Land use/land cover changes and its drivers in Shekayboru area of Chifra District in Afar Region, Ethiopia

Land use/land cover changes and its drivers in Shekayboru area of Chifra District in Afar Region, 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Land use/land cover changes and its drivers in Shekayboru area of Chifra District in Afar Region, Ethiopia Truset Fetene, Nahusenay Abate Dessie, Tilahun Amede This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2104836/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 For long-term land management, understanding the scope, direction, and agents of LULC change, as well as their configuration across geographical and temporal dimensions, is essential. This study looked at the patterns and extents of LULC change in Chifra District's Shekayboru Area for that aim. Landsat imagery from TM and ETM+ (2009/10) and OLI were used to investigate the dynamics of LULCC (2015 and 2020). A hybrid method was used to categorize the LULC maps for each period using the results of supervised classification and intense on-screen-digitizing approaches. Three major LULC types (vegetation, cultivation, and bare lands) were identified, with overall accuracies ranging from 91.3 to 100%. According to the 2009/10 LULC classification map, bare lands had the highest area coverage (around 87%), while in 2015 and 2020, both vegetation and cultivated lands showed increment trends of 26 and 28 percent (2015), and 29 and 44 percent (2020), respectively, and bare lands showed decrement trends of 46 and 28 percent, respectively. The study area's Focus Group Discussions and interviews further revealed that, as a result of the water spreading weir, vegetation and cultivated fields have increased over time, and local villagers have taken up maize farming. Better soil and water conservation methods may be responsible for the increase in plant cover. The outcomes of this study will help design future soil and water conservation measures in response to sustainable land management. According to the researcher, a study of soil fertility, quality, and moisture content should be conducted in the future. Image classification LULC normalization index validation assessment water spreading weir Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Natural and socio-economic factors, as well as human activity in time and space, influence land use and land cover (LULC) change in a given location. Changes in LULC are primarily driven by population expansion (Lambin et al. , 2003), economic growth, and physical factors such as terrain, slope condition, soil type, and climate (Setegn et al. , 2009; Yalew et al. , 2016). When it comes to how people use the land, land-use change is a historical process. It alters the availability of many resources such as plants, soil, and water (Ahmad, 2014). Changes in land use have a direct impact on evapotranspiration, groundwater infiltration, and overland runoff. When it comes to global dynamics and their responses to environmental and socio-economic causes, LULC change is a major concern (Akpoti et al. , 2016; Bewket, 2002; Hurni et al. , 2005). On a global and local scale, changes in LULC have a negative impact on climatic patterns, natural hazards, and socio-economic dynamics (Chakilu and Moges, 2017; Hegazy and Kaloop, 2015; Sewnet, 2015). To satisfy the increasing demands for basic human requirements and welfare, information on LULC and potentials for their optimal use is critical for selection, planning, sustainable land resource management, and understanding changes in hydrological processes. Many applications connected to LULC changes detection, such as cultivation, urban expansion, and landscape changes, benefit from change detection (Hegazy and Kaloop, 2015; Imbernon, 1999; Solaimani et al. , 2010). Understanding landscape patterns, changes, and interactions between human activities and natural phenomena is critical for effective land management and improved decision-making (Rawat and Kumar, 2015). Remote sensing (RS) and Geographic Information Systems (GIS) are powerful and cost-effective methods for analyzing the spatial and temporal change of LULC (Herold et al. , 2003; Serra et al. , 2008). Remote sensing data is now useful and appropriate for LULC change detection research (Yuan et al. , 2005). Due to its recurring data collecting, suitability for processing, and ease of use, remote sensing data is the most popular source for detection, quantification, and mapping of LULC patterns (Chen et al ., 2005; Jensen, 1996). The GIS and Remote Sensing techniques are useful for studying the region's land use dynamics, as well as monitoring, mapping, and managing natural resources. The use of remote sensing and GIS, combined with ground survey methods, is critical for identifying LULC change dynamics (Milanova and Telnova, 2007; Rindfuss, et al ., 2004). As per Getnet et al. (2019) investigated the impacts of the water spreading weir on soil moisture gradient and fertility, crop yields, and biomass production. In the research region, the direction and degree of LULC change and normalized difference vegetation index (NDVI) dynamics as indicators of rangeland restoration as a result of the water spreading weir have not been explored. Therefore, this study will fill the gap. Due to the aforementioned gaps, the study's goal was to determine the total LULC change and NDVI in the Shekayboru area of Chifra District during the last few decades. The study's specific aims were to measure LULC changes, investigate the trend, rate, and extent of LULC change, and evaluate vegetation diversity using the Normalized Difference Vegetation Index, all in keeping with the overall goal. 2. Materials And Methods 2.1. Study Area The study was conducted at Shekayboru Area of Chifra District in Afar Regional state, located at 11°37′43′′N and 40°02′30′′E near the base of the eastern escarpment of the Ethiopian highlands (Fig. 1 ). The study site covers 49.3 ha which is the drought prone area where annual rainfall ranges from 200 to 500 mm, with the rain season extends from July to September. In related to the rainfall times of the Wollo highlands, flooding always comes to the study area from March to April and from July to September because the adjacent highlands have received higher rainfall in both seasons. The mean annual, minimum and maximum temperatures are 27.8, 18.3 and 37.6°C, respectively (NMA, 2007). The soils are variable, ranging from deep alluvial soils in the valley bottoms bordering the highlands to shallow and mostly gravel-dominated soils in degraded rangelands (Mezegebu et al ., 2019). 2.2. Data and Data Sources To quantify the magnitude and directions of LU/LC change, the digital image data files were organized and used from satellite imageries downloaded in zipped files from the United State Geological Survey (USGS) archive website: http://earthexplorer.usgs.gov . Image processing and image classification is carried out by using QGIS 3.10 software. The GIS analysis is conducted by using QGIS 3.10. The three periods Landsat satellite images (Thematic Mapper/TM, Enhanced Thematic Mapper/ETM+, and Operational Land Imagery/OLI) were accessed. The selection of satellite images primarily considered: (i) open source data availability, (ii) the image quality to reduce the effect of cloud cover on data reliability. Since the projects started in 2015 (Getnet et al ., 2019), the Landsat Image of 2009, 2015 (Project started) and 2020 were download from USGS( https://earthexplorer.usgs.gov ). The details of remote sensing data used sources and description of the data used in the study area summarized in Table 1 . Path/row Date/year Sensor Cloud cover (percent) Purpose Source Spatial Resolution /Sharpened** Sources Table 1 Serious of Landsat Images and Other Data’s Used for the Study 168/052 06 Dec. 2009 TM 6 LULC 30/15 USGS 168/052 15 Jan. 2010 ETM+ 0.00 B8 for pan-sharpening* 15 USGS 168/052 06 Feb.2015 OLI 0.00 LULC 30/15 USGS 168/052 19 Jan.2020 OLI 0.01 LULC 30/15 USGS Google Earth/Engine Each periods Ground Truthing Points collection Google LLC Study area boundary To set the spatial scope of the study (Getnet et al. , 2019) *Scan line error is corrected using “Landsat Toolbox” extension for QGIS. ** All images used for LULC was sharpened to 15m spatial resolution using corresponding/ available panchromatic band (B8). 