Seasonal and Multi-Decadal Dynamics of Land Surface Temperature and Urban Heat Island in a Semi-Arid Secondary City: A Case Study of Adama, Ethiopia

Seasonal and Multi-Decadal Dynamics of Land Surface Temperature and Urban Heat Island in a Semi-Arid Secondary City: A Case Study of Adama, Ethiopia | 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 Seasonal and Multi-Decadal Dynamics of Land Surface Temperature and Urban Heat Island in a Semi-Arid Secondary City: A Case Study of Adama, Ethiopia Dejene Tesema Bulti, Eyoel Mekonnen Bogale This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7802451/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Ethiopia is experiencing rapid urbanization, which is drastically changing land use and land cover (LULC), increasing land surface temperature (LST), and aggravating urban heat island (UHI) effects. The paper provides a new seasonally disaggregated, multi-decadal analysis of UHI processes in a fast-growing secondary city in the semi-arid Rift Valley of Ethiopia, namely, Adama City. We measure seasonal and interannual patterns of LST based on multi-seasonal Landsat images of 1991, 2000, 2010, and 2022 and examine how this information is related to LULC changes and spectral indices (NDVI, NDBI). The Belg season is when peak heating takes place due to low vegetation and high solar radiation, and the Kiremt is a cooling influence of thick vegetation and moisture. Urban growth is the primary cause of the LST increase, with built-up areas experiencing a massive eightfold increase. Vegetation helps to mitigate the heat effects, as it modulates the temperatures on the surfaces in recent years. This research contributes to understanding urban climate seasonality and long-term trends beyond megacities, which is crucial for urban planning that aligns with climate-resistant development in semi-arid secondary cities. Urban heat Island (UHI) Land Surface Temperature (LST) Urban Climate Adaptation Seasonal Change Land Use Land Cover change NDVI and NDBI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Urbanization has a major alteration effect on the local climates through the shift in the balance of surface energy, hydrological cycles, and atmospheric dynamics. One of the major forms of this is the urban heat island (UHI) effect, where urban regions have higher temperatures when compared to their rural environment owing to albedo, evapotranspiration, and heat storage change. Whereas UHI phenomena are well-known in major metropolises, secondary cities, especially those in fast-urbanizing areas such as Sub-Saharan Africa, are still relatively little studied. The criticality of this gap lies in the fact that knowledge gained in megacities does not necessarily apply to these more basic, yet more susceptible, urban areas, in which the urban population is already expected to increase threefold by 2050 (UN-Habitat, 2022). The dynamics of UHI in big cities across the globe have been widely reported earlier, showing the relevance of the degree of impervious surface, urban structure, and human activities in increasing urban temperatures (Oke et al., 2021; Santamouris, 2021 ). Remote sensing methods have also been successful at attributing urban growth to rises in land surface temperature (LST) through spectral indices (i.e., NDVI and NDBI) to measure vegetation and urbanized areas (Firozjaei et al., 2020 ; Wang et al., 2023). The seasonal changes also affect the UHI intensity because of the variability in rainfall, vegetation, and the moisture content of the soil (Eestoque et al., 2022; Ahmed and Rahman, 2024 ). The seasonal view here is, however, rarely applied to the area of the secondary cities that are in the fast urbanization stage in semi-arid climates with wet and dry seasons that in turn produce complicated thermal conditions. Poor infrastructure and fast horizontal development are some of the challenges that are unique to these cities and can change the conventional UHI drivers and reactions (Khan and Hassan, 2023 ; Choi and Park, 2022). Most of the UHI research in Ethiopia concerns Addis Ababa, where the morphology and LST of the city are examined (Terfa et al., 2020 ; Balew and Korme, 2020 ). There are few studies that have been conducted on other cities like Hawassa and Bahir Dar, usually within the framework of water bodies' moderating effect on microclimates (Wubneh, 2023 ; Zewdie et al., 2022 ). Nevertheless, the seasonal and long-term weather effects of urban development in the Ethiopian secondary cities are poorly comprehended. This is an important omission, as these cities are located in part of varied agro-ecological areas, in which vegetation cycles due to rainfall have an immense effect on the surface energy processes. Adama, a fast-growing secondary city in the Ethiopian Rift Valley, provides a unique case. Unlike the mixed vertical and horizontal expansions of Addis Ababa, the growth of Adama has been more horizontal, and the agricultural and vegetated land has quickly been turned into an impervious surface. The semi-arid climate of the city with three different seasons, Belg (short rains, February - May), Kiremt (main rains, June - September), and Bega (dry season, October - January), brings high seasonal variability of evapotranspiration and vegetation phenology and soil moisture. These elements play significant modulating roles in LST, and thus Adama is a perfect environment to test the interaction between climatic seasonality and land cover change to determine urban thermal environments. The prior UHI research in the Sub-Saharan region and Ethiopia as well frequently depends on the annual or one-season averages (Balew and Korme, 2020 ; Terfa et al., 2020 ). Although informative, these methods hide vital intra-annual changes and vegetation cycles in response to rainfall. A seasonal approach is particularly important in semi-arid climatic conditions where the urban heat hazards could be higher in dry seasons and reduced in rainy seasons. Moreover, there is a need to conduct a long-term analysis to determine the interaction of sustained urban growth with climate variability to change surface thermal regimes. Addressing this knowledge gap, the present study analyzes seasonal and interannual LST and UHI dynamics in Adama from 1991 to 2022 using multi-temporal Landsat imagery and statistical methods. The research adds to the existing body of knowledge on urban climate by (1) having made the first multi-decadal, seasonally resolved study on the dynamics of UHI in an Ethiopian secondary city; (2) establishing a better understanding of the role of climatic seasonality and horizontal urban development in the formation of thermal patterns within a semi-arid climate; and (3) creating results applicable to other rapidly urbanizing secondary cities in similar climates around the world. This has direct implications for climate-responsive city planning and the necessity of seasonally responsive measures to avert the impact of heat risks. 2. Materials and Methods 2.1. Study Area Adama City is a fast growing a secondary city located in the Oromia Region of the Rift Valley plain approximately 99 kilometers to the southeast of Addis Ababa geographically ba at 8°33′N latitude and 39°16′E longitude averagely about 1713 meters above the Sea level. The climate of the city is in the tropical highland with 3 seasons including; Belg or transition between rain and rising warmth during February to May; Kiremt (June-September) the rainy season when the vegetation is at the peak and temperatures moderate; and Bega (October- January) a dry and cooler season of variable surface temperature. Such climatic background, fast urban growth and rapid land use change offers the city of Adama as a good example of studying seasonal and long-term processes of land surface temperature and heat island effects of cities. 2.2. Data Acquisition and Image Preprocessing This study used multi-seasonal and multi-decadal Landsat satellite images (Landsat 5 TM, 7 ETM+, and 8 OLI/TIRS) from the years 1991, 2000, 2010, and 2022 to capture seasonal and long-term change in the patterns of land surface temperature and land use/land cover. The images were selectively taken to guarantee they were cloud-free and representative of the three target seasons, viz., January (Bega), April (Belg), and August (Kiremt), to record intra-annual variability. Radiometric calibration, atmospheric correction using the FLAASH module in ENVI software, and a geometrical correction of all images into UTM Zone 37N using the WGS84 datum were accomplished to ensure that the multispectral images are aligned with standard map projections. Such rigorous preprocessing guarantees both inter-temporal and inter-seasonal comparability. 2.3. Land Use/Land Cover Classification We mapped the LULC of the study area across all years and climate seasons using supervised classification of processed LandSAT images with the Maximum Likelihood Classifier (MLC) algorithm. Four major land use and land cover classes: built-up areas, vegetation (including forest, shrubland, and grassland), agricultural land, and bare land. Training data were collected through visual interpretation cross-referenced with high-resolution Google Earth imagery and field validation. Classification accuracy was assessed for all years and seasons using independent validation samples and confusion matrices, yielding overall accuracy above 85% and Kappa coefficients greater than 0.80, showing strong LULC mapping and robust LULC mapping essential to interpret thermal landscapes. 2.4. Land Surface Temperature Retrieval The Landsat thermal infrared data (mainly band 10 of Landsat 8 OLI/TIRS) was used to obtain Land Surface Temperature by the well-established Single-Channel Algorithm (SCA). The approach transforms the raw thermal band data of satellites into LST after adjusting them to atmospheric effects and surface emissivity variations, which are instrumental in estimating temperature correctly in a heterogeneous urban environment (Firozjaei et al., 2020 ). The processing steps involve: Conversion of raw thermal digital numbers (DN) to spectral radiance at sensor calibration parameters. Spectral radiance to at-sensor brightness temperature conversion using the inverse Planck function. Surface emissivity estimation using the Normalized Difference Vegetation Index (NDVI) threshold approach to distinguish between vegetated and non-vegetated surfaces. The atmospheric correction based on parameters like transmittance, up/downwelling radiance, etc., was used based on atmospheric profiles existing at the date of acquisition of the image. Lastly, the surface radiance with the correction factored was reverted to LST values in degrees Celsius to be analyzed. Landsat-derived LST was validated by comparing it with the current MODIS LST (lower spatial resolution but higher temporal frequency). The validation confirmed that the Landsat-based LST was reliable based on its ability to support seasonal and spatial variations. Overall, this approach guarantees high accuracy and reproducibility of LST measurements needed to study the dynamics of urban heat islands in a spatially and seasonally resolved context. 2.5. Seasonal Disaggregation and Analysis The experiment clearly breaks down information into three important seasons in Ethiopia, which Belg, Kiremt, and Bega based on the rainfall distribution and vegetation reaction that vary greatly to affect the urban thermal settings. This seasonal design elucidates intra-annual variations hidden in annual or single-season studies and adds to the knowledge of the effects of season-specific climate factors on urban heat island intensity. The LST and LULC maps of seasonal scale allowed conducting intra- and intersecting seasonal comparisons of thermal dynamic processes, which provided new knowledge of the cyclical operations of heat exposure in an urbanizing secondary city environment. 