Land Use Land Cover Change Detection in Asabla Watershed, Northern Highlands of Ethiopia

Land Use Land Cover Change Detection in Asabla Watershed, Northern Highlands of 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 Article Land Use Land Cover Change Detection in Asabla Watershed, Northern Highlands of Ethiopia Abebe Amare, Zewde Alemayehu, Cherinet Miju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5733358/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Land use change is a fundamental variable that impacts and links many parts of the human and physical environments. The analysis and monitoring of changes in land use and resources using Landsat imagery is important to understand the extent and magnitude of the changes. Therefore, the study was conducted to analyze the land use land cover change in Asabla watershed Northwest Ethiopia during 2020/22. Methods This study examines land use/land cover changes from 1986 to 2020 using Landsat satellite images and field data from (DEM) from USGS using supervised methods in ERDAS Imagine 2010 and ArcGIS 10.3. LU/LC classes, including forestland, grazing land, cultivated land, and settlements, were analyzed. Accuracy assessments were performed using 150 ground control points, with measures like producer and user accuracy, overall accuracy, and Kappa coefficient to evaluate classification precision. The percent change and annual rate of change to assess the magnitude of LU/LC over time was calculated. Result The (LU/LC) changes in a watershed from 1986 to 2020, revealing significant transformations across four primary classes: cultivated land, settlements, grazing land, and forestland. Accuracy assessments for the classified images indicated high reliability in 2020 (88.65%) with Kappa values 0.80, signaling strong agreement with ground truth data. Cultivated land increased steadily from 1986 to 2020, primarily at the expense of grazing lands and forest cover. In contrast, forestland showed a consistent increase, largely due to the expansion of eucalyptus plantations. Conclusion The study highlights significant LU/LCC in the watershed from 1986 to 2020. These changes are primarily driven by population growth, agricultural expansion, and the establishment of eucalyptus plantations. Therefore, sustainable land management strategies and policy, balancing agricultural expansion with the preservation of grazing lands and agroforestry practices to mitigate the land use change on the environment and local communities could be implemented. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Ecology/Community ecology Earth and environmental sciences/Ecology/Conservation Earth and environmental sciences/Ecology/Ecosystem services Land use change land use Asabla watershed Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION 1.1. Background and Justification Ethiopia has potentially rich natural resources, with land being the most important soil that supports the agricultural sector, which forms the basis of the country's economy. Ethiopia's economy is dependent on agriculture, which accounts for 40 percent of GDP and 80 percent of exports. However, the productivity of land resources is continuously declining due to population growth associated with deforestation, continuous cultivation, inadequate land management practices and changing land use patterns (UNDP, 2014).. Land use is the arrangement, activities and inputs that people make in a particular land cover type to produce and change land use/cover values ​​(Ufot et al., 2016 ). According to Mariye et al. ( 2020 ), land use is defined as the way the land is used by people and their habitats, usually emphasizing the functional role of land for economic activities, while land cover is a physical feature of the Earth's surface. Land use/land cover change (LULCC) is the change in the Earth's surface caused by human activities (Mengistu et al., 2012 ). Land use/land cover changes (LULCCs) are triggered by the interaction of socioeconomic and natural environmental factors. Inappropriate practices, overgrazing, rapid human population growth (Assefa and Singh, 2017 ), and weak institutional setup Dinka and Chaka ( 2019 )) are among the key anthropogenic driving variables of LULCCs. Changes in land uses, mainly the conversion of natural forests to agricultural land and settlement are the most widely practiced activities in Ethiopia (Eyayu Molla et al., 2010 ). The LULCC process has a negative impact on biodiversity, climate, soil and air, as well as the ecosystem in general, and has become the most serious environmental problem for humans in recent years (Hailemariam et al., 2016 ). LULC changes have a variety of negative socioeconomic and environmental consequences, including reduced agricultural yields, increased vulnerability to natural hazards (floods, droughts, fires), altered ecosystem services, and surface runoff trends (Moisa et al., 2022 ). Agricultural expansion is responsible for almost 90% of global deforestation, a much greater impact than previously thought (FAO, 2018). One of the most serious challenges facing the planet is forest degradation. More than half of the world’s forest loss is due to the conversion of forests to agricultural land. To feed a rapidly growing global population, agriculture is placing increasing pressure on natural forest resources (Negassa et al., 2020). Using Landsat imagery to assess land use change at the basin and sub-basin levels, as well as to identify the rate and extent of land cover change, is an important approach to improve the appropriate management of natural resources (Meshesha et al., 2016). Assessing land use changes and the factors causing these changes is the subject of ongoing scientific investigation that has aroused the curiosity of a wide range of scientists (Angessa et al., 2019 ). Several studies on land use change analysis have been conducted in different regions of Ethiopia (Feyissa and Gebremariam, 2018; Geeraert et al., 2019; mint etc., 2018; Tolessa etc., 2017). Therefore, the analysis and monitoring of changes in land use and resources is important to understand the extent and magnitude of the changes, as well as to manage appropriate integrated watershed management (Shimizu et al., 2019). However, very little is known about the current status and extent of land use change in the Asabla basin. In addition, a recent study revealed a decrease in land use caused by the natural environment, which was largely attributed to human activities such as population growth and economic development. Due to population growth and climate change, anthropogenic activities in the watershed have significantly modified the natural landscapes. The Asabla basin is suitable for agricultural production of certain crops. However, it is severely affected by land use change and soil erosion. Thus, understanding the pattern of these LULC changes is important for efficient watershed management. Accurate and up-to-date information about land use land cover dynamics and the effect of land use types on soil properties is critical in developing smart policies regarding the sustainable management of Ethiopia’s natural resources. Therefore, the aim of this research paper was to investigate the magnitude and rate of LULC change of Asabla watershed northwestern highland of Ethiopia. 2. Materials and methods 2.1. Description of the Study Area The study was conducted in the Asabla watershed, located in South Achefer District, Northwestern Ethiopia (ANRS) (Fig. 1 . Map location of the study area). It is located approximately 250 km south of Bahir Dar and 725 km from Addis Ababa. The watershed coordinates are between 13° 01' 37.50'' north latitude and 38° 58' 36.50'' east longitude with an elevation of 1953 meters above sea level. The study area covers a total area of 819 hectares. Average annual rainfall in the area between 1500 and 1975 mm and the average temperature is 17°C to 30°C (Getahun, 2015). The area is characterized by bimodal precipitation characteristics and a significant amount of precipitation occurs in July and August. Topographically, the watershed is grouped as the flat slope (0–3%), gentle slope (3–8%), slopping (8–16%), moderately steep (16–30%), steep slope (30–50%) and very steep (> 50%) slope classes (SADOA, 2023). Nitisols is the dominant soil type in the study area (Abebe, 1998). The total human population of the study watershed is 1263 of which 49% are males and 51% are females (SADOA, 2023). The main crops grown in the study area are wheat (Triticum aestivum), barley (Hordeumvulgare), potato (Solanum tuberosum) Maize and figuremillet. The dominant plant species found in the area includes Acacia abyssinica, Eucalyptus globules, Eucalyptus camaldulensis and Podocarpus falcatus (SADOA, 2023). The population size of South Achefer district has been increased from year to year at both rural and urban. From the year 2007–2019, the population size increased from 148,456 to 188,082 by 21.226%. From the total population 68,989 (49.76%) were male and 69,663 (50.24%) were female in 2007. In 2020, from the total population, 81,859 (48.7%) are male and 86,223 (51.3%) female (South Achefer District Demography Study Office, 2020). On the other hand, in 2019, the total populations of the study watershed were 1263. Of which 620 (49.01%) are male and 643 (50.99%) are female (SADOA, 2023). The total area coverage of the watershed is 809.56 ha with different land use types; cultivated land (46.96%), grazing land (30.34%), natural/plantation forest (12.52%) and settlement area (10.18%) as show (Table 1 ). Small-scale farmers with an average landholding size of less than one hectare per household dominate the area. The community in the study area practice subsistence-mixed farming which is crop production (rain-fed and irrigated) and livestock production. The study area is known for its high potential for wheat ( Eleusine coracana ), barley ( Hordeum vulgare ), finger millet ( Eleusine coracana ) and maize ( Zea mays ) production. Livestock population includes cattle, 3,847- sheep and goat, 7,763- poultry, 5,622- and beehives- 312, including modern and traditional. A wide range of plant species occur in the area including Acacia abyssinica , Eucalyptus globules , Eucalyptus camaldulensis and Podocarpus falcatus (SADOA, 2020). 