2.3. Data processing and analysis Landsat imageries of three bands (4, 3, and 2) for Landsat TM and Landsat ETM + whereas bands (5, 4, and 3) to Landsat-8 were used in image enhancement to identify changes in land use/land-cover features. All satellite images had original format in TIFF. They were exported to image format in QGIS 3.10 by using layer stack function. The images were geo-referenced in to the same map projection of WGS (World Geodetic System) 1984 Zone 37N. All satellite images were sub mapped (subset) for covering only the study area. In order to interpret and discriminate the surface features clearly, all satellite images were composed using the Red Green Blue (RGB) colour composition. False Colour Composites (FCC) of satellite imageries were prepared for the years 2009/10 and 2015 using band 4 (NIR), band 3 (Red), and band 2 (Green) and for the year 2020 Landsat 8 using band 5 (NIR), band 4 (Red) and band 3 (Green) combination. Descriptions of the land-cover categories of the study area are shown in Table 1 . Normalized Difference Vegetation Index (NDVI) is the most widely used indicator for vegetation distribution which can be estimated as: The value ranges from − 1 to 1 where − 1 implies for non-vegetative electromagnetic radiation absorbing feature and 1 refers to the presence of deep green vegetation. In this section of the study, NDVI was calculated and NDVI ranges were adapted from Ghebrezgabher et al. (2016) then extent of each NDVI ranges was calculated. 3. Results And Discussions 3.1. Land Use and Land Cover Change Information about the any change in land use/land cover (LULC) changes have become a key component in current strategies for managing natural resources and monitoring environmental changes. The LULC maps were correctly classified independently according to the selected land use classes including bare land, vegetation and cultivated land for each periods (2009, 2015 and 2020). The LULC classification maps for the study site are depicted in Fig. 3 . The spatial distribution of LULC categories of the study area during the period 2015 and 2020 in Fig. 2 showed that the continuous increments of the cultivated land and vegetation land increased, while the bare land declined continuously from 2015 till 2020. The comparison of different LULC between those years is shown in Table 2 as follows. Table 2 Area Comparison of the LULC Changes in Year 2009/10, 2015 and 2020 Land use class Pre-treatment Post treatment 2009/10 2015 2020 Area(ha) % Area(ha) % Area(ha) % Bare land 45.39 87.14 24.01 46.12 14.36 27.5 Cultivated land 0 0 14.62 28.08 22.93 44 Vegetation land 6.7 12.86 13.43 25.8 14.89 28.5 Total 52.09 100 52.06 100 52.18 100 Source : Field Survey Data (2020) The 2009/10 LULC classification map showed, the highest area coverage was the bare lands which accounted about 87%, while, in the year 2015 and 2020, both vegetation and cultivated lands showed the increment trends by 26 and 28% (2015), and 29 and 44% (2020), respectively and the bare lands were the decrement trends by 46 and 28%, respectively (Table 2 ). The Focus Group Discussions and interview in study area also indicated that due to the water spreading weir the vegetation and cultivated lands become increased from time to time and the local residents have engaged in growing maize (Fig. 4 ). 3.2. Analysis of Trend, Rate and Extent of Land Use and Land Cover Change From the results of LULC classification (Table 3 ) in the period’s 2009/10 (pre-treatment) of the area, the vegetation covers were dramatically decreased (about 13%) and nil of the cultivated lands in the water spreading weir site. After treating the area using the water spreading weir, the under cultivation land and vegetation covers have become increasing by 28%, and 6.73 ha (13%), respectively, while the bare lands were decreased by 41%. The detection of LULC changes for a period of 2015–2020 showed, cultivated and vegetation lands coverage were increased by 15.96% and 2.80%, respectively while the bare lands were decreased by 18.53%. The change detection from 2009/10-2020 showed that the cultivated and vegetation lands coverage were increased by 43.94%, 15.69%, respectively and the bare lands were decreased by 59.46% (Table 3 ). The growth of cultivated and vegetation lands might be attributed to the conversion of bare lands to agricultural land as a result of water spreading weirs. Table 3 Trend, Rate and Extent of LULC Change Over the Period (2009/10-2020) 2009/10- 2015 2015–2020 2009/10- 2020 Land Use Class AC RAC AC RAC AC RAC (ha) (Ha/yr.) (%/yr.) (ha) (Ha/yr.) (%/yr.) (ha) (Ha/yr.) (%/yr.) Bare land -21.38 -4.28 -9.42 -9.65 -1.93 -8.04 -31.03 -3.10 -6.84 Cultivated land 14.62 2.92 0 8.31 1.66 11.37 22.93 2.29 0 Vegetation land 6.73 1.35 20.09 1.46 0.29 2.17 8.19 0.82 12.22 RAC = Rate of Area Change, AC = Area Change Source Field Survey Data (2020) 3.3. Accuracy/Validation Assessment of Land-use/Land-cover Mapping The ground truth data were utilized in the classification report as the independent data set from which the classification accuracy was compared. An error report containing the error matrix and accuracy report summarizing the agreement and disagreement are produced (Appendix 2 ). The accuracy is essentially a measure of how many ground truth pixels were classified correctly. An overall accuracy of 100%, 91.3% and 93.5% was achieved with a Kappa coefficient of 1, 0.86 and 0.897 for the three Scenes (Landsat TM and ETM + 2009, OLI 2015 and OLI 2020), respectively. The overall accuracy is a similar average with the accuracy of each class weighted by the proportion of test samples for that class in the total training or testing sets. Thus, the overall accuracy is a more accurate estimate of accuracy. The Kappa coefficient represents the proportion of agreement obtained after removing the proportion of agreement that could be expected to occur by chance. The Kappa coefficient lies typically on a scale between 0 and 1, where the latter indicates complete agreement, and is often multiplied by 100 to give a percentage measure of classification accuracy. 3.4. Assessment of Vegetation Cover Using NDVI Normalized Difference Vegetation Index (NDVI) is calculated for the Landsat images of the 2009, 2015 and 2020 using the NDVI formula to see how the vegetation cover of the water spreading weir practice area changes over the past periods. The NDVI values for vegetation range from the low of -0.0377358 to high 0.288462 for the year 2010, from the low of 0.0972257 to high 0.420435 for the year 2015 and low 0.116988 to high 0.444969 for the year 2020. Non vegetated areas have NDVI values of less than zero (-0.0377358) and the highest NDVI values have represented 0.444969 the maximum vegetation at that period (Table 5 ). Table 5 Status and Extents of Vegetation on the Three Periods Vegetation status 2009 2015 2020 Minimum NDVI value -0.0377358 0.0972257 0.116988 Maximum NDVI value 0.288462 0.420435 0.444969 Vegetation Distribution NDVI range Status (contextual) Area (hectare) < 0.20 No Vegetation 49.95 17.1225 8.73 0.20 to 0.24 Low vegetation 1.8 18.585 11.61 0.24 to 0.31 Moderate vegetation 0.270 12.5775 23.3325 ≥ 0.31 High vegetation - 3.7125 8.325 Total Area 52 52 52 Source : Filed Survey Data (2020) The NDVI map in Fig. 6 indicates that the vegetation decreased dramatically during the time from 2009 to 2015 (pre-treatment). This might be due to inappropriate land use management and charcoal production in the area as well as it might be high soil and plant species erosion. During the post treatment period from 2015 to 2020, there was significant increments have observed in the area. Thus, the researcher has observed good practices and would remind to scale up widely the water spreading weir structures in the District as well as in other areas of the region and the country. The rehabilitation of vegetation in many places of the rangeland has improved the vegetation cover. Agro-pastoralists also confirmed during focus group discussions, thanks to the water spreading weir, the natural vegetation of the area and the availability of fodder for livestock improve the land and their livelihoods. This increment of the vegetation might be due to adoption of soil and water conservation practices, better utilization of surface and ground water. 