purpose of this paper is to analyze and examine satellite communication as a tool for the attainment of an integrated, prosperous and peaceful Africa by means of combatting environmental hazards in the continent.
References
[1]
The World Bank (2016) Global Economic Prospects.
[2]
African Union (2015) Agenda 2063: The Africa We Want. Popular Version African Union, Addis Ababa.
[3]
Rich, R.C., Edelstein, M., Hallman, W.K. and Wandersman, A.H. (1995) Citizen Participation and Emprowerment: The Case of Local Environmental Hazards. American Journal of Community Psychology, 23, 657-676.
https://doi.org/10.1007/BF02506986
[4]
Mitchell, V.G. (2006) Applying Integrated Urban Water Management Concepts: A Review of Australian Experience. Environmental Management, 37, 589-605.
https://doi.org/10.1007/s00267-004-0252-1
[5]
Chowdhary, P., Raj, A. and Bharagava, R.N. (2018) Environmental Pollution and Health Hazards from Distillery Wastewater and Treatment Approaches to Combat the Environmental Threats: A Review. Chemosphere, 194, 229-246.
https://doi.org/10.1016/j.chemosphere.2017.11.163
[6]
Poursanidis, D. and Chrysoulakis, N. (2017) Remote Sensing, Natural Hazards and the Contribution of ESA Sentinels Missions. Remote Sensing Applications: Society and Environment, 6, 25-38. https://doi.org/10.1016/j.rsase.2017.02.001
[7]
Subramanian, S.G.V. (2017) Environment Problems and Hazards Lecture 01.
https://www.youtube.com/watch?v=RTQ6dEXn9i4
[8]
Ikhlayel, M. (2018) An Integrated Approach to Establish E-Waste Management Systems for Developing Countries. Journal of Cleaner Production, 170, 119-130.
https://doi.org/10.1016/j.jclepro.2017.09.137
[9]
Tramutoli, V. (2007) Robust Satellite Techniques (RST) for Natural and Environmental Hazards Monitoring and Mitigation: Theory and Applications. International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Leuven, 18-20 July 2007, 1-6. https://doi.org/10.1109/MULTITEMP.2007.4293057
[10]
Vorovencii, I. (2011) Satellite Remote Sensing in Environmental Impact Assessment: An Overview. Bulletin of the Transilvania University of Braşov, 4, 73-80.
[11]
Akinyede, J., Adepoju, K., Akinluyi, F. and Anifowose, A. (2015) Developing a Sustainable Satellite-Based Environmental Monitoring System in Nigeria. Proceedings of Earth Resources and Environmental Remote Sensing/GIS Applications VI, 9644, 96440Q. https://doi.org/10.1117/12.2195625
[12]
Li, G. and Cao, C. (2010) Development of Environmental Monitoring Satellite Systems in China. Science China Earth Sciences, 53, 1-7.
https://doi.org/10.1007/s11430-010-4140-7
[13]
Duan, H., Cao, Z., Shen, M., Liu, D. and Xiao, Q. (2019) Detection of Illicit sand Mining and the Associated Environmental Effects in China’s Fourth Largest Freshwater Lake Using Daytime and Nighttime Satellite Images. Science of the Total Environment, 647, 606-618. https://doi.org/10.1016/j.scitotenv.2018.07.359
[14]
Zotin, A., Zuev, D., Kashkin, V., Kurako, M. and Simonov, K. (2018) Environmental Risk Zones Mapping Using Satellite Monitoring Data. Procedia Computer Science, 126, 1597-1605. https://doi.org/10.1016/j.procs.2018.08.133
[15]
Manzo, C., Mei, A., Zampetti, E., Bassani, C., Paciucci, L. and Manetti, P. (2017) Top-Down Approach from Satellite to Terrestrial Rover Application for Environmental Monitoring of Landfills. Science of the Total Environment, 584-585, 1333-1348.