2.2. Methods of Data Collection 2.2.1 Spatial data In this study land use/land cover changes were monitored at three time series (1986–2020). Landsat satellite image was used for the LU/LC change detection. It was acquired in January for the three-time series (1986, 2001 and 2020). Because there is no/less cloud cover (Table 3.2). The Landsat images were freely downloaded from united geological survey (USGS) earth explorer (https://earthexplorer.usgs.gov /) in zip format and then saved in tagged image file (TIF) format. A thirty meter DEM obtained from the United States Geological Survey (USGS) (https://earthexplorer.usgs.gov /) was used to delineate the watershed. After different image enhancement, performed image classification schemes were done. Besides, the history of each land use/land cover and the prevailing ones in the study area of the field data obtained from key informants, Google Earth and field survey as primary datasets. The field data points were captured using a handheld GPS for ground truth of the study area. Table 1 Satellite image type, resolution, acquisition date and number of bands Satellite image /Sensor Path /row Cloud cover (%) Resolution (pixel size) Acquisition Date No_ of Bands Landsat5 TM Landsat7 ETM+ Landsat8 OLI 172/54 172/54 172/54 0 0 0 30m x 30m 30m x 30m 30m x 30m 1986-01-12 2001-01-24 2020-01-29 6 6 6 2.2 Method of Data Analysis 2.2.1. Watershed delineation A 30 meter digital elevation model (DEM) obtained from the United States Geological Survey (USGS) ( https://earthexplorer.usgs.gov/ was used to delineate the watershed. The major LU/LC types in the watershed namely: forestland, grazing land, cultivated land and settlements were quantified. For land use change detection, forestland is the combination of natural forest and plantation forest similarly grassland and grazing land was grouped as grazing land use class. This is due to the difficulties to differentiate each other since they had the same tone of the visual image during classification in order to draw out useful thematic information. 2.2.2 LU/LC image classification The Landsat images were classified using a supervised image classification method. It used to classify the training site of the LU/LC change by using ERDAS Imagine 2010 and Arc GIS 10.3 software. ERDAS imagines 2010 model was carried out for high classification and accurate images (Asmala Ahmad, 2012). The truth ground point data was used for the classification scheme of each LU/LC class, for the creation of training sites and for signature generation to perform supervised classification. The land use/cover classes from the 2020 land sat image (Land sat-ETM) were produced by supervised digital image classification method in ENVI (Environment for Visualizing Images) 4.3 software using training area taken on the basis of false color composite (reflectance characteristics) of each land use/cover classes. The use of arc GIS 10.3software was made to link the polygon lines to labels of specific land use/cover classification and to calculate the statistics of each polygon. Then, three time series land use/cover maps were quantified corresponding to the three time series (1986, 2001 and 2020). Finally, percent of change (Ebrahim Esa and Mohamed Assen. 2017) and anual rate of change (Temesgen Gashaw et al. 2014a) was also computed to demonstrate the magnitude of the changes experienced between the periods of the years using \(\:\text{E}\text{q}\text{u}\text{a}\text{t}\text{i}\text{o}\text{n}\:\left(1\right)\text{a}\text{n}\text{d}\:\left(2\right)\) respectively. $$\:\text{p}\text{e}\text{r}\text{c}\text{e}\text{n}\text{t}\:\text{o}\text{f}\:\text{c}\text{h}\text{a}\text{n}\text{g}\text{e}\:=\frac{X-Y}{Y}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\text{E}\text{q}\text{u}\text{a}\text{t}\text{i}\text{o}\text{n}\:\left(1\right)\:$$ $$\:\text{R}\text{a}\text{t}\text{e}\:\text{o}\text{f}\:\text{c}\text{h}\text{a}\text{n}\text{g}\text{e}\:=\frac{X-Y}{Z}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\text{E}\text{q}\text{u}\text{a}\text{t}\text{i}\text{o}\text{n}\:\left(2\right)\:$$ Where, X = LULC area of recent year in ha, Y = LULC area of initial year of in ha Z = is Time interval between X and Y in years. 2.2.2. Accuracy assessment Accuracy assessment was done to understand the representation of the classified images on the ground. Thus, the classification was verified by accuracy assessment using ground control points in ordered to check the precision of the classified LU/LC images and what actually exists on the ground. It was commonly done with reference to other images. To do accuracy assessment for the classified images, 150 representative sample ground truth points using handheld GPS while taken from Google Earth by imported into the classified map in ArcGIS 10.3 was created. Reference points were collected for the 1986, 2001 and 2020 classified images from the corresponding Google Earth images following the procedure of Abineh Tilahun and Bogale Teferi (2015) and Temesgen et al . (2017). The accuracy assessment was done using 70 representative ground truth points collected from the fieldwork points recorded by using handheld GPS while taken from Google Earth by imported into the classified map. Various measures of accuracy assessment such as producer accuracy, user accuracy overall accuracy and Kappa coefficient were done. Then, the accuracy assessment of the classification image was reported using the four performance criteria including producer accuracy, user accuracy, overall accuracy and kappa coefficient were generated from classified images. Moreover, the measure of producer’s accuracy (Sensitivity) reflects the accuracy of prediction of the particular category. The User’s accuracy reflects the reliability of the classification to the user. User’s accuracy is the more relevant measure of the classification’s actual utility in the field. To calculate the overall accuracy, add the number of correctly classified sites and divide it by the total number of reference site. We could also express this as an error percentage, which would be the complement of accuracy: error + accuracy = 100%. User accuracy refers how actually classified map is real on the ground. For example your user accuracy is 80% means your classified item is 80% of mapped area in actually that items other may not referred to that item. Producer accuracy refers to the classification scheme. It is also the number of reference sites classified accurately divided by the total number of reference sites for that class. The User's Accuracy is calculating by taking the total number of correct classifications for a particular class and dividing it by the row total. The kappa coefficient measures the agreement between classification and truth values. It is computed as follows: i is the class number and N is the total number of classified values compared to truth values m i,i is the number of values belonging to the truth class i that have also been classified as class i (i.e., values found along the diagonal of the confusion matrix) C i is the total number of predicted values belonging to class I and G i is the total number of truth values belonging to class 3. RESULTS AND DISCUSSION 3.1. Accuracy Assessment The overall accuracy assessment and kappa coefficient of the land use assessment were computed for three respective years (1986, 2001 and 2020). The overall accuracy and kappa coefficient were 83.81%, 83.79%, 88.65% and 0.80, 0.82, 0.81 in 1986, 2001 and 2020 years, respectively (Table 2 ). The Kappa statistics of a value greater than 0.80 indicates a strong agreement between the ground truth and classified LULC classes (Anderson, 2003). The result of accuracy assessment showed accuracy of 2020 was found more reliable with 88.65% while for 1986 and 2001 was found moderate reliable. This might be due to a severe confusion of each land use classes land with other land cover classes. Table 2 Accuracy assessments of classified images LU/LC Class 1986 2001 2020 UA PA UA PA UA PA Forest land 86.70 90.63 92.11 88.32 89.06 88.56 Grazing land 80.30 79.78 96.77 85.71 93.33 92.78 Cultivated and 89.00 88.64 97.14 89.47 82.73 93.74 Settlement 79.25 82.56 90.00 89.23 85.71 87.65 Over all accuracy 83.81 83.79 88.65 Kappa coefficient 0.80 0.82 0.81 UA = user accuracy; PA = producer accuracy 3.2. Land Use Land Cover Change In the studied watershed land use land cover changes of four major LULC classes namely, cultivated land, settlement, grazing land, and forest land from 1986 to 2020 were detected. Normally, the analyzed LU/LC patterns in the studied watershed indicated there were significant land use land cover change between the three time series over 1986–2020 (Table 5 and Fig. 3 ). 3.2.1. Cultivated land The analyzed LULC change for the study watershed revealed that cultivated land was predominant land use class through the studied years and showed slight increment over the period of (1986–2001) and (1986–2020) at the expense of grazing lands and forest land (Table 4 and Fig. 3 ). In those years cultivated land use class was changed from 358.34 ha (44.26%) in 1986 to 384.23 ha (47.46%) in 2001 or increased by 25.89 ha (3.20%) over fifteen years (Table 3 ). Similarly, it had increased by 21.83 ha (2.69%) over thirty four years of 1986–2020 (Table 4 ). On the other hand cultivated land was decreased in a time series of 2001–2020 by 4.06 ha (0.5%) as presented in Table (4) and Figure (3). This might be associated with the expansion of plantation forest. As shown Table 5 annually the cultivated land had increased by 0.64 ha (0.08%) in 1986 to 2020 years at the expenses of mainly grazing land. The increased in cultivated land could be due to population growth increases associated with increases in demands of more cultivated land. In addition to this the reduction of land productivity underpinning the intension of the people for getting new fertile cultivable lands might be led to the expansion of cultivated land in the study watershed. Further expansion of cultivated land has been reduced since 2001 it could be associated with the expansion of plantation forest and settlement area due to population growth as evidenced in different parts of Ethiopia (Molla et al , 2010). In addition to this, it could be due to lack of suitable land that would be converted to farm-land. According to the elder (2020), mentioned that farmers were settle and cultivate crops on these lands legally and illegally particularly at the expense of grazing land. The study was in line with the work of Alemu et al. (2020) reported an increase in area coverage for the cultivated land-use system over 30 years in Meki river watershed, Western Lake Ziway Sub-Basin, Central Rift Valley of Ethiopia. Kiros and Desalegn (2019) reported an increase in cultivated land cover by 36.70% over 60 years (1957–2017) in North-eastern Addis Ababa, central highlands of Ethiopia. Similarly, Gashaw et al . (2017a) reported increased coverage of cultivated land cover over 30 years (1985–2015) with similar predicted trends for 2030 and 2045 in the Andassa watershed, Blue Nile Basin, Ethiopia. Table 3 LULC of Asabela watershed between 1986, 2001, and 2020 LU/LC Class 1986 2001 2020 Area(ha) (%) Area(ha) (%) Area(ha) (%) Cultivated land 358.34 44.26 384.23 47.46 380.17 46.96 Settlement 49.88 6.16 62.15 7.68 82.41 10.18 Grazing land 335.79 41.48 293.67 36.28 245.66 30.34 Forest Land 65.55 8.10 69.51 8.58 101.32 12.52 Total 809.56 100 809.56 100 809.56 100 LU = land use, LC = land cover, ha = hectare 3.2.2. Settlement area Settlement land use class had smallest area coverage. However, the settlement area increased steadily in a referenced period (Table 4 ). Settlement was increased by 12.27 ha (1.52%), 20.26 ha (2.5%), and 32.53 ha (4.02%) from 1986 to 2001, 2001 to 2020, and 1986 to2020 respectively (Table 4 ) and annually it was increased by 0.97 ha (0.12%) over thirty four years (Table 5 ). This might be the increased in population size associated with the demands of additional settlement in the rural areas and mood of scarce settlement rather than populated. The result was similar to Asmame and Abegaz (2017), that reported increased rural settlement land due to increasing population pressure in Gelana sub-watershed of North of highlands of Ethiopia. Moreover, the study result in line with Sabiela Fekad et al. ( 2020 ) who reported that settlement increased by 10% from 1989 to 2019 in the Tejibara watershed, Northwest Ethiopia. 