thesis on remote sensing and gis

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Dambe, Natalia. "Riverine flooding using GIS and remote sensing." Master's thesis, Faculty of Engineering and the Built Environment, 2020. https://hdl.handle.net/11427/31738.

Gustavsson, Andreas, and Selberg Martin. "Delineation of Ditches in Wetlandsby Remote Sensing." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-354612.

Al, Sghair Fathi Goma. "Remote sensing and GIS for wetland vegetation study." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4581/.

Tyoda, Zipho. "Landslide susceptibility mapping : remote sensing and GIS approach." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79856.

Ahmadzadeh, M. R. "Reasoning with uncertainty in remote sensing." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/804/.

Almond, Simon John. "Remote sensing within GIS for woodland inventory and monitoring." Thesis, University of Portsmouth, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386832.

Firoozi, Nejad Behnam. "Population mapping using census data, GIS and remote sensing." Thesis, Queen's University Belfast, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.705917.

Mason, Philippa Jane. "Landslide hazard assessment using remote sensing and GIS techniques." Thesis, Imperial College London, 1999. http://hdl.handle.net/10044/1/8899.

Blackburn, George Alan. "Remote sensing of deciduous woodlands : a tool for ecological investigations." Thesis, University of Southampton, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239872.

Berberoglu, Suha. "Optimising the remote sensing of Mediterranean land cover." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285646.

McNulty, Wendy Lynn. "THE CREATION OF A GIS DATABASE AND THE DETERMINATION OF SLUDGE'S SPECTRAL SIGNATURE IN AN AGRICULTURAL SETTING." Bowling Green State University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1120596906.

Sahar, Liora. "Using remote-sensing and gis technology for automated building extraction." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37231.

Ratnayake, Ranitha. "Remote sensing and GIS application for monitoring forest management operations." Thesis, University of Nottingham, 2004. http://eprints.nottingham.ac.uk/11309/.

Murnion, Shane D. "Neural and genetic algorithm applications in GIS and remote sensing." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337024.

Saini, Aditya. "Mapping snow cover in Siberia using GIS and remote sensing." College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/94.

Huang, Junyi. "Investigation on landslide susceptibility using remote sensing and GIS methods." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/33.

Jennings, Laura. "A Storm Water Runoff Investigation Using Gis and Remote Sensing." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149613/.

Faraklioti, M. "Classification of sets of mixed pixels in remote sensing." Thesis, University of Surrey, 2000. http://epubs.surrey.ac.uk/844613/.

Villeneuve, Julie. "Delineating wetlands using geographic information system and remote sensing technologies." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3135.

Solomon, Semere. "Remote Sensing and GIS : Applications for Groundwater Potential Assessment in Eritrea." Doctoral thesis, KTH, Civil and Architectural Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3491.

An integrated approach with remote sensing, GeographicInformation Systems (GIS) and more traditional fieldworktechniques was adopted to assess the groundwater potential inthe central highlands of Eritrea. Digitally enhanced colorcomposites and panchromatic images of Landsat TM and Spot wereinterpreted to produce thematic maps such as lithology andlineaments. The potential of the Advanced Spaceborne ThermalEmission and Reflection Radiometer (ASTER) data forlithological and lineament mapping was evaluated. Topographicparameters such as surface curvature, slope and drainagesystems were derived from digital elevation models and used tomap landforms. Digital elevation models (DEM) derived fromcontours and acquired in the Shuttle Radar Topographic Mission(SRTM) were compared in relation to location, drainage networksand lineament extraction. Fracture patterns and spacing weremeasured in the field in different rock types and compared withlineaments. Selected springs and wells were visited to studytheir topographic and hydrogeological setting. Well logs,pumping tests, water table depth in dry and wet season as wellas location of wells were collected. All thematic layersincluding hydrogeological data were integrated and analysed ina geographic information system. A groundwater potential mapwas generated and compared with yield data. Groundwaterrecharge was estimated based on water level fluctuations inlarge dug wells and chloride mass-balance method.

Principal component analysis for rock type mapping providedbetter results with ASTER than with Landsat TM data. DEM datapermitted to create detailed landform maps useful torgroundwater potential assessment. DEM derived from SRTM dataare better for detection of drainage systems and linearfeatures than those derived from contours. Most of the fracturesystems corresponding to lineaments are either extensionalrelated to normal faults and dykes, or shear fractures relatedto strike-slip faults. N-S, NW-SE, WNW-ESE, NE-SW and ENE-WSWare dominant fracture orientations with often very densespacing. High yielding wells and springs are often related tolarge lineaments and corresponding structural features such asdykes. Typically wells and springs in basaltic areas havehigher yields mainly due to primary joints. Young alluvialsediments with high permeability and deeply weathered rocklayers are important for water supply especially in hydraulicconnection with fracture systems in crystalline bedrock.Groundwater potential zones demarcated through the model are inagreement with bore well yield data. The spatial distributionof groundwater potential zones shows regional patterns relatedto lithologies, lineaments, drainage systems and landforms.Recharge rates of 10 - 50 mm were estimated in this region. Theresults demonstrate that the integration of remote sensing,GIS, traditional fieldwork and models provide a powerful toolin the assessment and management of water resources anddevelopment of groundwater exploration plans.

Key words: Remote sensing, Geographic InformationSystems, groundwater, geomorphology, Digital elevation model,lithology, hard rock, lineament, structures, hydrogeology,Eritrea

Zhang, Bo. "Data Mining, Gis And Remote Sensing: Application In Wetland Hydrological Investigation." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1220021657.

Yang, Ming-Der. "Adaptive short-term water quality forecasts using remote sensing and GIS /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu148794273980509.

Emery, Guy Stephen. "Determining a classifier optimisation process which uses temporal sequences of remotely sensed images." Thesis, Staffordshire University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389100.

Buyuksalih, Gurcan. "Geometric and radiometric calibration of video infrared imagers for photogrammetric applications." Thesis, University of Glasgow, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284703.

Higgins, Neil Anthony. "Information content of ATSR-2 dual-view angle spectral data." Thesis, University of Salford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244821.

Archer, David John. "Monitoring geological processes on the Chott el Djerid playa using the ERS-1 SAR." Thesis, University of Reading, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296630.

Harris, Andrew John Lang. "Thermal monitoring of volcanoes from space at low spatial resolution." Thesis, Open University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309863.

Krzys, Bethaney L. "Remote identification of wetlands in Mahoning and Trumbull County, Ohio." [Kent, Ohio] : Kent State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1227650462.

Harwood, Joseph Walter IV. "Delineation and GIS Mapping of Urban Heat Islands Using Landsat TM Imagery." Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1208562366.

Kelgenbaeva, Kamilya. "Agronomic Suitability Studies in the Russian Altai Using Remote Sensing and GIS." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1212669959876-32328.

Belden, Deborah Jeanne. "Geomorphological mapping of the K2 area, Pakistan using GIS and remote sensing." Diss., [Missoula, Mont.] : The University of Montana, 2008. http://etd.lib.umt.edu/theses/available/etd-06112008-121208/.

Paul, Frank. "The new Swiss Glacier Inventory 2000 : application of remote sensing and GIS /." Zürich : Geographisches Institut der Universität Zürich, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016135827&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA.

Zhang, Xiaoyang. "Soil-erosion modelling at the global scale using remote sensing and GIS." Thesis, King's College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321948.

Koon, Michael. "A spatial and temporal analysis of conifers using remote sensing and GIS." Huntington, WV : [Marshall University Libraries], 2004. http://www.marshall.edu/etd/descript.asp?ref=401.

Ivits-Wasser, Eva. "Potential of remote sensing and GIS as landscape structure and biodiversity indicators." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11259425.

Rocha, Stella Procopio da. "Análise espaço temporal do uso e cobertura da terra no entorno da BR-101 - trecho Angra dos Reis e Parati/RJ." Universidade do Estado do Rio de Janeiro, 2005. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=313.

Roy, David Paul. "The geometric correction of airborne remotely sensed scanner imagery." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318207.

Cheesman, Joanne E. "Modelling long-term runoff from upland catchments." Thesis, Manchester Metropolitan University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389290.

Chopping, M. J. "Linear semi-empirical kernel-driven bidirectional reflectance distribution function models in monitoring semi-arid grasslands from space." Thesis, University of Nottingham, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262949.

Reunanen, P. (Pasi). "Landscape responses of the Siberian flying squirrel ( Pteromys volans ) in northern Finland:the effect of scale on habitat patterns and species incidence." Doctoral thesis, University of Oulu, 2001. http://urn.fi/urn:isbn:9514264967.

Ranatunga, Thushara D. "Development of a GIS and Remote Sensing Based Study Tool for Tree Identification." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1227241623.

Crosta, Alvaro Penteado. "Mapping of residual soils by remote sensing for mineral exploration in SW Minas Gerais State, Brazil." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/47830.

Shen, Lin. "GIS-based Multi-criteria Analysis for Aquaculture Site Selection." Thesis, University of Gävle, Department of Industrial Development, IT and Land Management, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-7532.

The pearl oyster Pinctada martensii or Pinctada fucata is the oyster for produce the South China Sea Pearl, and the production of pearl oyster Pinctada martensii plays a key role for the economic and social welfare of the coastal areas. To guarantee both rich and sustainability of providing pearl oyster productions, addressing the suitable areas for aquaculture is a very important consideration in any aquaculture activities. Relatively rarely, in the case of site selection research, the researchers use GIS analysis to identify suitable sites in fishery industry in China. Therefore, I decided to help the local government to search suitable sites form the view of GIS context. This study was conducted to find the optimal sites for suspended culture of pearl oyster Pinctada martensii using GIS-based multi-criteria analysis. The original idea came from the research of Radiarta and his colleagues in 2008 in Japan. Most of the parameters in the GIS model were extracted from remote sensing data (Moderate Resolution Imaging Spectroradiometer and Landsat 7). Eleven thematic layers were arranged into three sub-models, namely: biophysical model, social-economic model and constraint model. The biophysical model includes sea surface temperature, chlorophyll-α concentration, suspended sediment concentration and bathymetry. The criteria in the social-economic model are distance to cities and towns and distance to piers. The constraint model was used to exclude the places from the research area where the natural conditions cannot be fulfilled for the development of pearl oyster aquaculture; it contains river mouth, tourism area, harbor, salt fields / shrimp ponds, and non-related water area. Finally those GIS sub-models were used to address the optimal sites for pearl oyster Pinctada martensii culture by using weighted linear combination evaluation. In the final result, suitability levels were arranged from 1 (least suitable) to 8 (most suitable), and about 2.4% of the total potential area had the higher levels (level 6 and 7). These areas were considered to be the places that have the most suitable conditions for pearl oyster Pinctada martensii for costal water of Yingpan.

Sumaryono, Sumaryono. "Assessing Building Vulnerability to Tsunami Hazard Using Integrative Remote Sensing and GIS Approaches." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-123909.

Haq, Mohammed Rajibul. "Development of a remote sensing and GIS-based landslide susceptibility model for scotland." Thesis, University of Dundee, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.510683.