https://doi.org/10.1016/j.scitotenv.2017.01.033
[16]
Adamo, F., Attivissimo, F., Carducci, C.G.C. and Lanzolla, A. (2014) Monitoring Apulia Region Coastline: A Case Study. 2014 IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS), Naples, 17-18 September 2014, 1-5.
https://doi.org/10.1109/EESMS.2014.6923270
[17]
Traore, B.B., Kamsu-Foguem, B. and Tangara, F. (2017) Data Mining Techniques on Satellite Images for Discovery of Risk Areas. Expert Systems with Applications, 72, 443-456. https://doi.org/10.1016/j.eswa.2016.10.010
[18]
Skouloudis, A.N. and Rickerby, D.G. (2015) In-Situ and Remote Sensing Networks for Environmental Monitoring and Global Assessment of Leptospirosis Outbreaks. Procedia Engineering, 107, 194-204. https://doi.org/10.1016/j.proeng.2015.06.074
[19]
Li, T., Bai, F., Han, P. and Zhang, Y. (2016) Non-Point Source Pollutant Load Variation in Rapid Urbanization Areas by Remote Sensing, GIS and the L-THIA Model: A Case in Bao’an District, Shenzhen, China. Environmental Management, 58, 873-888.
https://doi.org/10.1007/s00267-016-0743-x
[20]
Christopher, S., Wang, J., Gupta, P. and Box, M. (2018) Satellite Remote Sensing of Air Pollution in Mega Cities.
https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=c62e37e9f13330b9a6472aeb6baf317532278030
[21]
Kamarul Zaman, N., Kanniah, K. and Kaskaoutis, D.G. (2018) Satellite Data for Upscalling Urban Air Pollution in Malaysia. IOP Conference Series Earth and Environmental Science, 169, Article 012036.
https://doi.org/10.1088/1755-1315/169/1/012036
[22]
Engel-Cox, J.A., Hoff, R.M. and Haymet, A. (2004) Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality. Journal of the Air & Waste Management Association, 54, 1360-1371.
https://doi.org/10.1080/10473289.2004.10471005
[23]
Le, T.H., Thanh Nguyen, T.N., Lasko, K., Ilavajhala, S., Vadrevu, K.P. and Justice, C. (2014) Vegetation Fires and Air Pollution in Vietnam. Environmental Pollution, 195, 267-275. https://doi.org/10.1016/j.envpol.2014.07.023
[24]
Sannazzaro, F., Filizzola, C., Marchese, F., Corrado, R., Paciello, R., Mazzeo, G., et al. (2014) Identification of Dust Outbreaks on Infrared MSG-SEVIRI Data by Using a Robust Satellite Technique (RST). Acta Astronautica, 93, 64-70.
https://doi.org/10.1016/j.actaastro.2013.07.003
[25]
Kourtidis, K., Georgoulias, A., Mijling, B., van der A, R., Zhang, Q. and Ding, J. (2017) A New Method for Deriving Trace Gas Emission Inventories from Satellite Observations: The Case of SO2 over China. Science of the Total Environment, 612, 923-930. https://doi.org/10.1016/j.scitotenv.2017.08.313
[26]
Tao, M., Chen, L., Wang, Z., Ma, P., Tao, J. and Jia, S. (2014) A Study of Urban Pollution and Haze Clouds over Northern China during the Dusty Season Based on Satellite and Surface Observations. Atmospheric Environment, 82, 183-192.
https://doi.org/10.1016/j.atmosenv.2013.10.010
[27]
Vadrevu, K.P., Lasko, K., Giglio, L. and Justice, C. (2014) Analysis of Southeast Asian Pollution Episode during June 2013 Using Satellite Remote Sensing Datasets. Environmental Pollution, 195, 245-256.