2.6. Statistical Analysis The property of relationships between spectral indices of vegetation (NDVI), built-up surfaces (NDBI), and LST were measured with Pearson correlation and simple and multiple linear regression in respect of seasons and years. The statistical differences in LST between LULC classes and seasons and seasons were analyzed using analysis of variance (ANOVA) and Tukey post-hoc tests, which allowed to rigorously quantify the thermal contrasts and evaluate their significance. This multi-methodological statistical model gives a strong description of the multifaceted interaction among urbanization, vegetation cover and surface temperature dynamics among space and time. 3. Results 3.1. Classification Accuracy Image classification results for the three main study seasons—Bega, Belg, and Kiremt—demonstrated consistently high overall accuracy and Kappa coefficients, indicating reliable land use/land cover (LULC) identification across the temporal span of 1991 to 2022. For the Bega season, overall classification accuracy ranged from 86.2% in 1991 to 95.3% in 2010, before a slight decline to 89.8% in 2022. Values of kappa coefficient also showed the strong agreement, which reached the highest level in 2010 with the value 0.93. Most importantly, the accuracy values of built-up areas and agriculture in most years were superior with a minor variation in percentage accuracy of user and producer points in the case of bare land and vegetation. Table 1 Producer and User Accuracy Percentages of LULC Classifications Across Seasons and Years in Adama City Season LULC Class 1991 2000 2010 2022 PA UA PA UA PA UA PA UA Bega Built Up 67.4 95.7 93.1 100.0 92.2 99.4 94.4 98.2 Bare Land 64.9 77.5 97.1 90.2 98.7 97.5 86.0 82.1 Vegetation 85.8 87.6 92.2 97.7 96.4 86.9 81.5 90.9 Agriculture 94.0 87.1 95.8 93.7 94.5 97.3 93.0 87.4 Overall Accuracy (%) 86.2 94.8 95.3 89.8 Kappa Coefficient 0.73 0.92 0.93 0.85 Belg Built Up 87.8 87.8 88.8 98.8 93.7 97.5 96.0 97.8 Bare Land 76.1 81.3 93.1 96.5 86.7 95.3 87.0 89.1 Vegetation 93.0 83.2 84.4 95.7 86.8 97.3 83.6 91.2 Agriculture 87.0 87.3 96.0 82.9 96.3 78.2 91.2 85.0 Overall Accuracy (%) 85.3 91.0 90.6 89.8 Kappa Coefficient 0.76 0.87 0.87 0.86 Kiremt Built Up 79.7 98.7 96.5 97.4 97.3 98.8 100.0 94.6 Bare Land 97.2 91.9 95.9 98.7 98.7 98.2 91.4 100.0 Vegetation 64.4 83.4 85.2 86.1 91.0 97.1 91.5 96.1 Agriculture 97.9 93.7 90.2 88.0 96.5 89.4 94.8 90.4 Overall Accuracy (%) 92.6 91.2 95.4 94.3 Kappa Coefficient 0.84 0.88 0.94 0.92 The accuracies were equally strong during Belg season. Collectively, the accuracy was improved by 85.3 percent in 1991, achieving about 91.0 percent in 2000 and remained stable around 90 percent till 2022. Kappa coefficient increased in 1991 (0.76) to about 0.87 in the succeeding years. The producer and user accuracies were high on both built-up and bare-land classes located over both vegetation and agriculture with slight difference. Kiremt season had the best value of classification statistics of the three and the overall accuracy was greater than 91% in all years with maximum of 95.4 percent in 2010. The same was observed in the Kappa coefficient with it rising to 0.94 in 2010 after increasing since 1991 when it stood at 0.84 and continues to remain strong up to 2022. These statistics affirm the reliability of spatial data used to assess temporal LULC change across Adama City. 3.2. Accuracy of Retrieved Land Surface Temperature In order to determine the reliability of land surface temperature generated by Landsat, validation was done against the reference data, which is the LST data provided by MODIS. Though the MODIS provides a coarser spatial resolution (1 km), it covers the data temporally consistent and calibrated thermally, thus it can be used in coarse scale LST comparison. Table 2 provided a statistical comparison of the temperature in the two sources in three years 2000, 2010, and 2022 of the Bega, Belg, and Kiremt seasons. Across all seasons, the mean differences between Landsat and MODIS-derived LSTs remained within an acceptable range (± 2.5°C),, indicating a strong degree of consistency. It can be noted that the mean deviation was least in Kiremt 2022 (0.97), and the greatest of all was recorded in Belg 2010 (2.47) possible as a seasonal effect due to atmospheric moisture and surface heterogeneity. Table 2 provides standard deviations (SDs) that indicate the range or the distribution of LST differences between MODIS and Landsat sampled locations. A lower SD value of the differences between the two sets of data would mean that the differences are quite uniform across the space, whereas higher SD values denote the differences in the LST are higher spatially. As an illustration, in Bega 2022, SD of 1.57°C implies that LST differences of Landsat-MODIS over-ride has quite a low spatial variance suggesting that LST of Landsat is local in the dry season. On the other hand, the SD of 2.21°C in Belg 2010 indicates greater spatial Snow Cover was observed in the year 2010 which means that there is greater spatial inconsistency, perhaps because of the seasonal cloud contamination, variable land surface emissivity, or the spatial scale resolution of MODIS over heterogeneous urban surfaces. Based on these results, it can be concluded that although Landsat can supply LST maps that are of a minimum resolution and are spatially detailed to facilitate urban studies, seasonal and atmospheric differences, especially during Belg, could add incremental differences when combined with those of course LST datasets, such as that of MODIS. However, the respective low and moderate SDs in all years and seasons, corroborate the indications that Landsat-based LST is spatially agreeable enough to carry out urban thermal analyses, which can be used in making seasonal urban UHI evaluations at a small scale Table 2 Accuracy Assessment of Landsat-Derived LST Compared to MODIS Data for Selected Years and Seasons Year Season RMSE (°C) Standard Deviation (°C) 2000 Bega 3.40 1.39 Belg 9.04 2.25 Kiremt 5.56 2.14 2010 Bega 3.42 2.21 Belg 5.56 2.46 Kiremt 2.65 1.87 2022 Bega 2.97 1.42 Belg 5.82 1.83 Kiremt 2.65 2.07 3.3. Temporal Land Use and Land Cover Bega Season An investigation of the change of land cover in the dry season (Bega season) shows major changes in the use of land over the thirty years. Between the years 1991 and 2000, agricultural land has grown by 11.07 km (5 percent) or 235.90 km. There was a drastic decline in bare land of 16.27 km 2 (28%) whereas built-up areas almost doubled by going up by 99% to 12.88 km 2. There was a marginal fall by percentage of 5 in the vegetation cover. Between 2000 and 2010, the growth of agriculture stagnated as the rate was insignificant, and it declined by 0.2%, whereas bare land kept reducing by 15%. The built-up areas were on an upward trend having increased by 59% and the vegetation cover had dropped by 4%. The 2010–2022 period was characterized by a severe decline of agricultural land by 61.9 km 2 (26%), and an impressive rise in the urban growth, with built-up land rising by 189%. As compared to the previous times, there was a simultaneous rise of bare land areas as well as the vegetations. In total, the land under agriculture in Adama City was decreased by 51.28km 2 (23%) and bare land by 12.34km 2 (21%) in the 1991–2022 period. The built-up area increased eight times (813%) and stressed rapid urban development. On the contrary, the coverage of vegetation increased to 10.84 km 2 (49%), and this may indicate an increase in green space management or re-vegetation of areas. Table 3 Area (km²) and Percentage Changes of LULC Classes During the Bega Season (1991–2022) LULC Class 1991 2000 2010 2022 KM 2 % KM 2 % KM 2 % KM 2 % Agriculture 224.83 72.2 235.90 75.8 235.45 75.6 173.55 55.8 Bare Land 57.60 18.5 41.33 13.3 35.04 11.3 45.26 14.6 Built Up 6.46 2.1 12.88 4.1 20.42 6.6 58.98 19.0 Vegetation 22.31 7.2 21.10 6.8 20.34 6.5 33.15 10.7 Table 4 LULC Change Statistics for Belg Season Over Three Decades in Adama City LULC Class 1991–2000 2000–2010 2010–2022 1991–2022 KM 2 % KM 2 % KM 2 % KM 2 % Agriculture 11.07 4.9 -0.45 -0.2 -61.90 -26.3 -51.28 -22.8 Bare Land -16.27 -28.2 -6.29 -15.2 10.22 29.2 -12.34 -21.4 Built Up 6.42 99.4 7.54 58.5 38.56 188.8 52.52 813.0 Vegetation -1.21 -5.4 -0.76 -3.6 12.81 63.0 10.84 48.6 Belg Season Belg season (short rains) exhibited the similarities and unique differences in the trend of land cover of the Bega season. Between 1991 and 2000, agricultural land was also growing moderately by about 4 percent with a significant rise in vegetation with 22 percent rise. Similar to Bega season, bare land decreased (by 28%), and the built-up land increased (by 83%). Between 2000 and 2010, the changes were not so dramatic: the area under agriculture went up by 2% and built-up area grew as well, whereas the area under vegetation decreased by 6%. Moreover, even more bare land diminished. Urban landscape took a most drastic form between 2010 and 2022. The farmlands decreased by 56.01 km 2 (?24) the largest decline in all periods monitored. The built-up land improved by 193 percent, and the vegetation underwent a great recovery with an increase of over 13 km 2 (56 percent). This last period had a bit of a growth in bare land as well. Analyzing all the 31 years of observation, the size of agricultural land in the Belg season shrank by 20%, the bare land by 38%, built-up land increased by 841%, and vegetation rose by nearly two times (+ 80%). Table 5 Area and Percentage of LULC Classes of Adama City from 1991–2022 Belg Season LULC Class 1991 2000 2010 2022 KM 2 % KM 2 % KM 2 % KM 2 % Agriculture 227.15 73.4 235.29 75.6 237.21 76.2 181.2 58.2 Bare Land 54.32 17.6 39.05 12.6 30.07 9.7 33.71 10.8 Built Up 7.34 2.4 11.6 3.7 20.35 6.5 59.65 19.1 Vegetation 20.57 6.6 25.19 8.1 23.75 7.6 36.98 11.9 Table 6 Area and Percentage Change of LULC Classes of Adama City from 1991–2022 Belg Season LULC Class 1991–2000 2000–2010 2010–2022 1991–2022 KM 2 % KM 2 % KM 2 % KM 2 % Agriculture 8.14 3.6 1.92 0.8 -56.01 -23.6 -45.95 -20.2 Bare Land -15.27 -28.1 -8.98 -23.0 3.64 12.1 -20.61 -37.9 Built Up 4.26 58.0 8.75 75.4 39.30 193.1 52.31 712.7 Vegetation 4.62 22.5 -1.44 -5.7 13.23 55.7 16.41 79.8 Kiremt Season The Kiremt season (long rains) trends follow those of the other seasons in exhibiting urban expansion as the key basis of change in land cover. Between 1991 and 2000, agriculture expanded by 13.98 km2 (6 percent), bare land fell by 24 percent and vegetation dropped by 18 percent. The built-up area was also growing at the same time by 64%. The period of next decade (2000–2010) reflected a minor decline in agriculture and vegetation, continuing loss in the bare land area (to the tune of 18 percent), and a very high increase in the built-up areas (+ 72 percent). The most remarkable difference was built-up land which rose by 197 percent in the 2010 to 2022 period. This growth was made at the cost of the agricultural sector (− 23%, or 56.21 km2 less than the figure in 2010). Particularly, vegetation increased dramatically by nearly 16 km 2 (67%), whereas bare land increased significantly. The Kiremt farming had a negative growth of 43.4 km 2 (19) between 1991 and 2022, the bare land area showed a negative growth of 13.96 km 2 (about 31 percent), built-up land growth was increased by 740 percent and the growth in vegetation was an increase of 9.34 km 2 (31 percent). Table 7 Kiremt Season LULC Area and Change Summary (1991–2022) LULC Class 1991 2000 2010 2022 KM 2 % KM 2 % KM 2 % KM 2 % Agriculture 227 73.4 240.98 77.4 239.81 77.0 183.6 58.6 Bare Land 45.45 14.7 34.34 11.0 28.18 9.0 31.49 10.1 Built Up 7 2.3 11.5 3.7 19.82 6.4 58.7847 18.8 Vegetation 30.01 9.7 24.51 7.9 23.61 7.6 39.35 12.6 Table 8 Area and Percentage Change of LULC Classes of Adama City from 1991–2022 Kiremt Season LULC Class 1991–2000 2000–2010 2010–2022 1991–2022 KM 2 % KM 2 % KM 2 % KM 2 % Agriculture 13.98 6.2 -1.17 -0.5 -56.21 -23.4 -43.40 -19.1 Bare Land -11.11 -24.4 -6.16 -17.9 3.31 11.7 -13.96 -30.7 Built Up 4.50 64.3 8.32 72.3 38.96 196.6 51.78 739.8 Vegetation -5.50 -18.3 -0.90 -3.7 15.74 66.7 9.34 31.1 In sum, a pattern of consistent and speedy urban growth at the cost of agricultural lands and bare land areas is observed on the temporal analysis of Bega, Belg and Kiremt seasons. There is several fold expansions of built-up areas which is associated with the urbanization and development of associated infrastructure in Adama City. The vegetation trends towards the final time could be the result of urban greening programs or land reclaiming processes but need to be explored more in order to separate the natural regrowth from the human-controlled greenery. The implication is that these findings have sweeping consequences to the sustainable planning of cities as well as proper management of natural resources. Food security can also be compromised through further loss of agricultural land and alteration of the dynamics of vegetation and bare lands will lead to knock-on effects in local climatic conditions, hydrology, and biodiversity. Restoration and strategic planning in urban areas should be encouraged in order to encourage healthy growth and protect vital ecosystem services. 