3.2.3. Grazing land The result revealed that grazing land had the highest area converge in 1986, 2001, and 2020 next to cultivated land (Table 3 ). However, this land use class showed slightly declination throughout the studied period. Normally, it was decreased by 2.65 ha (0.33%) from1986 to 2020 suggestion years (Table 5 ). The reason for a decline in grazing land might be the increased population size that changed grazing land to cultivated land and settlement. Besides, the communal grazing land was reserved by institutional sectors as mention by key informants and focus group discussion (2020). Furthermore, as local elders elaborated that the communal grazing land was given to the landless local youth age people. Therefore, the demand of cultivated lands and settlement area was fulfilled by decreasing the area of grazing land. Furthermore, as local elders elaborated that the communal grazing land was given to the landless local youth age people. Therefore, the demand of cultivated lands and settlement area was fulfilled by decreasing the area of grazing land. Furthermore, as local elders elaborated that the communal grazing land was given to the landless local youth age people. Therefore, the demand of cultivated lands and settlement area was fulfilled by decreasing the area of grazing land (Fig. 3 ). The result was in line with the findings of Fisseha et al. (2011) who found a decline trends in grazing land from 1957–2008 due to the transformation of grazing land into eucalyptus plantation, cultivated land and settlement areas in Debre-Mewi watershed Northwest, Ethiopia. Gashaw et al. (2017) also reported that a shifting of grassland in to settlement and forest from 1985 to 2015 in the Andass a watershed, Blue Nile Basin, Ethiopia. Similarly, Shiferaw (2011) reported that grazing land was decreased from 1972–1985 in Borena Woreda South Wollo Highlands of Ethiopia. As reported by Molla et al . (2010), grassland was decreased in Tara Gedam and adjacent agro ecosystem Northwest Ethiopia. Furthermore, the study result was in line with Fekad et al . (2020) who reported a decline of grazing land from 1989 to 2019 while increase in cultivated land and settlement areas in Tejibara watershed, Northwest Ethiopia 3.2.4. Forestland There was an increment of forestland area coverage of the studied watershed from 65.55 ha to 69.51 ha in 1986 to 2001 and from 69.51 ha to 101.32 ha in 2001 to 2020 referenced years (Table 3 ). This might be associated with the conversion in extent of grazing land to plantation forestland. The forestland use annually expanded by 1.05 ha (0.13%) in the referenced year of 1986–2020 (Table 5 ). The expansion of forestland was largely attributed to the predominantly established of plantation of eucalyptus tree in the study area. As key informant mentioned that the reason for the increment of the plantation forest because the demand of income, energy, and construction purpose was increased throughout the year. Table 4 LULC change of Asabela watershed in 1986–2001, 2001–2020 and 1986–2020 LU/LC Class Land use change in (ha) and (%) 1986–2001 2001–2020 1986–2020 area(ha) Area (%) Area(ha) Area (%) Area(ha) Area (%) Cultivated Land + 25.89 + 3.20 -4.06 -0.50 + 21.83 2.69 Settlement + 12.27 + 1.52 + 20.26 + 2.50 + 32.53 4.02 Grazing land -42.12 -5.20 -48.01 -5.93 -90.13 -11.13 Forest + 3.96 + 0.49 + 31.81 + 3.93 + 35.77 4.42 Total 0.00 0.00 0.00 0.00 0.00 0.00 The result in Table 4 and Table 5 was in line with Bewket (2003) and Sebhatleab (2014), who reported the increased in forestland coverage as the year increased. This could be the plantation program of the eucalyptus tree species in Chemoga watershed Blue Nile Basin Ethiopia and in the highlands of Ethiopia, respectively. Similarly, in the Beressa watershed Northern central high-lands of Ethiopia Meshesha et al. , (2016) also found increased forestland area by 6.5% between 1984 and 2015 years. This associated to the increase in plantation of eucalyptus trees. Moreover, Getachew Fisseha et al. (2011) reported that the individual farmers around homesteads establish the eucalyptus plantations for different purposes at Debre-Mewi watershed, northwest Ethiopia. Table 5 Annual percentage LU/LC change of Asabela watershed LU/LC Class Land use change in ha/year 1986–2001 2001–2020 1986–2020 Area(ha) % Area(ha) % Area(ha) % Cultivated Land + 1.73 + 0.21 -0.21 -0.03 + 0.64 + 0.08 Settlement + 0.82 + 0.10 + 1.07 + 0.13 + 0.97 + 0.12 Grazing land -2.81 -0.35 -2.53 -0.31 -2.65 -0.33 Forest + 0.26 + 0.03 + 1.67 + 0.21 + 1.05 + 0.13 Total 0.00 0.00 0.00 0.00 0.00 0.00 4. CONCLUSION The land use/land cover (LULC) analysis of Asabela watershed between 1986 and 2020 reveals significant changes in the region's land use dynamics, reflecting the influence of population growth, agricultural practices, and forest management. The accuracy assessment, with overall accuracies of 83.81%, 83.79%, and 88.65% in 1986, 2001, and 2020 respectively, indicates a generally reliable classification over the three periods, with the 2020 results being the most reliable. The kappa coefficients for these years (0.80, 0.82, and 0.81) suggest strong agreement between the ground truth data and the classified LULC maps. Regarding land use changes, cultivated land has consistently increased throughout the study period, particularly from 1986 to 2020, expanding by 21.83 ha (2.69%), primarily at the expense of grazing lands. This is likely due to population pressures and the increasing demand for arable land, although the rate of increase slowed between 2001 and 2020, possibly due to the expansion of plantation forests. The settlement area has steadily grown by 32.53 ha (4.02%) between 1986 and 2020, reflecting rural population growth and the demand for residential space. Grazing land, has declined over the study period, shrinking by 90.13 ha (11.13%) from 1986 to 2020. This trend can be attributed to the conversion of grazing land into cultivated land and settlement areas, in addition to institutional reservations of communal grazing lands. The decrease in grazing land aligns with trends observed in other regions of Ethiopia, where similar land use transitions have been noted. Forest land has shown a positive change, increasing by 35.77 ha (4.42%) from 1986 to 2020, driven largely by the expansion of eucalyptus plantations. This change is consistent with broader national trends of afforestation and reforestation programs in Ethiopia. The steady increase in forest area could be attributed to the growing demand for forest products, including wood for energy, construction, and income generation. These findings suggest that future land use planning should consider balancing agricultural expansion with the preservation of grazing lands and sustainable forest management to ensure long-term ecological and socio-economic stability. Declarations Author contribution statement : Abebe Amare Kassie Designed the study through the experiments; carried them out; evaluated and interpreted the results; and composed the research article. Zewude Alemayehu Tilahun Composed the paper; conducted data analysis and interpretation. Chernet Miju Collect primary data; organize raw data and test its validity. Conflict of Interest: No conflicts of interest are disclosed by the Authors. Funding statement: No funding was for this investigation. Author Contribution Abebe Amare Kassie: Designed the study through the experiments; carried them out; evaluated and interpreted the results; and composed the research article.Zewde Alemayehu Tilahun: Composed the paper; conducted data analysis and interpretation.Chernet Miju: Collect primary data; organize raw data and test its validity. Acknowledgement Thank you for your positive response!!!! Data Availability The data that support the findings of this study are available from various sources. Weather data, including mean annual temperature and rainfall, were collected from four weather stations around the study area and are publicly accessible from the Ethiopian National Meteorological Agency (NMA). Elevation, outlet, shape file and DEM data were sourced from the USGS Earth Explorer to delineate the watershed and digitize on HEC-RAS (https://earthexplorer.usgs.gov). These datasets are available upon reasonable request from the corresponding author, ensuring that the data is accessible for further research and validation purposes. All data were utilized under the regulations and permissions provided by the respective agencies and organizations. References Angessa, A. T., Lemma, B., & Yeshitela, K. (2019). Land-use and land-cover dynamics and their drivers in the central highlands of Ethiopia with special reference to the Lake Wanchi watershed. GeoJournal , 86(3), 1225–1243. Assefa A., Singh K. N. The implications of land use and land cover change for rural household food insecurity in the north eastern highlands of Ethiopia: the case of Teleyayen sub-watershed. Agric and Food Secure . 2017;6 Deribewn Kiros and Dalacho Desalegn. 