https://doi.org/10.1016/j.envpol.2014.06.017
[28]
Ialongo, I., Fioletov, V., McLinden, C., Jåfs, M., Krotkov, N., Li, C., et al. (2018) Application of Satellite-Based Sulfur Dioxide Observations to Support the Cleantech Sector: Detecting Emission Reduction from Copper Smelters. Environmental Technology & Innovation, 12, 172-179. https://doi.org/10.1016/j.eti.2018.08.006
[29]
Aliyu, Y.A. and Botai, J.O. (2018) Appraising City-Scale Pollution Monitoring Capabilities of Multi-Satellite Datasets Using Portable Pollutant Monitors. Atmospheric Environment, 179, 239-249. https://doi.org/10.1016/j.atmosenv.2018.02.034
[30]
Evans, J., van Donkelaar, A., Martin, R.V., Burnett, R., Rainham, D.G., Birkett, N.J., et al. (2013) Estimates of Global Mortality Attributable to Particulate Air Pollution Using Satellite Imagery. Environmental Research, 120, 33-42.
https://doi.org/10.1016/j.envres.2012.08.005
[31]
Liu, B., Ma, Y., Gong, W., Zhang, M. and Yang, J. (2018) Study of Continuous Air Pollution in Winter over Wuhan Based on Ground-Based and Satellite Observations. Atmospheric Pollution Research, 9, 156-165.
https://doi.org/10.1016/j.apr.2017.08.004
[32]
Christodoulakis, J., Varotsos, C.A., Cracknell, A.P. and Kouremadas, G.A. (2018) The Deterioration of Materials as a Result of Air Pollution as Derived from Satellite and Ground Based Observations. Atmospheric Environment, 185, 91-99.
https://doi.org/10.1016/j.atmosenv.2018.04.052
[33]
Luo, M., Shephard, M.W., Cady-Pereira, K.E., Henze, D.K., Zhu, L., Bash, J.O., et al. (2015) Satellite Observations of Tropospheric Ammonia and Carbon Monoxide: Global Distributions, Regional Correlations and Comparisons to Model Simulations. Atmospheric Environment, 106, 262-277.
https://doi.org/10.1016/j.atmosenv.2015.02.007
[34]
Yang, T., Gbaguidi, A., Yan, P., Zhang, W., Zhu, L., Yao, X., et al. (2017) Model Elucidating the Sources and Formation Mechanisms of Severe Haze Pollution over Northeast Mega-City Cluster in China. Environmental Pollution, 230, 692-700.
https://doi.org/10.1016/j.envpol.2017.06.007
[35]
de Hoogh, K., Héritier, H., Stafoggia, M., Künzli, N. and Kloog, I. (2018) Modelling daily PM2.5 Concentrations at High Spatio-Temporal Resolution across Switzerland. Environmental Pollution, 233, 1147-1154.
https://doi.org/10.1016/j.envpol.2017.10.025
[36]
Huang, K., Xiao, Q., Meng, X., Geng, G., Wang, Y., Lyapustin, A., et al. (2018) Predicting Monthly High-Resolution PM2.5 Concentrations with Random Forest Model in the North China Plain. Environmental Pollution, 242, 675-683.
https://doi.org/10.1016/j.envpol.2018.07.016
[37]
Liang, F., Xiao, Q., Gu, D., Xu, M., Tian, L., Guo, Q., et al. (2018) Satellite-Based Short- and Long-Term Exposure to PM2.5 and Adult Mortality in Urban Beijing, China. Environmental Pollution, 242, 492-499.