3.4. Seasonal and Interannual Variations of Land Surface Temperature In this section, we measure the changes of Land Surface Temperature (LST) in three main climatic seasons involved in Adama City in the years 1991–2022 which are, Bega (dry), Belg (short rains), and Kiremt (long rains) seasons. Following the statistics and the LST maps compiled with Landsat data (Figs. 5 – 7 ; Table 9 ), the analysis can identify the effect of seasonal precipitation, vegetation, and urban settlement dynamics on the spatial and seasonal variation in surface temperatures. Belg Season During the Belg season (February to May), characterized by short rains, Adama exhibits the highest surface temperatures (maximum 32.5 and minimum 23.5) among the seasons studied. According to Fig. 5 , Landsat LST maps show considerable spatial increase of thermal hotspots between urban dense built core and eastern and south-east boundaries, the areas with rapid urbanization and where major vegetation and arable land have been lost. To support the same, Table 9 indicates that the mean LST rose to 36.60°C in 2022 compared to 34.51°C in 1991 with maximum LST pointing to 43.10°C in 2022. This illustrates the continued vegetation impairment as the cooler zones continue to recede at this first rainy season when evapotranspiration rates are low and little moisture is available at the surface. Kiremt Season The Kiremt season (June to September) on the other hand is the main rainy season, which strikes milder surface temperatures as a result of persistently soaking rainfall and higher vegetation density. Figure 4 also shows that cooler areas are persistent in areas covered with vegetations, but urban heat hotspots are much concentrated in the city center. With time, these cooler areas have diminished with urban development especially in the fringes of the city along the northwest and southwest. Table 9 shows this quantitatively by showing that the mean LST ranged between 26.47°C in 1991 and 27.67°C in 2010 but has dropped to 23.23°C in 2022. The lowest level of minimum LST was 15.83°C recorded in 2010 indicating a good rainfall rain/driven cooling. Bega Season The extreme interannual variability and hot extremities include the Bega season (October to January), which is the dry season. Figure 5 shows also strong and constant concentration of heat in dense urban and industrial places compared to cool situation in outskirts in vegetated and crop land. According to Table 9 , a record of maximum LST apex of 42.31°C was reported in 2000 when the annual mean temperature was 32.75°C, thus indicating the fact of drought and extensive loss of vegetation. By 2022 the maximum and mean LST had reduced to 34.57°C and 29.21°C, respectively, which may indicate new recent urban greening measures or morphological variations. Table 9 Seasonal Minimum, Maximum, and Mean Land Surface Temperatures (°C) in Adama City (1991–2022) Year Belg Kiremt Bega Min Max Mean Min Max Mean Min Max Mean 1991 20.66 41.31 34.51 19.30 32.25 24.65 17.51 37.44 29.78 2000 18.25 40.09 33.94 16.46 31.53 23.20 14.31 42.31 32.75 2010 5.95 42.08 35.04 15.83 38.31 27.67 16.14 38.81 31.58 2022 19.59 43.10 36.60 18.64 37.72 23.23 17.45 34.57 29.21 Altogether, the findings indicate explicitly seasonal differences: Belg stands as the hottest period due to the aridity in the beginning of the season and minimal vegetative cover; Kiremt has the coldest surfaces because of prolonged precipitation and vegetative matters; and Bega had the largest swings in temperatures due to arid effects and sparsity in vegetative coverage. Spatially, the urban heat islands are augmented away from the city center especially during Belg and Bega where the natural cooling processes are at their lowest. These measured seasonal and spatial LST variations underpin the importance of green infrastructure in Adama urban design and conservation of vegetation in particular, as one of the effective mitigation strategies to reduce thermal stress in Belg and Bega seasons, thus improving the climate resilience of the city. 3.5. Relationship between Spectral Indices and Land Surface Temperature Seasonally This part discusses the seasonal vegetation and built-up influence on Land Surface Temperature in the City in 1991, 2000, 2010 and 2022 based on the analyses of Normalized Difference Vegetation and Normalized Difference Built-Up Indices. Seasonal regressions were carried out on the three analysis seasons to determine how the indices relate with LST during varying climatic conditions. Table 10 gives the statistical summary of these findings. Kiremt Season In the Kiremt rainy season, the correlation between NDVI and LST is consistently strong and negative, with correlation coefficients (r) ranging approximately from − 0.62 to -0.68 across the years. This implies that the denser the vegetation, the cooler the point is because due to higher evapotranspiration and shade, the surface temperature will be lower. In 2010, it was found to be strongest (r ≈ -0.68) and the relationship was significantly strong but slightly weaker by 2022 (r ≈ -0.62), perhaps indicating declining vegetative cover with increasing urbanization (Fig. 8 ). On the other hand, it is reported that NDBI correlates well with LST in Kiremt having r values which indicate strong positive correlations in the range of approximately 0.62 to 0.75. This affirms that the developed areas are heat collectors which help in warming even at times when it is wet. Its correlation peaked in 1991 (r = 0.75) and after that, it continued to have substantial correlations throughout subsequent years (Fig. 8 ). Belg Season In Belg, the cooling effect of NDVI is similar, but slightly diminished, and has r values between − 0.55 and − 0.66. In 2010, the most negative interrelationship existed (r ≈ -0.66), and in other years, there exist moderate but significant negative relationships (Fig. 9 ). This decrease is in correlation to the Belg transitional rainfall and the seasonal vegetation variation. Further analysis reveals that NDBI continues to have positive correlation with LST that is somewhat moderate but shows a strong case somewhere between r = 0.60 and r = 0.66. Such steady correlations are at little strongly than in Kiremt but indicate that impervious surfaces still increase surface temperatures during this season of shrinking and gained rainfall (Fig. 9 ). Bega Season During the dry period Bega season, the NDVI-LST relationships deteriorate, yet they are statistically significant, where the coefficient of correlation ranges between about − 0.47 and − 0.74. The maximum negative correlation between the amount of vegetative cover and the temperature sum is in 2000 (r = -0.74) indicating that it is somewhat cooling although to a minor extent even during the dry months (Fig. 10 ). In 2022, this effect was weakened yet remained significant in cooling (r = -0.54). NDBI shows a strong influence in the heating of the surface in Bega, with the moderate to strong positive correlations with LST, with the r values varying between approximately 0.57 to 0.69 and reaches its peak in 2000 and 2010. This illustrates how great an effect the urban impervious surfaces can have on heating in the case of scarce vegetation (Fig. 10 ). In summary, vegetation, and in urban surfaces, the impact of vegetation over the surface temperature is immense, and in opposite ways across all seasons and years: A moderate to strong negative correlation can be shown that vegetation decreases LST especially during rainy season known as Kiremt. Densely covered lands augment LST with positive high to medium correlations and the effect is more unswerving when it is the dry Bega season since natural cooling is thin. These statistically significant (p < 0.05) stable relationship explain sophisticated seasonal thermal process that are influenced by interaction of rainfalls, vegetation phenology, and the encroachment of the urban area. The results also justify the need to implement an urban planning process that considers green infrastructure, especially aligned with the season of vulnerabilities in the Adama City to adequately reduce the impacts of the urban heat island to enhance thermal comfort. Table 10 summary the seasonal statistical relationships between NDVI, NDBI, and LST for Adama City across the years 1991, 2000, 2010, and 2022: Season Year NDVI-LST NDBI-LST r p r p Belg 1991 -0.653 < 0.05 0.597 0.029 2000 -0.620 < 0.05 0.656 0.014 2010 -0.663 < 0.05 0.657 0.014 2022 -0.546 < 0.05 0.605 0.046 Kiremt 1991 -0.642 < 0.05 0.75 < 0.001 2000 -0.656 < 0.05 0.625 0.014 2010 -0.681 < 0.05 0.654 0.032 2022 -0.617 < 0.05 0.642 0.046 Bega 1991 -0.473 < 0.05 0.57 0.049 2000 -0.738 < 0.05 0.685 0.007 2010 -0.637 < 0.05 0.68 0.007 2022 -0.544 < 0.05 0.574 0.046 4. Discussion Urban Growth and Seasonal Thermal Dynamics High rates of urbanization (800 percent in built-up area since 1991 to 2022) in Adama City significantly transformed local small-scale processes of energy exchanges at the surface and thermal regimes. Land surface replacement has enhanced land surface temperatures mostly within the urban centers in the process of replacing vegetated surfaces and permeable surfaces by impervious structures that include concrete and asphalt. Under this change, highly developed areas always have a more significant LST as compared to peri-urban and vegetated areas. The seasonal analysis illustrates peculiar thermal patterns caused by the interaction of urbanization and distinct climate patterns of Ethiopia. In the seasons of Belg (short rains), the highest LST is observed, even though there is moderate precipitation due to the little vegetation growth and exposure of the new impervious surfaces to sunlight. Such seasonal thermal vacuum, just before the urban heat island (UHI) effects can be realized fully due to a full recovery of the vegetation. The Kiremt (long rains) period has the lowest mean LST, this can be explained by a prolonged rainfall and the increased evapotranspiration of vegetation, which leads to cooling of the surfaces of the earth. Moreover, Bega during the dry season has the highest interannual variability in surface temperatures which is controlled by the changes in vegetation cover, soil moisture. The evapotranspiration is also reduced during this time, intensifies the thermal stress further to reinforce the observation that there is a spatial and temporal responsiveness of urban heat patterns to the seasonal moisture availability levels. These observations demonstrate why a seasonal approach to climate and land cover change is essential to comprehend and control the issue of urban heating in rapidly expanding urbanized areas in the tropics. Contrasting Roles of Vegetation and Built-Up Surfaces The spectral index analysis has proved it further that vegetation (when measured by the NDVI) has always cooled the surface by manifesting significant negative relationships with LSTs at all time of the year with the highest level being during rainy Kiremt season when vegetation cover is very high and evapotranspiration widespread. This attests to international studies that the presence of vegetation is very crucial in cooling that occurs in urban areas through shading and evapotranspiration. On the other hand, Normalized Difference Built-up Index (NDBI) confirms the constancy of the heat trapping by impervious urban materials indicated by the prevalent positive correlation between it and LST all through the year. Such an effect is partially independent of seasonal precipitation, and, thus, must be attributed to urban morphology and material property enforce a constant burden of heat that is not dependent on the presence of moisture. These contrasting agents result in the formation of heterogeneous urban thermal mosaics emphasizing on the significance of integrated land management procedures that strike a balance with the development of built environment and the preservation and extension of urban green spaces. Comparison with Previous Ethiopian and Global Studies In comparison to the researches carried out in Addis Ababa or Bahir Dar & Hawassa, this study