4. Conclusion And Recommendation Assessment of the impacts of water spreading weir using satellite data are paramount importance in order to evaluate the pre and post water spreading weir intervention conditions, and generate baseline information that helps to monitor and evaluate real time situation in the future for different options within the relatively large geographical area and repetitive time scale coverage. Major changes in the water spreading weir due to implementation of sustainable land management programs a reflected in the development of vegetation cover, agricultural land-use, reduced soil erosion and rehabilitation of degraded rangelands. The improvement in vegetation cover could be attributed to the better soil and water conservation practices. The findings of this research will contribute to developing future soil and water conservation strategies in response to sustainable land management. The researcher recommends the soil fertility, quality and moisture content analysis will be done in the future. Abbreviations ANOVA: Analysis of variance; CSA: Central statistical authority; SPSS: Statistical Packages for Social Scientists; SWC: Soil and water conservation Declarations Acknowledgements The author is grateful to GIZ for providing the required financial support and to Samar University for allowing him to participate in the program. My heartfelt thanks go out to my supportive families and coworkers for their ideas, morals, and supplies, as well as the Chifra District Authorities for their help and knowledge throughout the process. Authors’ detail Truset Fetene is an expert of Disaster Risk Management and Pastoral Development in Afar regional state Bureau of Livestock Agriculture and Natural Resources Development. Truset Fetene attended her Bachelor Degree at Department of Natural resources management and Master’s Degree at Geography and Environmental Studies (specialization in Disaster Risk Management and Pastoral Development) in samara University, Ethiopia. E-mail address: [email protected] (+251 920 54 91 06) Afar, Ethiopia. Nahusenay Abate Dessie is an Associate Professor and Lecturer in the Department of Geography and Environmental Studies (PhD in Soil Science- Soil Genesis and Land Evaluation), Samara University, Samara, Ethiopia. E-mail address: [email protected] , Tel: + 251913 86 61 85, Box 132 Samara University, samara, Ethiopia. Tilahun Amede is an Associate Professor and Principal Scientist in Systems Agronomy and Integrated Agriculture, International Crops Research Institute for the Semiarid Tropics (ICRISAT) and C/o International Livestock Research Institute (ILRI) [email protected] / [email protected] (Mobile: +251911230135) Funding The first author acknowledges GIZ for financial support of this study. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Authors’ contributions TF has made significant contribution in conception and designing of the study, sample collection, analysis, and interpretation; NA and TA have contributed in designing the study, interpretation of results and editing, commenting and suggesting ideas in the manuscript preparation process. Finally, all authors read and approved the final manuscript for publication. Ethics approval and consent to participate Not applicable. Consent for publication All authors agreed and approved the manuscript for publication in Environmental Systems Research . Competing interests The authors declare that they have no competing interests. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details 1a Afar Regional State, Bureau of Livestock Agriculture and Natural Resources Development, [email protected] (+251 920 54 91 06) Afar, Ethiopia. 2 Department of Geography and Environmental Studies, Department of Geography and Environmental Studies, Samara University, Samara, Ethiopia. E-mail address: [email protected] , Tel: + 251913 86 61 85, Box 132 Samara University, samara, Ethiopia. 3 International Crops Research Institute for the Semiarid Tropics (ICRISAT) and C/o International Livestock Research Institute (ILRI) [email protected] / [email protected] (Mobile: +251911230135) References Ahmad, S. (2014). Land use change detection using remote sensing and artificial neural network:Application to Birjand, Iran. Computational Ecology and Software, 4, 276. http://www.iaees.org/publica tions/journals/ces/onlineversion.asp. Akpoti, K., Antwi, E. O., & Kabo-Bah, A. T. (2016). Impacts of rainfall variability, land use and land cover change on stream flow of the black Volta Basin, West Africa. Hydrology , 3 (3), 26. https://doi.org/10.3390/hydrology3030026 Bewket, W. (2002). Land cover dynamics since the 1950s in Chemoga watershed, Blue Nile basin, Ethiopia. Mountain Research and Development , 22 (3), 263–269. https://doi. org/10.1659/0276-4741(2002)022[0263:LCDSTI]2.0.CO;2 Chakilu, G., & Moges, M. (2017). Assessing the land use/cover dynamics and its impact on the low flow of Gumara Watershed, Upper Blue Nile Basin, Ethiopia. Hydrol Current Res , 7 , 2. doi: 10.4172/2157-7587.1000268 Chen, X., Vierling, L., & Deering, D. (2005). A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment , 98 (1),63–79.https://doi.org/10.1016/j.rse.2005.05.021 Congalton, R. G., Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practice : Lweis Publishers. Forkuor, G., & Cofie, O. (2011). Dynamics of land-use and land cover change in Freetown, Sierra Leone and its effects on urban and peri-urban agriculture – A remote sensing approach. International Journal of Remote Sensing , 32 (4), 1017–1037. https://doi.org/10.1080/01431160903505302 Ghebrezgabher, M. G., Yang T., Yang X., Wang X., and Khan M.,(2016). Extracting and analyzing forest and woodland cover change in Eritrea based on Landsat data using supervised classification. The Egyptian Journal of Remote Sensing and Space Sciences (19), 37–47. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment , 4 (1), 117–124. https://doi.org/10.1016/j. ijsbe.2015.02.005 Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The spatiotemporal form of urban growth: Measurement, analysis and modeling. Remote Sensing of Environment , 86 (3), 286–302. https://doi.org/10. 1016/S0034-4257(03)00075-0 Hurni, H., Tato, K., & Zeleke, G. (2005). The implications of changes in population, land use, and land management for surface runoff in the upper Nile basin area of Ethiopia. Mountain Research and Development , 25 (2), 147–154. https://doi.org/10.1659/0276-4741 (2005)025[0147:TIOCIP]2.0.CO;2 Imbernon, J. (1999). Pattern and development of land-use changes in the Kenyan highlands since the 1950s. Agriculture, Ecosystems & Environment , 76 (1), 67–73. https://doi.org/10.1016/S0167-8809(99)00061-4 Jensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective. Prentice-Hall Inc. Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources , 28 , 205–241. https://doi.org/10.1146/annurev.energy.28.050302.105459 Lille sand, T.M. and Kiefer, R.W. (2000). Remote Sensing and Image Interpretation: John Wiley and Sons Inc., New York. Mezegebu, G., Tilahun , A., Gebeyaw, T., Gizachew, L., Murali Krishna, G., Hunegnaw, A., et al. (2019). Water spreading weirs altering flood, nutrient distribution and crop productivity in upstream–downstream settings in dry lowlands of Afar, Ethiopia. Renewable Agriculture and Food Systems 1–11 . NMSA (2007), NMS (National Meteorological Services). 