2019. Land use and forest cover dynamics in the North-eastern Addis Ababa, central highlands of Ethiopia. Environmental Systems Research , 8 (1), pp.1-18. Dinka M. O., Chaka D. D. Analysis of land use land cover change in adei watershed, central highlands of Ethiopia. Journal of Water and Land Development . 2019;41(1):146–153. doi: 10.2478/jwld-2019-0038. Eyayu Molla, Heluf Gebrekidan, Tekalign Mamo and Mohammed Assen. 2010. Patterns of land use/cover dynamics in the mountain landscape of Tara Gedam and adjacent agro-ecosystem, Northwest Ethiopia. SINET: Ethiopian Journal of Science , 33 (2), pp.74-88. FAO Remote Sensing Survey Reveals Tropical Rainforests under Pressure as AgriculturalFood and Agricultural Organizations of the United Nations (2018). Feyissa and Gebremariam, 2018 G. Feyissa, E. Gebremariam Mapping of landscape structure and forest cover change detection in the mountain chains around Addis Ababa: the case of Wechecha Mountain, Ethiopia Remote Sens. Appl., 11 (2018), pp. 254-264. Gashaw Temesgen, Tulu Taffa, Argaw Mekuria and Abeyou W.Worqlul. 2017. Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia. Environmental Systems Research , 6 (1), pp.1-15. Geeraert L., E. Hulsmans, K. Helsen, G. Berecha, R. Aerts, O. Honnay Rapid diversity and structure degradation over time through continued coffee cultivation in remnant Ethiopian Afromontane forests Biol. Conserv., 236 (2019), pp. 8-16. Getahun Bore and Bobe Bedadi. 2015. Impacts of land use types on selected soil physico-chemical properties of Loma Woreda, Dawuro Zone, and Southern Ethiopia. Science, Technology and Arts Research Journal 4(4): 40-48. Hailemariam S. N., Soromessa T., Teketay D. Land use and land cover change in the Bale mountain eco-region of Ethiopia during 1985 to 2015. Land . 2016;5(4):p. 41. doi: 10.3390/land5040041. K. Shimizu, T. Ota, N. Mizoue Detecting forest changes using dense landsat 8 and Sentinel-1 time series data in tropical seasonal forests Remote Sensing 2019, 11 (2019), p. 1899. Mariye, M.; Mariyo, M.; Changming, Y.; Teffera, Z.L.; Weldegebrial, B. Effects of land use and land cover change on soil erosion potential in Berhe district: A case study of Legedadi watershed, Ethiopia. Int. J. River Basin Manag. 2020 , 1–13. Mengistu D. A., Waktola D. K., Woldetsadik M. Detection and analysis of land-use and land-cover changes in the midwest escarpment of the Ethiopian rift valley. Journal of Land Use Science . 2012;7(3):239–260. doi: 10.1080/1747423x.2011.562556. Meshesha, D.T., et al, Tsunekawa, A., & Tsubo, M., et al (2014). Land-use change and its socio-environmental impact in Eastern Ethiopia’s highland. Regional Environmental Change , 14, 757–768. Minta M., K. Kibret, P. Thorne, T. Nigussie, L. Nigatu Land use and land cover dynamics in Dendi-Jeldu hilly-mountainous areas in the central Ethiopian highlands Geoderma, 314 (2018), pp. 27-36. Moisa M.B., I.N. Dejene, O. Hirko, D.O. Gemeda Impact of deforestation on soil erosion in the highland areas of western Ethiopia using geospatial techniques: a case study of the Upper Anger watershed Asia-Pacific J. Reg. Sci., 6 (2022), pp. 489-514. Negassa, D.T. Mallie, D.O. Gemeda Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia Environmental Systems Research, 9 (2020), pp. 1-14. Sabiela Fekad, Kehali Jembere, Edalkachew Fekadu and Dessalew Wasie. 2020. Land Use and Land Cover Dynamics and Properties of Soils under Different Land Uses in the Tejibara Watershed Northwest Ethiopia. Scientific World Journal , 1-12. SADAO ( South Achefer District Agricultural office). 2020. Annual report on land use system, soil type, and topographical features. Unpublished Documents . South Achefer District Durbete, Ethiopia. Tolessa T, F. Senbeta, M. Kidane The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia Ecosyst. Serv., 23 (2017), pp. 47-54. Ufot, U.O., Iren, O.B., Chikere Njoku, C.U., 2016. Effects of land use on soil physical and chemical properties in Akokwa area of Imo State, Nigeria. International Journal of Life Sciences Scientific Research 2(3): 273-278. United Nations Development Program (UNDP). 2014. Ethiopia fact sheet: Agricultural Growth and Transformation. UNDP. http:// www.undp.org/content/dam/ethiopia/docs/ UNDP. (accessed on March, 2015). Woldeamilak Bewket and Stroosnijder L. 2003. Effects of agro ecological land use succession on soil properties in Chemoga watershed, Blue Nile basin, Ethiopia. Geoderma, 111(1-2): 85-98. Additional Declarations No competing interests reported. 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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-5733358","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400460538,"identity":"2011f0cc-dcf2-474f-9bc4-0f27abe8206f","order_by":0,"name":"Abebe Amare","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACCQY2KIv54OM/FSCauYFYLWzJBjxnQFoYidbCYybB2wZiENDCP7v52IMPNdvkzdvbEiQk59VG87cDtfyo2IbbkjvH0g1nHLttOOfM4QMGhtuO5844zNjA2HPmNm5rbuSYSfOw3WacIZGWkJC47VhuA1ALM2Mbbi3yN/K/Sf/5d9t+hvwbgwMH5xzLnU9Ii8GNHDZpoILEGRI8ho2NDTW5GwhpMbxzzNywt+928gyetGRmhmMHcjcCtRzE5xe5283PHvz4dtt2Bvvh478Zaupy550/fPDBjwo83kcDh8HkAaLVA0EdKYpHwSgYBaNghAAAnYximMhUMagAAAAASUVORK5CYII=","orcid":"","institution":"Oda Bultum University","correspondingAuthor":true,"prefix":"","firstName":"Abebe","middleName":"","lastName":"Amare","suffix":""},{"id":400460539,"identity":"150abb1e-7d2c-4ca7-8ea8-4710e980005d","order_by":1,"name":"Zewde Alemayehu","email":"","orcid":"","institution":"Kebri Dehar University","correspondingAuthor":false,"prefix":"","firstName":"Zewde","middleName":"","lastName":"Alemayehu","suffix":""},{"id":400460540,"identity":"fbd20076-c5b7-4ed2-86a3-4acc0bb82bee","order_by":2,"name":"Cherinet Miju","email":"","orcid":"","institution":"Oda Bultum University","correspondingAuthor":false,"prefix":"","firstName":"Cherinet","middleName":"","lastName":"Miju","suffix":""}],"badges":[],"createdAt":"2024-12-30 07:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5733358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5733358/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73780702,"identity":"85b7f0d1-b58d-4092-a809-fd647f80fda4","added_by":"auto","created_at":"2025-01-14 15:15:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":485338,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5733358/v1/66ae66a843f046f9d3f6bfd5.png"},{"id":73779266,"identity":"9112ecde-a166-4818-a502-0f1b2e2c751e","added_by":"auto","created_at":"2025-01-14 15:07:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135805,"visible":true,"origin":"","legend":"\u003cp\u003eMean monthly distribution of rainfall and maximum and minimum temperature of the study area from 2000-2020 (Source: Bahir Dar Meteorological Station Durbete, 2020).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5733358/v1/c1310b71dc5e4ff222600ec5.png"},{"id":73779268,"identity":"63aeed24-c8c7-43a8-b479-a3c76e4474af","added_by":"auto","created_at":"2025-01-14 15:07:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":751388,"visible":true,"origin":"","legend":"\u003cp\u003eLand use land cover change map of ASabla watershed in 1986, 2001, and 2020 (LULCC classification, 2020)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5733358/v1/8b4a635b4dcb85ea4f809aa6.png"},{"id":87174278,"identity":"0a5042d9-e8ac-4ab5-ba53-62d0a4c285a2","added_by":"auto","created_at":"2025-07-21 08:17:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2044420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5733358/v1/49bc472b-d3ad-41dc-877d-e90e4ee1ae56.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Land Use Land Cover Change Detection in Asabla Watershed, Northern Highlands of Ethiopia ","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Background and Justification\u003c/h2\u003e \u003cp\u003eEthiopia has potentially rich natural resources, with land being the most important soil that supports the agricultural sector, which forms the basis of the country's economy. Ethiopia's economy is dependent on agriculture, which accounts for 40 percent of GDP and 80 percent of exports. However, the productivity of land resources is continuously declining due to population growth associated with deforestation, continuous cultivation, inadequate land management practices and changing land use patterns (UNDP, 2014)..\u003c/p\u003e \u003cp\u003eLand use is the arrangement, activities and inputs that people make in a particular land cover type to produce and change land use/cover values ​​(Ufot et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to Mariye et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), land use is defined as the way the land is used by people and their habitats, usually emphasizing the functional role of land for economic activities, while land cover is a physical feature of the Earth's surface. Land use/land cover change (LULCC) is the change in the Earth's surface caused by human activities (Mengistu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLand use/land cover changes (LULCCs) are triggered by the interaction of socioeconomic and natural environmental factors. Inappropriate practices, overgrazing, rapid human population growth (Assefa and Singh, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and weak institutional setup Dinka and Chaka (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)) are among the key anthropogenic driving variables of LULCCs. Changes in land uses, mainly the conversion of natural forests to agricultural land and settlement are the most widely practiced activities in Ethiopia (Eyayu Molla et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The LULCC process has a negative impact on biodiversity, climate, soil and air, as well as the ecosystem in general, and has become the most serious environmental problem for humans in recent years (Hailemariam et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLULC changes have a variety of negative socioeconomic and environmental consequences, including reduced agricultural yields, increased vulnerability to natural hazards (floods, droughts, fires), altered ecosystem services, and surface runoff trends (Moisa et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Agricultural expansion is responsible for almost 90% of global deforestation, a much greater impact than previously thought (FAO, 2018). One of the most serious challenges facing the planet is forest degradation. More than half of the world\u0026rsquo;s forest loss is due to the conversion of forests to agricultural land. To feed a rapidly growing global population, agriculture is placing increasing pressure on natural forest resources (Negassa et al., 2020).