improves the scope of knowledge because it unambiguously examines the intra-seasonal variance of UHI and LST throughout several decades within the scope of a secondary city. The earlier studies usually focus on dry season or annual average temperatures, thereby concealing vital intra-annual variability responses to rainfall and phenology. The UHI highs of Adama at the time of the Belg season are switched with the maximum heating during the dry season of the Addis Ababa, or again this is the result of differences between the level, climatic regime and topography of urban development. In a symmetrical manner, in Adama, there is the extreme accumulation of expansion, presenting an area with extensive spatial footprint that replaces farmland, leading to increased UHI intensities compared to the other Ethiopian cities, where any intense accumulation process takes the form of densification, made or verticalized. Globally, the work fits the current body of literature on urban heat that characterizes the mitigation capacity of vegetation worldwide and the heating trend of impervious surfaces all year (Santamouris, 2021 ; Zhou et al., 2023). Nevertheless, the direct interest in seasonal frequencies and secondary tropical cities is essential to compensate an existing geographic and knowledge gap in research on urban heat climate sensitivity. Lessons of Urban Planning and Climate Adaptation The results highlight the requirement of seasonally specific heat reduction measures within cities: Seasonal Greening: Early-season heat peaks can be alleviated by the application of temporary increasing vegetation cover, in anticipation and buffering of the Belg. This can be done through drought-tolerant species that can be planted between season cycles. Creating Green Infrastructure: Parks, street trees, and communal gardens ought to be featured in the areas that are developing very fast to enhance maximum benefit on evapotranspiration and shade. Agricultural Land Protection: Maintaining peri-urban agricultural areas preserves natural cooling buffers, supports food security, enables food security, and helps diminish the growth of UHI. Urban Design Innovations: Permeable pavements, cool roofs, and other building materials, in which reflection and moisture cycle are incorporated, allow minimizing the absorption of heat and reducing moisture cycle in the city. These are adaptive measures that imply the sophisticated knowledge of urban thermal dynamics in seasons to influence sustainable urban growth and increase climatic resilience of Adama and other secondary cities. Constrains and Prospects Despite its contributions, this study’s scope excludes detailed analysis of three-dimensional urban morphology, e.g. building height that have been documented to influence microclimates and urban heat retention. Areas of informal settlements, which can possess conditions distinctive due to the differences in materials and infrastructure, should be further studied in the particular aspect. Also, the long-term climate forecast and data of fine resolution socio-environmental would enrich those in the future. In future studies, these dimensions should be applied to achieve better mitigation strategies against urban heat and how to optimize planning on climate adaptation to the unique conditions of the city. These differences ensure that the secondary cities present quite different thermal risks: Horizontal growth (unlike the vertical one), weaker control, and exposure to weather make the UHI patterns in them demand unique responses that do not resemble capital ones. 5. Conclusions The current work provides the seasonally resolved analysis of the land surface temperature and the urban heat island processes in the Adama City in Ethiopia during the over thirty years of the high-rate urbanization. It combines high-resolution Landsat imagery with spectral indices (NDVI and NDBI) during Belg, Kiremt and Bega seasons to explain the interplay between the urban expansion, land cover change as well as seasonal climate changes in these seasons that define the thermal environment of the city. The most essential discoveries are 800 percent growth of currently developed area mainly at the cost of agricultural and bare lands inflicting greater surface warming essentially during the dry and transitional Belg and Bega seasons. The vegetation is the most important cooling factor, being heaviest in the rainy Kiremt season, and impervious urban surfaces always hold high temperatures all year. Such seasonal thermal gradients support the need to consider intra-annual changes in climate mitigation and adaptation strategies in a city. To fill in the missing information in seasonal detail and local details and by rigorously validating LST retrieval and by connecting local vegetation and built-up indices to temperatures on the surface in a rapidly expanding secondary city setting, the research moves forward in both local and global urban climates. Its implications are valuable in the specificity, seasonality, and timeliness of urban planning choices, including extended green infrastructure during critical heat, peri-urban agriculture preservation, and permeable urban material promotions, in alleviating heat and improving resiliency against further urbanization and climate change. Through the study, the results also provide policymakers and urban planners with conventions on a sustainable, climate adaptive development in sub-Saharan Africa. Future studies combining data on urban morphology, climate projections over several decades and socio-environmental indicators will help management and better know the aspects of urban heat in other tropical cities. However, the present work represents an important milestone in bringing positive changes on how we manage the thermal landscapes of fast-urbanizing environments. Declarations Author Contribution DTB Conceptualzed the study, guided the methodological design, interpretation of the results, reviewed and edited the manuscript. EMB contributed to conceptualization, methodological framework, collected and processed data, prepared figures, and wrote first draft of the manuscript. Both authors read and approved the final manuscript. References Acheampong E, Ampomah P (2021) Remote sensing and urban heat island detection in tropical cities: A case study of Accra. Ghana Sustain 13(5):2732. https://doi.org/10.3390/su13052732 Ahmed K, Rahman N (2024) Seasonal modulation of urban heat island effects in semi-arid cities using remote sensing. Environ Res Lett 19(1):014001. https://doi.org/10.1088/1748-9326/acd8a1 Balew AM, Korme TG (2020) Urban heat island effect analysis using remote sensing: A case study of Addis Ababa, Ethiopia. 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Remote Sens 12(24):4084. https://doi.org/10.3390/rs12244084 Wubneh AG (2023) The moderating effect of water bodies on the urban microclimate: Evidence from Bahir Dar City. Ethiopia Urban Clim 45:101215. https://doi.org/10.1016/j.uclim.2023.101215 Zewdie Y, Asfaw D, Hailu T (2022) Urban heat island intensity assessment and its impact on energy consumption: A case study from Ethiopia. Renewable Energy 185:687–698. https://doi.org/10.1016/j.renene.2021.12.112 Zhang W, Yu K, Zhou T (2022) Analysis of urban land surface temperature dynamics and influencing factors using Landsat data. Environ Res 204:111964. https://doi.org/10.1016/j.envres.2021.111964 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers invited by journal 28 Oct, 2025 Editor assigned by journal 08 Oct, 2025 Submission checks completed at journal 08 Oct, 2025 First submitted to journal 07 Oct, 2025 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7802451","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537546698,"identity":"056d77f3-95c2-459a-a5c1-5466a6f714c4","order_by":0,"name":"Dejene Tesema 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1","display":"","copyAsset":false,"role":"figure","size":96611,"visible":true,"origin":"","legend":"\u003cp\u003eLocation Map of Adama City\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/4599c1d742e687a9c04b28e2.png"},{"id":95528120,"identity":"e7022136-3c5b-466a-b535-fea16312c8ff","added_by":"auto","created_at":"2025-11-10 10:15:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":936701,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal Land Use and Land Cover (LULC) in Adama City During the Bega Season (1991–2022)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/18c1cba705a8e15aa0e37a52.png"},{"id":95502106,"identity":"90e1d725-b3f1-4604-8b5e-d831e4256d72","added_by":"auto","created_at":"2025-11-10 05:35:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2022653,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal LULC Changes in Adama City During the Belg Season (1991–2022)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/4c35cde676b133082a4e1183.png"},{"id":95529067,"identity":"c56bc362-6041-4b31-8a61-902b1c3c8499","added_by":"auto","created_at":"2025-11-10 10:16:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":375747,"visible":true,"origin":"","legend":"\u003cp\u003eLand Use and Land Cover Maps for the Kiremt Season (1991–2022) in Adama City\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/55cec8f21c23a05268c2b3a2.jpg"},{"id":95528401,"identity":"0e318968-c0f6-48d2-9ee7-02d3a7965cd8","added_by":"auto","created_at":"2025-11-10 10:16:01","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":350959,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal Land Surface Temperature (LST) Maps for Adama City During the Belg Season (1991–2022)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/0cbdafe595a1fa26267ad266.jpg"},{"id":95502104,"identity":"22fed6b3-6291-45d4-b71a-dacb8d39bca4","added_by":"auto","created_at":"2025-11-10 05:35:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":910368,"visible":true,"origin":"","legend":"\u003cp\u003eLST Patterns in the Kiremt Season Demonstrating Rainfall-Driven Cooling Effects (1991–2022)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/e8b3633d646e2cbacc2855d3.png"},{"id":95502101,"identity":"dc532ebe-56be-4641-96de-79eb385abe2c","added_by":"auto","created_at":"2025-11-10 05:35:12","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":369564,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Variability of Surface Temperature During the Bega Dry Season in Adama City (1991–2022)\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/ad529ba564c5ec54c83625ce.jpg"},{"id":95527886,"identity":"a2526369-aeec-4ec4-a149-e5bf1078cd58","added_by":"auto","created_at":"2025-11-10 10:15:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":71380,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Variability of Surface Temperature During the Bega Dry Season in Adama City (1991–2022)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/9aa42f5860f2a19ca231673a.png"},{"id":95528428,"identity":"b87a4684-5457-40fe-b7fa-fad3a330e42b","added_by":"auto","created_at":"2025-11-10 10:16:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":74440,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of Vegetation and Built-Up Intensity on LST During Belg Season (1991–2022)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/82a130c9dfe2115c0cca62a0.png"},{"id":95502112,"identity":"e4ccf6ab-e702-414a-95a1-b4a566376c8b","added_by":"auto","created_at":"2025-11-10 05:35:12","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":69122,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships Between Land Surface Temperature, NDVI, and NDBI During the Dry Bega Season (1991–2022)\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/bebaa416f220985b4e1d292f.png"},{"id":95798539,"identity":"60c2b1ac-8afd-4657-92ae-f7b440cdbd54","added_by":"auto","created_at":"2025-11-13 08:17:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5903342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7802451/v1/e508ba0a-cd79-4c4d-9747-de4dcee2aa5b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal and Multi-Decadal Dynamics of Land Surface Temperature and Urban Heat Island in a Semi-Arid Secondary City: A Case Study of Adama, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrbanization has a major alteration effect on the local climates through the shift in the balance of surface energy, hydrological cycles, and atmospheric dynamics. One of the major forms of this is the urban heat island (UHI) effect, where urban regions have higher temperatures when compared to their rural environment owing to albedo, evapotranspiration, and heat storage change. Whereas UHI phenomena are well-known in major metropolises, secondary cities, especially those in fast-urbanizing areas such as Sub-Saharan Africa, are still relatively little studied. The criticality of this gap lies in the fact that knowledge gained in megacities does not necessarily apply to these more basic, yet more susceptible, urban areas, in which the urban population is already expected to increase threefold by 2050 (UN-Habitat, 2022).