2007. Climate Change National Adaptation Program of Action (NAPA) of Ethiopia. NMS, Addis Ababa, Ethiopia. Setegn, S. G., Srinivasan, R., Dargahi, B., & Melesse, A. M. (2009). Spatial delineation of soil erosion vulnerability in the Lake Tana Basin, Ethiopia. Hydrological Processes: An International Journal , 23 , 3738–- 3750. https://doi.org/10.1002/hyp.7476 Sewnet, A. (2015). Land use/cover change at infraz Watershed, Northwestren Ethiopia. Journal of Landscape Ecology , 8 (1), 69–83. https://doi.org/10. 1515/jlecol-2015-0005 Solaimani, K., Arekhi, M., Tamartash, R., & Miryaghobzadeh, M. (2010). Land use/cover change detection based on remote sensing data (A case study; Neka Basin). Agriculture and Biology Journal of North America , 1 (6), 1148–1157. https://doi.org/10. 5251/abjna.2010.1.6.1148.1157 USGS (https://earthexplorer.usgs.gov Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multi-temporal Landsat remote sensing. Remote Sensing of Environment , 98 (2–3), 317–328.https://doi.org/10.1016/j.rse.2005.08.006 Additional Declarations No competing interests reported. Supplementary Files APPENDIXES.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies 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-2104836","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":140633099,"identity":"6dafa202-955d-487f-bc81-fd8cbc42200e","order_by":0,"name":"Truset Fetene","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYPACOQiVUGEDJBkbD+BTywNRagxhPDiTBtLSQLwWxodth8F8vFrspc8Yf/j4w0DOnr334IMEtvN2a9sPA22psYnGaQtfjpnkjAQDYx6ec8kGCTy3k7edSQRqOZaW24BLCw+PGTNPwp/EHokcM4kEidvJZgeAWhgbDuPTYvz5T4JBfY/8G/MfCQbnks3OPySoxUCaIQHoJAkeM4aEhAN2ZjcI2XKGrUyyJ83AsOdMjrFEwoHkBLMbQFsS8PiFvYd584cfNgby7O1nDD/+/Gdnb3Y+/eGDDzU2OLVggESwygRilYOAPSmKR8EoGAWjYGQAAEycXSkyO0RIAAAAAElFTkSuQmCC","orcid":"","institution":"Samara University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Truset","middleName":"","lastName":"Fetene","suffix":""},{"id":140633100,"identity":"4b8b2b22-b793-439e-94a3-8e9b86523ec5","order_by":1,"name":"Nahusenay Abate Dessie","email":"","orcid":"","institution":"Samara University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nahusenay","middleName":"Abate","lastName":"Dessie","suffix":""},{"id":140633101,"identity":"fc350370-c820-4270-b3c3-229067e83b14","order_by":2,"name":"Tilahun Amede","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tilahun","middleName":"","lastName":"Amede","suffix":""}],"badges":[],"createdAt":"2022-09-26 13:29:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2104836/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2104836/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27475938,"identity":"0cc2098a-be2a-400d-b4a9-a16838632f23","added_by":"auto","created_at":"2022-10-07 15:24:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":344290,"visible":true,"origin":"","legend":"\u003cp\u003eLocation Map of the Study Area in Shekayboru Site at Chifra \u003cem\u003eDistrict\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2104836/v1/d3908f39330018ed782f07f8.png"},{"id":27475937,"identity":"87007aee-5737-47c4-97d8-d05b40af08c9","added_by":"auto","created_at":"2022-10-07 15:24:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157624,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Procedure for Deriving LU/LC Data and Change of Detection from Remotely Sensed Data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Field Survey Data (2020)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2104836/v1/4346455ea0d6a96383aff92f.png"},{"id":27474605,"identity":"4593c99f-5717-47c0-b5ba-f3550f0dc0b5","added_by":"auto","created_at":"2022-10-07 15:14:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":355528,"visible":true,"origin":"","legend":"\u003cp\u003eLand-use/Land-cover Maps 2009, 2015 and 2020 in Shekayboru Site at Chifra \u003cem\u003eDistrict\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Field Survey Data (2020)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2104836/v1/0be3704a421f5995418dc134.png"},{"id":27475135,"identity":"d28b778d-d02f-46b6-a19b-6d19d02b91e7","added_by":"auto","created_at":"2022-10-07 15:19:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10007,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Change in the Study Area from 2009/10-2020 in Shekayboru Site at Chifra \u003cem\u003eDistrict\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Field Survey Data (2020)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2104836/v1/a2155eb46c1c6b0f94c47601.png"},{"id":27474599,"identity":"0dc6788f-525a-4688-9356-f996647e82d4","added_by":"auto","created_at":"2022-10-07 15:14:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11576,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Changes during 2009/10 -2015, 2015-2020 and 2009/10-2020 in Shekayboru Site at Chifra \u003cem\u003eDistrict\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Field Survey Data (2020)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2104836/v1/d1f32f9cd658c8cb4657b8bc.png"},{"id":27474604,"identity":"7c94f471-90e8-4bf4-9902-94688046546b","added_by":"auto","created_at":"2022-10-07 15:14:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":514674,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation Distribution in the Study Area (2009, 2015 and 2020)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003eField survey Data (2020)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2104836/v1/6e1902fb144c392a7fd20f9b.png"},{"id":27836516,"identity":"ad5ec0be-c672-4266-8366-f0ce10c6f514","added_by":"auto","created_at":"2022-10-17 00:44:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1951294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2104836/v1/2782a1d2-2e37-4834-bf36-807b8e5608f5.pdf"},{"id":27475136,"identity":"c7cb69b3-7c06-4bda-b943-3458fc155b71","added_by":"auto","created_at":"2022-10-07 15:19:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24597,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIXES.docx","url":"https://assets-eu.researchsquare.com/files/rs-2104836/v1/b37a42371fc7e2685ce96d54.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Land use/land cover changes and its drivers in Shekayboru area of Chifra District in Afar Region, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNatural and socio-economic factors, as well as human activity in time and space, influence land use and land cover (LULC) change in a given location. Changes in LULC are primarily driven by population expansion (Lambin \u003cem\u003eet al.\u003c/em\u003e, 2003), economic growth, and physical factors such as terrain, slope condition, soil type, and climate (Setegn \u003cem\u003eet al.\u003c/em\u003e, 2009; Yalew \u003cem\u003eet al.\u003c/em\u003e, 2016). When it comes to how people use the land, land-use change is a historical process. It alters the availability of many resources such as plants, soil, and water (Ahmad, 2014). Changes in land use have a direct impact on evapotranspiration, groundwater infiltration, and overland runoff. When it comes to global dynamics and their responses to environmental and socio-economic causes, LULC change is a major concern (Akpoti \u003cem\u003eet al.\u003c/em\u003e, 2016; Bewket, 2002; Hurni \u003cem\u003eet al.\u003c/em\u003e, 2005). On a global and local scale, changes in LULC have a negative impact on climatic patterns, natural hazards, and socio-economic dynamics (Chakilu and Moges, 2017; Hegazy and Kaloop, 2015; Sewnet, 2015). To satisfy the increasing demands for basic human requirements and welfare, information on LULC and potentials for their optimal use is critical for selection, planning, sustainable land resource management, and understanding changes in hydrological processes.