GIS-Based Assessment of Urban Environmental Quality (UEQ): Spatial Analysis and Implications for Sustainable Urban Planning: The Case of Finfinnee and Shaggar Cities

GIS-Based Assessment of Urban Environmental Quality (UEQ): Spatial Analysis and Implications for Sustainable Urban Planning: The Case of Finfinnee and Shaggar Cities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GIS-Based Assessment of Urban Environmental Quality (UEQ): Spatial Analysis and Implications for Sustainable Urban Planning: The Case of Finfinnee and Shaggar Cities Zenebe Reta Roba, Mitiku Badasa Moisa, Fedhasa Benti Chalchissa, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8142228/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The quality of the urban environment in developing countries including Ethiopia is facing significant challenges due to swift urban growth-related alterations in land use, and a worsening of the Urban Heat Island (UHI) effect. This research offers an extensive GIS-based evaluation of urban environmental quality (UEQ) in Shaggar City, emphasizing the spatial differences among sub-cities and important land use categories.Key indicators of urban environmental quality such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Land Use/Land Cover (LULC), and Urban Thermal Comfort Level (UTCL) were utilized with the help of geospatial techniques to pinpoint regions experiencing significant thermal stress, loss of vegetation, and high levels of built-up areas. The findings indicated that, across the entire study area of 2,038.5 km², the most critical regions, characterized by dense urban development and bare land, comprised 6.0% of the city, while critical and more critical areas together made up 84.3%, highlighting significant environmental strain. Conversely, forested areas, green spaces, and water bodies constituted the least critical regions, offering vital ecological benefits and helping to regulate the climate. An analysis at the sub-city level revealed that Finfinne, Kura Jida, and Sebeta are areas significantly affected by environmental degradation, whereas Melka Nonno and Mana Abichu displayed relatively better environmental conditions. The use of Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) helped to identify both high-risk thermal zones and ecologically resilient locations, offering valuable insights for focused interventions. The findings highlight the urgent necessity for sustainable urban planning approaches, which should include green infrastructure, urban forest initiatives, reflective construction materials, and community-based conservation efforts to alleviate heat stress, rehabilitate degraded areas, and improve overall urban livability. This study underscores the importance of detailed Urban Environment Quality (UEQ) assessments for informing evidence-based policy and advancing climate-resilient, sustainable urban development. LST Land Use Land Cover Urban Thermal comfort Level NDVI NDBI Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Urbanization is a global phenomenon that has been accelerating at an unprecedented rate, posing both opportunities and challenges for sustainable development. According to the United Nations, more than 55% of the world’s population resides in urban areas, a figure projected to increase to nearly 70% by 2050 (United Nations, 2018 ). This rapid urban expansion has led to significant environmental concerns, including air and water pollution, loss of green spaces, increased waste generation, and socio-economic inequalities (United Nations, 2018 ). As cities grow, the demand for effective urban planning and environmental management becomes more critical in ensuring a balance between development and sustainability (Seto, Güneralp, & Hutyra, 2012 ). Population growth mainly due to rural-to-urban migration, and economic shifts have all contributed to Africa's particularly rapid urbanization (UN-Habitat, 2020; African Development Bank, 2018 ). Africa's urban population is predicted to triple by 2050, exacerbating environmental issues like poor waste management, poor infrastructure, air pollution, and deteriorating water quality (UN-Habitat, 2020). Living conditions and urban sprawl worsen in many African cities due to poor spatial planning strategies (Kamana, 2024 ; Mwenje, 2024 ). Geographic Information Systems (GIS) and Remote Sensing approaches have been acknowledged as a powerful tools for spatial analysis, monitoring urban growth trends, and directing sustainable urban policies in urban environmental assessments (Weng, 2019 ; Seto et al., 2022 ). Cities like Addis Ababa, Shaggar, and other secondary urban centers have seen rapid development, making Ethiopia one of African countries with the fastest rate of urbanization (Moisa et al., 2025 ; Woldemichael et al., 2023 ). But this expansion hasn't been properly planned for, which has resulted in poor service delivery, and environmental degradation. Uncontrolled land use changes, dwindling green spaces, rapid expansion of built-up areas, worsening air and water pollution, poor waste management, and unequal access to basic urban services are just a few of the environmental issues that both Finfinne and the recently formed metropolitan city of Shaggar must deal with. GIS-based methods for evaluating the environmental quality of urban areas, such as Finfinne and Shaggar cities, can support evidence-based decision-making for sustainable urban planning, highlighting priority areas for intervention, and offering insightful information about the spatial distribution of the various environmental factors. This study aims to assess the urban environmental quality of Finfinne and Shaggar Cities using GIS-based spatial analysis. By integrating geospatial data with key environmental indicators, the research seeks to identify critical environmental challenges, analyze spatial disparities, and pinpoint areas that require targeted interventions to enhance urban sustainability. The findings are supposed to immensely contribute to the growing body of knowledge on urban environmental management while also offering a practical framework for policymakers and urban planners to promote resilient and sustainable urban development. Moreover, this study supports the Ethiopian government’s initiative for green urban development by identifying critical sub-cities that require targeted interventions, thereby aligning with national efforts to foster environmentally sustainable urban areas. Materials and Methods 2.1. Description of the Study area The study was conducted in Finfinne and Shaggar Cities. Shaggar City is one of Ethiopia’s newly established metropolitan areas located in the Oromia National Regional State. The total area of Fifinne City is about 54,000 ha (Moisa et al., 2025 ). Geographically, Shaggar city lies approximately between latitude 9°02′N to 9°10′N and longitude 38°42′E to 38°50′E engulfing Finfinne City in all directions. The average elevation of Shaggar City is about 2,400 meters above sea level and itcovers an estimated area of 2038.5 km², comprising urban, peri-urban, and emerging suburban zones (Fig. 1 ). Climatically, both Finfinne and Shaggar Cities experience a subtropical highland climate, with a unimodal rainfall pattern concentrated between June and September. The average annual precipitation ranges from 1,200 to 1,600 mm, and mean annual temperatures vary between 15°C and 25°C, creating favorable conditions for both urban greenery and peri-urban agriculture. The cities have undergone rapid urban expansion in recent years due to rapid population growth, high rate of rural-to-urban migration, and economic development. However, this expansion has not been accompanied by adequate urban planning, resulting in challenges such as unregulated land use, loss of green spaces, environmental pollution, and pressure on infrastructure and public services. Key environmental features include rivers, streams, and remaining patches of vegetation, which are critical for urban ecology, drainage, and climate regulation. Land uses in Finfinne and Shaggar Citiies are heterogeneous, consisting of built-up areas, roads and transportation networks, urban green spaces, water bodies, and peri-urban agricultural lands. These characteristics make GIS-based urban environmental quality assessment a suitable tool for investigating the spatial distribution of environmental factors and stressors. This is extremely crucial for effective analysis and mapping for guiding sustainable urban planning. 2.2. Data Types and source In this study, Landsat OLI/TIRS imagery from 2024 was utilized to derive the parameters necessary for evaluating the environmental quality of the study area. The data were obtained from the United States Geological Survey (USGS) Earth Explorer platform ( https://earthexplorer.usgs.gov/ ) and selected from the dry season to minimize cloud cover. From the 2024 Landsat images, key indicators such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Land Use/Land Cover (LULC) classes, and Urban Thermal Comfort Level (UTCL) were generated for the assessment. Table 1 Table 1 Landsat images used in this study Sensor Path/Row Spatial Resolution Spectral resolution Year Source Landsat OLI/TIRS 168,169/054,055 30m 11 bands , 2024 https://earthexplorer.usgs.gov/ 2.3. Data analysis In this study, urban environmental quality was assessed using five key indicators: LULC, UTCL, NDVI, NDBI, and LST-each classified into five categories ranging from least to most critical based on established scientific literature (Assaye et al., 2017 ) (Table 2 ). Favorable conditions such as forest cover, water bodies, high vegetation density, negative NDBI values, and lower surface temperatures were rated least critical, whereas built-up areas, poor thermal comfort, sparse or negative vegetation, high NDBI values, and elevated LST were considered most critical. These parameters were then integrated through a weighted overlay analysis to generate the composite urban environmental quality index. Table 2 Rating parameters for urban environmental quality analysis Parameters Classification criteria and scale Unit Least critical Marginal Critical More critical Most critical LULC Class Forest land, water body agricultural land, Grassland bare land Built-up area Built-up area UTCL Class Excellent, Good, Normal Bad Worse Worst Worst NDVI Value 0.26 to 0.59 0.2 to 0.26 0.12 to 0.2 -0.02 to 0.12 -0.02 to (-0.02) NDBI Value -0.16 to -0.46 -0.08 to -0.16 -0.23 to -0.08 -0.23 to 0.02 0.02 to 0.38 LST Value 39.6 2.3.1. Retrieval of Land surface temperature Land Surface Temperature (LST) for 2024 was derived from the thermal band (band 10) of Landsat OLI/TIRS using the Mono-window algorithm (Wang etal., 2015 ) Step 1: Digital Number (DN) to Radiance Conversion The thermal infrared sensor (TIRS) digital numbers, which range between 0 and 255, were converted into spectral radiance using Eq. (1). L \(\:\lambda\:=\frac{LMAX\lambda\:-LMIN\lambda\:}{QCALMAX-QCALMIN}*\left(DN-QCALMIN\right)+LMIN\lambda\:\) (Eq. 1) where; $$\:QCAL\:is\:the\:quantized\:calibrated\:pixel\:value\:in\:DN$$ $$\:LMIN\lambda\:\:is\:the\:spectral\:radiance\:that\:is\:scaled\:to\:QCALMIN\:in\:Watts/(meter\:squared*ster*\mu\:m$$ $$\:LMAX\lambda\:\:is\:the\:spectral\:radiance\:that\:is\:scaled\:to\:QCALMAX\:in\:Watts/(meter\:squared*ster*\mu\:m\:$$ $$\:QCALMIN\:is\:the\:minimum\:quantized\:calibrated\:pixel\:value\:\left(corresponding\:to\:LMIN\lambda\:\right)in\:DN$$ $$\:QCALMAX\:is\:the\:maximum\:quantized\:calibrated\:pixel\:value\:\left(corresponding\:to\:LMAX\lambda\:\right)in\:DN=255$$ The digital number of band 10 from Landsat 8 TIRS was changed into radiance values using (Eq. 2). \(\:{L}_{\lambda\:}=\left({M}_{L}*{Q}_{Cal}\right)+{A}_{L}\) (Eq. 2) where; $$\:L\lambda\:\:is\:the\:Top\:of\:Atmosphere\:\left(TOA\right)spectral\:radiance\:Watts/(meter\:squared*ster*\mu\:m\:$$ $$\:ML\:is\:Band\:specific\:multiplicative\:rescaling\:factor\:from\:the\:metadata\left(Radiance\:multi\:band\:x\right)$$ \(\:,where\:x\:\:is\:the\:band\:number\) ) $$\:AL\:is\:Band\:specific\:additive\:rescaling\:factor\:from\:the\:metadata\:(Radiance\:Add\:Band\:x,\:$$ $$\:where\:x\:is\:the\:band\:number)\:$$ $$\:Qcal\:is\:quantized\:and\:calibrated\:standard\:product\:pixel\:values\:of\:DN\:$$ Step 2: Conversion to brightness temperature LST was calculated based on land surface emissivity. TIRS values from band 10 were transformed from spectral radiance to brightness temperature (Eq. 3). \(\:\text{T}\text{B}=\frac{K2}{ln\left(\frac{K1}{L\lambda\:}+1\right)}\) (Eq. 3) $$\:where\:TB\:is\:effective\:at\:satellite\:temperature\:in\:Kelvin$$ $$\:K2\:is\:calibration\:constant\:2,\:k1\:is\:caliberation\:constant\:1$$ $$\:L\lambda\:\:is\:spectral\:radiance\:in\:Wm2\:ster-1\mu\:m-1$$ Step 3: Estimation of land surface emissivity using NDVI In this study, Landsat 8 band 5 (Near-Infrared, NIR) and band 4 (Red) were utilized to compute the Normalized Difference Vegetation Index (NDVI). NDVI was derived using (Eq. 4) to assess the spatial and temporal variations in vegetation cover (Li et al., 2013 ). This index is widely applied in vegetation studies because it highlights differences between healthy and sparse or degraded vegetation, thereby providing valuable insights into ecosystem conditions and land cover changes over time. \(\:NDVI=\frac{\:NIR-R}{NIR+R}\) (Eq. 4) A proportional vegetation (Pv) calculation (Eq. 5) was done based on the NDVI measurements to calculate land surface emissivity. \(\:Pv=\left\{\frac{NDVI-NDVImin}{NDVImax-NDVImin}\right\}2\) (Eq. 5) where; $$\:Pv\:is\:proportion\:vegetation,\:NDVI\:is\:Normalized\:difference\:vegeation\:index,\:$$ $$\:NDVImin\:is\:minimum\:value\:of\:NDVI,\:NDVImax\:is\:maximum\:value\:of\:NDVI\:$$ An estimation of LST (Eq. 8) was performed (Eq. 6) \(\:ԑ=0.004*Pv+0.986\) (Eq. 7) where; $$\:\text{Ƹ}\:\text{i}\text{s}\:\text{e}\text{m}\text{i}\text{s}\text{s}\text{i}\text{v}\text{i}\text{t}\text{y}\:\text{a}\text{n}\text{d}\:\text{p}\text{v}\:\text{i}\text{s}\:\text{v}\text{e}\text{g}\text{e}\text{t}\text{a}\text{t}\text{i}\text{o}\text{n}\:\text{p}\text{r}\text{o}\text{p}\text{o}\text{r}\text{t}\text{i}\text{o}\text{n}\:$$ Finally, the LST was calculated using (Eq. 8) \(\:\text{L}\text{S}\text{T}\left(0\text{C}\right)=\:\:\frac{\text{T}\text{B}}{1\:\left({\lambda\:}\frac{\text{T}\text{B}}{\text{P}}\right)\text{I}\text{n}\text{Ƹ}}-273.15\) (Eq. 8) $$\:where\:LST\:is\:land\:surface\:temperature\:in\:Kelvin$$ $$\:TB\:is\:radiant\:surface\:temperature\:in\:Kelvin$$ $$\:\lambda\:\:is\:the\:wavelength\:of\:emitted\:radiance\:\left(10.8\mu\:m\right),\:P\:is\:h*c/\alpha\:(1.438*10-1\:mK)$$ $$\:h\:is\:Planck^{\prime\:}\:constant\:(6.26*1010-34\:Js),\:c\:is\:the\:veleocity\:of\:light\:(2.998*108m/s)$$ $$\:\alpha\:\:is\:Stefan\:Boltzmans\:constant\:\left(1.38*10-23\:JK-1\right)ԑ\:\:is\:land\:surface\:emissivity$$ . 2.3.2. Normalized Difference Vegetation index (NDVI) The Normalized Difference Vegetation Index (NDVI) is one of the most widely applied indices for monitoring vegetation dynamics, as it provides information on plant health, density, and vigor by contrasting the reflectance of near-infrared (NIR), which vegetation strongly reflects, with that of red light, which is absorbed during photosynthesis (Roba et al., 2025 ). In this study, NDVI was calculated from Landsat OLI/TIRS multispectral imagery for the year 2025, using Band 5 (NIR) and Band 4 (Red) as inputs to (Eq. 9). The resulting NDVI values were used to evaluate the spatial distribution and density of vegetation cover. Higher NDVI values indicate healthy and dense vegetation, while lower or negative values correspond to sparse vegetation, bare soil, built-up areas, or water bodies, thereby offering a reliable measure of vegetation condition across the study area. It calculated Using Formula: \(\:NDVI=\frac{NIR-Red}{NIR+Red}\) (Eq. 9) Where: NIR = Reflectance in the near-infrared band (Band 5 of Landsat OLI/TIRS) Red = Reflectance in the red band (Band 4 of Landsat OLI/TIRS) 2.3.3. The normalized difference built-up index (NDBI) The Normalized Difference Built-up Index (NDBI) was employed to identify and map impervious surfaces within the urban areas (Moisa et al., 2025 ). For its computation, multispectral bands from different Landsat sensors were utilized: Bands 4 and 5 from Landsat 5 and 7, and Bands 5 and 6 from Landsat 8. The index was derived using (Eq. 10). NDBI is particularly useful for distinguishing built-up areas from vegetation and other land cover types, as higher values typically correspond to urbanized or impervious surfaces, while lower values indicate vegetated or non-built-up areas. \(\:\text{N}\text{D}\text{B}\text{I}=\frac{\text{S}\text{W}\text{I}\text{R}-\text{N}\text{I}\text{R}}{\text{S}\text{W}\text{I}\text{R}+\text{N}\text{I}\text{R}}\) (Eq. 10) SWIR is the short wave infrared calculated from Bands 5 and 7 (Landsat 5), and Band 6 (Landsat 8. NIR stands for near infrared, and is calculated using band 4 of Landsat 5 and 7, and band 5 of Landsat 8. 2.3.4. Land Use and Land Cover (LULC) Classification Land use/land cover (LULC) for the study area was derived from the 2024 Landsat OLI/TIRS imagery using a supervised classification technique based on the maximum likelihood algorithm. For this analysis, the landscape was classified into six major categories: agricultural land, bare land, built-up area, forest land, grassland, and water bodies. This classification provided a clear representation of the spatial distribution of different land cover types, serving as a basis for assessing environmental quality in the study area. 2.3.5. Urban Thermal Confort Level (UTCL) The Urban Thermal Field Variance Index (UTFVI) was applied to estimate the urban thermal level and evaluate the ecological effects of urban heat conditions (Moisa et al., 2022 ). It is calculated as (Eq. 11): \(\:UTFVI=\frac{LST-LST\:mean}{LST}\) (Eq. 11) Where: LST is the land surface temperature of a pixel and LST mean is the mean land surface temperature of the study area. Table 3 shows the classification of urban thermal comfort levels based on UTFVI scores. Table 3 Threshold values of UTCL UTFVI UHI Phenomena Urban Thermal comfort level (UTCL) 0.02 Strongest Worst Multi-Criteria Decision Analysis (MCDA) model Multi-Criteria Evaluation (MCE) in GIS is a technique used to determine land suitability for various applications by considering several factors, each weighted and ranked according to its importance (Worqlul et al., 2017 , Negeri, et al., 2025 ). The Analytical Hierarchy Process (AHP), a multi-criteria evaluation (MCE) technique, was employed to assess land suitability for urban environmental quality, following the 1–9 scale of relative importance proposed by Saaty ( 2002 ). Pairwise comparisons were conducted among the selected parameters to determine their relative significance. Based on these comparisons, each parameter was reclassified and assigned weights that reflect its influence and contribution to urban environmental quality in the study area (Table 4 ). Table 4 pair wise comparison matrix of selected parameters Factors LST NDVI NDBI LULC UTCL Weight LST 1 2 2 2 3 0.34 NDVI 0.5 1 2 2 2 0.24 NDBI 0.5 0.5 1 2 2 0.18 LULC 0.5 0.5 0.5 1 2 0.14 UTCL 0.33 0.5 0.5 0.5 1 0.10 Σ 2.83 4.5 6 7.5 10 1 λ max= (2.83*0.34) +(4.5*0.24)+ (6*0.18) +(7.5*0.14)+ (10*0.10) = 5.1722, n = 5, CI = 0.04305, RI = 1.12, CR = 0.038 The validity and clarity of the pairwise parameter comparisons were evaluated using the Consistency Ratio (CR). According to the guideline, the CR value must be less than 10% to be considered acceptable (Moisa et al., 2023). The CR was calculated as the ratio of the Consistency Index (CI) to the Random Consistency Index (RI), as shown in (Eq. 12). \(\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\text{C}\text{R}=\frac{\text{C}\text{I}}{\:\text{R}\text{I}}\) (Eq. 1 2 ) Where CI is consistency index and RI is random consistency index. Consistency index is the measure of parameters consistency as the degree of consistency by using the following formula (Eq. 13): $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\text{C}\text{I}=\frac{\lambda\:{max}-\:n}{n-1}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:(\:\text{E}\text{q}.13)\:\:\:\:\:\:\:\:\:\:$$ Where, n represents the number of parameters, while λ max denotes the principal eigenvalue, which is obtained by multiplying the total horizontal summation of the assigned intensity importance values with the normalized principal eigenvector values of the parameters. The normalized principal eigenvector was derived by averaging the normalized relative weights of the parameters. The Random Consistency Index (RI) is a constant value assigned to each parameter set, depending on the number of parameters considered and their intensity importance scale (Connett., et al., 2019) (Table 5 ). Table 5 Random index value table Intensity importance 1 2 3 4 5 6 7 8 9 10 Constant number 0.00 0.00 0.58 0.90 1.12 1 1.32 1.41 1.45 1.49 Urban Environmental Quality Analysis The final assessment of land suitability for urban environmental quality was conducted using a weighted overlay analysis, in which all the selected parameters were integrated based on their assigned weights (Faisal and shaker, 2017 ) (Eq. 14). This approach allowed for the combination of multiple spatial criteria, reflecting their relative importance, to generate a comprehensive map of areas suitable for maintaining or improving urban environmental quality. The resulting suitability map provides a clear spatial representation of zones with varying potential for supporting sustainable urban development and environmental management. \(\:SI={\Sigma\:}\text{W}\text{i}\text{*}\text{X}\text{i}\) (Eq. 14 ) Where SI is suitability index, Wi is weight of factor I, Xi is normalized criterion score of factors. Finally, the weight was assigned based on their degree of influence. Result and Discussions 3.1. Factors for Urban environmental quality Assessment 3.1.1. Land surface Temperature Land Surface Temperature (LST), which reflects the thermal properties of land cover and its influence on microclimate, is a crucial indicator of urban environmental quality (Moisa et al., 2025 ; Zhao et al., 2020; Naserikia et al., 2022, 2023). The most crucial conditions for the prevalence of the observed reduced environmental quality and increased LST in the studied urban areas are associated with deforestation, expansion of bare lands, and remarkable increase in population pressure. Severe ecological and social stress may happen such high LST hotspot areas. The observedheightened urban heat island effects and the elevated heat stress, leads to decreased human thermal comfort, increased energy demands or reducing temperature, and can definitely result in possible loss of biodiversity. Green spaces, forests, and water bodies, on the other hand, represent the least critical conditions, while regions with relatively lower LST, such as agricultural and grass lands, are moderately critical. The study revealed that areas covered with forest and greenery spaces are characterized by reduced evapotranspiration and increased shading. This in turn leads to more sustained biodiversity and local climate regulation resulting in better thermal comfort and improved quality of the urban environment. Thus, mapping and examining spatial variations in LST makes it possible to identify both ecologically resilient and high-risk thermal zones, which is crucial information for taking appropriate climate adaptation measures and implementation of sustainable urban planning. The use of LST in assessing the health of the urban environment and directing heat stress mitigation interventions is strengthened when it is combined with vegetation and built-up indices. The northern and western regions of the study area were characterized by the lowest increase in LST and hence were characterized by the most comfortable thermal comfort implying better suitability for life. In contrast, the central, southern, and eastern regions of the area under study are classified as highly-critical to moderately-critical areas that require sustainable land management (Fig. 2 a). 3.1.2. Normalized Difference Vegetation Index (NDVI) The NDVI, which gauges the amount and health of vegetation, is a crucial indicator of urban environmental quality. Green spaces that lower heat, enhance air quality, and promote biodiversity are indicated by high NDVI values. On the other hand, bare or built-up areas with unfavorable environmental conditions are indicated by low values. When paired with LST and built-up indices, NDVI mapping facilitates climate resilience and sustainable urban planning by highlighting areas that require greening (Fig. 2 b). As shown in Fig. 2 b, the northern and northwestern parts of the study area are characterized by better vegetation coverage which in turn has resulted in higher NDVI values and lower average LST. 3.1.3. Normalized Difference Built-up Index ( NDBI) Normalized difference built-up index highlights regions with a high density of built-up surfaces and little vegetation. The Built-up Index (NDBI) identifies the most important areas for urban environmental intervention. The high NDBI areas are generally considered as priority targets for greening, urban forest development, reflective surfaces, and other climate-resilient strategies because they are most susceptible to heat stress, characterized by poor air quality, and ecosystem degradation. Planners can identify hotspots where interventions will most successfully enhance thermal comfort, environmental quality, and overall urban resilience by combining NDBI with LST mapping (Fig. 2 c). As shown in Fig. 2 c, the central part of the study area, which is dominantly occupied by Finfinne City is characterized by higher NDBI experiences higher LST implying deterioration of the urban environmental quality. 