\u003c/p\u003e \u003cp\u003eUsing Landsat imagery to assess land use change at the basin and sub-basin levels, as well as to identify the rate and extent of land cover change, is an important approach to improve the appropriate management of natural resources (Meshesha et al., 2016). Assessing land use changes and the factors causing these changes is the subject of ongoing scientific investigation that has aroused the curiosity of a wide range of scientists (Angessa et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several studies on land use change analysis have been conducted in different regions of Ethiopia (Feyissa and Gebremariam, 2018; Geeraert et al., 2019; mint etc., 2018; Tolessa etc., 2017). Therefore, the analysis and monitoring of changes in land use and resources is important to understand the extent and magnitude of the changes, as well as to manage appropriate integrated watershed management (Shimizu et al., 2019).\u003c/p\u003e \u003cp\u003eHowever, very little is known about the current status and extent of land use change in the Asabla basin. In addition, a recent study revealed a decrease in land use caused by the natural environment, which was largely attributed to human activities such as population growth and economic development. Due to population growth and climate change, anthropogenic activities in the watershed have significantly modified the natural landscapes. The Asabla basin is suitable for agricultural production of certain crops. However, it is severely affected by land use change and soil erosion. Thus, understanding the pattern of these LULC changes is important for efficient watershed management. Accurate and up-to-date information about land use land cover dynamics and the effect of land use types on soil properties is critical in developing smart policies regarding the sustainable management of Ethiopia\u0026rsquo;s natural resources. Therefore, the aim of this research paper was to investigate the magnitude and rate of LULC change of Asabla watershed northwestern highland of Ethiopia.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Description of the Study Area\u003c/h2\u003e\n \u003cp\u003eThe study was conducted in the Asabla watershed, located in South Achefer District, Northwestern Ethiopia (ANRS) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Map location of the study area). It is located approximately 250 km south of Bahir Dar and 725 km from Addis Ababa. The watershed coordinates are between 13\u0026deg; 01\u0026apos; 37.50\u0026apos;\u0026apos; north latitude and 38\u0026deg; 58\u0026apos; 36.50\u0026apos;\u0026apos; east longitude with an elevation of 1953 meters above sea level. The study area covers a total area of \u0026nbsp;819 hectares. Average annual rainfall in the area between 1500 and 1975 mm and the average temperature is 17\u0026deg;C to 30\u0026deg;C (Getahun, 2015). The area is characterized by bimodal precipitation characteristics and a significant amount of precipitation occurs in July and August.\u003c/p\u003e\n \u003cp\u003eTopographically, the watershed is grouped as the flat slope (0\u0026ndash;3%), gentle slope (3\u0026ndash;8%), slopping (8\u0026ndash;16%), moderately steep (16\u0026ndash;30%), steep slope (30\u0026ndash;50%) and very steep (\u0026gt;\u0026thinsp;50%) slope classes (SADOA, 2023). Nitisols is the dominant soil type in the study area (Abebe, 1998). The total human population of the study watershed is 1263 of which 49% are males and 51% are females (SADOA, 2023). The main crops grown in the study area are wheat (Triticum aestivum), barley (Hordeumvulgare), potato (Solanum tuberosum) Maize and figuremillet. The dominant plant species found in the area includes Acacia abyssinica, Eucalyptus globules, Eucalyptus camaldulensis and Podocarpus falcatus (SADOA, 2023).\u003c/p\u003e\n \u003cp\u003eThe population size of \u003cem\u003eSouth Achefer\u003c/em\u003e district has been increased from year to year at both rural and urban. From the year 2007\u0026ndash;2019, the population size increased from 148,456 to 188,082 by 21.226%. From the total population 68,989 (49.76%) were male and 69,663 (50.24%) were female in 2007. In 2020, from the total population, 81,859 (48.7%) are male and 86,223 (51.3%) female (South Achefer District Demography Study Office, 2020). On the other hand, in 2019, the total populations of the study watershed were 1263. Of which 620 (49.01%) are male and 643 (50.99%) are female (SADOA, 2023).\u003c/p\u003e\n \u003cp\u003eThe total area coverage of the watershed is 809.56 ha with different land use types; cultivated land (46.96%), grazing land (30.34%), natural/plantation forest (12.52%) and settlement area (10.18%) as show (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Small-scale farmers with an average landholding size of less than one hectare per household dominate the area. The community in the study area practice subsistence-mixed farming which is crop production (rain-fed and irrigated) and livestock production. The study area is known for its high potential for wheat (\u003cem\u003eEleusine coracana\u003c/em\u003e), barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e), finger millet (\u003cem\u003eEleusine coracana\u003c/em\u003e) and maize (\u003cem\u003eZea mays\u003c/em\u003e) production. Livestock population includes cattle, 3,847- sheep and goat, 7,763- poultry, 5,622- and beehives- 312, including modern and traditional. A wide range of plant species occur in the area including \u003cem\u003eAcacia abyssinica\u003c/em\u003e, \u003cem\u003eEucalyptus globules\u003c/em\u003e, \u003cem\u003eEucalyptus camaldulensis\u003c/em\u003e and \u003cem\u003ePodocarpus falcatus\u003c/em\u003e (SADOA, 2020).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Methods of Data Collection\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Spatial data\u003c/h2\u003e\n \u003cp\u003eIn this study land use/land cover changes were monitored at three time series (1986\u0026ndash;2020). Landsat satellite image was used for the LU/LC change detection. It was acquired in January for the three-time series (1986, 2001 and 2020). Because there is no/less cloud cover (Table 3.2). The Landsat images were freely downloaded from united geological survey (USGS) earth explorer \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e(https://earthexplorer.usgs.gov\u003c/span\u003e\u003c/span\u003e/) in zip format and then saved in tagged image file (TIF) format. A thirty meter DEM obtained from the United States Geological Survey (USGS) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e(https://earthexplorer.usgs.gov\u003c/span\u003e\u003c/span\u003e/) was used to delineate the watershed. After different image enhancement, performed image classification schemes were done. Besides, the history of each land use/land cover and the prevailing ones in the study area of the field data obtained from key informants, Google Earth and field survey as primary datasets. The field data points were captured using a handheld GPS for ground truth of the study area.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\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\u003eSatellite image type, resolution, acquisition date and number of bands\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSatellite image\u003c/p\u003e\n \u003cp\u003e/Sensor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003cp\u003e/row\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCloud cover\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResolution\u003c/p\u003e\n \u003cp\u003e(pixel size)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcquisition\u003c/p\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo_ of\u003c/p\u003e\n \u003cp\u003eBands\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\u003eLandsat5 TM\u003c/p\u003e\n \u003cp\u003eLandsat7 ETM+\u003c/p\u003e\n \u003cp\u003eLandsat8 OLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172/54\u003c/p\u003e\n \u003cp\u003e172/54\u003c/p\u003e\n \u003cp\u003e172/54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m x 30m\u003c/p\u003e\n \u003cp\u003e30m x 30m\u003c/p\u003e\n \u003cp\u003e30m x 30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1986-01-12\u003c/p\u003e\n \u003cp\u003e2001-01-24\u003c/p\u003e\n \u003cp\u003e2020-01-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e6\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 \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Method of Data Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1. Watershed delineation\u003c/h2\u003e\n \u003cp\u003eA 30 meter digital elevation model (DEM) obtained from the United States Geological Survey (USGS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003c/span\u003e was used to delineate the watershed. The major LU/LC types in the watershed namely: forestland, grazing land, cultivated land and settlements were quantified. For land use change detection, forestland is the combination of natural forest and plantation forest similarly grassland and grazing land was grouped as grazing land use class. This is due to the difficulties to differentiate each other since they had the same tone of the visual image during classification in order to draw out useful thematic information.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 LU/LC image classification\u003c/h2\u003e\n \u003cp\u003eThe Landsat images were classified using a supervised image classification method. It used to classify the training site of the LU/LC change by using ERDAS Imagine 2010 and Arc GIS 10.3 software. ERDAS imagines 2010 model was carried out for high classification and accurate images (Asmala Ahmad, 2012). The truth ground point data was used for the classification scheme of each LU/LC class, for the creation of training sites and for signature generation to perform supervised classification. The land use/cover classes from the 2020 land sat image (Land sat-ETM) were produced by supervised digital image classification method in ENVI (Environment for Visualizing Images) 4.3 software using training area taken on the basis of false color composite (reflectance characteristics) of each land use/cover classes. The use of arc GIS 10.3software was made to link the polygon lines to labels of specific land use/cover classification and to calculate the statistics of each polygon. Then, three time series land use/cover maps were quantified corresponding to the three time series (1986, 2001 and 2020). Finally, percent of change (Ebrahim Esa and Mohamed Assen. 2017) and anual rate of change (Temesgen Gashaw \u003cem\u003eet al.