\u003c/p\u003e \u003cp\u003eMany applications connected to LULC changes detection, such as cultivation, urban expansion, and landscape changes, benefit from change detection (Hegazy and Kaloop, 2015; Imbernon, 1999; Solaimani \u003cem\u003eet al.\u003c/em\u003e, 2010). Understanding landscape patterns, changes, and interactions between human activities and natural phenomena is critical for effective land management and improved decision-making (Rawat and Kumar, 2015). Remote sensing (RS) and Geographic Information Systems (GIS) are powerful and cost-effective methods for analyzing the spatial and temporal change of LULC (Herold \u003cem\u003eet al.\u003c/em\u003e, 2003; Serra \u003cem\u003eet al.\u003c/em\u003e, 2008). Remote sensing data is now useful and appropriate for LULC change detection research (Yuan \u003cem\u003eet al.\u003c/em\u003e, 2005). Due to its recurring data collecting, suitability for processing, and ease of use, remote sensing data is the most popular source for detection, quantification, and mapping of LULC patterns (Chen \u003cem\u003eet al\u003c/em\u003e., 2005; Jensen, 1996).\u003c/p\u003e \u003cp\u003eThe GIS and Remote Sensing techniques are useful for studying the region's land use dynamics, as well as monitoring, mapping, and managing natural resources. The use of remote sensing and GIS, combined with ground survey methods, is critical for identifying LULC change dynamics (Milanova and Telnova, 2007; Rindfuss, \u003cem\u003eet al\u003c/em\u003e., 2004).\u003c/p\u003e \u003cp\u003eAs per Getnet \u003cem\u003eet al.\u003c/em\u003e (2019) investigated the impacts of the water spreading weir on soil moisture gradient and fertility, crop yields, and biomass production. In the research region, the direction and degree of LULC change and normalized difference vegetation index (NDVI) dynamics as indicators of rangeland restoration as a result of the water spreading weir have not been explored. Therefore, this study will fill the gap.\u003c/p\u003e \u003cp\u003eDue to the aforementioned gaps, the study's goal was to determine the total LULC change and NDVI in the Shekayboru area of Chifra District during the last few decades. The study's specific aims were to measure LULC changes, investigate the trend, rate, and extent of LULC change, and evaluate vegetation diversity using the Normalized Difference Vegetation Index, all in keeping with the overall goal.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Study Area\u003c/h2\u003e\n \u003cp\u003eThe study was conducted at Shekayboru \u003cem\u003eArea\u003c/em\u003e of Chifra \u003cem\u003eDistrict\u003c/em\u003e in Afar Regional state, located at 11\u0026deg;37\u0026prime;43\u0026prime;\u0026prime;N and 40\u0026deg;02\u0026prime;30\u0026prime;\u0026prime;E near the base of the eastern escarpment of the Ethiopian highlands (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The study site covers 49.3 ha which is the drought prone area where annual rainfall ranges from 200 to 500 mm, with the rain season extends from July to September. In related to the rainfall times of the Wollo highlands, flooding always comes to the study area from March to April and from July to September because the adjacent highlands have received higher rainfall in both seasons. The mean annual, minimum and maximum temperatures are 27.8, 18.3 and 37.6\u0026deg;C, respectively (NMA, 2007). The soils are variable, ranging from deep alluvial soils in the valley bottoms bordering the highlands to shallow and mostly gravel-dominated soils in degraded rangelands (Mezegebu \u003cem\u003eet al\u003c/em\u003e., 2019).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Data and Data Sources\u003c/h2\u003e\n \u003cp\u003eTo quantify the magnitude and directions of LU/LC change, the digital image data files were organized and used from satellite imageries downloaded in zipped files from the United State Geological Survey (USGS) archive website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://earthexplorer.usgs.gov\u003c/span\u003e\u003c/span\u003e. Image processing and image classification is carried out by using QGIS 3.10 software. The GIS analysis is conducted by using QGIS 3.10. The three periods Landsat satellite images (Thematic Mapper/TM, Enhanced Thematic Mapper/ETM+, and Operational Land Imagery/OLI) were accessed. The selection of satellite images primarily considered: (i) open source data availability, (ii) the image quality to reduce the effect of cloud cover on data reliability. Since the projects started in 2015 (Getnet \u003cem\u003eet al\u003c/em\u003e., 2019), the Landsat Image of 2009, 2015 (Project started) and 2020 were download from USGS(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov\u003c/span\u003e\u003c/span\u003e). The details of remote sensing data used sources and description of the data used in the study area summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath/row\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDate/year\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCloud cover (percent)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePurpose\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource Spatial Resolution /Sharpened**\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSources\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSerious of Landsat Images and Other Data\u0026rsquo;s Used for the Study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168/052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06 Dec. 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168/052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 Jan. 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETM+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB8 for pan-sharpening*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168/052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e06 Feb.2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168/052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 Jan.2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGoogle Earth/Engine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEach periods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGround Truthing Points collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGoogle LLC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStudy area boundary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTo set the spatial scope of the study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Getnet \u003cem\u003eet al.\u003c/em\u003e, 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e*Scan line error is corrected using \u0026ldquo;Landsat Toolbox\u0026rdquo; extension for QGIS.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e** All images used for LULC was sharpened to 15m spatial resolution using corresponding/ available panchromatic band (B8).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3. Data processing and analysis\u003c/h2\u003e\n \u003cp\u003eLandsat imageries of three bands (4, 3, and 2) for Landsat TM and Landsat ETM\u0026thinsp;+\u0026thinsp;whereas bands (5, 4, and 3) to Landsat-8 were used in image enhancement to identify changes in land use/land-cover features. All satellite images had original format in TIFF. They were exported to image format in QGIS 3.10 by using layer stack function. The images were geo-referenced in to the same map projection of WGS (World Geodetic System) 1984 Zone 37N. All satellite images were sub mapped (subset) for covering only the study area. In order to interpret and discriminate the surface features clearly, all satellite images were composed using the Red Green Blue (RGB) colour composition. False Colour Composites (FCC) of satellite imageries were prepared for the years 2009/10 and 2015 using band 4 (NIR), band 3 (Red), and band 2 (Green) and for the year 2020 Landsat 8 using band 5 (NIR), band 4 (Red) and band 3 (Green) combination. Descriptions of the land-cover categories of the study area are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eNormalized Difference Vegetation Index (NDVI) is the most widely used indicator for vegetation distribution which can be estimated as:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe value ranges from \u0026minus;\u0026thinsp;1 to 1 where \u0026minus;\u0026thinsp;1 implies for non-vegetative electromagnetic radiation absorbing feature and 1 refers to the presence of deep green vegetation. In this section of the study, NDVI was calculated and NDVI ranges were adapted from Ghebrezgabher \u003cem\u003eet al.