Yang, Lisa S. M. Massachusetts Institute of Technology. "Application of high resolution remote sensing and GIS techniques for evaluating urban infrastructure." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120199.

Suzuoki, Yukihiro. "Human Impacts Study on Cuyahoga Valley National Park using GIS and Remote Sensing." Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1216649639.

Smith, Steven Murray. "Assessing variability in the production of pasture using GIS and remote sensing techniques." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/29293.

Martinez-Rodriguez, Juan Guillermo 1958. "Sensitivity analysis across scales and watershed discretization schemes using ARDBSN hydrological model and GIS." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/282879.

Akbar, A. Ali Mohd Sadiq. "Application of remote sensing methods for discrimination of surficial sand types in Qatar Peninsula, the Arabian Gulf." Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295012.

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Remote sensing and GIS for wetland vegetation study

Al Sghair, Fathi Goma (2013) Remote sensing and GIS for wetland vegetation study. PhD thesis, University of Glasgow.

Remote Sensing (RS) and Geographic Information System (GIS) approaches, combined with ground truthing, are providing new tools for advanced ecosystem management, by providing the ability to monitor change over time at local, regional, and global scales.

In this study, remote sensing (Landsat TM and aerial photographs) and GIS, combined with ground truthing work, were used to assess wetland vegetation change over time at two contrasting wetland sites in the UK: freshwater wetland at Wicken Fen between 1984 and 2009, and saltmarsh between 1988 and 2009 in Caerlaverock Reserve. Ground truthing studies were carried out in Wicken Fen (UK National Grid Reference TL 5570) during 14th - 18th June 2010: forty 1 m2 quadrats were taken in total, placed randomly along six transects in different vegetation types. The survey in the second Study Area Caerlaverock Reserve (UK National Grid Reference NY0464) was conducted on 5th - 9th July 2011, with a total of forty-eight 1 m2 quadrats placed randomly along seven transects in different vegetation types within the study area. Two-way indicator species (TWINSPAN) was used for classification the ground truth samples, taking separation on eigenvalues with high value (>0.500), to define end-groups of samples. The samples were classified into four sample-groups based on data from 40 quadrats in Wicken Fen, while the data were from 48 quadrats divided into five sample-groups in Caerlaverock Reserve.

The primary analysis was conducted by interpreting vegetation cover from aerial photographs, using GIS combined with ground truth data. Unsupervised and supervised classifications with the same technique for aerial photography interpretation were used to interpret the vegetation cover in the Landsat TM images. In Wicken Fen, Landsat TM images were used from 18th August 1984 and 23rd August 2009; for Caerlaverock Reserve Landsat TM imagery used was taken from 14th May 1988 and 11th July 2009. Aerial photograph imagery for Wicken Fen was from 1985 and 2009; and for Caerlaverock Reserve, from 1988 and 2009.

Both the results from analysis of aerial photographs and Landsat TM imagery showed a substantial temporal change in vegetation during the period of study at Wicken Fen, most likely primarily produced by the management programme, rather than being due to natural change. In Cearlaverock Reserve, results from aerial photography interpretation indicated a slight change in the cover of shrubs during the period 1988 to 2009, but little other change over the study period.

The results show that the classification accuracy using aerial photography was higher than that of Landsat TM data. The difference of classification accuracy between aerial photography and Landsat TM, especially in Caerlaverock Reserve, was due to the low resolution of Landsat TM images, and the fact that some vegetation classes occupied an area less than that of the pixel size of the TM image. Based on the mapping exercise, the aerial photographs produced better vegetation classes (when compared with ground truthing data) than Landsat TM images, because aerial photos have a higher spatial resolution than the Landsat TM images.

Perhaps the most important conclusion of this study is that it provides evidence that the RS/GIS approach can provide useful baseline data about wetland vegetation change over time, and across quite expansive areas, which can therefore provide valuable information to aid the management and conservation of wetland habitats.

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The University of Glasgow is a registered Scottish charity: Registration Number SC004401

Thesis - Geo-information Science and Remote Sensing

The thesis is a compulsory part of every Master study programme of Wageningen University & Research. A major thesis is between 24 and 39 Ects and is at least 36 Ects for the master programme Geo-information Science (MGI).

Geo-information science thesis topics are narrowly related to the research programme of the Laboratory of Geo-informationt Science and Remote Sensing (GIRS). This research program covers a wide range of subjects. The following main themes have been selected to delineate the laboratory's identity:

• Sensing & measuring
• Modelling & visualization
• Integrated land monitoring
• Human - space interaction
• Empowering & engaging communities

The thesis research is conducted under supervision of a staff member of the GIRS group, but might also take place in another institute or company.

• Students have to follow the GRS thesis procedure. Complete guidelines for doing a thesis Geo-information Science are available via Brightspace.
• Required documents and forms are available via Brightspace. Contact us for access to this Brightspace page.
• For the planning of the thesis research an overview with dates for the midterm presentations and colloquia are scheduled.
• Thesis topics can be selected from the GRS thesis topic list or under conditions be proposed by individual students.
• Past thesis projects can be consulted.

Compulsory course in

• Master Geo-Information Science (MGI)

Restricted Optional in

• Master Biosystems Engineering (MBE)
• Master Urban Environmental Management (MUE)

• Open access
• Published: 06 February 2021

GIS-based multi‐criteria analysis for sustainable urban green spaces planning in emerging towns of Ethiopia: the case of Sululta town

• Eshetu Gelan 1  

Environmental Systems Research volume  10 , Article number:  13 ( 2021 ) Cite this article

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Urban green spaces are important components, contributing in different ways to the quality of human well-being. In the planning and management of urban centres, attention to the appropriate site selection of urban green spaces with regard to the importance that these spaces have from the perspectives of ecology, socioeconomic, mentality, etc., is an inevitable requirement. In present decades, land suitability mapping methods and GIS have been used to support urban green space planners in developed countries; however, its application and practices are limited in developing countries, like Ethiopia. Therefore, the aim of this study has to select potential sites for green spaces in Sululta town that assist an effective planning process of green areas in a sustainable way.

In this study, GIS-based Multi-criteria analysis (MCA) has been adopted to select suitable sites for urban green spaces. Existing land use, proximity to settlement, road and water body, population density, land ownership, topography, and scenic attractiveness were recognized as the key factor affecting urban green land suitability.

The results showed that 13.6%, 34%, 28%, and 18.9% of the study area are highly suitable, suitable, moderately suitable, and poorly suitable, respectively, for urban green spaces development. Furthermore, out of the total area of the study town 5.5% of the landmass is not suitable for urban green spaces development.

Conclusions

Therefore, the application of GIS-based MCA has provided an effective methodology to solve a complex decisional problem in urban green spaces site selection in the study town and urban planning all over the country.

Introduction

With more than 50 % of the global population now living in urban areas, the world has experienced unprecedented urban growth in recent decades (Wu  2014 ). The global urban population is projected to be 6.3 billion by 2050, almost double the global population of 3.5 billion urban dwellers in 2010 (SCBD  2012 ). This rapid urbanization has posed greater pressure on natural resources and the environment (Rees and Wackernagel  1996 ; Shi  2002 ) and the amount of land exploited for infrastructure development and buildings has increased at the expense of urban green spaces (Sandstrom  2002 ).

Urban green spaces are of crucial importance, especially in an urbanized world, as they are the key providers of ecosystem services and improve the quality of life of urban residents. For instance, by increasing water infiltration, it promotes the regulation of ecosystem services (Haase and Nuissl  2007 ; Pauleit and Duhme  2000 ) and has positive impacts on microclimate regulation (Gill et al. 2000 ; Hamada and Ohta  2010 ). It also provides benefits to city residents, such as exercise, socialization, interaction with nature and connection with places of rich cultural heritage (Crompton  2005 ; Cho et al.  2006 ; Sarev 2011). It is important to understand in this sense that green spaces are main components of urban environments (Tratalos et al. 2007 ) not only for their recreation but also for social contributions (Jones et al. 2013 ), health (Kimberlee et al. 2011) and environmental outcomes (Patel et al. 2009 ).

Despite the numerous aforementioned benefits, urban green spaces are unable to provide urban dwellers with the desirable facilities due to increased urbanization and unplanned urban growth (Wright and Nebel  2002 ), lack of proper site selection and planning and lack of attention to population thresholds (Ahmadi et al. 2016 ). As a result, both quality and quantity of urban green spaces are adversely affected and do not deliver what urban centres demand from urban green spaces as a living organism (Crompton  2001 ). Therefore, by taking into consideration environmental and social-economic factors, well planned, and well-designed green spaces within the reach of the community are mandatory in order to maximize the value that green spaces bring to urban residents and their environment in a sustainable way (Giles-Corti et al.  2005 ).

Land suitability analysis is vital in urban green spaces planning as it gives room for choosing the most suitable site from among various alternatives (Sahabo and Mohammed  2016 ). For suitable site selection, the multi-criteria analysis (MCA) approach that is integrated with the Geographical Information System (GIS) has been increasingly used (Uy and Nakagoshi  2008 ; Van Berkel et al.  2014 ; Ustaoglu, and Aydınoglu  2020 ). In order to determine different land problems considering the alternatives, MCA focuses on various parameters such as biophysical, socio-economic and policy-related factors in decision-making processes (Pramanik, 2016 ).

The MCA methods have been widely applied in both developed and developing countries to select agricultural sites, industrial sites, residential areas, landfill sites, wind farms, disaster area, health centres, and education centres (Rikalovic et al. 2014 ; Rahmati et al. 2016 ; Marsh et al. 2016 ; Demesouka et al. 2016 ; Vasileiou et al. 2017 ). However, the MCA methodology has not commonly used in developing countries such as Ethiopia to select suitable site for urban green spaces development and using MCA in urban planning, as decision-making tools are not practiced.

In parts of Europe, North America and Asia, MCA approach that is integrated with the GIS to identify suitable site for urban green spaces has been receiving more attention and it is considered as one of the essential tools for urban green spaces planning (Nowak et al. 2003 ; Ustaoglu and Aydinoglu  2020 ). In order to specifically analyse the characteristics of green areas and possible sites suitable for green spaces in either the European or overseas context, numerous studies were conducted (Kienast et al.  2012 ; La Rosa and Privitera  2013 ; Chandio et al.  2014 ; Morckel  2017 ; Merry et al.  2018 ; Ustaoglu and Aydınoglu  2020 ). However, in developing countries, while some green spaces studies have been performed, the available studies have concentrated largely on the evaluation of urban green spaces with less emphasis on the study of the suitability analysis for green spaces site selection. For instances, the studies in sub-Saharan African countries are primarily related to street trees’ abundance and composition (Kuruneri-Chitepo and Shackleton  2011 ), green space degradation (Mensah  2014 ), green space extent (McConnachie et al. 2008 ; McConnachie and Shackleton 2010 ) and planning aspects (Cilliers 2009 ; Fohlmeister et al. 2015 ).