\u003c/em\u003e 2014a) was also computed to demonstrate the magnitude of the changes experienced between the periods of the years using \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{E}\\text{q}\\text{u}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\left(1\\right)\\text{a}\\text{n}\\text{d}\\:\\left(2\\right)\\)\u003c/span\u003e\u003c/span\u003e respectively.\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{p}\\text{e}\\text{r}\\text{c}\\text{e}\\text{n}\\text{t}\\:\\text{o}\\text{f}\\:\\text{c}\\text{h}\\text{a}\\text{n}\\text{g}\\text{e}\\:=\\frac{X-Y}{Y}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\text{E}\\text{q}\\text{u}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\left(1\\right)\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\text{R}\\text{a}\\text{t}\\text{e}\\:\\text{o}\\text{f}\\:\\text{c}\\text{h}\\text{a}\\text{n}\\text{g}\\text{e}\\:=\\frac{X-Y}{Z}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\text{E}\\text{q}\\text{u}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\left(2\\right)\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, X\u0026thinsp;=\u0026thinsp;LULC area of recent year in ha,\u003c/p\u003e\n \u003cp\u003eY\u0026thinsp;=\u0026thinsp;LULC area of initial year of in ha\u003c/p\u003e\n \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;is Time interval between X and Y in years.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2. Accuracy assessment\u003c/h2\u003e\n \u003cp\u003eAccuracy assessment was done to understand the representation of the classified images on the ground. Thus, the classification was verified by accuracy assessment using ground control points in ordered to check the precision of the classified LU/LC images and what actually exists on the ground. It was commonly done with reference to other images. To do accuracy assessment for the classified images, 150 representative sample ground truth points using handheld GPS while taken from Google Earth by imported into the classified map in ArcGIS 10.3 was created. Reference points were collected for the 1986, 2001 and 2020 classified images from the corresponding Google Earth images following the procedure of Abineh Tilahun and Bogale Teferi (2015) and Temesgen \u003cem\u003eet al\u003c/em\u003e. (2017). The accuracy assessment was done using 70 representative ground truth points collected from the fieldwork points recorded by using handheld GPS while taken from Google Earth by imported into the classified map. Various measures of accuracy assessment such as producer accuracy, user accuracy overall accuracy and Kappa coefficient were done. Then, the accuracy assessment of the classification image was reported using the four performance criteria including producer accuracy, user accuracy, overall accuracy and kappa coefficient were generated from classified images. Moreover, the measure of producer\u0026rsquo;s accuracy (Sensitivity) reflects the accuracy of prediction of the particular category. The User\u0026rsquo;s accuracy reflects the reliability of the classification to the user. User\u0026rsquo;s accuracy is the more relevant measure of the classification\u0026rsquo;s actual utility in the field.\u003c/p\u003e\n \u003cp\u003eTo calculate the overall accuracy, add the number of correctly classified sites and divide it by the total number of reference site. We could also express this as an error percentage, which would be the complement of accuracy: error\u0026thinsp;+\u0026thinsp;accuracy\u0026thinsp;=\u0026thinsp;100%. User accuracy refers how actually classified map is real on the ground. For example your user accuracy is 80% means your classified item is 80% of mapped area in actually that items other may not referred to that item. Producer accuracy refers to the classification scheme. It is also the number of reference sites classified accurately divided by the total number of reference sites for that class. The User\u0026apos;s Accuracy is calculating by taking the total number of correct classifications for a particular class and dividing it by the row total. The kappa coefficient measures the agreement between classification and truth values. It is computed as follows:\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"686\" height=\"121\"\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003ei is the class number and N is the total number of classified values compared to truth values\u003c/li\u003e\n \u003cli\u003em\u003csub\u003ei,i\u003c/sub\u003e is the number of values belonging to the truth class i that have also been classified as class i (i.e., values found along the diagonal of the confusion matrix)\u003c/li\u003e\n \u003cli\u003eC\u003csub\u003ei\u003c/sub\u003e is the total number of predicted values belonging to class I and G\u003csub\u003ei\u003c/sub\u003e is the total number of truth values belonging to class\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Accuracy Assessment\u003c/h2\u003e \u003cp\u003eThe overall accuracy assessment and kappa coefficient of the land use assessment were computed for three respective years (1986, 2001 and 2020). The overall accuracy and kappa coefficient were 83.81%, 83.79%, 88.65% and 0.80, 0.82, 0.81 in 1986, 2001 and 2020 years, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Kappa statistics of a value greater than 0.80 indicates a strong agreement between the ground truth and classified LULC classes (Anderson, 2003). The result of accuracy assessment showed accuracy of 2020 was found more reliable with 88.65% while for 1986 and 2001 was found moderate reliable. This might be due to a severe confusion of each land use classes land with other land cover classes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy assessments of classified images\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLU/LC Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrazing land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivated and\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOver all accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKappa coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eUA\u0026thinsp;=\u0026thinsp;user accuracy; PA\u0026thinsp;=\u0026thinsp;producer accuracy\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Land Use Land Cover Change\u003c/h2\u003e \u003cp\u003eIn the studied watershed land use land cover changes of four major LULC classes namely, cultivated land, settlement, grazing land, and forest land from 1986 to 2020 were detected. Normally, the analyzed LU/LC patterns in the studied watershed indicated there were significant land use land cover change between the three time series over 1986\u0026ndash;2020 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Cultivated land\u003c/h2\u003e \u003cp\u003eThe analyzed LULC change for the study watershed revealed that cultivated land was predominant land use class through the studied years and showed slight increment over the period of (1986\u0026ndash;2001) and (1986\u0026ndash;2020) at the expense of grazing lands and forest land (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In those years cultivated land use class was changed from 358.34 ha (44.26%) in 1986 to 384.23 ha (47.46%) in 2001 or increased by 25.89 ha (3.20%) over fifteen years (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, it had increased by 21.83 ha (2.69%) over thirty four years of 1986\u0026ndash;2020 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the other hand cultivated land was decreased in a time series of 2001\u0026ndash;2020 by 4.06 ha (0.5%) as presented in Table\u0026nbsp;(4) and Figure (3). This might be associated with the expansion of plantation forest. As shown Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e annually the cultivated land had increased by 0.64 ha (0.08%) in 1986 to 2020 years at the expenses of mainly grazing land.\u003c/p\u003e \u003cp\u003eThe increased in cultivated land could be due to population growth increases associated with increases in demands of more cultivated land. In addition to this the reduction of land productivity underpinning the intension of the people for getting new fertile cultivable lands might be led to the expansion of cultivated land in the study watershed. Further expansion of cultivated land has been reduced since 2001 it could be associated with the expansion of plantation forest and settlement area due to population growth as evidenced in different parts of Ethiopia (Molla \u003cem\u003eet al\u003c/em\u003e, 2010). In addition to this, it could be due to lack of suitable land that would be converted to farm-land. According to the elder (2020), mentioned that farmers were settle and cultivate crops on these lands legally and illegally particularly at the expense of grazing land.\u003c/p\u003e \u003cp\u003eThe study was in line with the work of Alemu \u003cem\u003eet al.\u003c/em\u003e (2020) reported an increase in area coverage for the cultivated land-use system over 30 years in Meki river watershed, Western Lake Ziway Sub-Basin, Central Rift Valley of Ethiopia. Kiros and Desalegn (2019) reported an increase in cultivated land cover by 36.70% over 60 years (1957\u0026ndash;2017) in North-eastern Addis Ababa, central highlands of Ethiopia. Similarly, Gashaw \u003cem\u003eet al\u003c/em\u003e. (2017a) reported increased coverage of cultivated land cover over 30 years (1985\u0026ndash;2015) with similar predicted trends for 2030 and 2045 in the Andassa watershed, Blue Nile Basin, Ethiopia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC of \u003cem\u003eAsabela\u003c/em\u003e watershed between 1986, 2001, and 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLU/LC Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e358.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e384.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e380.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrazing land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e293.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e809.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e809.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e809.