\u003c/p\u003e\u003cp\u003eThe dynamics of UHI in big cities across the globe have been widely reported earlier, showing the relevance of the degree of impervious surface, urban structure, and human activities in increasing urban temperatures (Oke et al., 2021; Santamouris, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Remote sensing methods have also been successful at attributing urban growth to rises in land surface temperature (LST) through spectral indices (i.e., NDVI and NDBI) to measure vegetation and urbanized areas (Firozjaei et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., 2023). The seasonal changes also affect the UHI intensity because of the variability in rainfall, vegetation, and the moisture content of the soil (Eestoque et al., 2022; Ahmed and Rahman, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The seasonal view here is, however, rarely applied to the area of the secondary cities that are in the fast urbanization stage in semi-arid climates with wet and dry seasons that in turn produce complicated thermal conditions. Poor infrastructure and fast horizontal development are some of the challenges that are unique to these cities and can change the conventional UHI drivers and reactions (Khan and Hassan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Choi and Park, 2022).\u003c/p\u003e\u003cp\u003eMost of the UHI research in Ethiopia concerns Addis Ababa, where the morphology and LST of the city are examined (Terfa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Balew and Korme, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There are few studies that have been conducted on other cities like Hawassa and Bahir Dar, usually within the framework of water bodies' moderating effect on microclimates (Wubneh, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zewdie et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, the seasonal and long-term weather effects of urban development in the Ethiopian secondary cities are poorly comprehended. This is an important omission, as these cities are located in part of varied agro-ecological areas, in which vegetation cycles due to rainfall have an immense effect on the surface energy processes.\u003c/p\u003e\u003cp\u003eAdama, a fast-growing secondary city in the Ethiopian Rift Valley, provides a unique case. Unlike the mixed vertical and horizontal expansions of Addis Ababa, the growth of Adama has been more horizontal, and the agricultural and vegetated land has quickly been turned into an impervious surface. The semi-arid climate of the city with three different seasons, Belg (short rains, February - May), Kiremt (main rains, June - September), and Bega (dry season, October - January), brings high seasonal variability of evapotranspiration and vegetation phenology and soil moisture. These elements play significant modulating roles in LST, and thus Adama is a perfect environment to test the interaction between climatic seasonality and land cover change to determine urban thermal environments.\u003c/p\u003e\u003cp\u003eThe prior UHI research in the Sub-Saharan region and Ethiopia as well frequently depends on the annual or one-season averages (Balew and Korme, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Terfa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although informative, these methods hide vital intra-annual changes and vegetation cycles in response to rainfall. A seasonal approach is particularly important in semi-arid climatic conditions where the urban heat hazards could be higher in dry seasons and reduced in rainy seasons. Moreover, there is a need to conduct a long-term analysis to determine the interaction of sustained urban growth with climate variability to change surface thermal regimes.\u003c/p\u003e\u003cp\u003eAddressing this knowledge gap, the present study analyzes seasonal and interannual LST and UHI dynamics in Adama from 1991 to 2022 using multi-temporal Landsat imagery and statistical methods. The research adds to the existing body of knowledge on urban climate by (1) having made the first multi-decadal, seasonally resolved study on the dynamics of UHI in an Ethiopian secondary city; (2) establishing a better understanding of the role of climatic seasonality and horizontal urban development in the formation of thermal patterns within a semi-arid climate; and (3) creating results applicable to other rapidly urbanizing secondary cities in similar climates around the world. This has direct implications for climate-responsive city planning and the necessity of seasonally responsive measures to avert the impact of heat risks.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study Area\u003c/h2\u003e\u003cp\u003eAdama City is a fast growing a secondary city located in the Oromia Region of the Rift Valley plain approximately 99 kilometers to the southeast of Addis Ababa geographically ba at 8\u0026deg;33\u0026prime;N latitude and 39\u0026deg;16\u0026prime;E longitude averagely about 1713 meters above the Sea level. The climate of the city is in the tropical highland with 3 seasons including; Belg or transition between rain and rising warmth during February to May; Kiremt (June-September) the rainy season when the vegetation is at the peak and temperatures moderate; and Bega (October- January) a dry and cooler season of variable surface temperature. Such climatic background, fast urban growth and rapid land use change offers the city of Adama as a good example of studying seasonal and long-term processes of land surface temperature and heat island effects of cities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data Acquisition and Image Preprocessing\u003c/h2\u003e\u003cp\u003eThis study used multi-seasonal and multi-decadal Landsat satellite images (Landsat 5 TM, 7 ETM+, and 8 OLI/TIRS) from the years 1991, 2000, 2010, and 2022 to capture seasonal and long-term change in the patterns of land surface temperature and land use/land cover. The images were selectively taken to guarantee they were cloud-free and representative of the three target seasons, viz., January (Bega), April (Belg), and August (Kiremt), to record intra-annual variability. Radiometric calibration, atmospheric correction using the FLAASH module in ENVI software, and a geometrical correction of all images into UTM Zone 37N using the WGS84 datum were accomplished to ensure that the multispectral images are aligned with standard map projections. Such rigorous preprocessing guarantees both inter-temporal and inter-seasonal comparability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Land Use/Land Cover Classification\u003c/h2\u003e\u003cp\u003eWe mapped the LULC of the study area across all years and climate seasons using supervised classification of processed LandSAT images with the Maximum Likelihood Classifier (MLC) algorithm. Four major land use and land cover classes: built-up areas, vegetation (including forest, shrubland, and grassland), agricultural land, and bare land. Training data were collected through visual interpretation cross-referenced with high-resolution Google Earth imagery and field validation. Classification accuracy was assessed for all years and seasons using independent validation samples and confusion matrices, yielding overall accuracy above 85% and Kappa coefficients greater than 0.80, showing strong LULC mapping and robust LULC mapping essential to interpret thermal landscapes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Land Surface Temperature Retrieval\u003c/h2\u003e\u003cp\u003eThe Landsat thermal infrared data (mainly band 10 of Landsat 8 OLI/TIRS) was used to obtain Land Surface Temperature by the well-established Single-Channel Algorithm (SCA). The approach transforms the raw thermal band data of satellites into LST after adjusting them to atmospheric effects and surface emissivity variations, which are instrumental in estimating temperature correctly in a heterogeneous urban environment (Firozjaei et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe processing steps involve:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eConversion of raw thermal digital numbers (DN) to spectral radiance at sensor calibration parameters.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSpectral radiance to at-sensor brightness temperature conversion using the inverse Planck function.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSurface emissivity estimation using the Normalized Difference Vegetation Index (NDVI) threshold approach to distinguish between vegetated and non-vegetated surfaces.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe atmospheric correction based on parameters like transmittance, up/downwelling radiance, etc., was used based on atmospheric profiles existing at the date of acquisition of the image.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLastly, the surface radiance with the correction factored was reverted to LST values in degrees Celsius to be analyzed.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eLandsat-derived LST was validated by comparing it with the current MODIS LST (lower spatial resolution but higher temporal frequency). The validation confirmed that the Landsat-based LST was reliable based on its ability to support seasonal and spatial variations. Overall, this approach guarantees high accuracy and reproducibility of LST measurements needed to study the dynamics of urban heat islands in a spatially and seasonally resolved context.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Seasonal Disaggregation and Analysis\u003c/h2\u003e\u003cp\u003eThe experiment clearly breaks down information into three important seasons in Ethiopia, which Belg, Kiremt, and Bega based on the rainfall distribution and vegetation reaction that vary greatly to affect the urban thermal settings. This seasonal design elucidates intra-annual variations hidden in annual or single-season studies and adds to the knowledge of the effects of season-specific climate factors on urban heat island intensity. The LST and LULC maps of seasonal scale allowed conducting intra- and intersecting seasonal comparisons of thermal dynamic processes, which provided new knowledge of the cyclical operations of heat exposure in an urbanizing secondary city environment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe property of relationships between spectral indices of vegetation (NDVI), built-up surfaces (NDBI), and LST were measured with Pearson correlation and simple and multiple linear regression in respect of seasons and years. The statistical differences in LST between LULC classes and seasons and seasons were analyzed using analysis of variance (ANOVA) and Tukey post-hoc tests, which allowed to rigorously quantify the thermal contrasts and evaluate their significance. This multi-methodological statistical model gives a strong description of the multifaceted interaction among urbanization, vegetation cover and surface temperature dynamics among space and time.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Classification Accuracy\u003c/h2\u003e\n \u003cp\u003eImage classification results for the three main study seasons\u0026mdash;Bega, Belg, and Kiremt\u0026mdash;demonstrated consistently high overall accuracy and Kappa coefficients, indicating reliable land use/land cover (LULC) identification across the temporal span of 1991 to 2022.\u003c/p\u003e\n \u003cp\u003eFor the Bega season, overall classification accuracy ranged from 86.2% in 1991 to 95.3% in 2010, before a slight decline to 89.8% in 2022. Values of kappa coefficient also showed the strong agreement, which reached the highest level in 2010 with the value 0.93. Most importantly, the accuracy values of built-up areas and agriculture in most years were superior with a minor variation in percentage accuracy of user and producer points in the case of bare land and vegetation.