This situation also occurs in the case of Ethiopia, which is one of the fastest growing countries in sub-Saharan Africa (Lamson-Hall et al. 2018 ), and studies have focused on the impacts of urban growth on green space (Abebe and Megento  2016 ; Gashu and Gebre-Egziabher  2018 ; Abo El Wafa et al.  2018 ), climate change adaptation (Lindley et al.  2015 ), the development of functional green infrastructure and ecosystem service (Woldegerima et al. 2017 ), planning aspect (Girma et al.  2019 ), green spaces depletion (Girma et al., 2019a ) and utilization pattern (Yeshewazerf  2017 ; Molla et al.  2017 ; Girma et al.  2019b ). However, the topic of suitability analysis for green space in the urban environment by using methods such as GIS-based Multi-criteria analysis has not discussed in these studies. This study therefore aimed to fill the existing research gap by applying GIS-based Multi-criteria analysis method to identify suitable sites for urban green space development in Sululta town.

Materials and methods

Description of the study area.

Sululta town is located in Sululta district of the previous North Shewa administrative zone of Oromia region, currently under Oromia special zone surrounding Finfinne. It is situated very close to the district capital town Chancho and Addis Ababa, which are far about 15 and 23 km in the north and south direction, respectively. Astronomically, the study area is located between 9° 30′ 00″ N to 9° 12′ 15″ N latitude and 38° 42′ 0″ E to 38° 46′ 45″ E longitude. The administrative area of the town is about 4471 hectares. Sululta has the same general climatologically characteristics as that of Addis Ababa. Globally it is a part of tropical humid climatic region, which is characterized by warm temperature and high rainfall. The soils of the zone are basically derived from mesozoic sedimentary and volcanic rocks. The major soil types of Suluta area are Chromic Luvisols (Fig. 1 ).

Map of the study area

Factor map to make suitability analysis for urban green space

Urban green spaces have continuously played a significant role in enhancing the quality of life of urban inhabitants and in supporting urban metabolism. However, urban green spaces have experienced a physical and social decline, while its heterogeneity and richness is often neglected and its contribution to the well-being of a community ignored within current urban planning instruments in Sululta town (Girma et al.  2019 ; Girma et al.  2019a ). Under this circumstance, GIS-based multi-criteria land suitability analysis is becoming critical in determining the land resource that is suitable for urban green spaces (Cetin 2015). Continued development and refinement of suitability analysis, particularly with GIS technology, can enable urban planners to create a suitable urban green spaces system in the urban environment (Manlum 2003 ).

Several literatures have stated that MCA components are used in only a few GIS programs (e.g. IDRISI and ILWIS) to select appropriate places for different functions (Lesslie et al. 2008 ; Chen et al. 2001 ; Ozturk and Batuk  2011 ). MCA has not yet been implemented in the standard functions, according to the literatures, while ArcGIS is one of the most popular GIS applications. In this study, MCA has incorporated ArcGIS 10.2 as a method to select an appropriate location for the development of urban green space.

Therefore, this study proposed the application of GIS-based multi-criteria suitability analysis using analytical hierarchy process (AHP) to support the decision-making process on selecting an appropriate site for development urban green spaces. This approach will be used as a basis for the town’s administration and the planning authority to identify an appropriate and potential site for providing suitable, sufficient and accessible urban green spaces to the urban dwellers. Moreover, it will be used as a benchmark to guide the sustainable land use decision in the study area.

In this study, to select a suitable site for urban green spaces using GIS-based multi-criteria analysis the following five main steps were used:

Spatial and non-spatial data collection

Determination and rating of criteria and sub-criterion

Criteria standardization and factor map generation

Determination of weighting for factors and

Weighted overlay analysis.

Spatial and non‐spatial data collection

The primary data from the field survey were collected through interviews undertaken with different experts in the related field of study for identifying factors that are important for urban green spaces site selection. Various spatial data were also obtained from different secondary sources (Table  1 ). The data were analysed in ArcGIS 10.2 and ERDAS Imagine 2010 for further analysis and mapping purposes.

Determination and rating of criteria and sub‐criteria

In AHP process selection of criteria and their sub-criteria is a crucial stage as selection of criteria influences the judgment by segregating one criterion from other and at the same time, by giving more importance to one criterion over other (Ullah  2014 ). For urban green space planning, there were no universally agreed criteria and factors (Jabir and Arun 2014 ). Therefore, by synthesizing literature review, personal experiences, experts opinions and previous related studies conducted by different researchers (Manlun  2003 ; Uy and Nakagoshi  2008 ; Pantalone  2010 ; Ahmed et al.  2011 ; Kuldeep  2013 ; Heshmat et al.  2013 ; Elahe et al.  2014 ; Yousef et al. 2014 ; Abebe and Megento  2017 ; Li et al. 2018 ; Dagistanli et al.  2018 ; Ustaoglu and Aydinoglu 2020 ) 12 factors were considered for selection of suitable site for development of urban green spaces (Table  2 ). In this study, scientific standards review and personal experiences were used to ensure the reliability of the experts’ opinions.

Besides identifying appropriate criteria and sub-criteria to select a suitable site for urban green spaces the rating has been assigned for each factor. In order to assign a rating (score) for each criterion and sub-criteria, review of previous scientific experimental research findings and literature on parameters were undertaken. Furthermore, reviews were consolidated through consultations and discussion with experienced experts and researchers from various disciplines. Rating of factors has usually made in terms of five classes: highly suitable, suitable, moderately suitable, poorly suitable, and not suitable (FAO  2006 ).

Criteria standardization and factors map generation

In GIS-based multi-criteria decision-making analysis, there is a need to standardize the data in order to integrate the data measured in different units and mapped in different scale of measurement such as ordinal, interval, nominal and ratio scales (Pereira and Duckstein 1993 ). Even though there are different methods that can be used to standardize criterion maps, linear scale transformation is the most frequently used technique (Malczewski  2003 ). For criterion standardization in this study, all the vector maps of the criterion were converted to raster data formats. Afterward using the Spatial Analyst tool in ArcMap the raster maps were reclassified into five classes with the values that range from 1 to 5, where the value of 5 was taken as highly suitable while that of 1 was unsuitable for all factors considered. This approach will enable all measurements to have an equivalent value before any weights were applied. However, it was important to note that there were some variables that did not fulfil the whole range of the criteria. Once all the criteria maps were standardized, a weight of each criteria map was calculated using AHP.

Estimating weight for factors and sub‐factors

One component of GIS-Based multi-criteria decision-making analysis is assigning criteria weights for each factor maps. The purpose of weighing in this process is to express the importance or preference of each factor relative to another factor effect on urban green spaces. In this study, the AHP using pairwise comparison matrixes were used to calculate weights for the criteria maps. AHP is a widely used method in multi-criteria decision-making analysis and was introduced by Saaty ( 1980 ). In this study, the AHP was carried out in three steps. Firstly, pair-wise comparison of criteria was performed and results were put into a comparison matrix. A Pair-wise comparison is performed in the 9-degree preferences scale, which is suggested by Saaty ( 1980 ), each higher level of scale shows higher importance than the previous lower level (Table  3 ).

According to Saaty ( 1980 ), the values in the matrix need to be consistent, which means that if x is compared to y, it receives a score of 9 (strong importance), y to x should score 1/9 (little importance) and something compared to itself gets the score of 1 (equal importance). Experts are asked to rank the value of criterion map for pairwise matrix on a saaty’s scale. Moreover, the pairwise comparison matrices (Annexe 1) were developed by taking into account the information provided by the relevant literature (Uy and Nakagoshi  2008 ; Pantalone  2010 ; Elahe et al.  2014 ; Yousef et al. 2014 ; Abebe and Megento  2017 ; Dagistanli et al.  2018 ; Ustaoglu and Aydinoglu 2020 ).

The second step was calculating criterion weights, the weights are calculated by normalizing the eigenvector associated with the maximum eigenvalue of the (reciprocal) ratio matrix. In this study the computation of the criterion weights involves the following operations: (a) summing the values in each column of the pairwise comparison matrix (Annexe 1); (b) dividing each element in the matrix by its column total (the resulting matrix is referred to as the normalized pairwise comparison matrix, (Annexe 1)), and (c) computing the average of the elements in each row of the normalized matrix, that is, dividing the sum of normalized scores for each row by 12 (the number of criteria).

Once the pair-wise comparison was filled and the weight of the factor was determined, a consistency ratio (CR) was calculated to identify inconsistencies and develop the best-fit weights in the complete pair-wise comparison matrix. A consistency ratio was calculated for each pairwise comparison matrix to verify the degree of credibility of the relative weights, by using the following formula (Bunruamkaew and Yuji 2001).

where CR = Consistency ratio, CI = referred to as consistency index, RI = is the random inconsistency index whose value depends on the number (n) of factors being compared; as illustrated in Table  4 (Saaty 1980 ).

The consistency index (CI) was calculated by the following formula:

where n = the number of items being compared in the matrix, λ max  = Average value of the consistency vector.

Weighted overlay analysis

Once the criteria maps and weights have been developed and established, a decision rule of multi-criteria analysis was used. As pointed by Jiang and Eastman ( 2000 ) and Malczewski ( 2003 ) there are three common decision rules in multi-criteria analysis namely weighted linear overlay, Boolean overlay and ordered averaging. The weighted linear combination technique was applied to aggregate the standardized layers in this study. In weighted linear combination procedure, factors and parameters (Xi) are multiplied by the weight of the suitability parameters (Wi) to get composited weights and then summed. This can be expressed by using the following formula to derive the intended map i.e. urban green spaces suitability map for the towns.

where S = total suitability score, Wi = weight of the selected suitability criteria layer, Xi = assigned sub criteria score of suitability criteria layer i, n = total number of suitability criteria layer.

Result and discussion

Ahp weights.

The result of AHP shows that the derived factors have a different degree of influence on urban green spaces. As it is evident from Table  5 , the weight assigned to the factors reveals the relative importance of each parameter in exposing an area to urban green spaces evaluation. As a result shows, an area with high population density with the normalized weight of 0.22 has the highest priority. Proximity to settlement area with the weight of 0.21 is in the second priority. Slop with a normal weight of 0.13 has the third priority. Proximity to the road with a normal weight of 0/10 is in the fourth priority. Elevation with normal weight of 0/059 is of the fifth priority. The area with vegetation cover with normal weight of 0/048 is the next priority. The flood-prone area with the normal weight of 0/04 is in the low priority. Proximity to water sources, visibility and existing land with almost similar weight of 0/032, 0/032 and 0/039, respectively, have relatively lowest priority (Table  5 ). These imply that the higher the weight in the percentage of a factor, the more influence it has in suitable site selection for urban green spaces.

Saaty (2008) has shown that Consistency ratio of 0.1 or less is acceptable to continue the AHP analysis. But if it’s larger than 0.10, then there are inconsistencies in the evaluation process, and the AHP method may not yield a meaningful result. In this study, consistency ratio or CR of conducted comparisons has obtained 0.09, which is smaller than 0.1 and therefore the comparisons can be acceptable. The computation of consistency ratio is given in Table  5 , below.