https://doi.org/10.1016/j.envpol.2018.06.097
[38]
He, J. and Christakos, G. (2018) Space-Time PM2.5 Mapping in the Severe Haze Region of Jing-Jin-Ji (China) Using a Synthetic Approach. Environmental Pollution, 240, 319-329. https://doi.org/10.1016/j.envpol.2018.04.092
[39]
Chen, G., Wang, Y., Li, S., Cao, W., Ren, H., Knibbs, L.D., et al. (2018) Spatiotemporal Patterns of PM10 Concentrations over China during 2005-2016: A Satellite-Based Estimation Using the Random Forests Approach. Environmental Pollution, 242, 605-613. https://doi.org/10.1016/j.envpol.2018.07.012
[40]
Zhang, Y. and Li, Z. (2015) Remote Sensing of Atmospheric Fine Particulate Matter (PM2.5) Mass Concentration near the Ground from Satellite Observation. Remote Sensing of Environment, 160, 252-262. https://doi.org/10.1016/j.rse.2015.02.005
[41]
Chen, G., Knibbs, L.D., Zhang, W., Li, S., Cao, W., Guo, J., et al. (2018) Estimating Spatiotemporal Distribution of PM1 Concentrations in China with Satellite Remote Sensing, Meteorology, and Land Use Information. Environmental Pollution, 233, 1086-1094. https://doi.org/10.1016/j.envpol.2017.10.011
[42]
Meng, X., Fu, Q., Ma, Z., Chen, L., Zou, B., Zhang, Y., et al. (2016) Estimating Ground-Level PM10 in a Chinese City by Combining Satellite Data, Meteorological Information and a Land Use Regression Model. Environmental Pollution, 208, 177-184. https://doi.org/10.1016/j.envpol.2015.09.042
[43]
Liu, Y., Xing, J., Wang, S., Fu, X. and Zheng, H. (2018) Source-Specific Speciation Profiles of PM2.5 for Heavy Metals and Their Anthropogenic Emissions in China. Environmental Pollution, 239, 544-553.
https://doi.org/10.1016/j.envpol.2018.04.047
[44]
Wu, J., Zheng, H., Zhe, F., Xie, W. and Song, J. (2018) Study on the Relationship between Urbanization and Fine Particulate Matter (PM2.5) Concentration and Its Implication in China. Journal of Cleaner Production, 182, 872-882.
https://doi.org/10.1016/j.jclepro.2018.02.060
[45]
Casciello, D., Lacava, T., Pergola, N. and Tramutoli, V. (2007) Robust Satellite Techniques (RST) for Oil Spill Detection and Monitoring. 2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Leuven, 18-20 July 2007, 1-6. https://doi.org/10.1109/MULTITEMP.2007.4293040
[46]
Adamu, B., Tansey, K. and Ogutu, B. (2018) Remote Sensing for Detection and Monitoring of Vegetation Affected by Oil Spills. International Journal of Remote Sensing, 39, 3628-3645. https://doi.org/10.1080/01431161.2018.1448483
[47]
Soydan, H., Koz, A., Düzgün, H.Ş. and Alatan, A.A. (2015) Oil Spill Determination with Hyperspectral Imagery: A Comparative Study. 2015 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, 16-19 May 2015, 2404-2407. https://doi.org/10.1109/SIU.2015.7130366
[48]
Gade, M., Mayer, B., Meier, C., Pohlmann, T., Putri, M. and Setiawan, A. (2017) Oil Pollution in Indonesian Waters: Combining Statistical Analyses of ENVISAT ASAR and Sentinel-1A C-SAR Data with Numerical Tracer Modelling. International Archives of the Photogrammetry. Remote Sensing & Spatial Information Sciences, 42, 71-77. https://doi.org/10.5194/isprs-archives-XLII-3-W2-71-2017
[49]
Arellano, P., Tansey, K., Balzter, H. and Boyd, D.S. (2015) Detecting the Effects of Hydrocarbon Pollution in the Amazon Forest Using hyperSpectral Satellite Images. Environmental Pollution, 205, 225-239.
https://doi.org/10.1016/j.envpol.2015.05.041
[50]
Asadzadeh, S. and de Souza Filho, C.R. (2017) Spectral Remote Sensing for Onshore Seepage Characterization: A Critical Overview. Earth-Science Reviews, 168, 48-72.
https://doi.org/10.1016/j.earscirev.2017.03.004
[51]
Emengini, E.J., Blackburn, G.A. and Theobald, J.C. (2013) Discrimination of Plant Stress Caused by Oil Pollution and Waterlogging Using Hyperspectral and Thermal Remote Sensing. Journal of Applied Remote Sensing, 7, Article 073476.