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eLU\u0026thinsp;=\u0026thinsp;land use, LC\u0026thinsp;=\u0026thinsp;land cover, ha\u0026thinsp;=\u0026thinsp;hectare\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Settlement area\u003c/h2\u003e \u003cp\u003eSettlement land use class had smallest area coverage. However, the settlement area increased steadily in a referenced period (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Settlement was increased by 12.27 ha (1.52%), 20.26 ha (2.5%), and 32.53 ha (4.02%) from 1986 to 2001, 2001 to 2020, and 1986 to2020 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and annually it was increased by 0.97 ha (0.12%) over thirty four years (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This might be the increased in population size associated with the demands of additional settlement in the rural areas and mood of scarce settlement rather than populated. The result was similar to Asmame and Abegaz (2017), that reported increased rural settlement land due to increasing population pressure in Gelana sub-watershed of North of highlands of Ethiopia. Moreover, the study result in line with Sabiela Fekad et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who reported that settlement increased by 10% from 1989 to 2019 in the \u003cem\u003eTejibara\u003c/em\u003e watershed, Northwest Ethiopia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Grazing land\u003c/h2\u003e \u003cp\u003eThe result revealed that grazing land had the highest area converge in 1986, 2001, and 2020 next to cultivated land (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, this land use class showed slightly declination throughout the studied period. Normally, it was decreased by 2.65 ha (0.33%) from1986 to 2020 suggestion years (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The reason for a decline in grazing land might be the increased population size that changed grazing land to cultivated land and settlement. Besides, the communal grazing land was reserved by institutional sectors as mention by key informants and focus group discussion (2020). Furthermore, as local elders elaborated that the communal grazing land was given to the landless local youth age people. Therefore, the demand of cultivated lands and settlement area was fulfilled by decreasing the area of grazing land. Furthermore, as local elders elaborated that the communal grazing land was given to the landless local youth age people. Therefore, the demand of cultivated lands and settlement area was fulfilled by decreasing the area of grazing land. Furthermore, as local elders elaborated that the communal grazing land was given to the landless local youth age people. Therefore, the demand of cultivated lands and settlement area was fulfilled by decreasing the area of grazing land (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe result was in line with the findings of Fisseha \u003cem\u003eet al.\u003c/em\u003e (2011) who found a decline trends in grazing land from 1957\u0026ndash;2008 due to the transformation of grazing land into eucalyptus plantation, cultivated land and settlement areas in \u003cem\u003eDebre-Mewi\u003c/em\u003e watershed Northwest, Ethiopia. Gashaw \u003cem\u003eet al.\u003c/em\u003e (2017) also reported that a shifting of grassland in to settlement and forest from 1985 to 2015 in the \u003cem\u003eAndass\u003c/em\u003ea watershed, Blue Nile Basin, Ethiopia. Similarly, Shiferaw (2011) reported that grazing land was decreased from 1972\u0026ndash;1985 in Borena Woreda South Wollo Highlands of Ethiopia. As reported by Molla \u003cem\u003eet al\u003c/em\u003e. (2010), grassland was decreased in \u003cem\u003eTara Gedam\u003c/em\u003e and adjacent agro ecosystem Northwest Ethiopia. Furthermore, the study result was in line with Fekad \u003cem\u003eet al\u003c/em\u003e. (2020) who reported a decline of grazing land from 1989 to 2019 while increase in cultivated land and settlement areas in \u003cem\u003eTejibara\u003c/em\u003e watershed, Northwest Ethiopia\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Forestland\u003c/h2\u003e \u003cp\u003eThere was an increment of forestland area coverage of the studied watershed from 65.55 ha to 69.51 ha in 1986 to 2001 and from 69.51 ha to 101.32 ha in 2001 to 2020 referenced years (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This might be associated with the conversion in extent of grazing land to plantation forestland. The forestland use annually expanded by 1.05 ha (0.13%) in the referenced year of 1986\u0026ndash;2020 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The expansion of forestland was largely attributed to the predominantly established of plantation of \u003cem\u003eeucalyptus\u003c/em\u003e tree in the study area. As key informant mentioned that the reason for the increment of the plantation forest because the demand of income, energy, and construction purpose was increased throughout the year.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC change of \u003cem\u003eAsabela\u003c/em\u003e watershed in 1986\u0026ndash;2001, 2001\u0026ndash;2020 and 1986\u0026ndash;2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLU/LC Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eLand use change in (ha) and (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1986\u0026ndash;2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2001\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1986\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003earea(ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivated Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;25.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;21.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;12.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;20.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;32.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrazing land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-42.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-48.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-90.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-11.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;31.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;35.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe result in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e was in line with Bewket (2003) and Sebhatleab (2014), who reported the increased in forestland coverage as the year increased. This could be the plantation program of the eucalyptus tree species in \u003cem\u003eChemoga\u003c/em\u003e watershed Blue Nile Basin Ethiopia and in the highlands of Ethiopia, respectively. Similarly, in the \u003cem\u003eBeressa\u003c/em\u003e watershed Northern central high-lands of Ethiopia Meshesha \u003cem\u003eet al.\u003c/em\u003e, (2016) also found increased forestland area by 6.5% between 1984 and 2015 years. This associated to the increase in plantation of \u003cem\u003eeucalyptus\u003c/em\u003e trees. Moreover, Getachew Fisseha \u003cem\u003eet al.\u003c/em\u003e (2011) reported that the individual farmers around homesteads establish the eucalyptus plantations for different purposes at \u003cem\u003eDebre-Mewi\u003c/em\u003e watershed, northwest Ethiopia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual percentage LU/LC change of \u003cem\u003eAsabela\u003c/em\u003e watershed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLU/LC Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eLand use change in ha/year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1986\u0026ndash;2001\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2001\u0026ndash;2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1986\u0026ndash;2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArea(ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivated Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrazing land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eThe land use/land cover (LULC) analysis of Asabela watershed between 1986 and 2020 reveals significant changes in the region's land use dynamics, reflecting the influence of population growth, agricultural practices, and forest management. The accuracy assessment, with overall accuracies of 83.81%, 83.79%, and 88.65% in 1986, 2001, and 2020 respectively, indicates a generally reliable classification over the three periods, with the 2020 results being the most reliable. The kappa coefficients for these years (0.80, 0.82, and 0.81) suggest strong agreement between the ground truth data and the classified LULC maps.\u003c/p\u003e \u003cp\u003eRegarding land use changes, cultivated land has consistently increased throughout the study period, particularly from 1986 to 2020, expanding by 21.83 ha (2.69%), primarily at the expense of grazing lands. This is likely due to population pressures and the increasing demand for arable land, although the rate of increase slowed between 2001 and 2020, possibly due to the expansion of plantation forests. The settlement area has steadily grown by 32.53 ha (4.02%) between 1986 and 2020, reflecting rural population growth and the demand for residential space. Grazing land, has declined over the study period, shrinking by 90.13 ha (11.13%) from 1986 to 2020. This trend can be attributed to the conversion of grazing land into cultivated land and settlement areas, in addition to institutional reservations of communal grazing lands. The decrease in grazing land aligns with trends observed in other regions of Ethiopia, where similar land use transitions have been noted. Forest land has shown a positive change, increasing by 35.77 ha (4.42%) from 1986 to 2020, driven largely by the expansion of eucalyptus plantations. This change is consistent with broader national trends of afforestation and reforestation programs in Ethiopia. The steady increase in forest area could be attributed to the growing demand for forest products, including wood for energy, construction, and income generation. These findings suggest that future land use planning should consider balancing agricultural expansion with the preservation of grazing lands and sustainable forest management to ensure long-term ecological and socio-economic stability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e:\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eAbebe Amare Kassie\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesigned the study through the experiments; carried them out; evaluated and interpreted the results; and composed the research article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZewude Alemayehu Tilahun\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComposed the paper; conducted data analysis and interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChernet Miju\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollect primary data; organize raw data and test its validity.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest:\u003c/h2\u003e\n\u003cp\u003eNo conflicts of interest are disclosed by the Authors.