Based on the result of this study, AHP is a highly efficient instrument for determining factor weights and is more beneficial than alternative approaches since the inconsistency of the factor weights’ pair-wise comparison matrix can be calculated and controlled by the Consistency Ratio (CR). In various studies (Dong et al.  2008 ; Tudes and Yigiter 2010 ; Kumar and Shaikh  2012 ; Bagheri et al.  2013 ; Romano et al.  2015 ; Abebe and Megento  2017 ; Ustaoglu and Aydinoglu 2020 ), this has been confirmed.

Suitability values of each factors

Studies have shown that current land use must be considered when choosing suitable sites for the development of urban green spaces and have identified the suitability of different land uses based on their use type (Uy and Nakagoshi  2008 ; Zhang et al. 2013 ; Malmir et al. 2016 ; Abebe and Megento 2017 ; Dagistanli et al. 2018 ). Open spaces and forest land were considered to be highly suitable for urban green spaces in this study, based on knowledge obtained from the analysis of literature and expert opinion. To rehabilitate the quarry area they are considered as suitable for urban green spaces. Additional, in this study, existing building area and water body has considered as moderately suitable for urban green spaces. In this study, agriculture is regarded as poorly suited to urban green spaces (Fig.  2 i; Table  2 ).

Various researchers have shown that low-slope areas are highly suitable for the development of urban green spaces (Heshmat et al. 2013 ; Mahdavi and Niknejad, 2014 ; Pramanik, 2016 ; Abebe and Megento, 2017 ; Dagistanli et al. 2018 ) and 0–10 slope areas are suitable for urban green spaces such as open spaces and parks. This study therefore considered the lower slope land to be more suitable than the higher slope land and area with slope of 0–5 %, 5–10 %, 10–15 % and 15–20 % has considered as highly suitable, suitable, moderately suitable, and poorly suitable, respectively, for identify suitable site for urban green spaces development. Area with the slope greater than 20 % considered as unsuitable for developing urban green spaces in this study (Fig.  2 d; Table  2 ).

In selecting suitable sites for urban green spaces, elevation have also significant role and should be considered as the major factor (Gül et al. 2006 ; Mahmoud and El-Sayed 2011 ; Li et al. 2018 ; Dagistanli et al. 2018 ). Based on the information acquired from literature review and expert opinion, in this study the elevations between 2550 and 2600m, 2600–26500m, 2650–2700m and 2700–2800m were considered as highly suitable, suitable, moderately suitable and poorly suitable, respectively. In this analysis, areas with elevations greater than 2800 m were considered to be unsuitable for the development of urban green spaces (Fig.  2 h; Table  2 ).

In any geographic analysis, proximity is always significant. Green spaces must be accessible to settlement areas in urban areas, since they have numerous ecological, social and economic benefits (Zhang et al. 2013 ; Malmir et al. 2016 ; Ustaoglu and Aydinoglu 2020 ). Hornsten and Fredman ( 2000 ) argued that a significant distance between settlement areas and green spaces had an adverse impact on users and reported that green spaces such as playground, parks and sport field closest to settlement areas are most popular. Therefore, the proximity of green spaces to the settlement area in terms of distance is very important to consider. In this research, the proximity of the settlement area has taken as a criterion. Based on this, areas that have identified within 500 m distances from the settlement area has considered as highly suitable by making Euclidian distances and the area with distances from 500 m to 1000 m has been considered suitable (Fig.  2 g; Table  2 ). In addition, the area with distances of 1000 m to 2000 m, 2000 m to 3000 m and greater than 3000 m form settlement area has considered to be moderately appropriate, poorly suited and unsuitable for the development of urban green spaces.

The road proximity also plays a vital role in providing convenient and feasible routes to the local population to reach local green areas in their surroundings (Bunruamkaew and Murayama 2011 ; Kienast et al. 2012 ; Morckel  2017 ). Elahe et al. ( 2014 ) and Ahmed et al. ( 2011 ) indicated that if it is situated at an acceptable distance from roads in order to access transport, the green space site is preferable. As a result, the road network proximity has been given due consideration as one aspect of infrastructural facilities in the mapping suitable site for urban green areas. Based on this, by making Euclidian distances, areas within the 400 m radius of the road network has considered as highly suitable, area within the 400 m-800 m range was considered suitable, and area within the 800 m-1000 m range was considered as moderately suitable. In addition, the area between 1000 m and 1500 m has considered as poorly suitable and the area more than 1500 m from the road network has considered as not suitable (Fig.  2 f; Table  2 ). Studies have also shown that the types of roads have an effect on the selection of suitable urban green spaces (Gül et al. 2006 ; 2011). Research conducted by Gül et al. ( 2006 ) and Chandio et al., ( 2011 ) found that areas with access to major roads are highly appropriate for the development of urban green spaces than areas with access to local roads such as gravel-soil roads, forest soil roads. Therefore, arterial and collector roads are considered to be highly suitable in this study for the selection of suitable locations for urban green spaces, as these types of roads are highly distributed in the town. In addition, main roads and local roads are regarded as suitable and moderately suitable, respectively (Fig.  2 j; Table  2 ).

Manlun ( 2003 ), Heshmat et al. ( 2013 ), Kuldeep ( 2013 ) and Abebe and Megento ( 2017 ) have noted that for the development of green space, lands closest to rivers, lakes and reservoirs are highly suitable. Therefore, on the basis of this claim, the distance less than 250 m from the river considered to be highly suitable and between 250 m and 500 m is considered as suitable in this study. Moreover, distances between 500 m and 1000 m and 1000 m to 1500 m is considered as moderately suitable and poorly suitable for urban green spaces, respectively. Whereas distance greater that 1500 m relatively considered as totally unsuitable (Fig.  2 e; Table  2 ).

Flood-prone areas have also introduced as parameters for the study of suitability. Studies found that the area within the lower flood-prone area has more suitable than the land with higher flood-prone area for urban green spaces development and they indicated that urban green spaces must be free from flood prone area as most as possible (Piran et al. 2013 ; Peng et al. 2016 ). Based on the information obtained from the literature review and expert opinion, high flood risk areas has considered as unsuitable for the development of urban green spaces in this study, and low and medium flood risk areas are considered as highly and moderately suitable (Fig.  2 a; Table  2 ).

Urban green space suitability assessment is directly or indirectly correlated with different socio-economic factors. Population density is known to be one of the socio-economic factors influencing the appropriate selection of green space in urban areas. Places with a higher number of people with crowded places near the high population density required access to the open green spaces (Schipperijn et al. 2010 ). Some researchers (Gül et al. 2006 ; Pantalone 2010 ; Ahmed et al. 2011 ; Heshmat et al. 2013 ; Elahe et al. 2014 ; Dagistanli et al. 2018 ) recommend that areas that have high population density are highly suitable for developing green space. On the basis of this claim, the study area is densely populated in the northwest, north, south and southeast, and it is considered as highly suitable for the development of urban green space. The eastern portion is sparsely populated and believed to be insufficiently suited to urban green spaces development. As it has a medium population density, the central and western parts of the town has considered as moderately suitable for urban green spaces development (Fig.  2 b; Table  2 ).

Environmental criteria are the most significant and important criteria for the evaluation of urban green spaces in any locality. Factor like vegetation cover plays an important role (Gül et al.  2006 ; Mahmoud, & El-Sayed  2011 ; Li et al. 2018 ; Dagistanli et al. 2018 ). Based on the information obtained from the literature review and expert opinion, in this study area with high vegetation cover has considered as highly suitable for urban green space development. Moreover, area with medium and low vegetation cover has considered as moderately and poorly suitable, respectively (Fig.  2 k; Table  2 ).

The availability of land is often considered as significant factor in the selection of appropriate sites for urban green spaces. Studies have shown that public land is highly suitable for urban green space development as compared to private land (Chandio et al. 2011 ). The study undertaken by Wang and Chan ( 2019 ) suggest that the situation with initial public land ownership status backed up by regulatory instruments is more advantageous for providing urban green spaces than that with the initial private land ownership status relying on market-based instruments. On the basis of this claim, in this study public land is considered as highly suitable and private land has considered as moderately suitable for selecting optimal location for urban green spaces in the town (Fig.  2 g, i Table  2 ).

In this study, as suggested by Gül et al. ( 2006 ) and Nur ( 2017 ), scenic beauty is also considered to decide the best or potentially acceptable sites for urban green space development. Based on the information obtained from the literature review and expert opinion, in this study area with high, moderate and low scenic attractiveness has considered as highly, moderately and poorly suitable for appropriate site selection of urban green space development, respectively.

Final suability analysis for urban green spaces

After weighting the criteria, as regards the relative importance of each criterion as well as suitability index, all the criterion maps were overlaid and final urban green spaces suitability map was prepared. According to GIS-based multi-criteria analysis, the final suitability maps have five classes for the study town that are highly suitable, suitable, moderately suitable, poorly suitable and unsuitable. Suitability maps of Sululta towns are demonstrated in Fig.  3 .

Final suitability map for urban green spaces

According to the overall suitability map, southern, central and south eastern part of the study area is more adequate for urban green space such as playground, sport field, parks and the like development purposes. It is because the lands mass in this area are fall in suitable and highly suitable classes.

Based on Table  6 , out of the total area of the Sululta, town, about 13.6 % (610.7 ha) area fall under the highly suitable category. The suitable area covers an area of 34 % (1523.9 ha) of Sululta town. The area which is shaded by blue colour covers 28 % (1276.6 ha) of Sululta town representing the moderately suitable class. Moreover, based on the Table 6, out of the total area 18.9 % (813 ha) of Sululta towns have been covered by poorly suitable class. Out of the total area 5.5 % of Sululta towns land mass is not suitable for urban green spaces.

The final suitability maps show a series of spaces following a pattern and connectivity. These can be adapted to form the urban green spaces system, complete with corridors and hubs within the study area. This can increase opportunities for residents and biodiversity to enjoy the nature and benefits of urban green spaces. Moreover, as the maps show the town have a high potential for developing the urban green spaces such as playground, sport field, parks and the like as more than half of the town’s lands mass are suitable. Therefore, the planning authority and the towns’ administration can take this approach as a benchmark to provide suitable, accessible, interconnected and sufficient urban green spaces in town under study.

Literature shows that many studies have used multi-criteria analysis based on GIS for land use planning in different countries. Ustaoglu and Aydinoglu (2020), for example, performed a site suitability study for the development of green space in the Pendik district of Turkey. Similar to this study, they considered geophysical factors, accessibility, blue and green amenities, settlement centres and land use/cover as the key factors affecting urban green land suitability and they also concluded that undertaking suitability analysis for green space through GIS based multi criteria analysis is mandatory for optimising land use planning and decision support. Giordano and Riedel ( 2008 ) conducted land suitability assessment of greenways in the city of Rio Claro, Brazil. They combined the AHP method with GIS for the analysis of land suitability, similar to this study. Uy and Nakagoshi ( 2008 ) used the ecological threshold factor approach and GIS in Hanoi, Vietnam, for land suitability study for green areas. Their research considered the concepts of landscape-ecology in the organisation of urban green spaces. Chandio et al. ( 2011 ) used GIS-integrated AHP strategy to evaluate factors such as land availability, land price/value, accessibility and socio-economic factors for the development of public parks in Larkana City, Pakistan. Similar to this study, Abebe and Megento ( 2017 ) also considered land use/cover, density, road network and river as the main factor undertake to site suitability analysis of urban green space development for the city of Addis Ababa.