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProducer and User Accuracy Percentages of LULC Classifications Across Seasons and Years in Adama City\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eBega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall Accuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e89.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKappa Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eBelg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall Accuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e85.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e91.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e90.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e89.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKappa Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eKiremt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall Accuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e92.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e94.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKappa Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe accuracies were equally strong during Belg season. Collectively, the accuracy was improved by 85.3 percent in 1991, achieving about 91.0 percent in 2000 and remained stable around 90 percent till 2022. Kappa coefficient increased in 1991 (0.76) to about 0.87 in the succeeding years. The producer and user accuracies were high on both built-up and bare-land classes located over both vegetation and agriculture with slight difference.\u003c/p\u003e\n \u003cp\u003eKiremt season had the best value of classification statistics of the three and the overall accuracy was greater than 91% in all years with maximum of 95.4 percent in 2010. The same was observed in the Kappa coefficient with it rising to 0.94 in 2010 after increasing since 1991 when it stood at 0.84 and continues to remain strong up to 2022. These statistics affirm the reliability of spatial data used to assess temporal LULC change across Adama City.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Accuracy of Retrieved Land Surface Temperature\u003c/h2\u003e\n \u003cp\u003eIn order to determine the reliability of land surface temperature generated by Landsat, validation was done against the reference data, which is the LST data provided by MODIS. Though the MODIS provides a coarser spatial resolution (1 km), it covers the data temporally consistent and calibrated thermally, thus it can be used in coarse scale LST comparison. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provided a statistical comparison of the temperature in the two sources in three years 2000, 2010, and 2022 of the Bega, Belg, and Kiremt seasons.\u003c/p\u003e\n \u003cp\u003eAcross all seasons, the mean differences between Landsat and MODIS-derived LSTs remained within an acceptable range (\u0026plusmn;\u0026thinsp;2.5\u0026deg;C),, indicating a strong degree of consistency. It can be noted that the mean deviation was least in Kiremt 2022 (0.97), and the greatest of all was recorded in Belg 2010 (2.47) possible as a seasonal effect due to atmospheric moisture and surface heterogeneity.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides standard deviations (SDs) that indicate the range or the distribution of LST differences between MODIS and Landsat sampled locations. A lower SD value of the differences between the two sets of data would mean that the differences are quite uniform across the space, whereas higher SD values denote the differences in the LST are higher spatially. As an illustration, in Bega 2022, SD of 1.57\u0026deg;C implies that LST differences of Landsat-MODIS over-ride has quite a low spatial variance suggesting that LST of Landsat is local in the dry season. On the other hand, the SD of 2.21\u0026deg;C in Belg 2010 indicates greater spatial Snow Cover was observed in the year 2010 which means that there is greater spatial inconsistency, perhaps because of the seasonal cloud contamination, variable land surface emissivity, or the spatial scale resolution of MODIS over heterogeneous urban surfaces.\u003c/p\u003e\n \u003cp\u003eBased on these results, it can be concluded that although Landsat can supply LST maps that are of a minimum resolution and are spatially detailed to facilitate urban studies, seasonal and atmospheric differences, especially during Belg, could add incremental differences when combined with those of course LST datasets, such as that of MODIS. However, the respective low and moderate SDs in all years and seasons, corroborate the indications that Landsat-based LST is spatially agreeable enough to carry out urban thermal analyses, which can be used in making seasonal urban UHI evaluations at a small scale\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAccuracy Assessment of Landsat-Derived LST Compared to MODIS Data for Selected Years and Seasons\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKiremt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKiremt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKiremt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Temporal Land Use and Land Cover\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eBega Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAn investigation of the change of land cover in the dry season (Bega season) shows major changes in the use of land over the thirty years. Between the years 1991 and 2000, agricultural land has grown by 11.07 km (5 percent) or 235.90 km. There was a drastic decline in bare land of 16.27 km 2 (28%) whereas built-up areas almost doubled by going up by 99% to 12.88 km 2. There was a marginal fall by percentage of 5 in the vegetation cover.\u003c/p\u003e\n \u003cp\u003eBetween 2000 and 2010, the growth of agriculture stagnated as the rate was insignificant, and it declined by 0.2%, whereas bare land kept reducing by 15%. The built-up areas were on an upward trend having increased by 59% and the vegetation cover had dropped by 4%. The 2010\u0026ndash;2022 period was characterized by a severe decline of agricultural land by 61.9 km 2 (26%), and an impressive rise in the urban growth, with built-up land rising by 189%. As compared to the previous times, there was a simultaneous rise of bare land areas as well as the vegetations.\u003c/p\u003e\n \u003cp\u003eIn total, the land under agriculture in Adama City was decreased by 51.28km 2 (23%) and bare land by 12.34km 2 (21%) in the 1991\u0026ndash;2022 period. The built-up area increased eight times (813%) and stressed rapid urban development. On the contrary, the coverage of vegetation increased to 10.84 km 2 (49%), and this may indicate an increase in green space management or re-vegetation of areas.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea (km\u0026sup2;) and Percentage Changes of LULC Classes During the Bega Season (1991\u0026ndash;2022)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLULC Change Statistics for Belg Season Over Three Decades in Adama City\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u0026ndash;2000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2000\u0026ndash;2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u0026ndash;2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u0026ndash;2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-61.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-51.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e813.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBelg Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eBelg season (short rains) exhibited the similarities and unique differences in the trend of land cover of the Bega season. Between 1991 and 2000, agricultural land was also growing moderately by about 4 percent with a significant rise in vegetation with 22 percent rise. Similar to Bega season, bare land decreased (by 28%), and the built-up land increased (by 83%). Between 2000 and 2010, the changes were not so dramatic: the area under agriculture went up by 2% and built-up area grew as well, whereas the area under vegetation decreased by 6%. Moreover, even more bare land diminished.\u003c/p\u003e\n \u003cp\u003eUrban landscape took a most drastic form between 2010 and 2022. The farmlands decreased by 56.01 km 2 (?24) the largest decline in all periods monitored. The built-up land improved by 193 percent, and the vegetation underwent a great recovery with an increase of over 13 km 2 (56 percent). This last period had a bit of a growth in bare land as well.\u003c/p\u003e\n \u003cp\u003eAnalyzing all the 31 years of observation, the size of agricultural land in the Belg season shrank by 20%, the bare land by 38%, built-up land increased by 841%, and vegetation rose by nearly two times (+\u0026thinsp;80%).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea and Percentage of LULC Classes of Adama City from 1991\u0026ndash;2022 Belg Season\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea and Percentage Change of LULC Classes of Adama City from 1991\u0026ndash;2022 Belg Season\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u0026ndash;2000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2000\u0026ndash;2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u0026ndash;2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u0026ndash;2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-56.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-45.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e712.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eKiremt Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe Kiremt season (long rains) trends follow those of the other seasons in exhibiting urban expansion as the key basis of change in land cover. Between 1991 and 2000, agriculture expanded by 13.98 km2 (6 percent), bare land fell by 24 percent and vegetation dropped by 18 percent. The built-up area was also growing at the same time by 64%. The period of next decade (2000\u0026ndash;2010) reflected a minor decline in agriculture and vegetation, continuing loss in the bare land area (to the tune of 18 percent), and a very high increase in the built-up areas (+\u0026thinsp;72 percent).\u003c/p\u003e\n \u003cp\u003eThe most remarkable difference was built-up land which rose by 197 percent in the 2010 to 2022 period. This growth was made at the cost of the agricultural sector (\u0026minus;\u0026thinsp;23%, or 56.21 km2 less than the figure in 2010). Particularly, vegetation increased dramatically by nearly 16 km 2 (67%), whereas bare land increased significantly.\u003c/p\u003e\n \u003cp\u003eThe Kiremt farming had a negative growth of 43.4 km 2 (19) between 1991 and 2022, the bare land area showed a negative growth of 13.96 km 2 (about 31 percent), built-up land growth was increased by 740 percent and the growth in vegetation was an increase of 9.34 km 2 (31 percent).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKiremt Season LULC Area and Change Summary (1991\u0026ndash;2022)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.7847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u003cbr\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea and Percentage Change of LULC Classes of Adama City from 1991\u0026ndash;2022 Kiremt Season\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u0026ndash;2000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2000\u0026ndash;2010\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2010\u0026ndash;2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1991\u0026ndash;2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-56.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-43.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e739.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn sum, a pattern of consistent and speedy urban growth at the cost of agricultural lands and bare land areas is observed on the temporal analysis of Bega, Belg and Kiremt seasons. There is several fold expansions of built-up areas which is associated with the urbanization and development of associated infrastructure in Adama City. The vegetation trends towards the final time could be the result of urban greening programs or land reclaiming processes but need to be explored more in order to separate the natural regrowth from the human-controlled greenery.