\u003c/p\u003e\n\u003ch2\u003eFunding statement:\u003c/h2\u003e\n\u003cp\u003eNo funding was for this investigation.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAbebe Amare Kassie: Designed the study through the experiments; carried them out; evaluated and interpreted the results; and composed the research article.Zewde Alemayehu Tilahun: Composed the paper; conducted data analysis and interpretation.Chernet Miju: Collect primary data; organize raw data and test its validity.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThank you for your positive response!!!!\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from various sources. Weather data, including mean annual temperature and rainfall, were collected from four weather stations around the study area and are publicly accessible from the Ethiopian National Meteorological Agency (NMA). Elevation, outlet, shape file and DEM data were sourced from the USGS Earth Explorer to delineate the watershed and digitize on HEC-RAS (https://earthexplorer.usgs.gov). These datasets are available upon reasonable request from the corresponding author, ensuring that the data is accessible for further research and validation purposes. All data were utilized under the regulations and permissions provided by the respective agencies and organizations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAngessa, A. T., Lemma, B., \u0026amp; Yeshitela, K. (2019). Land-use and land-cover dynamics and their drivers in the central highlands of Ethiopia with special reference to the Lake Wanchi watershed. \u003cem\u003eGeoJournal\u003c/em\u003e, 86(3), 1225\u0026ndash;1243.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003ccite\u003eAssefa A., Singh K. N. The implications of land use and land cover change for rural household food insecurity in the north eastern highlands of Ethiopia: the case of Teleyayen sub-watershed. Agric and Food Secure . 2017;6\u003c/cite\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDeribewn Kiros and Dalacho Desalegn. 2019. Land use and forest cover dynamics in the North-eastern Addis Ababa, central highlands of Ethiopia. \u003cem\u003eEnvironmental Systems Research\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), pp.1-18.\u003c/li\u003e\n \u003cli\u003eDinka M. O., Chaka D. D. Analysis of land use land cover change in adei watershed, central highlands of Ethiopia. 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Appl., 11 (2018), pp. 254-264.\u003c/li\u003e\n \u003cli\u003eGashaw Temesgen, Tulu Taffa, Argaw Mekuria and Abeyou W.Worqlul. 2017. Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia. \u003cem\u003eEnvironmental Systems Research\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), pp.1-15.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Geeraert L., E. Hulsmans, K. Helsen, G. Berecha, R. Aerts, O. Honnay Rapid diversity and structure degradation over time through continued coffee cultivation in remnant Ethiopian Afromontane forests Biol. Conserv., 236 (2019), pp. 8-16.\u003c/li\u003e\n \u003cli\u003eGetahun Bore\u0026nbsp;and Bobe Bedadi. 2015. Impacts of land use types on selected soil physico-chemical properties of Loma Woreda, Dawuro Zone, and Southern Ethiopia. Science, Technology and Arts Research Journal 4(4): 40-48.\u003c/li\u003e\n \u003cli\u003e\u003ccite\u003eHailemariam S. N., Soromessa T., Teketay D. Land use and land cover change in the Bale mountain eco-region of Ethiopia during 1985 to 2015. Land . 2016;5(4):p. 41. doi: 10.3390/land5040041.\u003c/cite\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eK.\u0026nbsp;Shimizu,\u0026nbsp;T.\u0026nbsp;Ota,\u0026nbsp;N.\u0026nbsp;Mizoue Detecting forest changes using dense landsat 8 and Sentinel-1 time series data in tropical seasonal forests Remote Sensing 2019,\u0026nbsp;11\u0026nbsp;(2019), p.\u0026nbsp;1899.\u003c/li\u003e\n \u003cli\u003eMariye, M.; Mariyo, M.; Changming, Y.; Teffera, Z.L.; Weldegebrial, B. Effects of land use and land cover change on soil erosion potential in Berhe district: A case study of Legedadi watershed, Ethiopia. \u003cem\u003eInt. J. River Basin Manag.\u003c/em\u003e \u003cstrong\u003e2020\u003c/strong\u003e, 1\u0026ndash;13.\u003c/li\u003e\n \u003cli\u003e\u003ccite\u003eMengistu D. A., Waktola D. K., Woldetsadik M. Detection and analysis of land-use and land-cover changes in the midwest escarpment of the Ethiopian rift valley. Journal of Land Use Science . 2012;7(3):239\u0026ndash;260. doi: 10.1080/1747423x.2011.562556.\u003c/cite\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMeshesha, D.T., et al, Tsunekawa, A., \u0026amp; Tsubo, M., et al (2014). Land-use change and its socio-environmental impact in Eastern Ethiopia\u0026rsquo;s highland. \u003cem\u003eRegional Environmental Change\u003c/em\u003e, 14, 757\u0026ndash;768.\u003c/li\u003e\n \u003cli\u003eMinta M.,\u0026nbsp;K.\u0026nbsp;Kibret,\u0026nbsp;P.\u0026nbsp;Thorne,\u0026nbsp;T.\u0026nbsp;Nigussie,\u0026nbsp;L.\u0026nbsp;Nigatu Land use and land cover dynamics in Dendi-Jeldu hilly-mountainous areas in the central Ethiopian highlands Geoderma,\u0026nbsp;314\u0026nbsp;(2018), pp.\u0026nbsp;27-36.\u003c/li\u003e\n \u003cli\u003eMoisa M.B.,\u0026nbsp;I.N.\u0026nbsp;Dejene,\u0026nbsp;O.\u0026nbsp;Hirko,\u0026nbsp;D.O.\u0026nbsp;Gemeda Impact of deforestation on soil erosion in the highland areas of western Ethiopia using geospatial techniques: a case study of the Upper Anger watershed Asia-Pacific J. Reg. Sci.,\u0026nbsp;6\u0026nbsp;(2022), pp.\u0026nbsp;489-514.\u003c/li\u003e\n \u003cli\u003eNegassa,\u0026nbsp;D.T.\u0026nbsp;Mallie,\u0026nbsp;D.O.\u0026nbsp;Gemeda Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia Environmental Systems Research,\u0026nbsp;9\u0026nbsp;(2020), pp.\u0026nbsp;1-14.\u003c/li\u003e\n \u003cli\u003eSabiela Fekad, Kehali Jembere, Edalkachew Fekadu and Dessalew Wasie. \u0026nbsp;2020. Land Use and Land Cover Dynamics and Properties of Soils under Different Land Uses in the Tejibara Watershed Northwest Ethiopia.\u0026nbsp;\u003cem\u003eScientific World Journal\u003c/em\u003e, 1-12.\u003c/li\u003e\n \u003cli\u003eSADAO (\u003cem\u003eSouth Achefer\u0026nbsp;\u003c/em\u003eDistrict Agricultural office). 2020. Annual report on land use system, soil type, and topographical features. \u003cem\u003eUnpublished Documents\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003eSouth Achefer District Durbete, Ethiopia.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTolessa T, F. Senbeta, M. Kidane The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia Ecosyst. Serv., 23 (2017), pp. 47-54.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eUfot, U.O., Iren, O.B., Chikere Njoku, C.U., 2016. Effects of land use on soil physical and chemical properties in Akokwa area of Imo State, Nigeria. International Journal of Life Sciences Scientific Research 2(3): 273-278.\u003c/li\u003e\n \u003cli\u003eUnited Nations Development Program (UNDP). 2014. Ethiopia fact sheet: Agricultural Growth and Transformation. UNDP. http:// www.undp.org/content/dam/ethiopia/docs/ UNDP. (accessed on March, 2015).\u003c/li\u003e\n \u003cli\u003eWoldeamilak Bewket and Stroosnijder L. 2003. Effects of agro ecological land use succession on soil properties in Chemoga watershed, Blue Nile basin, Ethiopia. Geoderma, 111(1-2): 85-98.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land use change, land use, Asabla watershed","lastPublishedDoi":"10.21203/rs.3.rs-5733358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5733358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eLand use change is a fundamental variable that impacts and links many parts of the human and physical environments. The analysis and monitoring of changes in land use and resources using Landsat imagery is important to understand the extent and magnitude of the changes. Therefore, the study was conducted to analyze the land use land cover change in Asabla watershed Northwest Ethiopia during 2020/22.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study examines land use/land cover changes from 1986 to 2020 using Landsat satellite images and field data from (DEM) from USGS using supervised methods in ERDAS Imagine 2010 and ArcGIS 10.3. LU/LC classes, including forestland, grazing land, cultivated land, and settlements, were analyzed. Accuracy assessments were performed using 150 ground control points, with measures like producer and user accuracy, overall accuracy, and Kappa coefficient to evaluate classification precision. The percent change and annual rate of change to assess the magnitude of LU/LC over time was calculated.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe (LU/LC) changes in a watershed from 1986 to 2020, revealing significant transformations across four primary classes: cultivated land, settlements, grazing land, and forestland. Accuracy assessments for the classified images indicated high reliability in 2020 (88.65%) with Kappa values 0.80, signaling strong agreement with ground truth data. Cultivated land increased steadily from 1986 to 2020, primarily at the expense of grazing lands and forest cover. In contrast, forestland showed a consistent increase, largely due to the expansion of eucalyptus plantations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study highlights significant LU/LCC in the watershed from 1986 to 2020. These changes are primarily driven by population growth, agricultural expansion, and the establishment of eucalyptus plantations. Therefore, sustainable land management strategies and policy, balancing agricultural expansion with the preservation of grazing lands and agroforestry practices to mitigate the land use change on the environment and local communities could be implemented.\u003c/p\u003e","manuscriptTitle":"Land Use Land Cover Change Detection in Asabla Watershed, Northern Highlands of Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 15:07:26","doi":"10.21203/rs.3.rs-5733358/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a4e27c6-2767-4f92-a821-da3a0dbcf645","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42681444,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":42681445,"name":"Earth and environmental sciences/Ecology/Community ecology"},{"id":42681446,"name":"Earth and environmental sciences/Ecology/Conservation"},{"id":42681447,"name":"Earth and environmental sciences/Ecology/Ecosystem services"}],"tags":[],"updatedAt":"2025-07-21T08:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-14 15:07:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5733358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5733358","identity":"rs-5733358","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}