In general, the factors used in this study to select suitable site for urban green spaces such as parks, play grounds and sport filed development is compliant with different studies undertaken in different part of the world. Moreover, similar to studies conducted by Giordano and Riedel ( 2008 ), Uy and Nakagoshi ( 2008 ), Chandio et al. ( 2011 ), Abebe and Megento ( 2017 ) and Ustaoglu and Aydinoglu (2020) the methodology applied in this study provide a base for future studies focusing on land suitability assessments. GIS-based multi criteria analysis suitability assessment technique can be utilised to produce land suitability maps regarding other land uses such as industrial, residential, landfill, urban land, water management and forest development. Moreover, the methodologies are complementary with the other green land assessment methods, such as landscape metrics analysis, landscape connectivity analysis, accessibility and network analysis and therefore can be used in green spaces planning to specify and quantify the suitable sites in line with the other approaches.

In this study, GIS-based multi-criteria analysis has been used to support the site selection process for the development of urban green spaces. The study results are very significant in evaluating the feasibility of the use of GIS-based multi-criteria analysis for the development of urban green space. Since, by using appropriate analytical methods, the evaluation of urban green space is necessary to recognize their potential and to better select the most suitable land uses to improve their integrity and maintain the benefits obtained from them.

In the present study, the sub-criteria for site suitability for urban green spaces in order of importance were area with high population density (22 %), Proximity to settlement area (21 %), Slop (13 %), Proximity to the road (10 %), elevation (5.9 %), vegetation cover (4.8 %), Proximity to water sources, visibility and existing land (3.2 %) and flood prone area (4 %). The GIS-based multi-criteria analysis performed in this study found that, in the current situation, the larger land mass (47 %) of the town is suitable for developing urban green spaces. The town, therefore, has great potential to develop adequate urban green spaces.

GIS technologies can play a crucial role in urban green space planning, as shown in this study, and AHP has been shown to be a flexible and realistic tool for selecting areas for urban green spaces in the study area. This can be attributed to participation of experts in the determination of the criteria and sub criteria using AHP. Furthermore, GIS may be used to support spatial decision-making, as it has excellent spatial problem solving capabilities. Therefore, this study can provide a framework for the planning process using GIS and AHP for Ethiopian County planning and the results can be useful in the planning of urban green space and future land use planning in study town.

Finally, future research should focus on assessing the suitable site selection for each urban green spaces component such as park, playground, sport field, and the like, independently. In this study, the same criteria and sub criteria were considered to select suitable site for all components of urban green space. Therefore, considering criteria and sub criteria for each component separately are necessary in order to provide a complete understanding of urban green space suitability analysis.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

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Flood hazard assessment and mapping using GIS integrated with multi-criteria decision analysis in upper Awash River basin, Ethiopia

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Floods have destroyed people’s lives as well as social and environmental assets. Flooding is becoming more severe and frequent as a result of climate change and an increase in human-induced land-use changes, which puts pressure on river channels and causes changes in river morphology. The study was aimed to assess flood danger and map inundation areas in Ethiopia’s Teji watershed, which is prone to flooding. The basic flood-producing factors in this study were derived from soil, slope, elevation, drainage-density and land use land cover data. The opinions of public institutions and expert decisions were gathered to determine the weight of the factors in the analytic hierarchy process. The collected data were processed using the ArcGIS environment and the analytic hierarchy method to produce a flood danger map. According to the findings of this study, approximately 43.28 and 13.09% of the area were vulnerable to high and very high flood risk zones, respectively. As a result, flood prediction, early warning and management practices could be implemented on a regular and sustainable basis.

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Introduction

Flooding is a natural part of the hydrological cycle. However, it has the potential to cause death, displacement, and environmental damage, all of which could jeopardize economic progress. Flooding is one of the most common natural disasters, often with disastrous consequences, affecting 170 million people worldwide each year (Kowalzig 2008 ; Mezgebedingil and Suryabhagavan 2018 ). Between 1980 and 2010, Ethiopia experienced 86 natural disasters, resulting in the loss of 313,486 human lives, the displacement of 57 million people, and an economic loss of US$ 31.7 million. Flood came in second place among natural disasters, trailing only drought (OFDA 2012 ).

Floods are among the most shocking natural disasters, according to Rozalis et al. ( 2010 ), and can cause irreversible damage. Flooding can occur in a number of different ways. River/stream overflow, heavy rain, breaches in flood protection systems, and rapid melting of ice in the mountains are among the most prominent. With the exception of flash flooding, which occurs only in the foothills, most floods build up over hours to days. River flooding is caused by excessive precipitation and/or melting snow, which causes rivers to overflow their banks and cover territory that is normally not covered by water. Kron ( 2002 ) defines formalized.

Flood-inundated areas have been mapped using a combination of geographical information system, remote sensing and multi-criteria decision analysis (MCDA) approaches (Fernández and Lutz 2010 ; Danumah et al. 2016 ; Gigovi ç et al. 2017 ; Samela et al. 2018 ; Morea and Samanta 2020 ). Several flood hazard assessment studies have made use of multi-criteria analysis (MCA) techniques. Several researchers (Blistanova et al. 2016 ; Vojtek and Vojteková 2019 ; Desalegn and Mulu 2020 ; Hussain et al. 2021 ) examined flood susceptibility zones in Slovakia and Ethiopia using a GIS-based multi-criteria evaluation. Wondim ( 2016 ) investigated the flood risk and hazard in Ethiopia's Lower Awash Subbasin. Elsheikh et al. ( 2015 ) and Danumah et al. ( 2016 ) investigated flood risk in Malaysia and Côte d'Ivoire, respectively. Argaz et al. ( 2019 ); Gazi et al. ( 2019 ); G.S. Ogato et al. ( 2020 ); and Arya and Singh ( 2021 ) used GIS-based multi-criteria flood hazard assessment in different parts of the world.

Ethiopia receives the most summer rainfall during the months of June, July, August and September, resulting in devastating floods in some regions of the country (Abebe 2007 ; Alemu 2015 ; Getahun and Gebre 2015 ; Amare and Okubay 2019 ; Legese and Gumi 2020 ; Ogato et al. 2020 ). According to Kefyalew ( 2003 ), the most frequently flooded areas in Ethiopia include the Baro-Akobo Basin, the Awash River basin, the Wabi Shebelle, Ribb and Gumara watersheds and the localized flooding risks of Lake Awassa, Lake Besseka and Dire Dawa. The Awash River basin is one of Ethiopia’s major river basins, located in the Rift Valley and prone to flooding (Wondim 2016 ).

Upper Awash sub basin is a section of the Awash basin that has been impacted by recurring flooding. Flooding has been a major issue in the region, affecting thousands of people and resulting in massive economic losses. Significant floods were reported in the woredas of Sebeta Hawas, Wolmera and Egeria in September 2017, Liben Chukuala and Bora woredas in 2014, 2016 and 2017, at Fentale in 2012, 2015 and 2017, and in 2018 and 2019 at Ilu and Sebeta Hawasa woredas. Flooding has forced thousands of people to flee their homes and sacrificed thousands of animals, particularly in the aforementioned woredas of the upper Awash basin. It also caused massive economic losses and environmental damage. Year after year, infrastructure, health and educational institutions deteriorate; schools in the basin frequently start late due to flooding, and health clinics are closed during the country’s rainy season. Although the downstream area is inundated for days or weeks every year during the rainy season, the Teji River has been flooded for brief periods following severe or prolonged rainfall storms. River flood records in the Teji watershed were recently recorded in the kebeles of Asgori, Teji, Bili, Jigdu Mida, and Tulu Mangora in 2018 and 2019.

Flood hazard mapping and analysis, which identifies the most vulnerable regions based on physical characteristics that indicate the propensity for flooding, is one of the most important parts of early warning systems or methods for the prevention and mitigation of future flood situations. Flood hazard mapping is a critical component of flood-prone land use planning and mitigation strategies (Bhatt et al. 2014 ). Flood hazard mapping provides easy-to-read charts and maps, allowing planners to identify risk areas and prioritize mitigation activities (Forkuo 2011 ; Wang et al. 2011 ; Ajin et al. 2013 ; Argaz et al. 2019 ).

The primary objective of this research is to investigate the spatial distribution of flood hazards and to assess potential strategies for protecting the community from displacement and economic loss in the upper Awash subbasin of the Teji watershed. The flood hazard assessment procedure was carried out with this goal in mind, using hazard concepts within an analytic hierarchy process (AHP) framework.

Description of the study area

The Teji watershed is a tributary of the Awash River basin in central Ethiopia, located between 8'23'05"N and 8'50'46"N and 38'7"E and 38'26'30"E, about 60 km from Addis Ababa (Fig. 1 ). The watershed has a total size of 699.023 km 2 . Eutric Vertisols dominate the Teji watershed with an aerial area of 50588.56 ha (72.37%), followed by Chromic Luvisols 10368.2 ha (14.83%), Humic Nitisols 6775.62 ha (9.69%), and Lithic Leptosols covering 2169.9 ha (3.10%). The slope of the watershed ranges from nearly flat to quite steep, and it gradually declines northeastward. A number of minor streams drain the watershed and join to form the Teji River. The elevation of the research area ranges from 2037 to 3575 meters above sea level (m.a.s.l). The research area is divided into three climate zones: Wurch (cold climate with an altitude of more than 3000 m), Dega (highland temperate climate with an altitude of 2500–3000 m), and Woina-Dega (warm climate with an altitude of 1500–2500 m) (NMSA ( 2001 )). Annual rainfall in the watershed ranges from 940 mm in the extreme northeast to 1158 mm in the high hills, with 1027 mm being the average. Cropland, grassland, forest land, shrubland, and built-up area cover (5.17%), (0.52%), (0.38%), and (0.19%) of the study area, respectively.

Location of the study area a River basins in Ethiopia, b Awash river basin, c Upper Awash sub-basin and d Teji Watershed

Materials and methods

Journals, design manuals, books, and other secondary sources were used to collect secondary data. The slope, elevation, drainage density, and proximity to the river of the research region were calculated using the Digital Elevation Model (DEM, 20 * 20 m resolution obtained from SRTM). Soil maps obtained from Ethiopia’s Ministry of Water, Irrigation, and Electricity were used to assess flood risk by evaluating soil type maps. The map of land use was obtained from http://geoportal.rcmd.org . Meteorological (precipitation) data for four selected meteorological stations were obtained from the National Meteorological Agency (NMA): Teji, Tulu bolo, Guranda Meda, and Hombole.