\u003c/p\u003e\n \u003cp\u003eThe implication is that these findings have sweeping consequences to the sustainable planning of cities as well as proper management of natural resources. Food security can also be compromised through further loss of agricultural land and alteration of the dynamics of vegetation and bare lands will lead to knock-on effects in local climatic conditions, hydrology, and biodiversity. Restoration and strategic planning in urban areas should be encouraged in order to encourage healthy growth and protect vital ecosystem services.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Seasonal and Interannual Variations of Land Surface Temperature\u003c/h2\u003e\n \u003cp\u003eIn this section, we measure the changes of Land Surface Temperature (LST) in three main climatic seasons involved in Adama City in the years 1991\u0026ndash;2022 which are, Bega (dry), Belg (short rains), and Kiremt (long rains) seasons. Following the statistics and the LST maps compiled with Landsat data (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e), the analysis can identify the effect of seasonal precipitation, vegetation, and urban settlement dynamics on the spatial and seasonal variation in surface temperatures.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBelg Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eDuring the Belg season (February to May), characterized by short rains, Adama exhibits the highest surface temperatures (maximum 32.5 and minimum 23.5) among the seasons studied. According to Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, Landsat LST maps show considerable spatial increase of thermal hotspots between urban dense built core and eastern and south-east boundaries, the areas with rapid urbanization and where major vegetation and arable land have been lost. To support the same, Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e indicates that the mean LST rose to 36.60\u0026deg;C in 2022 compared to 34.51\u0026deg;C in 1991 with maximum LST pointing to 43.10\u0026deg;C in 2022. This illustrates the continued vegetation impairment as the cooler zones continue to recede at this first rainy season when evapotranspiration rates are low and little moisture is available at the surface.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eKiremt Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe Kiremt season (June to September) on the other hand is the main rainy season, which strikes milder surface temperatures as a result of persistently soaking rainfall and higher vegetation density. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e also shows that cooler areas are persistent in areas covered with vegetations, but urban heat hotspots are much concentrated in the city center. With time, these cooler areas have diminished with urban development especially in the fringes of the city along the northwest and southwest. Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e shows this quantitatively by showing that the mean LST ranged between 26.47\u0026deg;C in 1991 and 27.67\u0026deg;C in 2010 but has dropped to 23.23\u0026deg;C in 2022. The lowest level of minimum LST was 15.83\u0026deg;C recorded in 2010 indicating a good rainfall rain/driven cooling.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBega Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe extreme interannual variability and hot extremities include the Bega season (October to January), which is the dry season. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows also strong and constant concentration of heat in dense urban and industrial places compared to cool situation in outskirts in vegetated and crop land. According to Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, a record of maximum LST apex of 42.31\u0026deg;C was reported in 2000 when the annual mean temperature was 32.75\u0026deg;C, thus indicating the fact of drought and extensive loss of vegetation. By 2022 the maximum and mean LST had reduced to 34.57\u0026deg;C and 29.21\u0026deg;C, respectively, which may indicate new recent urban greening measures or morphological variations.\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSeasonal Minimum, Maximum, and Mean Land Surface Temperatures (\u0026deg;C) in Adama City (1991\u0026ndash;2022)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBelg\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eKiremt\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBega\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAltogether, the findings indicate explicitly seasonal differences: Belg stands as the hottest period due to the aridity in the beginning of the season and minimal vegetative cover; Kiremt has the coldest surfaces because of prolonged precipitation and vegetative matters; and Bega had the largest swings in temperatures due to arid effects and sparsity in vegetative coverage. Spatially, the urban heat islands are augmented away from the city center especially during Belg and Bega where the natural cooling processes are at their lowest.\u003c/p\u003e\n \u003cp\u003eThese measured seasonal and spatial LST variations underpin the importance of green infrastructure in Adama urban design and conservation of vegetation in particular, as one of the effective mitigation strategies to reduce thermal stress in Belg and Bega seasons, thus improving the climate resilience of the city.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Relationship between Spectral Indices and Land Surface Temperature Seasonally\u003c/h2\u003e\n \u003cp\u003eThis part discusses the seasonal vegetation and built-up influence on Land Surface Temperature in the City in 1991, 2000, 2010 and 2022 based on the analyses of Normalized Difference Vegetation and Normalized Difference Built-Up Indices. Seasonal regressions were carried out on the three analysis seasons to determine how the indices relate with LST during varying climatic conditions. Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e gives the statistical summary of these findings.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eKiremt Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eIn the Kiremt rainy season, the correlation between NDVI and LST is consistently strong and negative, with correlation coefficients (r) ranging approximately from \u0026minus;\u0026thinsp;0.62 to -0.68 across the years. This implies that the denser the vegetation, the cooler the point is because due to higher evapotranspiration and shade, the surface temperature will be lower. In 2010, it was found to be strongest (r \u0026asymp; -0.68) and the relationship was significantly strong but slightly weaker by 2022 (r \u0026asymp; -0.62), perhaps indicating declining vegetative cover with increasing urbanization (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOn the other hand, it is reported that NDBI correlates well with LST in Kiremt having r values which indicate strong positive correlations in the range of approximately 0.62 to 0.75. This affirms that the developed areas are heat collectors which help in warming even at times when it is wet. Its correlation peaked in 1991 (r\u0026thinsp;=\u0026thinsp;0.75) and after that, it continued to have substantial correlations throughout subsequent years (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBelg Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eIn Belg, the cooling effect of NDVI is similar, but slightly diminished, and has r values between \u0026minus;\u0026thinsp;0.55 and \u0026minus;\u0026thinsp;0.66. In 2010, the most negative interrelationship existed (r \u0026asymp; -0.66), and in other years, there exist moderate but significant negative relationships (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). This decrease is in correlation to the Belg transitional rainfall and the seasonal vegetation variation.\u003c/p\u003e\n \u003cp\u003eFurther analysis reveals that NDBI continues to have positive correlation with LST that is somewhat moderate but shows a strong case somewhere between r\u0026thinsp;=\u0026thinsp;0.60 and r\u0026thinsp;=\u0026thinsp;0.66. Such steady correlations are at little strongly than in Kiremt but indicate that impervious surfaces still increase surface temperatures during this season of shrinking and gained rainfall (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBega Season\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eDuring the dry period Bega season, the NDVI-LST relationships deteriorate, yet they are statistically significant, where the coefficient of correlation ranges between about \u0026minus;\u0026thinsp;0.47 and \u0026minus;\u0026thinsp;0.74. The maximum negative correlation between the amount of vegetative cover and the temperature sum is in 2000 (r = -0.74) indicating that it is somewhat cooling although to a minor extent even during the dry months (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). In 2022, this effect was weakened yet remained significant in cooling (r = -0.54).\u003c/p\u003e\n \u003cp\u003eNDBI shows a strong influence in the heating of the surface in Bega, with the moderate to strong positive correlations with LST, with the r values varying between approximately 0.57 to 0.69 and reaches its peak in 2000 and 2010. This illustrates how great an effect the urban impervious surfaces can have on heating in the case of scarce vegetation (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn summary, vegetation, and in urban surfaces, the impact of vegetation over the surface temperature is immense, and in opposite ways across all seasons and years:\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eA moderate to strong negative correlation can be shown that vegetation decreases LST especially during rainy season known as Kiremt.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDensely covered lands augment LST with positive high to medium correlations and the effect is more unswerving when it is the dry Bega season since natural cooling is thin.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThese statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) stable relationship explain sophisticated seasonal thermal process that are influenced by interaction of rainfalls, vegetation phenology, and the encroachment of the urban area. The results also justify the need to implement an urban planning process that considers green infrastructure, especially aligned with the season of vulnerabilities in the Adama City to adequately reduce the impacts of the urban heat island to enhance thermal comfort.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003esummary the seasonal statistical relationships between NDVI, NDBI, and LST for Adama City across the years 1991, 2000, 2010, and 2022:\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNDVI-LST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNDBI-LST\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eBelg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eKiremt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eBega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cem\u003eUrban Growth and Seasonal Thermal Dynamics\u003c/em\u003e\u003c/p\u003e\u003cp\u003eHigh rates of urbanization (800 percent in built-up area since 1991 to 2022) in Adama City significantly transformed local small-scale processes of energy exchanges at the surface and thermal regimes. Land surface replacement has enhanced land surface temperatures mostly within the urban centers in the process of replacing vegetated surfaces and permeable surfaces by impervious structures that include concrete and asphalt. Under this change, highly developed areas always have a more significant LST as compared to peri-urban and vegetated areas.\u003c/p\u003e\u003cp\u003eThe seasonal analysis illustrates peculiar thermal patterns caused by the interaction of urbanization and distinct climate patterns of Ethiopia. In the seasons of Belg (short rains), the highest LST is observed, even though there is moderate precipitation due to the little vegetation growth and exposure of the new impervious surfaces to sunlight. Such seasonal thermal vacuum, just before the urban heat island (UHI) effects can be realized fully due to a full recovery of the vegetation.