3.1.4. Land use land cover types Three levels of sensitivity can be used to classify the patterns found in the 2,038.5 km² study area's land use and land cover (LULC) analysis. Built-up land (covering 605.5 km 2 , 29.7%) and bare land (114.2 km 2 , 5.6%), both which indicate extreme environmental stress, are the most critical areas. Bare lands are a sign of land degradation and productivity loss, while built-up areas increase urban heat and decrease vegetation cover. The agricultural land (684.4 km2, 33.6%) and grassland (526.5 km2, 25.8%) that make up marginally critical areas are vital for ecological services and food security, but they are still susceptible to overuse, degradation, and urbanization. The least critical areas are water bodies (5.3 km², 0.3%) and forest land (102.6 km², 5.0%), which, despite their relatively small extent, are vital for climate regulation, biodiversity support, and ecological stability, making their conservation a top priority (Table 6 , Fig. 2 d). Table 6 Land use and land cover classes of the study area LULC Types Area (km2) Area (%) Agricultural land 684.4 33.6 Bare land 114.2 5.6 Built up area 605.5 29.7 Forest land 102.6 5.0 Grassland 526.5 25.8 Water body 5.3 0.3 Total 2038.5 100.0 3.1.5. Urban Thermal Comfort Level (UTCL) Urban thermal comfort levels are greatly impacted by urban heat island (UHI) phenomena, which have an impact on human and environmental well-being. The classification of UHI intensities and their corresponding thermal comfort levels indicate substantial variations across different urban areas. The 45.4 km² of areas with no UHI effects and a high degree of thermal comfort make up 2.2% of the entire study area. Over 87.6 km 2 , or 4.3% of the total area, has weak UHI effects, which correspond to good thermal comfort. The 171.0 km² was classified under middle UHI category, which corresponds to normal thermal comfort levels, and this makes up 8.4% of the total study area. Thermal comfort levels gradually deteriorate with increasing UHI intensities. As shown in Table 7 , 501.0 km 2 , or 24.6% of the study area, are covered by strong UHI effects, which are categorized as having a bad thermal comfort level. Over 784.8 km², or 38.5% of the total area, fall into the "stronger" category, where thermal comfort is rated as worse. The most extreme UHI category, labeled as strongest, is associated with the worst thermal comfort conditions and encompasses 448.7 km², making up 22.0% of the study region (Table 7 , Fig. 2 e). The entire study area is 2038.5 km 2 , and a sizable section of it has strong to strongest UHI effects, which are indicative of low thermal comfort. This distribution highlights the need for effective urban planning and mitigation strategies to improve thermal comfort, particularly in areas with high UHI intensities. The negative effects of UHI could be lessened and a more sustainable and livable urban environment could result from actions like expanding green spaces, improving urban ventilation, and using reflective building materials. Table 7 UTCL and area coverage of the study area UHI phenomena Urban thermal comfort level Area (km 2 ) Area (%) None Excellent 45.4 2.2 Weak Good 87.6 4.3 Middle Normal 171.0 8.4 Strong Bad 501.0 24.6 Stronger Worse 784.8 38.5 Strongest Worst 448.7 22.0 Total 2038.5 100 3.2. Potential Assessment of Urban Environmental Quality There were noticeable differences in the environmental conditions throughout the 2,038.5 km² the evaluated study area based on the analysis of environmental quality classification. With 963.5 km² (47.3%) categorized as critical and 753.4 km² (37.0%) as more critical, the critical and more critical areas together made up the largest portion. This demonstrates the severe environmental stress in these areas, which calls for prompt action and long-term management plans. The 121.8 km² (6.0%), which is considered most critical area showed significant environmental degradation that urgently need restoration. Comparably, marginal areas made up 121.8 km² (6.0%), indicating moderate environmental conditions that still require conservation efforts to stop further deterioration. The least critically affected areas, on the other hand, represented areas with relatively better environmental quality but with smaller area coverage, spanning only 78.0 km² (3.8%). These results highlight the urgent need for focused urban planning and environmental policy interventions to preserve and improve the comparatively healthier areas while reducing degradation, especially in the most impacted areas (Table 8 , Fig. 3 ). These findings resonate with urban environmental studies globally. For example, Seto et al. ( 2012 ) highlighted that rapid urbanization disproportionately degrades the surrounding ecosystems, often leaving only limited areas with good environmental quality. The predominance of critical and more critical areas in the current study is consistent with the findings by Tewoldeberhan et al.'s (2020) which revealed that urban expansion in African urban contexts significantly increased environmental vulnerability, especially in peri-urban and low-planning zones. Similar to the limited scope of the least critical areas mentioned here, Li et al. (2018) noted that urban sprawl frequently results in fragmented green spaces and concentrated environmental stress. Thus, the findings highlight the need for policy and urban planning interventions that prioritize restoring severely degraded areas, putting green infrastructure in place, and protecting the remaining environmentally healthy zones. In areas that are rapidly urbanizing, strategies like community-based conservation, urban green belts, and sustainable land-use planning can help prevent degradation, increase resilience, and guarantee environmental sustainability. Table 8 Summarized results of urban environmental quality and corresponding area coverage Environmental quality Area (km 2 ) Area (%) Most Critical 121.8 6.0 More Critical 753.4 37.0 Critical 963.5 47.3 Marginal 121.8 6.0 Least Critical 78.0 3.8 Total 2038.5 100 3.3. Sub-City Level Environmental Quality Assessment The analysis of the results demonstrate that there are significant differences among the sub-cities' in the levels of environmental qualityies. Areas with the most environmental modification, specifically the expansion of built-up area, such as Finfinne (54.4 km²), Furi (12.1 km²), and Eka Tafo (9.2 km²) are characterized by the highest LST, most critical thermal discomfort and hence suffer from seriously deteriorated environmental quality. More specifically, the largest areas of those zones designated as more critical were found in Finfinne (257.1 km 2 ) and Sebeta (66.4 km 2 ), indicating high levels of environmental stress. Regarding the Shaggar sub-cities, Kura Jida had the greatest coverage of critical areas (173.0 km 2 ), followed by Koye (115.2 km 2 ) and Sebeta (105.6 km 2 ), highlighting serious environmental issues. Conversely, marginal and least critical areas were notably smaller in extent, with Mana Abichu (14.9 km²) and Sebeta (26.3 km²) exhibiting comparatively better environmental conditions.The study supports earlier findings that link land conversion in Ethiopian urban and peri-urban areas to declining environmental quality, and it shows that rapid urban expansion is a major driver of environmental stress (Alemayehu et al., 2021; Woldegerima et al., 2022). Critical and more critical zones spanned the largest portions of the 2,038.5 km² total area as evaluated across all sub-cities. The findings show that Finfinne is under extreme environmental stress and needs immediate assistance, as it has the largest total area of environmental concerns (429.4 km²). The need for urban planning strategies to mitigate environmental degradation is further highlighted by the extensively critical and more critical areas found in Kura Jida (245.4 km 2 ) and Sebeta (215.1 km 2 ). Sub-cities such as Melka Nonno (51.5 km 2 ) and Mana Abichu (98.5 km 2 ), on the other hand, show comparatively lower levels of environmental distress, indicating better conditions. To minimize further environmental deterioration, targeted conservation efforts are necessary even in these places. The disparity in environmental quality between sub-cities emphasizes the necessity of regional policies that are specific to the kind and extent of environmental issues that exist in each area. Degefa et al. (2021) emanated similar conclusions, stating that in order to address the disparate effects of land use change and urbanization on ecosystem services in Ethiopian cities, differentiated management strategies are necessary. Table: Environmental Quality Levels and corresponding area coverage in Shaggar Sub-cities Shaggar Sub-cities Environmental Quality (km 2 ) Most Critical More Critical Critical Marginal Least Critical Total Burayu 0.0 13.6 4.0 4.0 6.0 27.6 Eka Tafo 9.2 36.2 46.7 7.7 2.5 102.3 Finfinne 54.4 257.1 87.5 22.3 8.1 429.4 Furi 12.1 32.3 20.7 2.6 0.4 68.1 Galan 5.5 43.4 88.3 6.0 0.1 143.2 Galan Gudo 5.3 58.5 80.3 4.4 3.1 151.6 Gefersa Guje 2.6 54.5 88.2 18.9 6.9 171.0 Koye 10.3 48.1 115.2 8.6 5.0 187.2 Kura Jida 4.1 54.7 173.0 12.2 1.4 245.4 Mana Abichu 0.9 17.1 45.9 19.6 14.9 98.5 Melka Nonno 3.1 27.7 18.1 2.3 0.3 51.5 Sebeta 10.9 66.4 105.6 5.8 26.3 215.1 Sululta 3.3 43.7 89.9 7.5 3.3 147.7 Total 121.8 753.3 963.4 121.8 78.2 2038.5 Conclusions This study provides a comprehensive GIS-based assessment of urban environmental quality and thermal comfort in Finfinne and Shaggar City, revealing substantial spatial variations and identifying areas of critical concern that require imperative attention. The urban landscape is dominated by critical and more critical zones, which together make up 1,716.9 km² (84.3% of the total area). This indicates that environmental stress is widespread, and the most critical areas (121.8 km², 6.0%) need to be restored right away. Even though they are less extensive, marginal and least critical areas still require proactive conservation measures to stop further decline in environmental quality. According to an analysis of urban thermal comfort, only a small portion of the city experiences excellent or good conditions, while 1,734.5 km² (85.1%) of the city is affected by strong to strongest urban heat island (UHI) effects, which correspond to poor thermal comfort levels. Sub-city-level analysis revealed that Finfinne, Kura Jida, and Sebeta have the largest extents of critical and more critical areas, indicating environmental deprivation hotspots, whereas Melka Nonno and Mana Abichu exhibit comparatively better conditions,however still require targeted management. These results highlight the close connection between urban heat stress and environmental quality, highlighting the need for integrated planning strategies. Environmental quality and thermal comfort can be enhanced by strategic interventions like reforestation and sustainable land management to restore degraded areas, the implementation of green infrastructure and urban green belts, the adoption of reflective building materials, the improvement of urban ventilation, and the encouragement of community-based conservation.In order to benefit policymakers and planners there is an urgent intervention to mitigate dilapidation, improve resilience, and foster a livable, environmentally sustainable urban environment bycombining GIS-based spatial analysis with environmental and thermal assessments. To further improve urban sustainability strategies and guarantee long-term adaptive planning, future research should think about including more socioeconomic, demographic, and infrastructure factors in addition to climate projections. Declarations Declaration of Competing Interest: The author declare that there is no competing interest in this manuscript. Clinical trial number Not applicable (NA) Consent to Publish declarations: NA Funding No funding received for this work. Author Contribution Zenebe Reta Roba: Writing – review & editing, Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization. Mitiku Badasa Moisa: Writing – review & editing, Writing – original draft, Visualization, Supervision, Software, Formal analysis, Data curation, Conceptualization. Fedasa Benti Chalchisa, ,Harison Kiplagat Kipkulei, Kiros Tsegay Deribew,Aqil Tariq,and Dugasa Negash: Writing – original draft, Methodology, Data curation., Methodology, Data curation. KenateWorku Tabor, Tigist GirumAymeku, Merkato Markos Mena and Dessalegn Obsi Gemeda: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Acknowledgment The authors acknowledge Mattu University Bedele Campus, College of Natural Resource and Agricultural Economics, Department of Forestry, Wollega University Nekemte Campus College Natural and computational science, Department of Earth Science,Mississippi State University and Jimma University College of Agriculture and Veterinary Medicine for the existing facilities to conduct this study. Data Availability All data are available in the manuscript. References African Development Bank. (2018). African economic outlook 2018: Urbanization and industrialization. African Development Bank. https://www.afdb.org/en/knowledge/publications/african-economic-outlook Assaye R, Suryabhagavan KV, Balakrishnan M, Hameed S. Geo-spatial approach for urban green space and environmental quality assessment: a case study in Addis Ababa City. J Geographic Inform Syst. 2017;9(2):191–206. Connett B, O’Halloran BM, Pollman AG. Advancing the use of an analytical hierarchy process and improved random indexes for making prioritized decisions in systems. IEEE Trans Eng Manage. 