Factors that contribute to flood hazard

The major challenge in multi-criteria evaluation (MCE) is determining how to combine information from multiple criteria to generate a single index of assessment. To aid in the processing, data integration and operation of geographical information system (GIS) software, a set of base maps and images were created (Eastman 2001 ). All preparation procedures, such as downloading, extracting, georeferencing, formatting, and resampling digital data of the factors, were completed prior to analysis. To identify flood-causing variables, field surveys and literature were used. As a result, slope, elevation, drainage density, river proximity, rainfall, soil texture and land use were prioritized in terms of flood hazard relevance (Fig. 2 ).

Work flow of flood hazard and risk analysis of teji watershed

Slope factor

The slope is the ratio of a feature’s steepness or degree of inclination to the horizontal plane. Slope is an important indicator of flood-prone surface zones (Alemayehu 2007 ; Wondim 2016 ). The slope of a slope is an important factor in determining the rate and duration of water flow. Water moves more slowly, collects for a longer period of time, and accumulates on flatter surfaces, making them more vulnerable to flooding than steeper surfaces (Wondim 2016 ; Gigovi ç et al. 2017 ; Rimba et al. 2017 ; Rincón, et al. 2018 ; Desalegn and Mulu 2020 ; Singh et al. 2020a ). Slope has a significant impact on flood danger assessment because it affects the quantity of surface runoff generated by precipitation, the rate of precipitation, and the flow velocity of water over the equipotential surface. The slope percent map for the research area was created with ArcGIS 10.3.1’s spatial analysis tool and a DEM with a resolution of 20 meters (Fig. 3 ). The research region’s slope percentage ranges from 0 to 57.7. Lower slope values represented flatter topography that was especially vulnerable to flooding, whereas higher slope values represented steeper topography that was less vulnerable to flooding. Slopes were categorized into five levels based on their vulnerability to flooding. The slope of the study area was classified into five classes based on its impact on flood risk: extremely high (0–11°), high (11–22°), moderate (22–34°), low (34–46°) and very low (46–57.7°). Each slope class accounts for about 72, 23.7, 3.5, 0.5 and 0.04% of the total area of the watershed, respectively.

Slope map of the study area

The elevation raster layers are created with the help of the ArcGIS environment and the DEM. Using the reclassification tool in the ArcGIS environment, the elevation raster layers were further classified into five groups. Flooding was less of an issue higher elevation, and vice versa (Wondim 2016 ; Argaz et al. 2019 ; Choubin, et al. 2019 ; Gazi, et al. 2019 ; Ogato, et al. 2020 ). The elevation of the research area was divided into five categories based on its effect on flood hazard: extremely high (2031–2339 m), high (2339–2648 m), moderate (2648–2957 m), low (2957–3266 m) and very low (3266–3575 m). Each class covers approximately 54.8, 28.3, 8.4, 7.2 and 1.3% of the total area of watershed, respectively (Fig. 4 ).

Elevation map of the study area

Drainage density

The density of drainage is a major factor influencing flood hazard. The drainage system that develops in an area is entirely dependent on the slope, the type of bedrock, and the regional and local fracture pattern (Alemayehu 2007 ; Wondim 2016 ). The drainage density is an inverse function of soil permeability. A low permeable surface area is prone to high drainage density, and water from precipitation also leads to high runoff and vice versa. As a result, greater drainage density means that the area is less prone to flooding (Chibssa 2007 ; Wondim 2016 ). As a result, as drainage density increases, the rating for drainage density decreases. The technique has been proposed to extract drainage networks from DEMs with a resolution of 20 m using a spatial analysis tool in ArcGIS 10.3.1. Kernel Density was used in a GIS context to determine drainage density area from stream polyline features.

As a result, as drainage density increases, the rating for drainage density decreases. The algorithm has been proposed to extract drainage networks from DEMs with a resolution of 20 m using a spatial analysis tool in ArcGIS 10.3.1. In a GIS environment, Kernel Density was used to calculate drainage density area from stream polyline features (Fig. 5 ). The drainage density (DD) is calculated by dividing the total length of all streams and rivers in a drainage basin by the drainage basin’s total area. As shown in the equation below, drainage density is the total length of the stream segments divided by the unit area (Greenbaum 1985 ; Magesh et al. 2012 ; Ouma and Tateishi 2014 ).

where \(\mathop \sum \limits_{i = 1}^{n} L_{i}\) is the total length of drainage in Km, A is total area of study site in Km 2 , and n stand for number of drainage networks in the watershed.

Drainage density map of the study area

Finally, the drainage density was categorized into a continuous scale in accordance with the flood hazard rating. The watershed’s drainage density ranges from 0.006 to 8 km/km 2 . The class has been divided into five categories based on its effect on flood hazard: extremely high (0.006–2.5 km/km 2 ), high (2.5–4.5 km/km −2 ), moderate (4.5–6.0 km/km 2 ), low (6.0–7.5 km/km 2 ) and very low (7.5–8.1 km/km 2 ). Each drainage density class encompasses about 52.2, 37.0, 8.1, 2.3 and 0.5% of the total area of the watershed, respectively.

Proximity to river

One of the primary criteria used to evaluate flood hazard map generation in the study watershed is river proximity. Because river overtopping and flooding in the river buffer zone are the most common cases in the study area (Bapalu and Sinha 2005 ; Emin Tas 2017 ; Rincón et al. 2018 ; Vojtek and Vojteková 2019 ). This element is critical to include when mapping flood-prone areas in the Teji watershed. In the years 2019 and 2018, there have been reports of flood hazards affecting thousands of people and causing massive economic damage. Despite the fact that the river channel was deep, the river overflowed the bridge and flooded Asgori town during an observation at Asgori town on the Teji river crossing of Reta Desis Bridge. The Teji river is located about 400 m south west of Addis Ababa’s main asphalt road to Jima and overflows to the Ilu recreation center. It causes property damage in the Teji town center and beyond. In this study, the class was divided into five categories based on its effect on flood danger, namely extremely high (0–200 m), high (200–400 m), moderate (400–1000 m), low (1000–4700 m) and very low (4700–7680 m) which is derived from the watershed river network (Fig. 6 ). The proximity map was reclassified and combined with other criterion maps for overlay analysis. Each proximity class accounts for approximately 9.8, 8.6, 21.0, 54.9 and 5.7% of the total watershed area, respectively.

Proximity to River map of the study area

Rainfall is a significant factor in creating a flood danger map. The rainfall map was created using the inverse distance weight method from historical rainfall data collected from meteorological stations located in and around the research area (Ogato et al. 2020 ; Desalegn and Mulu 2020 ). The watershed’s mean annual rainfall ranges from 940 to 1158 mm, as shown in Fig. 7 . Rainfall intensity is important in causing flooding, so weight was assigned to rainfall classes. The greater the amount of rainfall, the greater the flood-producing runoff, and vice versa (Adiat et al. 2012 ; Blistanova et al. 2016 ; Gazi et al. 2019 ). The rainfall in the research area was classified into five categories based on its impact on flood risk: very low (940–983 mm), low (983–1027 mm), moderate (1027–1071 mm), high (1071–1114 mm) and very high (1114–1158 mm). Each rainfall volume class covers about 5.4, 2.8, 22.6, 31.9 and 37.4% of the total area of the watershed, respectively.

Rainfall distribution map of the study area

Soil texture

The type of soil has a significant impact on the rate of precipitated water infiltration and the water-holding capacity of the area. As a result, it may be considered one of the critical factors in defining flood-prone areas. Sandy soils have higher saturated hydraulic conductivities than finer grained soils due to the greater pore space between the soil particles. The ability of various soil textures to absorb water varies (Wondim 2016 ). Infiltration, according to Morgan ( 1995 ), has a significant impact on the availability and quantity of surface runoff produced by the rainfall-runoff process. As a result, clay soils infiltrate at a much lower rate than sandy soils (Ward and Robinson 1990 ; Wondim 2016 ). Soil physical characteristics, particularly soil texture, were considered when developing the soil texture factor. The statistical analysis of soil type reveals that the study area is primarily covered by clay (Eutric Vertisols) soil, accounting for 72.4% area coverage, followed by loam (Chromic Luvisols and Humic Nitisols) and sandy loam (Lithic Leptosols), which account for 24.5 and 3.1% of the total area of watershed (Fig. 8 ).

Soil texture map of the study area

Land use/land cover

Land use land cover (LULC) refers to the type of soil deposits and the distribution of built-up areas, cropland, grassland, shrubland and forestland within a given region. The LULC of a watershed play an important role in flood water movement by impeding, delaying or accelerating surface flow. The LULC of the watershed influences infiltration rates, the interaction of surface and groundwater, and debris flow. The study watershed region’s land use/land cover was reclassified into five classes based on its ability to increase or decrease the rate of floods. As cities expand in size, impervious cover increases while forest cover decreases, contributing to an increase in run-off (Tucci 2007 ; Fura 2013 ; Blistanova et al. 2016 ; Wondim 2016 ; Gazi et al. 2019 ; Arya & Singh 2021 ). As a result, built-up areas are classified as extremely high, whereas farmland, grassland and shrubland are classified as high, moderate, and low, respectively. Forestland, on the other hand, has a very low capacity to generate floods and is classified as extremely low, as seen in (Fig. 9 ). Cropland accounts for 93.7% of the land use in the research region, whereas built-up, grassland, shrubland, and forestland areas account for 0.2, 5.2, 0.4 and 0.5%, respectively.

Land use/Land cover map of the study area

AHP methodology

In AHP, weights (Table 2 , Table 3 ) and thematic layers of each level (criteria classes) are assigned and their relative importance is determined using Saaty’s 1–9 scale. The relevance or preference of each thematic layer relative to the other thematic layers on flood prone area delineation selection was conveyed by assigning weights. This was accomplished by utilizing related review literatures, field observation, and expert judgment to populate a pairwise comparison matrix from which a set of weights known as Eigenvectors, as well as consistency ratios, were generated for each of the criteria under consideration (Wondim 2016 ; Ogato et al. 2020 ; Arya and Singh 2021 ). Flood hazard factors are rated on a scale of 1 to 9, with 1 indicating that both elements are equally important and 9 indicating that one component is more important than the other. The reciprocal of 1 to 9 (1/1 and 1/9) denotes that one is less important than the other (Saaty 1980 ; Saaty and Vargas 1991 ). The factor weights were evaluated in order to conduct a multi-criteria assessment of the effect on flood generation in a study area. The following are the fundamental procedures for determining the indicator's weight and consistency ratio (CR) (Tables 1 , 2 , 3 , and 4 ):

Step 1. Establishment of judgment matrices ( P ) by pairwise comparison.

Where, n denote the n th row and m denotes the m th column elements of the judgment matrix.

Step 2. Calculation of normalized weight

This step is to normalize the matrix by totaling the numbers in each column. Each entry in the column is then divided by the column sum to yield its normalized score. The sum of each column is 1.

Where, the geometric mean of the i th row of the judgement matrices is calculated as:

Step 3. Calculates a consistency ratio (CR) to verify the coherence of the judgements. Now, calculate the consistency ratio and check its value. The purpose for doing this is to make sure that the original preference ratings were consistent (Table 8 ).

