GIS-Based Assessment of Urban Environmental Quality (UEQ): Spatial Analysis and Implications for Sustainable Urban Planning: The Case of Finfinnee and Shaggar Cities
A
Zenebe
Reta
Roba
1,2✉
Mitiku
Badasa
Moisa
3
Emailmitikubadasa10@gmail.com
Emailmitikumi@wollegauniversity.edu.et
Fedhasa
Benti
Chalchissa
4
Emailfedeesa@gmail.com
Merkato
Markos
Mana
5,6
Emailmarkyenatu19@gmail.com
Harison
Kiplagat
Kipkulei
7
Emailharison.kipkulei@uni-a.de
Kiros
Tsegay
Deribew
8
Emailcrosstsegaye@gmail.com
Kenate
WorkuTabor
1
Emailkeneni2009@gmail.com
Emailat2139@msstate.edu
Tigist
GirumAiymeku
9
Aqil
Tariq
10
Dugassa
Negash
1
Emaildugassanegash@gmail.com
Emaildasoobsi@gmail.com
Dessalegn
Obsi
Gemeda
1
1
Department of Forestry, College of Natural Resource and Agricultural Economics
Mattu University
Bedele Campus
Bedele
Ethiopia
2
A
Department of Earth Science, College of Natural and Computational Science
Wollega University
Nekemte campus, Nekemte
Ethiopia
3
Departmentof Environmental Science
Wollega University
Nekemte
Ethiopia
4
Department of Natural Resource Management, College of Natural Resource and Agricultural Economics
Mattu University
Bedele Campus
Bedele
Ethiopia
5
Climate Resilience of Human-made ecosystems, Centre for Climate Resilience
University of Augsburg
Universitätsstraße 12
86159
Augsburg
Germany
6
Department of Geomatic Engineering and Geospatial Information Systems
Jomo Kenyatta University of Agriculture and Technology (JKUAT)
P.O. Box 62000
00200
Nairobi
Kenya
7
Department of Geography and Environmental Studies
Raya University
Maichew
Ethiopia
8
Department of Geography and Environmental Studies, College of Social Science and Humanities
Jimma University
Jimma
Ethiopia
9
Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources
Mississippi State University
775 Stone Boulevard, Mississippi State
39762-9690
MS
USA
10
Department of Geography and Environmental Studies, College of Social Sciences and Humanities
Wollega University
Gimbi
Ethiopia
11
Department of Natural Resource Management, College of Agriculture and Veterinary Medicine
Jimma University
Jimma
Ethiopia
1*Zenebe Reta Roba,2Mitiku Badasa Moisa, 3Fedhasa Benti Chalchissa, 4Merkato Markos Mana 5,6Harison Kiplagat Kipkulei, 7Kiros Tsegay Deribew,8Kenate WorkuTabor ,1Tigist GirumAiymeku,9Aqil Tariq, 10Dugassa Negash; 11Dessalegn Obsi Gemeda
1Department of Forestry, College of Natural Resource and Agricultural Economics, Mattu University, Bedele Campus, Bedele, Ethiopia, Email: zenebereta98@gmail.com/zenebe.reta@mau.edu.et,.ORCID: https://orcid.org/0000-0001-9267-8588,
2Department of Earth Science, College of Natural and Computational Science, Wollega University, Nekemte campus, Nekemte, Ethiopia, Email: mitikubadasa10@gmail.com/ mitikumi@wollegauniversity.edu.et, ORCID: hhttps://orcid.org/0000-0003-1788-0035
3Departmentof Environmental Science, Wollega University, Nekemte, Ethiopia. Email: fedeesa@gmail.com, ORCID: https://orcid.org/0000-0002-7505-0139
4 Department of Natural Resource Management, College of Natural Resource and Agricultural Economics, Mattu University, Bedele Campus, Bedele, Ethiopia, email: markyenatu19@gmail.com
5Climate Resilience of Human-made ecosystems, Centre for Climate Resilience, University of Augsburg, Universitätsstraße 12, 86159 Augsburg, Germany. Email: harison.kipkulei@uni-a.de; ORCID: https://orcid.org/0000-0003-0643-2077
6Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box 62000, Nairobi 00200, Kenya.