2019;69(6):2791–805. Degefu MA, Argaw M, Feyisa GL, Degefa S. Impact of landscape dynamics and intensities on the ecological land of major cities in Ethiopia. Environ Syst Res. 2021;10., Article 32. https://doi.org/10.1186/s40068-021-00237-1 . Faisal K, Shaker A. An investigation of GIS overlay and PCA techniques for urban environmental quality assessment: a case study in Toronto. Ont Can Sustain. 2017;9(3):380. Kamana AA. Urban challenges and strategies in African cities. Urban Plann. 2024;10(1):1–17. https://doi.org/10.1016/j.up.2024.01.001 . Li ZL, Wu H, Wang N, Qiu S, Sobrino JA, Wan Z, Yan G. Land surface emissivity retrieval from satellite data. Int J Remote Sens. 2013;34(9–10):3084–127. Moisa MB, Dejene IN, Roba ZR, Gemeda DO. Impact of urban land use and land cover change on urban heat island and urban thermal comfort level: a case study of Addis Ababa City, Ethiopia. Environ Monit Assess. 2022;194(10):736. Moisa MB, Negash D, Reta Roba Z, DebesaWedajo G, Deribew T, Benti K, F., Gemeda O, D. Spatial analysis of urban expansion and its effects on land surface temperature using geospatial techniques in Addis Ababa and Shaggar Cities. Ethiopia All Earth. 2025;37(1):1–23. Mwenje E. Challenges for mainstreaming climate adaptation in African cities. Environ Sci Policy. 2024;145:1–10. https://doi.org/10.1016/j.envsci.2024.01.001 . Negeri BG, Xiuguang B, Moisa MB. Assessment of potential land suitability for rainfed wheat production using GIS and multi criteria decision analysis in the Southwestern parts of Ethiopia. PLoS ONE. 2025;20(6):e0324540. .https://doi.org/10.1371/journal.pone.0324540 . Roba ZR, Moisa MB, Purohit S, Deribew KT, Gemeda DO. Assessing the impacts of forest cover change on carbon stock and soil moisture dynamics using geospatial techniques: A case study of Nensebo forest, Southern Ethiopia. Environ Monit Assess. 2025;197(2):169. Saaty TL. Decision making with the analytic hierarchy process. Pittsburgh: RWS; 2002. Seto KC, Fragkias M, Güneralp B, Reilly MK. A meta-analysis of global urban land expansion. PLoS ONE. 2022;17(5):e0267646. https://doi.org/10.1371/journal.pone.0267646 . Seto KC, Güneralp B, Hutyra LR. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083–16088. https://doi.org/10.1073/pnas.1211658109 United Nations. (2018). World urbanization prospects: The 2018 revision. United Nations, Department of Economic and Social Affairs, Population Division. https://population.un.org/wup/ Wang F, Qin Z, Song C, Tu L, Karnieli A, Zhao S. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sens. 2015;7(4):4268–89. Weng Q. (2019). Remote sensing and GIS integration: Monitoring and modeling urban environmental changes. Springer. https://doi.org/10.1007/978-3-030-04155-2 Woldegerima T et al. (2021). Impact of landscape dynamics and intensities on the ecological land of major cities in Ethiopia. Environmental Systems Research, 10(32). (This article analyzes how built-up expansion has transformed ecological land across major Ethiopian cities, including Addis Ababa, Bahir Dar, Adama, and Hawassa between 1990 and 2020.). Woldemichael B, Teshome W, Alemu A. Urban growth dynamics and land use/cover changes in Ethiopia: Implications for sustainable urban planning. Sustainable Cities Soc. 2023;95:104654. https://doi.org/10.1016/j.scs.2023.104654 . Worqlul AW, Jeong J, Dile YT, Osorio J, Schmitter P, Gerik T, Clark N. Assessing potential land suitable for surface irrigation using groundwater in Ethiopia. Appl Geogr. 2017;85:1–13. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":359935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation Map of the Study Area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8142228/v1/52b3eb61f02eea53b5565edc.jpeg"},{"id":99217109,"identity":"01511f5e-a9ce-4077-82b6-ff378120385a","added_by":"auto","created_at":"2025-12-30 09:10:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":337691,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of \u0026nbsp;the \u0026nbsp;Environmental Quality Determinants\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8142228/v1/0528078f2463f958c1be1817.jpeg"},{"id":99217062,"identity":"d8d88e3b-d07d-47a9-bdc1-c5a2c7cc1feb","added_by":"auto","created_at":"2025-12-30 09:10:05","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":528373,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental Quality Levels' Map\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8142228/v1/c402849420d962ea33a93a6b.jpeg"},{"id":99217073,"identity":"8d56dc6e-d4c5-4e15-a2a8-67686b3a88f3","added_by":"auto","created_at":"2025-12-30 09:10:07","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":403578,"visible":true,"origin":"","legend":"\u003cp\u003eFigure: Environmental Quality Map of Shaggar Sub-cities\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8142228/v1/81f72c903c3acf713be6aa93.jpeg"},{"id":101881337,"identity":"48fded76-f137-4aa7-bc49-7bf69964f3b9","added_by":"auto","created_at":"2026-02-04 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According to the United Nations, more than 55% of the world\u0026rsquo;s population resides in urban areas, a figure projected to increase to nearly 70% by 2050 (United Nations, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This rapid urban expansion has led to significant environmental concerns, including air and water pollution, loss of green spaces, increased waste generation, and socio-economic inequalities (United Nations, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As cities grow, the demand for effective urban planning and environmental management becomes more critical in ensuring a balance between development and sustainability (Seto, G\u0026uuml;neralp, \u0026amp; Hutyra, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePopulation growth mainly due to rural-to-urban migration, and economic shifts have all contributed to Africa's particularly rapid urbanization (UN-Habitat, 2020; African Development Bank, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Africa's urban population is predicted to triple by 2050, exacerbating environmental issues like poor waste management, poor infrastructure, air pollution, and deteriorating water quality (UN-Habitat, 2020). Living conditions and urban sprawl worsen in many African cities due to poor spatial planning strategies (Kamana, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mwenje, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Geographic Information Systems (GIS) and Remote Sensing approaches have been acknowledged as a powerful tools for spatial analysis, monitoring urban growth trends, and directing sustainable urban policies in urban environmental assessments (Weng, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Seto et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCities like Addis Ababa, Shaggar, and other secondary urban centers have seen rapid development, making Ethiopia one of African countries with the fastest rate of urbanization (Moisa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Woldemichael et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e ). But this expansion hasn't been properly planned for, which has resulted in poor service delivery, and environmental degradation. Uncontrolled land use changes, dwindling green spaces, rapid expansion of built-up areas, worsening air and water pollution, poor waste management, and unequal access to basic urban services are just a few of the environmental issues that both Finfinne and the recently formed metropolitan city of Shaggar must deal with. GIS-based methods for evaluating the environmental quality of urban areas, such as Finfinne and Shaggar cities, can support evidence-based decision-making for sustainable urban planning, highlighting priority areas for intervention, and offering insightful information about the spatial distribution of the various environmental factors.\u003c/p\u003e \u003cp\u003eThis study aims to assess the urban environmental quality of Finfinne and Shaggar Cities using GIS-based spatial analysis. By integrating geospatial data with key environmental indicators, the research seeks to identify critical environmental challenges, analyze spatial disparities, and pinpoint areas that require targeted interventions to enhance urban sustainability. The findings are supposed to immensely contribute to the growing body of knowledge on urban environmental management while also offering a practical framework for policymakers and urban planners to promote resilient and sustainable urban development. Moreover, this study supports the Ethiopian government\u0026rsquo;s initiative for green urban development by identifying critical sub-cities that require targeted interventions, thereby aligning with national efforts to foster environmentally sustainable urban areas.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of the Study area\u003c/h2\u003e \u003cp\u003eThe study was conducted in Finfinne and Shaggar Cities. Shaggar City is one of Ethiopia\u0026rsquo;s newly established metropolitan areas located in the Oromia National Regional State. The total area of Fifinne City is about 54,000 ha (Moisa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Geographically, Shaggar city lies approximately between latitude 9\u0026deg;02\u0026prime;N to 9\u0026deg;10\u0026prime;N and longitude 38\u0026deg;42\u0026prime;E to 38\u0026deg;50\u0026prime;E engulfing Finfinne City in all directions. The average elevation of Shaggar City is about 2,400 meters above sea level and itcovers an estimated area of 2038.5 km\u0026sup2;, comprising urban, peri-urban, and emerging suburban zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimatically, both Finfinne and Shaggar Cities experience a subtropical highland climate, with a unimodal rainfall pattern concentrated between June and September. The average annual precipitation ranges from 1,200 to 1,600 mm, and mean annual temperatures vary between 15\u0026deg;C and 25\u0026deg;C, creating favorable conditions for both urban greenery and peri-urban agriculture.\u003c/p\u003e \u003cp\u003eThe cities have undergone rapid urban expansion in recent years due to rapid population growth, high rate of rural-to-urban migration, and economic development. However, this expansion has not been accompanied by adequate urban planning, resulting in challenges such as unregulated land use, loss of green spaces, environmental pollution, and pressure on infrastructure and public services. Key environmental features include rivers, streams, and remaining patches of vegetation, which are critical for urban ecology, drainage, and climate regulation.\u003c/p\u003e \u003cp\u003eLand uses in Finfinne and Shaggar Citiies are heterogeneous, consisting of built-up areas, roads and transportation networks, urban green spaces, water bodies, and peri-urban agricultural lands. These characteristics make GIS-based urban environmental quality assessment a suitable tool for investigating the spatial distribution of environmental factors and stressors. This is extremely crucial for effective analysis and mapping for guiding sustainable urban planning.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Types and source\u003c/h2\u003e \u003cp\u003eIn this study, Landsat OLI/TIRS imagery from 2024 was utilized to derive the parameters necessary for evaluating the environmental quality of the study area. The data were obtained from the United States Geological Survey (USGS) Earth Explorer platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and selected from the dry season to minimize cloud cover. From the 2024 Landsat images, key indicators such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Land Use/Land Cover (LULC) classes, and Urban Thermal Comfort Level (UTCL) were generated for the assessment. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLandsat images used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath/Row\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpectral resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat OLI/TIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168,169/054,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 bands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e, 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data analysis\u003c/h2\u003e \u003cp\u003eIn this study, urban environmental quality was assessed using five key indicators: LULC, UTCL, NDVI, NDBI, and LST-each classified into five categories ranging from least to most critical based on established scientific literature (Assaye et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Favorable conditions such as forest cover, water bodies, high vegetation density, negative NDBI values, and lower surface temperatures were rated least critical, whereas built-up areas, poor thermal comfort, sparse or negative vegetation, high NDBI values, and elevated LST were considered most critical. These parameters were then integrated through a weighted overlay analysis to generate the composite urban environmental quality index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRating parameters for urban environmental quality analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eClassification criteria and scale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeast critical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarginal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMore critical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMost critical\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest land, water body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eagricultural land,\u003c/p\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebare land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBuilt-up area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBuilt-up area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent, Good, Normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWorst\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26 to 0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2 to 0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12 to 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02 to 0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02 to (-0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16 to -0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08 to -0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.23 to -0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.23 to 0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02 to 0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3 to 31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.2 to 35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.4 to 39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;39.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Retrieval of Land surface temperature\u003c/h2\u003e \u003cp\u003eLand Surface Temperature (LST) for 2024 was derived from the thermal band (band 10) of Landsat OLI/TIRS using the Mono-window algorithm (Wang etal., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eStep 1: Digital Number (DN) to Radiance Conversion\u003c/p\u003e \u003cp\u003eThe thermal infrared sensor (TIRS) digital numbers, which range between 0 and 255, were converted into spectral radiance using Eq.\u0026nbsp;(1).\u003c/p\u003e \u003cp\u003eL\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:=\\frac{LMAX\\lambda\\:-LMIN\\lambda\\:}{QCALMAX-QCALMIN}*\\left(DN-QCALMIN\\right)+LMIN\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e (Eq.\u0026nbsp;1)\u003c/p\u003e \u003cp\u003ewhere;\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:QCAL\\:is\\:the\\:quantized\\:calibrated\\:pixel\\:value\\:in\\:DN$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:LMIN\\lambda\\:\\:is\\:the\\:spectral\\:radiance\\:that\\:is\\:scaled\\:to\\:QCALMIN\\:in\\:Watts/(meter\\:squared*ster*\\mu\\:m$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:LMAX\\lambda\\:\\:is\\:the\\:spectral\\:radiance\\:that\\:is\\:scaled\\:to\\:QCALMAX\\:in\\:Watts/(meter\\:squared*ster*\\mu\\:m\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:QCALMIN\\:is\\:the\\:minimum\\:quantized\\:calibrated\\:pixel\\:value\\:\\left(corresponding\\:to\\:LMIN\\lambda\\:\\right)in\\:DN$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:QCALMAX\\:is\\:the\\:maximum\\:quantized\\:calibrated\\:pixel\\:value\\:\\left(corresponding\\:to\\:LMAX\\lambda\\:\\right)in\\:DN=255$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe digital number of band 10 from Landsat 8 TIRS was changed into radiance values using (Eq.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{L}_{\\lambda\\:}=\\left({M}_{L}*{Q}_{Cal}\\right)+{A}_{L}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;2)\u003c/p\u003e \u003cp\u003ewhere;\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:L\\lambda\\:\\:is\\:the\\:Top\\:of\\:Atmosphere\\:\\left(TOA\\right)spectral\\:radiance\\:Watts/(meter\\:squared*ster*\\mu\\:m\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:ML\\:is\\:Band\\:specific\\:multiplicative\\:rescaling\\:factor\\:from\\:the\\:metadata\\left(Radiance\\:multi\\:band\\:x\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:,where\\:x\\:\\:is\\:the\\:band\\:number\\)\u003c/span\u003e \u003c/span\u003e)\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:AL\\:is\\:Band\\:specific\\:additive\\:rescaling\\:factor\\:from\\:the\\:metadata\\:(Radiance\\:Add\\:Band\\:x,\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:where\\:x\\:is\\:the\\:band\\:number)\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$\\:Qcal\\:is\\:quantized\\:and\\:calibrated\\:standard\\:product\\:pixel\\:values\\:of\\:DN\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep 2: Conversion to brightness temperature\u003c/p\u003e \u003cp\u003eLST was calculated based on land surface emissivity. TIRS values from band 10 were transformed from spectral radiance to brightness temperature (Eq.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{T}\\text{B}=\\frac{K2}{ln\\left(\\frac{K1}{L\\lambda\\:}+1\\right)}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;3)\u003cdiv id=\"Equk\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e\n$$\\:where\\:TB\\:is\\:effective\\:at\\:satellite\\:temperature\\:in\\:Kelvin$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equl\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equl\" name=\"EquationSource\"\u003e\n$$\\:K2\\:is\\:calibration\\:constant\\:2,\\:k1\\:is\\:caliberation\\:constant\\:1$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equm\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equm\" name=\"EquationSource\"\u003e\n$$\\:L\\lambda\\:\\:is\\:spectral\\:radiance\\:in\\:Wm2\\:ster-1\\mu\\:m-1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep 3: Estimation of land surface emissivity using NDVI\u003c/p\u003e \u003cp\u003eIn this study, Landsat 8 band 5 (Near-Infrared, NIR) and band 4 (Red) were utilized to compute the Normalized Difference Vegetation Index (NDVI). NDVI was derived using (Eq.\u0026nbsp;4) to assess the spatial and temporal variations in vegetation cover (Li et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This index is widely applied in vegetation studies because it highlights differences between healthy and sparse or degraded vegetation, thereby providing valuable insights into ecosystem conditions and land cover changes over time.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:NDVI=\\frac{\\:NIR-R}{NIR+R}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;4)\u003c/p\u003e \u003cp\u003eA proportional vegetation (Pv) calculation (Eq.\u0026nbsp;5) was done based on the NDVI measurements to calculate land surface emissivity.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Pv=\\left\\{\\frac{NDVI-NDVImin}{NDVImax-NDVImin}\\right\\}2\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;5)\u003c/p\u003e \u003cp\u003ewhere;\u003cdiv id=\"Equn\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equn\" name=\"EquationSource\"\u003e\n$$\\:Pv\\:is\\:proportion\\:vegetation,\\:NDVI\\:is\\:Normalized\\:difference\\:vegeation\\:index,\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equo\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equo\" name=\"EquationSource\"\u003e\n$$\\:NDVImin\\:is\\:minimum\\:value\\:of\\:NDVI,\\:NDVImax\\:is\\:maximum\\:value\\:of\\:NDVI\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAn estimation of LST (Eq.\u0026nbsp;8) was performed (Eq.\u0026nbsp;6)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:ԑ=0.004*Pv+0.986\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;7)\u003c/p\u003e \u003cp\u003ewhere;\u003cdiv id=\"Equp\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equp\" name=\"EquationSource\"\u003e\n$$\\:\\text{Ƹ}\\:\\text{i}\\text{s}\\:\\text{e}\\text{m}\\text{i}\\text{s}\\text{s}\\text{i}\\text{v}\\text{i}\\text{t}\\text{y}\\:\\text{a}\\text{n}\\text{d}\\:\\text{p}\\text{v}\\:\\text{i}\\text{s}\\:\\text{v}\\text{e}\\text{g}\\text{e}\\text{t}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{p}\\text{r}\\text{o}\\text{p}\\text{o}\\text{r}\\text{t}\\text{i}\\text{o}\\text{n}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, the LST was calculated using (Eq.\u0026nbsp;8)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{S}\\text{T}\\left(0\\text{C}\\right)=\\:\\:\\frac{\\text{T}\\text{B}}{1\\:\\left({\\lambda\\:}\\frac{\\text{T}\\text{B}}{\\text{P}}\\right)\\text{I}\\text{n}\\text{Ƹ}}-273.15\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;8)\u003cdiv id=\"Equq\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equq\" name=\"EquationSource\"\u003e\n$$\\:where\\:LST\\:is\\:land\\:surface\\:temperature\\:in\\:Kelvin$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equr\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equr\" name=\"EquationSource\"\u003e\n$$\\:TB\\:is\\:radiant\\:surface\\:temperature\\:in\\:Kelvin$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equs\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equs\" name=\"EquationSource\"\u003e\n$$\\:\\lambda\\:\\:is\\:the\\:wavelength\\:of\\:emitted\\:radiance\\:\\left(10.8\\mu\\:m\\right),\\:P\\:is\\:h*c/\\alpha\\:(1.438*10-1\\:mK)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equt\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equt\" name=\"EquationSource\"\u003e\n$$\\:h\\:is\\:Planck^{\\prime\\:}\\:constant\\:(6.26*1010-34\\:Js),\\:c\\:is\\:the\\:veleocity\\:of\\:light\\:(2.998*108m/s)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equu\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equu\" name=\"EquationSource\"\u003e\n$$\\:\\alpha\\:\\:is\\:Stefan\\:Boltzmans\\:constant\\:\\left(1.38*10-23\\:JK-1\\right)ԑ\\:\\:is\\:land\\:surface\\:emissivity$$\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Normalized Difference Vegetation index (NDVI)\u003c/h2\u003e \u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) is one of the most widely applied indices for monitoring vegetation dynamics, as it provides information on plant health, density, and vigor by contrasting the reflectance of near-infrared (NIR), which vegetation strongly reflects, with that of red light, which is absorbed during photosynthesis (Roba et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this study, NDVI was calculated from Landsat OLI/TIRS multispectral imagery for the year 2025, using Band 5 (NIR) and Band 4 (Red) as inputs to (Eq.\u0026nbsp;9). The resulting NDVI values were used to evaluate the spatial distribution and density of vegetation cover. Higher NDVI values indicate healthy and dense vegetation, while lower or negative values correspond to sparse vegetation, bare soil, built-up areas, or water bodies, thereby offering a reliable measure of vegetation condition across the study area. It calculated Using Formula:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:NDVI=\\frac{NIR-Red}{NIR+Red}\\)\u003c/span\u003e \u003c/span\u003e(Eq.