Consistency index (CI) is denoted as follows:

Max is the eigenvalue of judgment matrix and it is calculated as:

Where, W is the weight vector (column). Random index (RI) can be obtained from standard tables (Table 7 , Saaty 1980 ). In practice, a CR of 0.1 or below is considered acceptable. Any higher value at any level indicates that the judgments warrant re-examination.

In this study, seven factors (slope, elevation, drainage density, proximity to river, rainfall, soil texture and land use) were used to delineate flood prone zones. The impact of these factors on flood-prone area delineation is not the same. The weight of each factor was assigned based on its influence on the amount, flow velocity and other criteria related to rainfall-runoff, as well as references to literature (Elsheikh et al. 2015 ; Danumah et al. 2016 ; Blistanova et al. 2016 ; Wondim 2016 ; Argaz et al. 2019 ; Gazi et al. 2019 ; Vojtek and Vojteková 2019 ; Hussain et al. 2021 ) (Table 5 ).

A factor’s weight value indicates the proportion of its value in flood hazard prone area zonation, with the dominant influencing factor receiving a high weight value (Table 6 and Table 9 ). Slope, for example, has a score weight of 33.3%, followed by elevation, rainfall, drainage density, proximity to river, soil texture and land use, which all have score weights of 25.3, 15.9, 12.8, 7.0, 3.7 and 2.0%, respectively (Table 6 ).

Multi-Criteria Evaluation of flood hazard

A multi-criteria decision-making approach known as the AHP was used to determine the rankings and weights of the sub-factors and map layer based on their level of effect on the result. These layers were then subjected to a weighted overlay analysis, and the final resultant map was generated and classified based on the flood hazard model’s indication of their influence on flood danger (Eq. 9). In general, the flowchart depicted the study process (Fig. 2 ) ( Table 8 ).

Where W i  = weight of factor i ; X i   = criterion score of factors i .

Then in case of this study the final flood hazard map was determined using Eq. below.

Result and discussions

Flood hazard mapping.

The flooding hazard in the Teji watershed revealed that 2781.09 ha (3.98%), 14337.77 ha (20.51%), 13384.69 ha (19.15%), 30251.89 ha (43.28%), and 9146.85 ha (13.09%) were accordingly categorized to very low, low, moderate, high and very high flood susceptibility (Fig. 11 ). High to extremely high danger zones are primarily concentrated in the watershed’s center and lower reaches. These high to very high flood hazard zone regions are distinguished by flat areas with low slope gradient, lower elevation, low drainage density and proximity to the river, all of which are significant conditioning variables for flood hazard mapping. There were extremely low to low flood danger zones, which were primarily located along the upstream section of the watershed and were distinguished by their steep slope, higher elevation, and low drainage density (Figs. 10 and 11 ). This finding is similar to that of a flood vulnerability study conducted at Ethiopia’s Lower Awash Sub-basin (Wondim 2016 ); the Souss Watershed in middle western Morocco by Argaz et al. ( 2019 ); the northeastern part of Bangladesh by Gazi et al. ( 2019 ); the Fetam watershed in Ethiopia’s upper Abbay basin by Desalegn and Mulu ( 2020 ); and the Ghaghara River basin in Uttar Pradesh (2021) ( Table 9 ).

Flood Hazard Map of Teji Watershed

Pie chart shows the Teji watershed flood hazard zone area coverage in %age.

According to the findings of the spatial study, Illu and Becho woredas or districts are more vulnerable to very high flood risk (Table 10 ). This suggested that careful flood management and mitigation measures should be implemented first in these districts, before moving on to other districts. In contrast, the Kersana Malima district is less vulnerable to high and very high floods.

Validation of the flood hazard map

Model validation is the process of systematically comparing model outputs to independent real-world observations in order to assess quantitative and qualitative concordance with reality. Many models are used by researchers to assess flood susceptibility in various parts of the world, but it is critical to test the model’s outputs to ensure that the model adequately represents the actual ground conditions or recorded observations. By comparing model output to observable data, model calibration and validation can be accomplished.

To validate the Teji watershed flood hazard map results, the locations of historical flood occurrences were created using a field visit to collect flood markings and an interview with Teji watershed locals, who provided relevant data on 26 flooding sites (Fig. 12 ). These historical flood spots were superimposed on the model’s output. The watershed’s flooding history reveals that flash floods affect flat sloping regions such as much of the Ilu, Becho, Weliso, and some sections of other Woredas, whereas river flooding affects Teji town, Asgori town, as well as Bili, Jigdu Meda and Tulu Mangora Kebeles. Fig. 13 shows photographs taken in and around Teji and Asgori towns to depict flood marks for the 2019 flood event as well as flash flooded regions in Teji town in 2021. All historical flood points gathered, according to the predicted output, are located in the high and very high flood susceptibility zones, indicating the reliability of the flood vulnerability model used in this study.

Distribution of Ground Truth Points of Observed Flood Affected Areas in 2019 and Administrative Kebeles

Flood marks of 2019 river flood event in ( a ), ( c ) in Teji and ( b ) in Asgori towns, and ( d , e , f ) flash flood on Teji town around Ilu Police station and Hidasie Telecom 2021

Floods have disrupted people’s lives, as well as social and environmental assets. Flood simulation and risk assessments are strategic planning tools for effectively reducing flood risk and damage, despite the fact that they cannot be avoided. A flood management strategy must include the assessment of flood hazard areas. The proposed method was used to identify flood-prone areas in Ethiopia’s Teji watershed and upper Awash River basin. Many studies have used multi-criteria evaluation methods, which have proven to be an extremely effective tool in assisting decision-making processes. The seven distinct input maps that were created were slope, elevation, drainage density, river proximity, rainfall, soil texture and land use. Finally, the simulated result maps, such as floods, are presented. The obtained results were validated against data from previous floods in the watershed’s ground truth points of observed flood affected areas (hazard map, validation map). The collected data were analyzed using the analytic hierarchy method and mapped using geographic information system techniques, resulting in a land suitability map. According to the flood hazard model output, 4.0, 20.5, 19.2, 43.3 and 13.1% of land are at risk of flooding, with very low, low, moderate, high, and very high flood dangers, respectively. Remote sensing and GIS techniques have been shown to be extremely useful in detecting flood risk zones and developing flood susceptibility maps. It has also been demonstrated that the multi-criteria analysis technique may be useful in assisting local governments and government agencies in properly identifying flood-prone areas and assisting in the implementation of appropriate flood control strategies in such areas.

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Hagos, Y.G., Andualem, T.G., Yibeltal, M. et al. Flood hazard assessment and mapping using GIS integrated with multi-criteria decision analysis in upper Awash River basin, Ethiopia. Appl Water Sci 12 , 148 (2022). https://doi.org/10.1007/s13201-022-01674-8

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Thesis on GIS and Remote Sensing

Remote sensing is a technology that investigates the earth with help of satellites which is deployed with cameras and sensors. ...

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Remote sensing is a technology that investigates the earth with help of satellites which is deployed with cameras and sensors. Generally, the manual observation of natural resources is quite difficult. In the event of this challenge developers and technology engineers have developed a mapping tool called GIS.

The acronym stands for the Geographic Information System. GIS will gather data like economic improvement of the nation, population information, and the plant types in the farmlands. The effective regulation of the tool is based on the assimilation of the general server information and their arithmetical investigations corresponding with the remote sensing road maps.

This article will enumerate the general thesis on GIS and Remote Sensing in detail!!

Now we will see about remote sensing and their thesis on GIS. Every technology supports some tools to enhance the process effectively. For the ease of your understanding, the developers of our concern made the key points very short and sweet. Let us have an understanding of the thesis on GIS and remote sensing.

Overview of GIS and Remote Sensing

• The data gathered by the remote sensing tool will be used in the Geographical Information System (GIS) assimilation
• The gathered data are always represented in the form of images
• The GIS Geographical Information System, itself indicates that it is a location-oriented information gathering tool
• It is the advanced graphic visualization technology that eliminates the traditional spreadsheet in capabilities
• Furthermore it the visualization of the issues will lead to safety measures in an earlier stage

This is all about remote sensing and Geographical Information systems in general. Usually, every researcher needs an expert’s guidance for effective outcomes. Now we will see about the road maps of remote sensing.

Steps involved in the Remote Sensing

• The sunlight rays directly explore the environmental atmosphere
• The earth surface reflected by the rays
• The reflected rays will be gathered by the cameras
• The captured rays will be turned into images
• The sensor will gather the images
• The gathered images will be stored in the database
• Then it will be displayed for the analytical purpose

These are the main steps involved in remote sensing as they are presented in hierarchical this will be very easy to understand. As of now, we had seen the Geographical Information System, remote sensing, and its road map in detail to formulate Thesis on GIS and Remote Sensing. It is time to discuss the source of remote sensing in brief.

What is the Data Source for the Remote Sensing?

• Data of the streets
• Data of the buildings
• Data regarding vegetation
• Integration of the data

The above-mentioned key points are the sources of remote sensing. We hope that it will be very easy to remember. Furthermore, we will see about the application of the Geographical Information System in remote sensing.

• The gathered information of the living and non-living creatures on the earth surface is subject to the zero interaction
• Sensors in the remote sensing are assimilated with the Geographical Information System for visualization and analytical purpose which will be very understandable

How does Remote Sensing works?

• It is the primary source of the energy that is produced by the environment
• Direct solar radiance of the energy by the direct transmission of the primary source
• The intensity of the radiance is related to the improvement in the viewpoint of the related scene  renewable energy
• This is where the creatures of the earth is living, this surface is subject to the reflection of the radiance and the light produced
• Determination of the temperature and the radiation produced in the surface
• The terrain is the 3D landscape tool used to handle the queries and the generation of the mirrored reflection
• Visibility of the atmosphere
• The type of the atmosphere will be stated by the model on each particle presented in each layer
• The commotion of the images generated a lack of quality (Blurriness)
• The high volume of the particles leads to the haze in the absorption
• Transmission of the radiance which is produced in the atmosphere is subject to the remote sensing
• Upwelling and Downwelling Radiance

This is the way how the surface, atmosphere, and the ultimate source of remote sensing are integrated. The above-explained aspects will be effective for understanding. As our researchers made this article for ease of understanding which will lead the readers to explore the things in it.

We would like to share our victories with you, the company that served around 120+ countries in the world with our project and research ideas for Thesis on GIS and Remote Sensing. Additionally, we are having 5000+ happy customers who are served by our ideas. Without wasting time we will move to the next phase about the features of remote sensing.

Important Characteristics of Remote Sensing

• This is all about the least particles in the scenery which will be filtered out
• This is all about the sensors bandwidth and the specimen rate
• The satellites used in the sensors will repeatedly visit the Geolocation for several days based on their range
• The limitations of the SNR (Signal to Noise Ratio) and capacity lies on the high spatial or the low spectral ratio
• This has capable of tracking the isolated and vibrant range signals and systems
• The satellites used here is Landsat 7 which has the capacity of 8-bit images, 256 unique grey values produced by the energy, and 2048 radiometric grey values which leads to immediate monitoring

We are doing researches in the various fields that are emerging now a day. They are mentioned below for your understanding. It is a worthy note to be taken as of now. Without wasting time we will have a quick summary of our research areas Thesis on GIS and Remote Sensing.