https://doi.org/10.1117/1.JRS.7.073476
[52]
Dutta, S., Joseph, M., Kumari, E.V.S.S. and Prasad, A.V.V. (2018) Automated Approach for Extraction of Oil Spill from SAR Imagery. Journal of the Indian Society of Remote Sensing, 46, 633-639. https://doi.org/10.1007/s12524-017-0728-0
[53]
Subiyanto, S., Ramadhanis, Z. and Baktiar, A.H. (2018) Integration of Remote Sensing Technology Using Sentinel-2A Satellite images for Fertilization and Water Pollution Analysis in Estuaries Inlet of Semarang Eastern Flood Canal. E3S Web of Conferences, 31, Article No. 12008. https://doi.org/10.1051/e3sconf/20183112008
[54]
Chang, N.-B., Mostafiz, C., Sun, Z., Gao, W. and Chen, C.-F. (2017) Developing a Prototype Satellite-Based Cyber-Physical System for Smart Wastewater Treatment. 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Calabria, 16-18 May 2017, 339-344.
https://doi.org/10.1109/ICNSC.2017.8000115
[55]
Asian, A., Toklu, M., Karabulut, B., Yanm, C., Şimşek, M., Karyot, T., et al. (2017) Water Quality Management Using Nanosatellites. 2017 8th International Conference on Recent Advances in Space Technologies (RAST), Istanbul, 19-22 June 2017, 477-482.
https://doi.org/10.1109/RAST.2017.8002951
[56]
Sukojo, B.M. and Sianipar, R.E. (2018) Spatial Analysis of Water Pollution Using Multitemporal Satellite Imagery (Case Study: River Lamong Estuary, Surabaya). IOP Conference Series: Earth and Environmental Science, 165, Article 012015.
https://doi.org/10.1088/1755-1315/165/1/012015
[57]
Park, M.-H., Suffet, I.H. and Stenstrom, M.K. (2007) Utility of LANDSAT-Derived Land Use Data for Estimating Storm-Water Pollutant Loads in an Urbanizing Area. Journal of Environmental Engineering, 133, 203-210.
https://doi.org/10.1061/(ASCE)0733-9372(2007)133:2(203)
[58]
Liu, R., Xie, T., Wang, Q. and Li, H. (2010) Space-Earth Based Integrated Monitoring System for Water Environment. Procedia Environmental Sciences, 2, 1307-1314. https://doi.org/10.1016/j.proenv.2010.10.141
[59]
Klomp, J. (2016) Economic Development and Natural Disasters: A Satellite Data Analysis. Global Environmental Change, 36, 67-88.
https://doi.org/10.1016/j.gloenvcha.2015.11.001
[60]
Li, Q.-G., Kang, L., Tang, D.-Q. and Zhu, Y.-L. (2011) Applications on Spatial Information Technology in Natural Disasters. Procedia Environmental Sciences, 10, 1396-1400. https://doi.org/10.1016/j.proenv.2011.09.223
[61]
Clark, N.E. (2017) Towards a Standard Licensing Scheme for the Access and Use of Satellite Earth Observation Data for Disaster Management. Acta Astronautica, 139, 325-331. https://doi.org/10.1016/j.actaastro.2017.07.007
[62]
Liu, S. and Hodgson, M.E. (2016) Satellite Image Collection Modeling for Large Area Hazard Emergency Response. ISPRS Journal of Photogrammetry and Remote Sensing, 118, 13-21. https://doi.org/10.1016/j.isprsjprs.2016.04.007
[63]
Denis, G., de Boissezon, H., Hosford, S., Pasco, X., Montfort, B. and Ranera, F. (2016) The Evolution of Earth Observation Satellites in Europe and Its Impact on the Performance of Emergency Response Services. Acta Astronautica, 127, 619-633.