\u003c/em\u003e (2016) then extent of each NDVI ranges was calculated.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results And Discussions","content":"\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e3.1. Land Use and Land Cover Change\u003c/h2\u003e\n \u003cp\u003eInformation about the any change in land use/land cover (LULC) changes have become a key component in current strategies for managing natural resources and monitoring environmental changes. The LULC maps were correctly classified independently according to the selected land use classes including bare land, vegetation and cultivated land for each periods (2009, 2015 and 2020). The LULC classification maps for the study site are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe spatial distribution of LULC categories of the study area during the period 2015 and 2020 in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e showed that the continuous increments of the cultivated land and vegetation land increased, while the bare land declined continuously from 2015 till 2020. The comparison of different LULC between those years is shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e as follows.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea Comparison of the LULC Changes in Year 2009/10, 2015 and 2020\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLand use class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ePre-treatment Post treatment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2009/10\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea(ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea(ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea(ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultivated land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52.18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Field Survey Data (2020)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe 2009/10 LULC classification map showed, the highest area coverage was the bare lands which accounted about 87%, while, in the year 2015 and 2020, both vegetation and cultivated lands showed the increment trends by 26 and 28% (2015), and 29 and 44% (2020), respectively and the bare lands were the decrement trends by 46 and 28%, respectively (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The Focus Group Discussions and interview in study area also indicated that due to the water spreading weir the vegetation and cultivated lands become increased from time to time and the local residents have engaged in growing maize (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e3.2. Analysis of Trend, Rate and Extent of Land Use and Land Cover Change\u003c/h2\u003e\n \u003cp\u003eFrom the results of LULC classification (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) in the period\u0026rsquo;s 2009/10 (pre-treatment) of the area, the vegetation covers were dramatically decreased (about 13%) and nil of the cultivated lands in the water spreading weir site. After treating the area using the water spreading weir, the under cultivation land and vegetation covers have become increasing by 28%, and 6.73 ha (13%), respectively, while the bare lands were decreased by 41%. The detection of LULC changes for a period of 2015\u0026ndash;2020 showed, cultivated and vegetation lands coverage were increased by 15.96% and 2.80%, respectively while the bare lands were decreased by 18.53%. The change detection from 2009/10-2020 showed that the cultivated and vegetation lands coverage were increased by 43.94%, 15.69%, respectively and the bare lands were decreased by 59.46% (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The growth of cultivated and vegetation lands might be attributed to the conversion of bare lands to agricultural land as a result of water spreading weirs.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrend, Rate and Extent of LULC Change Over the Period\u003c/strong\u003e (2009/10-2020)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2009/10- 2015\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2015\u0026ndash;2020\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2009/10- 2020\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand Use Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Ha/yr.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%/yr.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Ha/yr.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%/yr.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Ha/yr.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%/yr.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-31.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCultivated land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eRAC\u0026thinsp;=\u0026thinsp;Rate of Area Change, AC\u0026thinsp;=\u0026thinsp;Area Change\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\u003cstrong\u003eSource\u003c/strong\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eField Survey Data (2020)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.3. Accuracy/Validation Assessment of Land-use/Land-cover Mapping\u003c/h2\u003e\n \u003cp\u003eThe ground truth data were utilized in the classification report as the independent data set from which the classification accuracy was compared. An error report containing the error matrix and accuracy report summarizing the agreement and disagreement are produced (Appendix \u003cstrong\u003e2\u003c/strong\u003e). The accuracy is essentially a measure of how many ground truth pixels were classified correctly. An overall accuracy of 100%, 91.3% and 93.5% was achieved with a Kappa coefficient of 1, 0.86 and 0.897 for the three Scenes (Landsat TM and ETM\u0026thinsp;+\u0026thinsp;2009, OLI 2015 and OLI 2020), respectively. The overall accuracy is a similar average with the accuracy of each class weighted by the proportion of test samples for that class in the total training or testing sets. Thus, the overall accuracy is a more accurate estimate of accuracy. The Kappa coefficient represents the proportion of agreement obtained after removing the proportion of agreement that could be expected to occur by chance. The Kappa coefficient lies typically on a scale between 0 and 1, where the latter indicates complete agreement, and is often multiplied by 100 to give a percentage measure of classification accuracy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.4. Assessment of Vegetation Cover Using NDVI\u003c/h2\u003e\n \u003cp\u003eNormalized Difference Vegetation Index (NDVI) is calculated for the Landsat images of the 2009, 2015 and 2020 using the NDVI formula to see how the vegetation cover of the water spreading weir practice area changes over the past periods. The NDVI values for vegetation range from the low of -0.0377358 to high 0.288462 for the year 2010, from the low of 0.0972257 to high 0.420435 for the year 2015 and low 0.116988 to high 0.444969 for the year 2020. Non vegetated areas have NDVI values of less than zero (-0.0377358) and the highest NDVI values have represented 0.444969 the maximum vegetation at that period (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatus and Extents of Vegetation on the Three Periods\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVegetation status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMinimum NDVI value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0377358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0972257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMaximum NDVI value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.420435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.444969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetation Distribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDVI range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus (contextual)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (hectare)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.1225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20 to 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 to 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.3325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Filed Survey Data (2020)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003eThe NDVI map in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e indicates that the vegetation decreased dramatically during the time from 2009 to 2015 (pre-treatment). This might be due to inappropriate land use management and charcoal production in the area as well as it might be high soil and plant species erosion. During the post treatment period from 2015 to 2020, there was significant increments have observed in the area. Thus, the researcher has observed good practices and would remind to scale up widely the water spreading weir structures in the District as well as in other areas of the region and the country.\u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe rehabilitation of vegetation in many places of the rangeland has improved the vegetation cover. Agro-pastoralists also confirmed during focus group discussions, thanks to the water spreading weir, the natural vegetation of the area and the availability of fodder for livestock improve the land and their livelihoods. This increment of the vegetation might be due to adoption of soil and water conservation practices, better utilization of surface and ground water.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion And Recommendation","content":"\u003cp\u003eAssessment of the impacts of water spreading weir using satellite data are paramount importance in order to evaluate the pre and post water spreading weir intervention conditions, and generate baseline information that helps to monitor and evaluate real time situation in the future for different options within the relatively large geographical area and repetitive time scale coverage. Major changes in the water spreading weir due to implementation of sustainable land management programs a reflected in the development of vegetation cover, agricultural land-use, reduced soil erosion and rehabilitation of degraded rangelands. The improvement in vegetation cover could be attributed to the better soil and water conservation practices. The findings of this research will contribute to developing future soil and water conservation strategies in response to sustainable land management. The researcher recommends the soil fertility, quality and moisture content analysis will be done in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANOVA:\u0026nbsp;Analysis of variance; CSA: Central statistical authority; SPSS: Statistical Packages for Social Scientists; SWC: Soil and water conservation\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe author is grateful to GIZ for providing the required financial support and to Samar University for allowing him to participate in the program. My heartfelt thanks go out to my supportive families and coworkers for their ideas, morals, and supplies, as well as the\u0026nbsp;Chifra District Authorities\u0026nbsp;for their help and knowledge throughout the process.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Authors\u0026rsquo; detail\u003c/p\u003e\n\u003cp\u003eTruset Fetene is an expert of Disaster Risk Management and Pastoral \u0026nbsp; Development in Afar regional state Bureau of Livestock Agriculture and Natural Resources Development. Truset Fetene attended her Bachelor Degree at Department of Natural resources management and Master\u0026rsquo;s Degree at Geography and Environmental Studies (specialization in Disaster Risk Management and Pastoral Development) in samara University, Ethiopia. E-mail address: [email protected] (+251 920 54 91 06) Afar, Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eNahusenay Abate Dessie\u003c/strong\u003e is an Associate Professor and Lecturer in \u0026nbsp;the Department of Geography and Environmental Studies (PhD in Soil Science- Soil Genesis and Land Evaluation), Samara University, Samara, Ethiopia. E-mail address: [email protected], Tel: + 251913 86 61 85, Box 132 Samara University, samara, Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTilahun Amede\u003c/strong\u003e is an Associate Professor and Principal Scientist in Systems Agronomy and Integrated Agriculture, International Crops Research Institute for the Semiarid Tropics (ICRISAT) and C/o International Livestock Research Institute (ILRI) \u0026nbsp;[email protected]/[email protected]\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003cu\u003e(Mobile:\u0026nbsp;\u003c/u\u003e+251911230135)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThe first author acknowledges GIZ for financial support of this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Availability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Authors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eTF has made significant contribution in conception and designing of the study, sample collection, analysis, and interpretation; NA and TA have contributed in designing the study, interpretation of results and editing, commenting and suggesting ideas in the manuscript preparation process. Finally, all authors read and approved the final manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All authors agreed and approved the manuscript for publication in \u003cstrong\u003eEnvironmental Systems Research\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Publisher\u0026rsquo;s Note\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author details\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1a\u003c/sup\u003e Afar Regional State, Bureau of Livestock Agriculture and Natural Resources Development, [email protected] (+251 920 54 91 06) Afar, Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Department of Geography and Environmental Studies,\u0026nbsp;Department of Geography and Environmental Studies, Samara University, Samara, Ethiopia. E-mail address:\u0026nbsp;[email protected], Tel: + 251913 86 61 85, Box 132 Samara University, samara, Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003e International Crops Research Institute for the Semiarid Tropics (ICRISAT) and C/o International Livestock Research Institute (ILRI) \u0026nbsp;[email protected]/[email protected]\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003cu\u003e(Mobile:\u0026nbsp;\u003c/u\u003e+251911230135)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, S. (2014). Land use change detection using remote sensing and artificial neural network:Application to Birjand, Iran. Computational Ecology and Software, 4, 276. http://www.iaees.org/publica tions/journals/ces/onlineversion.asp.\u003c/li\u003e\n\u003cli\u003eAkpoti, K., Antwi, E. O., \u0026amp; Kabo-Bah, A. T. (2016). Impacts of rainfall variability, land use and land cover change on stream flow of the black Volta Basin, West Africa. \u003cem\u003eHydrology\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 26. https://doi.org/10.3390/hydrology3030026\u003c/li\u003e\n\u003cli\u003eBewket, W. (2002). Land cover dynamics since the 1950s in Chemoga watershed, Blue Nile basin, Ethiopia. \u003cem\u003eMountain Research and Development\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3), 263\u0026ndash;269. https://doi. org/10.1659/0276-4741(2002)022[0263:LCDSTI]2.0.CO;2\u003c/li\u003e\n\u003cli\u003eChakilu, G., \u0026amp; Moges, M. (2017). Assessing the land use/cover dynamics and its impact on the low flow of Gumara Watershed, Upper Blue Nile Basin, Ethiopia. \u003cem\u003eHydrol Current Res\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 2. doi: 10.4172/2157-7587.1000268\u003c/li\u003e\n\u003cli\u003eChen, X., Vierling, L., \u0026amp; Deering, D. (2005). A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e,\u003cem\u003e98\u003c/em\u003e(1),63\u0026ndash;79.https://doi.org/10.1016/j.rse.2005.05.021 \u003c/li\u003e\n\u003cli\u003eCongalton, R. G., Green, K. (2009). \u003cem\u003eAssessing the Accuracy of Remotely Sensed Data:\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003ePrinciples and Practice\u003c/em\u003e: Lweis Publishers.