The researchers in our concern are habitually exploring them and transferring the technical facts in the research guidance!!

So far, we gave you a basic overview of remote sensing and the GIS in brief now is the time to understand the applications of the GIS in remote sensing. They come under the environmental, economic, and social development of sustainable cities.

What are the important aspects of GIS in Remote Sensing?

• Detecting the slum areas
• Population data and the computation of the quality of the life
• Assessment of challenges involved in the environment
• Observation of the rural and urban areas
• Assessing the remaining usable land
• Evaluating the lands and their allocation
• Discovering the impaired building infrastructures
• Evaluation of the GDP (Gross Domestic Product)
• Computation of the energy landscapes
• Renewable energy assessment
• Observing the land-use changes
• Evaluating the accurate population
• Evaluating the expansion of an urban area
• Evaluation of the pollution matrix
• Assessment of temperature conditions

Now we will see about the procedures or methods oriented with the Geographical Information System and Remote Sensing. The following techniques will let you know the key factors very keenly.

Methods for GIS and Remote Sensing

• DNA computing
• Cellular Automated Membrane computing
• Evolutionary computing
• Neuro-computing
• Charge system search
• Gravitational search
• Bee huddling algorithm
• An artificial flock of fishes
• Bacteria hunting
• Firefly optimization
• Intelligent water drops
• Cuckoo search
• Swarm particle optimization
• Biogeography optimization
• Dynamics of the river formation
• Ant colony optimization
• Wisdom technology
• Anticipatory computing
• Granular computing
• Rough set theory
• Fuzzy set theory
• Perception-based computing

These are the methods involved in the Geographical Information System and remote sensing. We hope that you will understand the aspects explained. Our developers and researchers have done this article with essential aspects which are very needy in technology. Next, we will see about the latest ideas oriented with Thesis on GIS and remote sensing in detail.

Ideologies are the eminent thing while researching a technology. We are there for you to assist in the research areas!!

As we are providing research and project-related assistance to our scholars and the college students get our expert’s assistance in research areas. We assist in the fields of  thesis writing, conference paper writing, system development, paper publication, and so on by well-organized experts. Do you surf for research guidance or thesis writing and then this is the right place to do it. We are providing 24/7 supports with a clear understanding.

Current trends on GIS and Remote Sensing

• Planning of urban development
• Disaster Alleviation and organization
• Earth observation and plotting following the remote sensing systems
• Organization of the water resources
• Impervious earthquake model
• Planning on traffic aspects and transportation construction
• Reintegration of the structures
• Ecological improvement and creation

These are the emerging ideas that are currently in trend. Knowing about the databases used in GIS and remote sensing is very important. To this, we have been mentioned the databases in the following passage. By observing the earth aspects, enormous volumes of the multi-spectrum, multi-temporal, multi-resolution pictures are produced which results in performance enhancements.

Databases used in the GIS and Remote Sensing

• This database represents the geospatial information with the help of NEO4J libraries
• These libraries collect the logs and
• stores them for analysis
• Layers of the NEO4J consisted of the geometrics and geo indices
• The data acquisition is pillared with the help of Open Street Map and Shapefiles
• This easy approach stores the geometric indices in their well-known text or format (WKT)
• This has a format called coordinate pairs which will be added in the planners, for instance, JSON planner will be denoted as GeoJSON
• This database represents the geospatial vectors which are built in the form of spatial queries
• The coordinate pairs indicate the different geographic data structures

Performance Metrics in GIS and Remote Sensing

• This is all about how the platform plotting the relevant scenes in the
• Evaluating the collection of the picture in each second to run a video
• The time taken for the capturing will result in the effective object
• This is about the distance between the platform range and the earth surface
• For the view of the geometric aspects
• Casual regulation of the camera
• This is about the time taken for the capturing of scenarios by per pixel

Master / doctoral thesis is a research study that is done by the students following real observation and experiments. This is an empirical research study that focuses on the student’s execution of the research till the climax. Now we will see about the components involved in the master thesis writing .

What are the chapters of Thesis Writing?

• Introduction of the Thesis
• Literature remarks
• Methods and modes

These are the aspects involved in the master thesis writing in general. Our researchers made this as simplest for ease of your understanding. It is time to write the thesis. There are four steps involved in effective thesis writing.

What are the phases in Thesis writing?

• Identify the theme and do a literature
• Identify the research gaps in the subject
• Enrich the results and give them reasons
• Frame the main theme

So far we gave you the overall perspective on the Thesis on GIS and Remote Sensing in wide. For any queries and guidance feel free to approach us.

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National gis and remote sensing officer.

• International Organization for Migration

Vacancy Number: SVN/IOMSO/010/2024 Duty Station: Nairobi, Kenya (IOM Somalia) Classification: National Officer Category, Grade NOB Type of Appointment: Special Short Term, six months with possibility of Extension Estimated Start Date: As soon as possible Closing Date: 01 May, 2024

Established in 1951, IOM is a Related Organization of the United Nations, and as the leading UN agency in the field of migration, works closely with governmental, intergovernmental and non-governmental partners. IOM is dedicated to promoting humane and orderly migration for the benefit of all. It does so by providing services and advice to governments and migrants.

Context: Somalia continues to face complex emergencies and post-crisis challenges that require effective coordination and response. IOM Somalia, through the Emergency and Post-Crisis (EPC) pillar, provides integrated support to affected populations in various sectors, including WASH, SNFI, CCCM, Health, Livelihoods, Climate, and Durable Solutions. The EPC pillar plays a critical role in addressing humanitarian needs, facilitating recovery, and building resilience among vulnerable communities in Somalia. Alongside frontline activities IOM Somalia has been implementing Displacement Tracking Matrix (DTM) program which encompasses mobility tracking, flow monitoring, and surveys that aim at supporting evidence-based programming. As a co-chair of the Information Management and Assessments Working Group (IMAWG), and key member of Internally Displaced Peoples Working Group (IDPWG), the DTM program is enabling humanitarian actors to plan and respond to needs in Somalia and across the region. Under the overall supervision of the Senior Programme Coordinator (EPC) and direct supervision of the Programme Manager (DTM) and in close collaboration with relevant units in the Country Office, DTM National GIS and Remote Sensing Officer will provide Geographic Information System (GIS) and remote sensing analysis to help strengthen the development, implementation, and data quality of DTM activities across Somalia. In particular, he/she will:

Core Functions / Responsibilities: 1.Provide technical guidance for GIS and remote sensing activities in IOM Somalia. 2.Participate in planning and development of the geographic data management tools (shapefiles and processes of DTM specifically related to data collection done in settlements and IDP sites), as well as in the maintenance of the IOM geo-database. 3.Carry out thematic geo-statistical data analysis as required relating to natural hazards and climate related displacement in the Somalia context. 4.Provide GIS technical throughout the design and implementation of DTM assessment and data initiatives related to durable solutions and anticipatory actions including the Local (Re)integration Assessment (LORA), the Urban Service Analysis and Projections Framework, the Movement Projections Model (MPM), the Transhumance Tracking Tool (TTT), and other activities as required. 5.Recommend needs and inform on GIS/mapping tools and products. Work closely with DTM and Information Management (IM) staffs to identify needs and produce the relevant products and maps; plan, develop, and provide training and develop skills for IM and GIS activities. 6.Participate in the development of data collection tools and training of field staff to facilitate the inclusion of GIS-related requirements and indicators. 7.Update and produce online maps (with ArcGIS Server for data review and analysis) and printable maps (with ArcGIS, and Adobe Illustrator/InDesign), as well as Dashboards, Graphs, Profiles, Shapefiles and KMZ files for reporting while ensuring accurate representation of spatial features with the most updated common operation datasets (CODs). 8.I Improve geo-referencing/Global Positioning System (GPS) cleaning and documentation of geo-coordinates. 9.Coordinate creation of country atlases and regularly update base maps in line with identified requirements. 10.Perform such other duties as may be assigned

Required Qualifications and Experience Education •Master’s degree related to GIS, Remote Sensing, Data Science, Geography, Innovation, or related field from an accredited academic institution with two years of relevant professional experience; or •University degree in the above fields with four years of relevant professional experience.

Experience •Advanced experience with geo-spatial information management and analysis, and excellent spatial analysis skills; •Experience in raster and vector GIS; •Experience working in international, regional organizations and the humanitarian community. •Experience in relevant issues such as migration, displacement, and humanitarian assistance; •Experience in the region an advantage

Skills •Advanced knowledge of ESRI suite software (ArcPro, ArcMap, ArcGIS Online); advanced knowledge of Microsoft office including Excel; knowledge of R, STATA, SPSS, Python or equivalent statistical software an advantage; •Skilled in managing Geo-databases and Relational Database Management System; •Good writing, communication, and negotiation skills; •Works effectively with local authorities, stakeholders, beneficiaries, and the broader community to advance country or regional objectives; •Demonstrated ability for training and capacity building of a wide variety of stakeholders including enumerators, field staff, local authorities

Languages For all applicants, Fluency in English (oral and written) Working knowledge in Somalia will be an added advantage

Required Competencies Values •Inclusion and respect for diversity: respects and promotes individual and cultural differences; encourages diversity and inclusion wherever possible. •Integrity and transparency: maintain high ethical standards and acts in a manner consistent with organizational principles/rules and standards of conduct. •Professionalism: demonstrates ability to work in a composed, competent, and committed manner and exercises careful judgment in meeting day-to-day challenges. •Courage: Demonstrates willingness to take a stand on issues of importance. •Empathy: Shows compassion for others, makes people feel safe, respected, and fairly treated.

Core Competencies – behavioural indicators level 2 •Teamwork: develops and promotes effective collaboration within and across units to achieve shared goals and optimize results. •Delivering results: produces and delivers quality results in a service-oriented and timely manner; is action oriented and committed to achieving agreed outcomes. •Managing and sharing knowledge continuously seeks to learn, share knowledge, and innovate. •Accountability: takes ownership for achieving the Organization’s priorities and assumes responsibility for own action and delegated work. •Communication: encourages and contributes to clear and open communication; explains complex matters in an informative, inspiring, and motivational way.

Other Any offer made to the candidate in relation to this special vacancy notice is subject to funding confirmation.

Appointment will be subject to certification that the candidate is medically fit for appointment and verification of residency, visa, and authorizations by the concerned Government, where applicable.

Only candidates residing in either the country of the duty station or from a location in a neighboring country that is within commuting distance of the duty station will be considered.

In all cases, a prerequisite for taking up the position is legal residency in the country of the duty station, or in the neighboring country located within commuting distance, and a work permit, as applicable.

How to apply

If you are interested, please submit your CV and Cover Letter via email with the subject of the position title and SVN number to [email protected] before the closing date.

No Fees: IOM does not charge a fee at any stage of its recruitment process (application, interview, processing, training, or other fee).

Posting period: From 24.04.2024 to 01.05.2024

Only shortlisted applicants will be contacted.

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