\u0026nbsp;9)\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eNIR\u0026thinsp;=\u0026thinsp;Reflectance in the near-infrared band (Band 5 of Landsat OLI/TIRS)\u003c/p\u003e \u003cp\u003eRed\u0026thinsp;=\u0026thinsp;Reflectance in the red band (Band 4 of Landsat OLI/TIRS)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. The normalized difference built-up index (NDBI)\u003c/h2\u003e \u003cp\u003eThe Normalized Difference Built-up Index (NDBI) was employed to identify and map impervious surfaces within the urban areas (Moisa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For its computation, multispectral bands from different Landsat sensors were utilized: Bands 4 and 5 from Landsat 5 and 7, and Bands 5 and 6 from Landsat 8. The index was derived using (Eq.\u0026nbsp;10). NDBI is particularly useful for distinguishing built-up areas from vegetation and other land cover types, as higher values typically correspond to urbanized or impervious surfaces, while lower values indicate vegetated or non-built-up areas.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{N}\\text{D}\\text{B}\\text{I}=\\frac{\\text{S}\\text{W}\\text{I}\\text{R}-\\text{N}\\text{I}\\text{R}}{\\text{S}\\text{W}\\text{I}\\text{R}+\\text{N}\\text{I}\\text{R}}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;10)\u003c/p\u003e \u003cp\u003eSWIR is the short wave infrared calculated from Bands 5 and 7 (Landsat 5), and Band 6 (Landsat 8. NIR stands for near infrared, and is calculated using band 4 of Landsat 5 and 7, and band 5 of Landsat 8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Land Use and Land Cover (LULC) Classification\u003c/h2\u003e \u003cp\u003eLand use/land cover (LULC) for the study area was derived from the 2024 Landsat OLI/TIRS imagery using a supervised classification technique based on the maximum likelihood algorithm. For this analysis, the landscape was classified into six major categories: agricultural land, bare land, built-up area, forest land, grassland, and water bodies. This classification provided a clear representation of the spatial distribution of different land cover types, serving as a basis for assessing environmental quality in the study area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5. Urban Thermal Confort Level (UTCL)\u003c/h2\u003e \u003cp\u003eThe Urban Thermal Field Variance Index (UTFVI) was applied to estimate the urban thermal level and evaluate the ecological effects of urban heat conditions (Moisa et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is calculated as (Eq.\u0026nbsp;11):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:UTFVI=\\frac{LST-LST\\:mean}{LST}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;11)\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eLST\u003c/em\u003e is the land surface temperature of a pixel and \u003cem\u003eLST mean\u003c/em\u003e is the mean land surface temperature of the study area.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the classification of urban thermal comfort levels based on UTFVI scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold values of UTCL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTFVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUHI Phenomena\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban Thermal comfort level (UTCL)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.005\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.01\u0026ndash;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.015\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStronger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorst\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eMulti-Criteria Decision Analysis (MCDA) model\u003c/h3\u003e\n\u003cp\u003eMulti-Criteria Evaluation (MCE) in GIS is a technique used to determine land suitability for various applications by considering several factors, each weighted and ranked according to its importance (Worqlul et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Negeri, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Analytical Hierarchy Process (AHP), a multi-criteria evaluation (MCE) technique, was employed to assess land suitability for urban environmental quality, following the 1\u0026ndash;9 scale of relative importance proposed by Saaty (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Pairwise comparisons were conducted among the selected parameters to determine their relative significance. Based on these comparisons, each parameter was reclassified and assigned weights that reflect its influence and contribution to urban environmental quality in the study area (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epair wise comparison matrix of selected parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUTCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eλ max= (2.83*0.34) +(4.5*0.24)+ (6*0.18) +(7.5*0.14)+ (10*0.10)\u0026thinsp;=\u0026thinsp;5.1722, n\u0026thinsp;=\u0026thinsp;5, CI\u0026thinsp;=\u0026thinsp;0.04305, RI\u0026thinsp;=\u0026thinsp;1.12, CR\u0026thinsp;=\u0026thinsp;0.038\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe validity and clarity of the pairwise parameter comparisons were evaluated using the Consistency Ratio (CR). According to the guideline, the CR value must be less than 10% to be considered acceptable (Moisa et al., 2023). The CR was calculated as the ratio of the Consistency Index (CI) to the Random Consistency Index (RI), as shown in (Eq.\u0026nbsp;12).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{C}\\text{R}=\\frac{\\text{C}\\text{I}}{\\:\\text{R}\\text{I}}\\)\u003c/span\u003e \u003c/span\u003e(Eq.\u0026nbsp;1\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eWhere CI is consistency index and RI is random consistency index.\u003c/p\u003e \u003cp\u003eConsistency index is the measure of parameters consistency as the degree of consistency by using the following formula (Eq.\u0026nbsp;13):\u003cdiv id=\"Equv\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equv\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{C}\\text{I}=\\frac{\\lambda\\:{max}-\\:n}{n-1}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:(\\:\\text{E}\\text{q}.13)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, n represents the number of parameters, while λ\u003csub\u003emax\u003c/sub\u003e denotes the principal eigenvalue, which is obtained by multiplying the total horizontal summation of the assigned intensity importance values with the normalized principal eigenvector values of the parameters. The normalized principal eigenvector was derived by averaging the normalized relative weights of the parameters. The Random Consistency Index (RI) is a constant value assigned to each parameter set, depending on the number of parameters considered and their intensity importance scale (Connett., et al., 2019) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRandom index value table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity importance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eUrban Environmental Quality Analysis\u003c/h3\u003e\n\u003cp\u003eThe final assessment of land suitability for urban environmental quality was conducted using a weighted overlay analysis, in which all the selected parameters were integrated based on their assigned weights (Faisal and shaker, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (Eq.\u0026nbsp;14). This approach allowed for the combination of multiple spatial criteria, reflecting their relative importance, to generate a comprehensive map of areas suitable for maintaining or improving urban environmental quality. The resulting suitability map provides a clear spatial representation of zones with varying potential for supporting sustainable urban development and environmental management.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:SI={\\Sigma\\:}\\text{W}\\text{i}\\text{*}\\text{X}\\text{i}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;\u003csub\u003e14\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eWhere SI is suitability index, Wi is weight of factor I, Xi is normalized criterion score of factors. Finally, the weight was assigned based on their degree of influence.\u003c/p\u003e"},{"header":"Result and Discussions","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Factors for Urban environmental quality Assessment\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Land surface Temperature\u003c/h2\u003e \u003cp\u003eLand Surface Temperature (LST), which reflects the thermal properties of land cover and its influence on microclimate, is a crucial indicator of urban environmental quality (Moisa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhao et al., 2020; Naserikia et al., 2022, 2023). The most crucial conditions for the prevalence of the observed reduced environmental quality and increased LST in the studied urban areas are associated with deforestation, expansion of bare lands, and remarkable increase in population pressure. Severe ecological and social stress may happen such high LST hotspot areas. The observedheightened urban heat island effects and the elevated heat stress, leads to decreased human thermal comfort, increased energy demands or reducing temperature, and can definitely result in possible loss of biodiversity. Green spaces, forests, and water bodies, on the other hand, represent the least critical conditions, while regions with relatively lower LST, such as agricultural and grass lands, are moderately critical. The study revealed that areas covered with forest and greenery spaces are characterized by reduced evapotranspiration and increased shading. This in turn leads to more sustained biodiversity and local climate regulation resulting in better thermal comfort and improved quality of the urban environment. Thus, mapping and examining spatial variations in LST makes it possible to identify both ecologically resilient and high-risk thermal zones, which is crucial information for taking appropriate climate adaptation measures and implementation of sustainable urban planning. The use of LST in assessing the health of the urban environment and directing heat stress mitigation interventions is strengthened when it is combined with vegetation and built-up indices.\u003c/p\u003e \u003cp\u003eThe northern and western regions of the study area were characterized by the lowest increase in LST and hence were characterized by the most comfortable thermal comfort implying better suitability for life. In contrast, the central, southern, and eastern regions of the area under study are classified as highly-critical to moderately-critical areas that require sustainable land management (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Normalized Difference Vegetation Index (NDVI)\u003c/h2\u003e \u003cp\u003eThe NDVI, which gauges the amount and health of vegetation, is a crucial indicator of urban environmental quality. Green spaces that lower heat, enhance air quality, and promote biodiversity are indicated by high NDVI values. On the other hand, bare or built-up areas with unfavorable environmental conditions are indicated by low values. When paired with LST and built-up indices, NDVI mapping facilitates climate resilience and sustainable urban planning by highlighting areas that require greening (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, the northern and northwestern parts of the study area are characterized by better vegetation coverage which in turn has resulted in higher NDVI values and lower average LST.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.1.3. Normalized Difference Built-up Index\u003c/b\u003e(\u003cb\u003eNDBI)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eNormalized difference built-up index highlights regions with a high density of built-up surfaces and little vegetation. The Built-up Index (NDBI) identifies the most important areas for urban environmental intervention. The high NDBI areas are generally considered as priority targets for greening, urban forest development, reflective surfaces, and other climate-resilient strategies because they are most susceptible to heat stress, characterized by poor air quality, and ecosystem degradation. Planners can identify hotspots where interventions will most successfully enhance thermal comfort, environmental quality, and overall urban resilience by combining NDBI with LST mapping (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, the central part of the study area, which is dominantly occupied by Finfinne City is characterized by higher NDBI experiences higher LST implying deterioration of the urban environmental quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4. Land use land cover types\u003c/h2\u003e \u003cp\u003eThree levels of sensitivity can be used to classify the patterns found in the 2,038.5 km\u0026sup2; study area's land use and land cover (LULC) analysis. Built-up land (covering 605.5 km\u003csup\u003e2\u003c/sup\u003e, 29.7%) and bare land (114.2 km\u003csup\u003e2\u003c/sup\u003e, 5.6%), both which indicate extreme environmental stress, are the most critical areas. Bare lands are a sign of land degradation and productivity loss, while built-up areas increase urban heat and decrease vegetation cover. The agricultural land (684.4 km2, 33.6%) and grassland (526.5 km2, 25.8%) that make up marginally critical areas are vital for ecological services and food security, but they are still susceptible to overuse, degradation, and urbanization. The least critical areas are water bodies (5.3 km\u0026sup2;, 0.3%) and forest land (102.6 km\u0026sup2;, 5.0%), which, despite their relatively small extent, are vital for climate regulation, biodiversity support, and ecological stability, making their conservation a top priority (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand use and land cover classes of the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (km2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e684.