\u003c/li\u003e\n\u003cli\u003eForkuor, G., \u0026amp; Cofie, O. (2011). Dynamics of land-use and land cover change in Freetown, Sierra Leone and its effects on urban and peri-urban agriculture \u0026ndash; A remote sensing approach. \u003cem\u003eInternational Journal of Remote Sensing\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(4), 1017\u0026ndash;1037. https://doi.org/10.1080/01431160903505302\u003c/li\u003e\n\u003cli\u003eGhebrezgabher, M. G., Yang T., Yang X., Wang X., and Khan M.,(2016). Extracting and analyzing forest and woodland cover change in Eritrea based on Landsat data using supervised classification. \u003cem\u003eThe Egyptian Journal of Remote Sensing and Space Sciences\u003c/em\u003e (19), 37\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eHegazy, I. R., \u0026amp; Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. \u003cem\u003eInternational Journal of Sustainable Built Environment\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 117\u0026ndash;124. https://doi.org/10.1016/j. ijsbe.2015.02.005\u003c/li\u003e\n\u003cli\u003eHerold, M., Goldstein, N. C., \u0026amp; Clarke, K. C. (2003). The spatiotemporal form of urban growth: Measurement, analysis and modeling. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(3), 286\u0026ndash;302. https://doi.org/10. 1016/S0034-4257(03)00075-0\u003c/li\u003e\n\u003cli\u003eHurni, H., Tato, K., \u0026amp; Zeleke, G. (2005). The implications of changes in population, land use, and land management for surface runoff in the upper Nile basin area of Ethiopia. \u003cem\u003eMountain Research and Development\u003c/em\u003e, \u003cem\u003e25 \u003c/em\u003e(2), 147\u0026ndash;154. https://doi.org/10.1659/0276-4741 (2005)025[0147:TIOCIP]2.0.CO;2\u003c/li\u003e\n\u003cli\u003eImbernon, J. (1999). Pattern and development of land-use changes in the Kenyan highlands since the 1950s. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(1), 67\u0026ndash;73. https://doi.org/10.1016/S0167-8809(99)00061-4\u003c/li\u003e\n\u003cli\u003eJensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective. Prentice-Hall Inc.\u003c/li\u003e\n\u003cli\u003eLambin, E. F., Geist, H. J., \u0026amp; Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. \u003cem\u003eAnnual Review of Environment and Resources\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e, 205\u0026ndash;241. https://doi.org/10.1146/annurev.energy.28.050302.105459\u003c/li\u003e\n\u003cli\u003eLille sand, T.M. and Kiefer, R.W. (2000). Remote Sensing and Image Interpretation: John Wiley and Sons Inc., New York. \u003c/li\u003e\n\u003cli\u003eMezegebu, G., Tilahun , A., Gebeyaw, T., Gizachew, L., Murali Krishna, G., Hunegnaw, A., et al. (2019). Water spreading weirs altering flood, nutrient distribution and crop productivity in upstream\u0026ndash;downstream settings in dry lowlands of Afar, Ethiopia. \u003cem\u003eRenewable Agriculture and Food Systems 1\u0026ndash;11\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eNMSA (2007), NMS (National Meteorological Services). 2007. Climate Change National Adaptation Program of Action (NAPA) of Ethiopia. NMS, Addis Ababa, Ethiopia.\u003c/li\u003e\n\u003cli\u003eSetegn, S. G., Srinivasan, R., Dargahi, B., \u0026amp; Melesse, A. M. (2009). Spatial delineation of soil erosion vulnerability in the Lake Tana Basin, Ethiopia. \u003cem\u003eHydrological Processes: An International Journal\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e, 3738\u0026ndash;- 3750. https://doi.org/10.1002/hyp.7476\u003c/li\u003e\n\u003cli\u003eSewnet, A. (2015). Land use/cover change at infraz Watershed, Northwestren Ethiopia. \u003cem\u003eJournal of\u003cbr\u003e Landscape Ecology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 69\u0026ndash;83. https://doi.org/10. 1515/jlecol-2015-0005\u003c/li\u003e\n\u003cli\u003eSolaimani, K., Arekhi, M., Tamartash, R., \u0026amp; Miryaghobzadeh, M. (2010). Land use/cover change\u003cbr\u003edetection based on remote sensing data (A case study; Neka Basin). \u003cem\u003eAgriculture and Biology Journal of North America\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(6), 1148\u0026ndash;1157. https://doi.org/10. 5251/abjna.2010.1.6.1148.1157\u003c/li\u003e\n\u003cli\u003eUSGS (https://earthexplorer.usgs.gov\u003c/li\u003e\n\u003cli\u003eYuan, F., Sawaya, K. E., Loeffelholz, B. C., \u0026amp; Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multi-temporal Landsat remote sensing. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, \u003cem\u003e98\u003c/em\u003e(2\u0026ndash;3), 317\u0026ndash;328.https://doi.org/10.1016/j.rse.2005.08.006 \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":"Image classification, LULC, normalization index, validation assessment, water spreading weir","lastPublishedDoi":"10.21203/rs.3.rs-2104836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2104836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFor long-term land management, understanding the scope, direction, and agents of LULC change, as well as their configuration across geographical and temporal dimensions, is essential. This study looked at the patterns and extents of LULC change in Chifra District's Shekayboru Area for that aim. Landsat imagery from TM and ETM+ (2009/10) and OLI were used to investigate the dynamics of LULCC (2015 and 2020). A hybrid method was used to categorize the LULC maps for each period using the results of supervised classification and intense on-screen-digitizing approaches. Three major LULC types (vegetation, cultivation, and bare lands) were identified, with overall accuracies ranging from 91.3 to 100%. According to the 2009/10 LULC classification map, bare lands had the highest area coverage (around 87%), while in 2015 and 2020, both vegetation and cultivated lands showed increment trends of 26 and 28 percent (2015), and 29 and 44 percent (2020), respectively, and bare lands showed decrement trends of 46 and 28 percent, respectively. The study area's Focus Group Discussions and interviews further revealed that, as a result of the water spreading weir, vegetation and cultivated fields have increased over time, and local villagers have taken up maize farming. Better soil and water conservation methods may be responsible for the increase in plant cover. The outcomes of this study will help design future soil and water conservation measures in response to sustainable land management. According to the researcher, a study of soil fertility, quality, and moisture content should be conducted in the future.\u003c/p\u003e","manuscriptTitle":"Land use/land cover changes and its drivers in Shekayboru area of Chifra District in Afar Region, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-07 15:14:44","doi":"10.21203/rs.3.rs-2104836/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":"0681da71-df0c-4174-a044-f6cf8af61fbb","owner":[],"postedDate":"October 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-17T00:44:12+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-07 15:14:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2104836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2104836","identity":"rs-2104836","version":["v1"]},"buildId":"re_ckhLnmML6MCF96OHNJ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}