7Department of Geography and Environmental Studies, Raya University, Maichew, Ethiopia. Email: crosstsegaye@gmail.com, ORCID: https://orcid.org/0000-0003-2433-8391.
8Department of Geography and Environmental Studies, College of Social Science and Humanities, Jimma University, Jimma Ethiopia.
Email: keneni2009@gmail.com
9Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Mississippi State, MS, 39762 − 9690, USA. Email : (at2139@msstate.edu).
10Department of Geography and Environmental Studies, College of Social Sciences and Humanities, Wollega University, Gimbi, Ethiopia. Email: dugassanegash@gmail.com ORCID: https://orcid.org/0000-0002-9806-7582
11Department of Natural Resource Management, College of Agriculture and Veterinary Medicine, Jimma University, Jimma, Ethiopia, Email: dasoobsi@gmail.com, ORCID: https://orcid.org/0000-0002-8635-260X
*Corresponding author e-mail: Email: zenebereta98@gmail.com/zenebe.reta@meu.edu.et,.ORCID: https://orcid.org/0000-0001-9267-8588
Abstract
A
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.Keywords:
LST
Land Use Land Cover
Urban Thermal comfort Level
NDVI
NDBI
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.
2. 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.
Fig. 1
Location Map of the Study Area
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
|
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.
|
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
|
< 27.3
|
27.3 to 31.2
|
31.2 to 35.4
|
35.4 to 39.6
|
> 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
(Eq. 1)
where;
The digital number of band 10 from Landsat 8 TIRS was changed into radiance values using (Eq. 2).
where;
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).
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.
A proportional vegetation (Pv) calculation (Eq. 5) was done based on the NDVI measurements to calculate land surface emissivity.
where;
An estimation of LST (Eq. 8) was performed (Eq. 6)
where;
Finally, the LST was calculated using (Eq. 8)
.
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:
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.
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):
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.
|
UTFVI
|
UHI Phenomena
|
Urban Thermal comfort level (UTCL)
|
|---|---|---|
|
< 0
|
None
|
Excellent
|
|
0-0.005
|
Weak
|
Good
|
|
0.005–0.01
|
Middle
|
Normal
|
|
0.01–0.015
|
Strong
|
Bad
|
|
0.015–0.02
|
Stronger
|
Worse
|
|
> 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).
|
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).
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):
Where, n represents the number of parameters, while λmax 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).
|
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.
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.
3. 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. 2a).
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. 2b). As shown in Fig. 2b, 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. 2c). As shown in Fig. 2c, 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 km2, 29.7%) and bare land (114.2 km2, 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. 2d).
|
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 km2, 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.
A
Thermal comfort levels gradually deteriorate with increasing UHI intensities. As shown in Table 7, 501.0 km2, 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. 2e).The entire study area is 2038.5 km2, 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.
|
UHI phenomena
|
Urban thermal comfort level
|
Area (km2)
|
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
|
Fig. 2
Maps of the Environmental Quality Determinants
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.
|
Environmental quality
|
Area (km2)
|
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
|
Fig. 3
Environmental Quality Levels' Map
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 km2) and Sebeta (66.4 km2), indicating high levels of environmental stress. Regarding the Shaggar sub-cities, Kura Jida had the greatest coverage of critical areas (173.0 km2), followed by Koye (115.2 km2) and Sebeta (105.6 km2), 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 km2) and Sebeta (215.1 km2). Sub-cities such as Melka Nonno (51.5 km2) and Mana Abichu (98.5 km2), 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 (km2)
|
|||||
|---|---|---|---|---|---|---|
|
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
|
Figure: Environmental Quality Map of Shaggar Sub-cities
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.
A
Funding
No funding received for this work.
Authorship contribution statement
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.
A
Data Availability
All data are available in the manuscript.
Declaration of Competing Interest:
The author declare that there is no competing interest in this manuscript.
Clinical trial number
Not applicable (NA)
Ethics, Consent to Participate: N/A
Consent to Publish
declarations: NA
A
A
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.
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