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt up area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e605.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e526.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2038.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5. Urban Thermal Comfort Level (UTCL)\u003c/h2\u003e \u003cp\u003eUrban thermal comfort levels are greatly impacted by urban heat island (UHI) phenomena, which have an impact on human and environmental well-being. The classification of UHI intensities and their corresponding thermal comfort levels indicate substantial variations across different urban areas. The 45.4 km\u0026sup2; of areas with no UHI effects and a high degree of thermal comfort make up 2.2% of the entire study area. Over 87.6 km\u003csup\u003e2\u003c/sup\u003e, or 4.3% of the total area, has weak UHI effects, which correspond to good thermal comfort. The 171.0 km\u0026sup2; was classified under middle UHI category, which corresponds to normal thermal comfort levels, and this makes up 8.4% of the total study area.\u003c/p\u003e \u003cp\u003eThermal comfort levels gradually deteriorate with increasing UHI intensities. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, 501.0 km\u003csup\u003e2\u003c/sup\u003e, or 24.6% of the study area, are covered by strong UHI effects, which are categorized as having a bad thermal comfort level. Over 784.8 km\u0026sup2;, or 38.5% of the total area, fall into the \"stronger\" category, where thermal comfort is rated as worse. The most extreme UHI category, labeled as strongest, is associated with the worst thermal comfort conditions and encompasses 448.7 km\u0026sup2;, making up 22.0% of the study region (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003eThe entire study area is 2038.5 km\u003csup\u003e2\u003c/sup\u003e, and a sizable section of it has strong to strongest UHI effects, which are indicative of low thermal comfort. This distribution highlights the need for effective urban planning and mitigation strategies to improve thermal comfort, particularly in areas with high UHI intensities. The negative effects of UHI could be lessened and a more sustainable and livable urban environment could result from actions like expanding green spaces, improving urban ventilation, and using reflective building materials.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUTCL and area coverage of the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUHI phenomena\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban thermal comfort level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e501.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStronger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e784.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrongest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e448.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2038.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Potential Assessment of Urban Environmental Quality\u003c/h2\u003e \u003cp\u003eThere were noticeable differences in the environmental conditions throughout the 2,038.5 km\u0026sup2; the evaluated study area based on the analysis of environmental quality classification. With 963.5 km\u0026sup2; (47.3%) categorized as critical and 753.4 km\u0026sup2; (37.0%) as more critical, the critical and more critical areas together made up the largest portion. This demonstrates the severe environmental stress in these areas, which calls for prompt action and long-term management plans. The 121.8 km\u0026sup2; (6.0%), which is considered most critical area showed significant environmental degradation that urgently need restoration. Comparably, marginal areas made up 121.8 km\u0026sup2; (6.0%), indicating moderate environmental conditions that still require conservation efforts to stop further deterioration. The least critically affected areas, on the other hand, represented areas with relatively better environmental quality but with smaller area coverage, spanning only 78.0 km\u0026sup2; (3.8%).\u003c/p\u003e \u003cp\u003eThese results highlight the urgent need for focused urban planning and environmental policy interventions to preserve and improve the comparatively healthier areas while reducing degradation, especially in the most impacted areas (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings resonate with urban environmental studies globally. For example, Seto et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) highlighted that rapid urbanization disproportionately degrades the surrounding ecosystems, often leaving only limited areas with good environmental quality. The predominance of critical and more critical areas in the current study is consistent with the findings by Tewoldeberhan et al.'s (2020) which revealed that urban expansion in African urban contexts significantly increased environmental vulnerability, especially in peri-urban and low-planning zones. Similar to the limited scope of the least critical areas mentioned here, Li et al. (2018) noted that urban sprawl frequently results in fragmented green spaces and concentrated environmental stress.\u003c/p\u003e \u003cp\u003eThus, the findings highlight the need for policy and urban planning interventions that prioritize restoring severely degraded areas, putting green infrastructure in place, and protecting the remaining environmentally healthy zones. In areas that are rapidly urbanizing, strategies like community-based conservation, urban green belts, and sustainable land-use planning can help prevent degradation, increase resilience, and guarantee environmental sustainability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummarized results of urban environmental quality and corresponding area coverage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental quality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost Critical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore Critical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e753.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e963.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeast Critical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2038.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Sub-City Level Environmental Quality Assessment\u003c/h2\u003e \u003cp\u003eThe analysis of the results demonstrate that there are significant differences among the sub-cities' in the levels of environmental qualityies. Areas with the most environmental modification, specifically the expansion of built-up area, such as Finfinne (54.4 km\u0026sup2;), Furi (12.1 km\u0026sup2;), and Eka Tafo (9.2 km\u0026sup2;) are characterized by the highest LST, most critical thermal discomfort and hence suffer from seriously deteriorated environmental quality. More specifically, the largest areas of those zones designated as more critical were found in Finfinne (257.1 km\u003csup\u003e2\u003c/sup\u003e) and Sebeta (66.4 km\u003csup\u003e2\u003c/sup\u003e), indicating high levels of environmental stress. Regarding the Shaggar sub-cities, Kura Jida had the greatest coverage of critical areas (173.0 km\u003csup\u003e2\u003c/sup\u003e), followed by Koye (115.2 km\u003csup\u003e2\u003c/sup\u003e) and Sebeta (105.6 km\u003csup\u003e2\u003c/sup\u003e), highlighting serious environmental issues. Conversely, marginal and least critical areas were notably smaller in extent, with Mana Abichu (14.9 km\u0026sup2;) and Sebeta (26.3 km\u0026sup2;) exhibiting comparatively better environmental conditions.The study supports earlier findings that link land conversion in Ethiopian urban and peri-urban areas to declining environmental quality, and it shows that rapid urban expansion is a major driver of environmental stress (Alemayehu et al., 2021; Woldegerima et al., 2022).\u003c/p\u003e \u003cp\u003eCritical and more critical zones spanned the largest portions of the 2,038.5 km\u0026sup2; total area as evaluated across all sub-cities. The findings show that Finfinne is under extreme environmental stress and needs immediate assistance, as it has the largest total area of environmental concerns (429.4 km\u0026sup2;). The need for urban planning strategies to mitigate environmental degradation is further highlighted by the extensively critical and more critical areas found in Kura Jida (245.4 km\u003csup\u003e2\u003c/sup\u003e) and Sebeta (215.1 km\u003csup\u003e2\u003c/sup\u003e). Sub-cities such as Melka Nonno (51.5 km\u003csup\u003e2\u003c/sup\u003e) and Mana Abichu (98.5 km\u003csup\u003e2\u003c/sup\u003e), on the other hand, show comparatively lower levels of environmental distress, indicating better conditions. To minimize further environmental deterioration, targeted conservation efforts are necessary even in these places. The disparity in environmental quality between sub-cities emphasizes the necessity of regional policies that are specific to the kind and extent of environmental issues that exist in each area. Degefa et al. (2021) emanated similar conclusions, stating that in order to address the disparate effects of land use change and urbanization on ecosystem services in Ethiopian cities, differentiated management strategies are necessary.\u003c/p\u003e \u003cp\u003eTable: Environmental Quality Levels and corresponding area coverage in Shaggar Sub-cities\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eShaggar Sub-cities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eEnvironmental Quality (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMost Critical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore Critical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarginal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeast Critical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurayu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEka Tafo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e102.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinfinne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e429.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e143.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalan Gudo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e151.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGefersa Guje\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e171.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKoye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e187.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKura Jida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e245.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMana Abichu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelka Nonno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSebeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e215.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSululta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e147.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e753.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e963.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2038.