https://doi.org/10.1016/j.actaastro.2016.06.012
[64]
Niu, X., Tang, H. and Wu, L. (2018) Satellite Scheduling of Large Areal Tasks for Rapid Response to Natural Disaster Using A Multi-Objective Genetic Algorithm. International Journal of Disaster Risk Reduction, 28, 813-825.
https://doi.org/10.1016/j.ijdrr.2018.02.013
[65]
Kaku, K. (2019) Satellite Remote Sensing for Disaster Management Support: A Holistic and Staged Approach Based on Case Studies in Sentinel Asia. International Journal of Disaster Risk Reduction, 33, 417-432.
https://doi.org/10.1016/j.ijdrr.2018.09.015
[66]
Pergola, N., Pietrapertosa, C., Lacava, T. and Tramutoli, V. (2001) Robust Satellite Techniques for Monitoring Volcanic Eruptions. Annali di Geofisica, 44, 167-177.
[67]
Flower, V.J., Carn, S.A. and Wright, R. (2016) The Impact of Satellite Sensor Viewing Geometry on Time-Series Analysis of Volcanic Emissions. Remote Sensing of Environment, 183, 282-293. https://doi.org/10.1016/j.rse.2016.05.022
[68]
Ortore, E., Laneve, G. and Bernini, G. (2014) Detection of Volcanic Gases and Particles by Satellite. Acta Astronautica, 93, 304-316.
https://doi.org/10.1016/j.actaastro.2013.07.025
[69]
Tramutoli, V., Di Bello, G., Pergola, N. and Piscitelli, S. (2001) Robust Satellite Techniques for Remote Sensing of Seismically Active Areas. Annali di Geofisica, 44, 295-312.
[70]
Bhardwaj, A., Singh, S., Sam, L., Joshi, P., Bhardwaj, A., Martín-Torres, F.J., et al. (2017) A Review on Remotely Sensed Land Surface Temperature Anomaly as an Earthquake Precursor. International Journal of Applied Earth Observation and Geoinformation, 63, 158-166. https://doi.org/10.1016/j.jag.2017.08.002
[71]
Marchetti, D. and Akhoondzadeh, M. (2018) Analysis of Swarm Satellites Data Showing Seismo-Ionospheric Anomalies around the Time of the Strong MEXICO (Mw = 8.2) Earthquake of 08 September 2017. Advances in Space Research, 62, 614-623. https://doi.org/10.1016/j.asr.2018.04.043
[72]
Korepanov, V. (2016) Possibility to Detect Earthquake Precursors Using Cubesats. Acta Astronautica, 128, 203-209. https://doi.org/10.1016/j.actaastro.2016.07.031
[73]
Ryu, K., Chae, J.-S., Lee, E. and Parrot, M. (2014) Fluctuations in the Ionosphere Related to Honshu Twin Large Earthquakes of September 2004 observed by the DEMETER and CHAMP Satellites. Journal of Atmospheric and Solar-Terrestrial Physics, 121, 110-122. https://doi.org/10.1016/j.jastp.2014.10.003
[74]
Feng, W., Samsonov, S., Tian, Y., Qiu, Q., Li, P., Zhang, Y., et al. (2017) Surface Deformation Associated with the 2015 Mw 8.3 Illapel Earthquake Revealed by Satellite-Based Geodetic Observations and Its Implications for the Seismic Cycle. Earth and Planetary Science Letters, 460, 222-233.
https://doi.org/10.1016/j.epsl.2016.11.018
[75]
Strozzi, T., Klimeş, J., Frey, H., Caduff, R., Huggel, C., Wegmüller, U., et al. (2018) Satellite SAR Interferometry for the Improved Assessment of the State of Activity of Landslides: A Case Study from the Cordilleras of Peru. Remote Sensing of Environment, 217, 111-125. https://doi.org/10.1016/j.rse.2018.08.014
[76]
Kaku, K., Aso, N. and Takiguchi, F. (2015) Space-Based Response to the 2011 Great East Japan Earthquake: Lessons Learnt from JAXA’s Support Using Earth Observation Satellites. International Journal of Disaster Risk Reduction, 12, 134-153.