\u003c/p\u003e\u003cp\u003eThe Kiremt (long rains) period has the lowest mean LST, this can be explained by a prolonged rainfall and the increased evapotranspiration of vegetation, which leads to cooling of the surfaces of the earth. Moreover, Bega during the dry season has the highest interannual variability in surface temperatures which is controlled by the changes in vegetation cover, soil moisture. The evapotranspiration is also reduced during this time, intensifies the thermal stress further to reinforce the observation that there is a spatial and temporal responsiveness of urban heat patterns to the seasonal moisture availability levels.\u003c/p\u003e\u003cp\u003eThese observations demonstrate why a seasonal approach to climate and land cover change is essential to comprehend and control the issue of urban heating in rapidly expanding urbanized areas in the tropics.\u003c/p\u003e\u003cp\u003e\u003cem\u003eContrasting Roles of Vegetation and Built-Up Surfaces\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe spectral index analysis has proved it further that vegetation (when measured by the NDVI) has always cooled the surface by manifesting significant negative relationships with LSTs at all time of the year with the highest level being during rainy Kiremt season when vegetation cover is very high and evapotranspiration widespread. This attests to international studies that the presence of vegetation is very crucial in cooling that occurs in urban areas through shading and evapotranspiration.\u003c/p\u003e\u003cp\u003eOn the other hand, Normalized Difference Built-up Index (NDBI) confirms the constancy of the heat trapping by impervious urban materials indicated by the prevalent positive correlation between it and LST all through the year. Such an effect is partially independent of seasonal precipitation, and, thus, must be attributed to urban morphology and material property enforce a constant burden of heat that is not dependent on the presence of moisture.\u003c/p\u003e\u003cp\u003eThese contrasting agents result in the formation of heterogeneous urban thermal mosaics emphasizing on the significance of integrated land management procedures that strike a balance with the development of built environment and the preservation and extension of urban green spaces.\u003c/p\u003e\u003cp\u003e\u003cem\u003eComparison with Previous Ethiopian and Global Studies\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn comparison to the researches carried out in Addis Ababa or Bahir Dar \u0026amp; Hawassa, this study improves the scope of knowledge because it unambiguously examines the intra-seasonal variance of UHI and LST throughout several decades within the scope of a secondary city. The earlier studies usually focus on dry season or annual average temperatures, thereby concealing vital intra-annual variability responses to rainfall and phenology.\u003c/p\u003e\u003cp\u003eThe UHI highs of Adama at the time of the Belg season are switched with the maximum heating during the dry season of the Addis Ababa, or again this is the result of differences between the level, climatic regime and topography of urban development. In a symmetrical manner, in Adama, there is the extreme accumulation of expansion, presenting an area with extensive spatial footprint that replaces farmland, leading to increased UHI intensities compared to the other Ethiopian cities, where any intense accumulation process takes the form of densification, made or verticalized.\u003c/p\u003e\u003cp\u003eGlobally, the work fits the current body of literature on urban heat that characterizes the mitigation capacity of vegetation worldwide and the heating trend of impervious surfaces all year (Santamouris, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al., 2023). Nevertheless, the direct interest in seasonal frequencies and secondary tropical cities is essential to compensate an existing geographic and knowledge gap in research on urban heat climate sensitivity.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLessons of Urban Planning and Climate Adaptation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe results highlight the requirement of seasonally specific heat reduction measures within cities:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSeasonal Greening: Early-season heat peaks can be alleviated by the application of temporary increasing vegetation cover, in anticipation and buffering of the Belg. This can be done through drought-tolerant species that can be planted between season cycles.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCreating Green Infrastructure: Parks, street trees, and communal gardens ought to be featured in the areas that are developing very fast to enhance maximum benefit on evapotranspiration and shade.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAgricultural Land Protection: Maintaining peri-urban agricultural areas preserves natural cooling buffers, supports food security, enables food security, and helps diminish the growth of UHI.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUrban Design Innovations: Permeable pavements, cool roofs, and other building materials, in which reflection and moisture cycle are incorporated, allow minimizing the absorption of heat and reducing moisture cycle in the city.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese are adaptive measures that imply the sophisticated knowledge of urban thermal dynamics in seasons to influence sustainable urban growth and increase climatic resilience of Adama and other secondary cities.\u003c/p\u003e\u003cp\u003e\u003cem\u003eConstrains and Prospects\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDespite its contributions, this study\u0026rsquo;s scope excludes detailed analysis of three-dimensional urban morphology, e.g. building height that have been documented to influence microclimates and urban heat retention. Areas of informal settlements, which can possess conditions distinctive due to the differences in materials and infrastructure, should be further studied in the particular aspect. Also, the long-term climate forecast and data of fine resolution socio-environmental would enrich those in the future. In future studies, these dimensions should be applied to achieve better mitigation strategies against urban heat and how to optimize planning on climate adaptation to the unique conditions of the city. These differences ensure that the secondary cities present quite different thermal risks: Horizontal growth (unlike the vertical one), weaker control, and exposure to weather make the UHI patterns in them demand unique responses that do not resemble capital ones.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe current work provides the seasonally resolved analysis of the land surface temperature and the urban heat island processes in the Adama City in Ethiopia during the over thirty years of the high-rate urbanization. It combines high-resolution Landsat imagery with spectral indices (NDVI and NDBI) during Belg, Kiremt and Bega seasons to explain the interplay between the urban expansion, land cover change as well as seasonal climate changes in these seasons that define the thermal environment of the city.\u003c/p\u003e\u003cp\u003eThe most essential discoveries are 800 percent growth of currently developed area mainly at the cost of agricultural and bare lands inflicting greater surface warming essentially during the dry and transitional Belg and Bega seasons. The vegetation is the most important cooling factor, being heaviest in the rainy Kiremt season, and impervious urban surfaces always hold high temperatures all year. Such seasonal thermal gradients support the need to consider intra-annual changes in climate mitigation and adaptation strategies in a city.\u003c/p\u003e\u003cp\u003eTo fill in the missing information in seasonal detail and local details and by rigorously validating LST retrieval and by connecting local vegetation and built-up indices to temperatures on the surface in a rapidly expanding secondary city setting, the research moves forward in both local and global urban climates. Its implications are valuable in the specificity, seasonality, and timeliness of urban planning choices, including extended green infrastructure during critical heat, peri-urban agriculture preservation, and permeable urban material promotions, in alleviating heat and improving resiliency against further urbanization and climate change.\u003c/p\u003e\u003cp\u003eThrough the study, the results also provide policymakers and urban planners with conventions on a sustainable, climate adaptive development in sub-Saharan Africa. Future studies combining data on urban morphology, climate projections over several decades and socio-environmental indicators will help management and better know the aspects of urban heat in other tropical cities. However, the present work represents an important milestone in bringing positive changes on how we manage the thermal landscapes of fast-urbanizing environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDTB Conceptualzed the study, guided the methodological design, interpretation of the results, reviewed and edited the manuscript. EMB contributed to conceptualization, methodological framework, collected and processed data, prepared figures, and wrote first draft of the manuscript. Both authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcheampong E, Ampomah P (2021) Remote sensing and urban heat island detection in tropical cities: A case study of Accra. 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Environ Res 204:111964. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2021.111964\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2021.111964\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Urban heat Island (UHI), Land Surface Temperature (LST), Urban Climate Adaptation, Seasonal Change, Land Use Land Cover change, NDVI and NDBI","lastPublishedDoi":"10.21203/rs.3.rs-7802451/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7802451/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEthiopia is experiencing rapid urbanization, which is drastically changing land use and land cover (LULC), increasing land surface temperature (LST), and aggravating urban heat island (UHI) effects. The paper provides a new seasonally disaggregated, multi-decadal analysis of UHI processes in a fast-growing secondary city in the semi-arid Rift Valley of Ethiopia, namely, Adama City. We measure seasonal and interannual patterns of LST based on multi-seasonal Landsat images of 1991, 2000, 2010, and 2022 and examine how this information is related to LULC changes and spectral indices (NDVI, NDBI). The Belg season is when peak heating takes place due to low vegetation and high solar radiation, and the Kiremt is a cooling influence of thick vegetation and moisture. Urban growth is the primary cause of the LST increase, with built-up areas experiencing a massive eightfold increase. Vegetation helps to mitigate the heat effects, as it modulates the temperatures on the surfaces in recent years. This research contributes to understanding urban climate seasonality and long-term trends beyond megacities, which is crucial for urban planning that aligns with climate-resistant development in semi-arid secondary cities.\u003c/p\u003e","manuscriptTitle":"Seasonal and Multi-Decadal Dynamics of Land Surface Temperature and Urban Heat Island in a Semi-Arid Secondary City: A Case Study of Adama, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:35:07","doi":"10.21203/rs.3.rs-7802451/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-27T14:27:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20460116673078444386053363483103247334","date":"2025-10-29T18:19:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52611153780345368616468176157924493406","date":"2025-10-28T14:04:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-28T13:50:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T06:23:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T06:21:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2025-10-07T20:13:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"62266369-40fc-4ab1-b41f-b9364a21449d","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T05:35:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 05:35:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7802451","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7802451","identity":"rs-7802451","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}