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eThis study provides a comprehensive GIS-based assessment of urban environmental quality and thermal comfort in Finfinne and Shaggar City, revealing substantial spatial variations and identifying areas of critical concern that require imperative attention. The urban landscape is dominated by critical and more critical zones, which together make up 1,716.9 km\u0026sup2; (84.3% of the total area). This indicates that environmental stress is widespread, and the most critical areas (121.8 km\u0026sup2;, 6.0%) need to be restored right away. Even though they are less extensive, marginal and least critical areas still require proactive conservation measures to stop further decline in environmental quality. According to an analysis of urban thermal comfort, only a small portion of the city experiences excellent or good conditions, while 1,734.5 km\u0026sup2; (85.1%) of the city is affected by strong to strongest urban heat island (UHI) effects, which correspond to poor thermal comfort levels. Sub-city-level analysis revealed that Finfinne, Kura Jida, and Sebeta have the largest extents of critical and more critical areas, indicating environmental deprivation hotspots, whereas Melka Nonno and Mana Abichu exhibit comparatively better conditions,however still require targeted management. These results highlight the close connection between urban heat stress and environmental quality, highlighting the need for integrated planning strategies. Environmental quality and thermal comfort can be enhanced by strategic interventions like reforestation and sustainable land management to restore degraded areas, the implementation of green infrastructure and urban green belts, the adoption of reflective building materials, the improvement of urban ventilation, and the encouragement of community-based conservation.In order to benefit policymakers and planners there is an urgent intervention to mitigate dilapidation, improve resilience, and foster a livable, environmentally sustainable urban environment bycombining GIS-based spatial analysis with environmental and thermal assessments. To further improve urban sustainability strategies and guarantee long-term adaptive planning, future research should think about including more socioeconomic, demographic, and infrastructure factors in addition to climate projections.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eDeclaration of Competing Interest:\u003c/h2\u003e \u003cp\u003eThe author declare that there is no competing interest in this manuscript.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003eNot applicable (NA)\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to Publish\u003c/h2\u003e \u003cp\u003edeclarations: NA\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding received for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZenebe Reta Roba:\u0026nbsp;Writing \u0026ndash; review \u0026amp;amp; editing, Writing \u0026ndash; original draft, Visualization, Methodology, Formal analysis, Data curation, Conceptualization.\u0026nbsp;Mitiku Badasa Moisa:\u0026nbsp;Writing \u0026ndash; review \u0026amp;amp; editing, Writing \u0026ndash; original draft, Visualization, Supervision, Software, Formal analysis, Data curation, Conceptualization.\u0026nbsp;Fedasa Benti Chalchisa, ,Harison Kiplagat Kipkulei, Kiros Tsegay Deribew,Aqil Tariq,and Dugasa Negash:\u0026nbsp;Writing \u0026ndash; original draft, Methodology, Data curation., Methodology, Data curation.\u0026nbsp;KenateWorku Tabor, Tigist GirumAymeku, Merkato Markos Mena and Dessalegn Obsi Gemeda:\u0026nbsp;Writing \u0026ndash; review \u0026amp;amp; editing, Writing \u0026ndash; original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eThe authors acknowledge Mattu University Bedele Campus, College of Natural Resource and Agricultural Economics, Department of Forestry, Wollega University Nekemte Campus College Natural and computational science, Department of Earth Science,Mississippi State University and Jimma University College of Agriculture and Veterinary Medicine for the existing facilities to conduct this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data are available in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfrican Development Bank. (2018). African economic outlook 2018: Urbanization and industrialization. African Development Bank. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.afdb.org/en/knowledge/publications/african-economic-outlook\u003c/span\u003e\u003cspan address=\"https://www.afdb.org/en/knowledge/publications/african-economic-outlook\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssaye R, Suryabhagavan KV, Balakrishnan M, Hameed S. Geo-spatial approach for urban green space and environmental quality assessment: a case study in Addis Ababa City. J Geographic Inform Syst. 2017;9(2):191\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnett B, O\u0026rsquo;Halloran BM, Pollman AG. Advancing the use of an analytical hierarchy process and improved random indexes for making prioritized decisions in systems. IEEE Trans Eng Manage. 2019;69(6):2791\u0026ndash;805.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDegefu MA, Argaw M, Feyisa GL, Degefa S. Impact of landscape dynamics and intensities on the ecological land of major cities in Ethiopia. Environ Syst Res. 2021;10., Article 32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40068-021-00237-1\u003c/span\u003e\u003cspan address=\"10.1186/s40068-021-00237-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaisal K, Shaker A. An investigation of GIS overlay and PCA techniques for urban environmental quality assessment: a case study in Toronto. Ont Can Sustain. 2017;9(3):380.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamana AA. Urban challenges and strategies in African cities. Urban Plann. 2024;10(1):1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.up.2024.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.up.2024.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi ZL, Wu H, Wang N, Qiu S, Sobrino JA, Wan Z, Yan G. Land surface emissivity retrieval from satellite data. Int J Remote Sens. 2013;34(9\u0026ndash;10):3084\u0026ndash;127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoisa MB, Dejene IN, Roba ZR, Gemeda DO. Impact of urban land use and land cover change on urban heat island and urban thermal comfort level: a case study of Addis Ababa City, Ethiopia. Environ Monit Assess. 2022;194(10):736.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoisa MB, Negash D, Reta Roba Z, DebesaWedajo G, Deribew T, Benti K, F., Gemeda O, D. Spatial analysis of urban expansion and its effects on land surface temperature using geospatial techniques in Addis Ababa and Shaggar Cities. Ethiopia All Earth. 2025;37(1):1\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMwenje E. Challenges for mainstreaming climate adaptation in African cities. Environ Sci Policy. 2024;145:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envsci.2024.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.envsci.2024.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegeri BG, Xiuguang B, Moisa MB. Assessment of potential land suitability for rainfed wheat production using GIS and multi criteria decision analysis in the Southwestern parts of Ethiopia. PLoS ONE. 2025;20(6):e0324540. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.1371/journal.pone.0324540\u003c/span\u003e\u003cspan address=\".10.1371/journal.pone.0324540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoba ZR, Moisa MB, Purohit S, Deribew KT, Gemeda DO. Assessing the impacts of forest cover change on carbon stock and soil moisture dynamics using geospatial techniques: A case study of Nensebo forest, Southern Ethiopia. Environ Monit Assess. 2025;197(2):169.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaaty TL. Decision making with the analytic hierarchy process. Pittsburgh: RWS; 2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeto KC, Fragkias M, G\u0026uuml;neralp B, Reilly MK. A meta-analysis of global urban land expansion. PLoS ONE. 2022;17(5):e0267646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0267646\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0267646\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeto KC, G\u0026uuml;neralp B, Hutyra LR. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083\u0026ndash;16088. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1211658109\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1211658109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations. (2018). World urbanization prospects: The 2018 revision. United Nations, Department of Economic and Social Affairs, Population Division. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://population.un.org/wup/\u003c/span\u003e\u003cspan address=\"https://population.un.org/wup/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang F, Qin Z, Song C, Tu L, Karnieli A, Zhao S. An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sens. 2015;7(4):4268\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng Q. (2019). Remote sensing and GIS integration: Monitoring and modeling urban environmental changes. Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-04155-2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-04155-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoldegerima T et al. (2021). Impact of landscape dynamics and intensities on the ecological land of major cities in Ethiopia. Environmental Systems Research, 10(32). (This article analyzes how built-up expansion has transformed ecological land across major Ethiopian cities, including Addis Ababa, Bahir Dar, Adama, and Hawassa between 1990 and 2020.).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoldemichael B, Teshome W, Alemu A. Urban growth dynamics and land use/cover changes in Ethiopia: Implications for sustainable urban planning. Sustainable Cities Soc. 2023;95:104654. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scs.2023.104654\u003c/span\u003e\u003cspan address=\"10.1016/j.scs.2023.104654\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorqlul AW, Jeong J, Dile YT, Osorio J, Schmitter P, Gerik T, Clark N. Assessing potential land suitable for surface irrigation using groundwater in Ethiopia. Appl Geogr. 2017;85:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"LST, Land Use Land Cover, Urban Thermal comfort Level, NDVI, NDBI","lastPublishedDoi":"10.21203/rs.3.rs-8142228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8142228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quality of the urban environment in developing countries including Ethiopia is facing significant challenges due to swift urban growth-related alterations in land use, and a worsening of the Urban Heat Island (UHI) effect. This research offers an extensive GIS-based evaluation of urban environmental quality (UEQ) in Shaggar City, emphasizing the spatial differences among sub-cities and important land use categories.Key indicators of urban environmental quality such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Land Use/Land Cover (LULC), and Urban Thermal Comfort Level (UTCL) were utilized with the help of geospatial techniques to pinpoint regions experiencing significant thermal stress, loss of vegetation, and high levels of built-up areas. The findings indicated that, across the entire study area of 2,038.5 km\u0026sup2;, the most critical regions, characterized by dense urban development and bare land, comprised 6.0% of the city, while critical and more critical areas together made up 84.3%, highlighting significant environmental strain. Conversely, forested areas, green spaces, and water bodies constituted the least critical regions, offering vital ecological benefits and helping to regulate the climate. An analysis at the sub-city level revealed that Finfinne, Kura Jida, and Sebeta are areas significantly affected by environmental degradation, whereas Melka Nonno and Mana Abichu displayed relatively better environmental conditions. The use of Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) helped to identify both high-risk thermal zones and ecologically resilient locations, offering valuable insights for focused interventions. The findings highlight the urgent necessity for sustainable urban planning approaches, which should include green infrastructure, urban forest initiatives, reflective construction materials, and community-based conservation efforts to alleviate heat stress, rehabilitate degraded areas, and improve overall urban livability. This study underscores the importance of detailed Urban Environment Quality (UEQ) assessments for informing evidence-based policy and advancing climate-resilient, sustainable urban development.\u003c/p\u003e","manuscriptTitle":"GIS-Based Assessment of Urban Environmental Quality (UEQ): Spatial Analysis and Implications for Sustainable Urban Planning: The Case of Finfinnee and Shaggar Cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 09:08:39","doi":"10.21203/rs.3.rs-8142228/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"287859d9-65c1-433b-84ca-1808e67575ef","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T10:00:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 09:08:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8142228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8142228","identity":"rs-8142228","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}