https://doi.org/10.1016/j.ijdrr.2014.12.009
[77]
Ovando, A., Martinez, J.-M., Tomasella, J., Rodriguez, D. and von Randow, C. (2018) Multi-Temporal Flood Mapping and Satellite Altimetry Used to Evaluate the Flood Dynamics of the Bolivian Amazon Wetlands. International Journal of Applied Earth Observation and Geoinformation, 69, 27-40.
https://doi.org/10.1016/j.jag.2018.02.013
[78]
Aghayev, A.T. and Rustamov, R.B. (2015) Remote Sensing and Geographic Information System/Geodatabase in River Flood Mapping. Journal of Surveying and Mapping Engineering, 3, 1-11.
[79]
Cai, X., Haile, A.T., Magidi, J., Mapedza, E. and Nhamo, L. (2017) Living with Floods-Household Perception and Satellite Observations in the Barotse Floodplain, Zambia. Physics and Chemistry of the Earth, Parts A/B/C, 100, 278-286.
https://doi.org/10.1016/j.pce.2016.10.011
[80]
Umar, Z., Akib, W.W.M. and Ahmad, A. (2014) Analysis of Debris Flow Kuranji River in Padang City Using Rainfall Data, Remote Sensing and Geographic Information System. IOP Conference Series: Earth and Environmental Science, 18, Article 012122. https://doi.org/10.1088/1755-1315/18/1/012122
[81]
Rao, G., Manjusree, P., Bhanumurthy, V. and Bhatt, C. (2013) Flood Monitoring and Management Using Remote Sensing. National Natural Resources Management System Bulletin, 38, 81-88.
[82]
Veerakachen, W. and Raksapatcharawong, M. (2015) Rainfall Estimation for Real Time Flood Monitoring Using Geostationary Meteorological Satellite Data. Advances in Space Research, 56, 1139-1145. https://doi.org/10.1016/j.asr.2015.06.016
[83]
Khan, S.I., Hong, Y., Wang, J., Yilmaz, K.K., Gourley, J.J., Adler, R.F., et al. (2011) Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins. IEEE Transactions on Geoscience and Remote Sensing, 49, 85-95.
https://doi.org/10.1109/TGRS.2010.2057513
[84]
Duti, B.M., Hossain, M.M., Khadim, F.K., Rahaman, A.Z. and Quadir, D. (2017) Mapping Floods in Bangladesh Using Satellite Data: An Overview. The Journal of NOAMI, 34, 57-68.
[85]
Koriche, S.A. and Rientjes, T.H. (2016) Application of Satellite Products and Hydrological Modelling for Flood Early Warning. Physics and Chemistry of the Earth, Parts A/B/C, 93, 12-23. https://doi.org/10.1016/j.pce.2016.03.007
[86]
Li, Y., Xu, X. and Zheng, B. (2018) Satellite Views of Cross-Strait Sediment Transport in the Taiwan Strait Driven by Typhoon Morakot (2009). Continental Shelf Research, 166, 54-64. https://doi.org/10.1016/j.csr.2018.07.004
[87]
Tappin, D.R., Evans, H.M., Jordan, C.J., Richmond, B., Sugawara, D. and Goto, K. (2012) Coastal Changes in the Sendai Area from the Impact of the 2011 Tōhoku-oki Tsunami: Interpretations of Time Series Satellite Images, Helicopter-Borne Video Footage and Field Observations. Sedimentary Geology, 282, 151-174.
https://doi.org/10.1016/j.sedgeo.2012.09.011
[88]
Hereher, M.E. (2014) Assessment of Sand Drift Potential along the Nile Valley and Delta Using Climatic and Satellite Data. Applied Geography, 55, 39-47.
https://doi.org/10.1016/j.apgeog.2014.09.004
[89]
Folmer, M.J., DeMaria, M., Ferraro, R., Beven, J., Brennan, M., Daniels, J., et al. (2015) Satellite Tools to Monitor and Predict Hurricane Sandy (2012): Current and Emerging Products. Atmospheric Research, 166, 165-181.
https://doi.org/10.1016/j.atmosres.2015.06.005
[90]
Li, X., Yu, L., Xu, Y., Yang, J. and Gong, P. (2016) Ten Years after Hurricane Katrina: Monitoring Recovery in New Orleans and the Surrounding Areas Using Remote Sensing. Science Bulletin, 61, 1460-1470. https://doi.org/10.1007/s11434-016-1167-y
[91]
Han, G., Ma, Z., Chen, N., Chen, N., Yang, J. and Chen, D. (2017) Hurricane Isaac Storm Surges off Florida Observed by Jason-1 and Jason-2 Satellite Altimeters. Remote Sensing of Environment, 198, 244-253.
https://doi.org/10.1016/j.rse.2017.06.005
[92]
Hoque, M.A.-A., Phinn, S., Roelfsema, C. and Childs, I. (2017) Tropical Cyclone Disaster Management Using Remote Sensing and Spatial Analysis: A Review. International Journal of Disaster Risk Reduction, 22, 345-354.
https://doi.org/10.1016/j.ijdrr.2017.02.008
[93]
Sureshkumar, M., Sivakumar, R. and Nagarajan, M. (2016) A Geo-Information System for Optimum Municipal Solid Waste Management (A Case Study of Kanchipuram Municipality, Tamil Nadu, India). Indian Journal of Science and Technology, 9, 1-8. https://doi.org/10.17485/ijst/2016/v9i37/99047
[94]
Zaman, A.U. and Lehmann, S. (2011) Urban Growth and Waste Management Optimization towards ‘Zero Waste City’. City, Culture and Society, 2, 177-187.
https://doi.org/10.1016/j.ccs.2011.11.007
[95]
Einaudi, F., Uccellini, L., Purdom, J., Rogers, D., Gelaro, R., Dodge, J., et al. (2001) Weather Prediction Improvement Using Advanced Satellite Technology. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, 9-13 July 2001, 150-153.
[96]
Garcia-Soto, C., Vazquez-Cuervo, J., Clemente-Colon, P. and Hernandez, F. (2012) Satellite Oceanography and Climate Change. Deep Sea Research Part II: Topical Studies in Oceanography, 77, 1-9. https://doi.org/10.1016/j.dsr2.2012.07.004
[97]
Jin, W., Wang, L., Zeng, X., Liu, Z. and Fu, R. (2014) Classification of Clouds in Satellite Imagery Using Over-Complete Dictionary via Sparse Representation. Pattern Recognition Letters, 49, 193-200. https://doi.org/10.1016/j.patrec.2014.07.015
[98]
Shrestha, N.K., Qamer, F.M., Pedreros, D., Murthy, M., Wahid, S.M. and Shrestha, M. (2017) Evaluating the Accuracy of Climate Hazard Group (CHG) Satellite Rainfall Estimates for Precipitation Based Drought Monitoring in Koshi Basin, Nepal. Journal of Hydrology: Regional Studies, 13, 138-151.
https://doi.org/10.1016/j.ejrh.2017.08.004
[99]
Mikuş, P. and Mahović, N.S. (2013) Satellite-Based Overshooting Top Detection Methods and an Analysis of Correlated Weather Conditions. Atmospheric Research, 123, 268-280. https://doi.org/10.1016/j.atmosres.2012.09.001
[100]
Ibiteye, O.K. and Awomoyi, M.E. (2023) Youth Restiveness and Socio-Economic Development: A Situation Study of Niger Delta Expanse. Social Science and Humanities Research, 6, 1-12.
