Browsing by Author "Tesfaw, Binyam (PhD)"

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    Agricultural Drought Risk Area Assessment and Mapping Using Remote Sensing and Gis: A Case Study of West Hararge Zone, Ethiopia
    (Addis Ababa Universty, 2017-06) Negassa, Wondwosan; Tesfaw, Binyam (PhD)
    Remote Sensing and GIS Technologies are currently important and timely for draught risk area assessment and mapping. In countries like Ethiopia, experiencing considerable annual regular period of dry seasons, drought is not a new phenomenon. The real drought problem, however, arises when the rainfalls fail to fit with the normal cropping seasons. As this gap brings about eminent crop failure and high yield reduction, drought can be perceived as the quantitative, spatial and temporal mismatch between rainfall and the cropping season. This study aims to identify, analyzing, determining and signifying the impacts of the virtual drought on the local environment and mapping agricultural drought risk prone areas of West Hararge Zone, Oromia region, Ethiopia. In order to effectively realize this goal, efforts were made to collect the ten years (2005-2014) drought behavior data, regarding its onset time, frequency of occurrence, spatial extent, duration and levels of severity on the local environment. In addition, this study utilizes remote sensing data, GIS technology and field observations data. Accordingly, reliable Geo-spatial and temporal data have been obtained through the use of modern Remote Sensing and Geographical Information System. The practical field observations, consultations and discussions played great roles in enhancing the acquisitions of commendable knowledge and experiences on the objective reality of the situation. These data were examined and analyzed to scale up the intensity level of the prevailing drought impacts on the agricultural activities of the local farmers. The general responses to drought and the particular existing correlations between rainfall and crop performances were confirmed using Normalized Difference Vegetation Index, Standard Precipitation Index and Water Requirement Satisfaction Index. Based on the confirmed drought pattern and frequency maps of the three indices, a comprehensive map was produced that indicates agricultural drought risk prone areas of West Hararge Zone. This map shows that 12.34%, 33.89% and 48.48% of the total geographical area of the Zone were slight, moderate and severe agricultural drought risk areas, respectively. The result map was validated based on ground based field data obtained from organizational documents and local field professional practitioners. The validation result show significant relation with a correlation coefficient of r = 94 or R² =88. This result map was based on the robust and timely methods and could be used as a guide for concerned government and non-government organizations for drought impacts mitigation activities in the Zone. Key words: Agricultural drought, GIS, NDVI, Remote Sensing, SPI, SPOT WRSI
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    Assessment of Ethiopian Lakes Level Change and Identification of Endangered Lakes for the Last four Decades Using Landsat Satellite Image
    (Addis Ababa Universty, 2016-05) Girma, Shimelis; Tesfaw, Binyam (PhD)
    Lake level change is fluctuation of water level within the lakes that resulted from the shifting of water balance from its static state. At national scale, the Ethiopian lakes level change was not studied yet using remote sensing method and there was no well-organized geodatabase that shows their spatial variation with specified time interval. Therefore, this was the biggest challenge in order to identify whether the lakes are in dynamic or in steady state. This study aims to solve the above mentioned problem through assessing the Ethiopian lakes level change by developing well-organized geodatabase and identifying the endangered lakes for the last four decades using Landsat satellite images (Landsat MSS, TM, ETM+ and OLI sensors). The result shows that there are 108 natural and man-lakes and from these lakes endangered lakes were identified with ranking their spatial extent fluctuation. Accordingly, Lake Abiyata, Chamo, Chelelek and Haromaya, ranked top three declining lakes, and Lake Beseka, top one rising lake, was identified as endangered lakes. Furthermore, Lake Abiyata and Chamo were shrunk by 33.59% (66.64 km2) and 8.98% (29.43 km2) respectively and Lake Beseke was drastically raised by above 900% compared to their 1970’s spatial extent. However, Lake Chelelek and Haromya were desiccated before 2010’s due to anthropogenic factors. The main factors considered in this study was the Land use land cover of the lakes surrounding and over consumption of the lakes water. Moreover, the verification for identified endangered lakes were done using the field collected coordinates of the lakes edge and the trend of water level of the lakes measured by Ministry of Water, Irrigation and Energy. Due to the drastic decline, Lake Abiyata needs special rehabilitation practice and Lake Beseka also needs proper treatment. Furthermore, the final result of this study used as a benchmark for the coming researches. Key words: Landsat image, lakes level change, endangered lakes, Ethiopia, geodatabase
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    Assessment of Soil Erosion Risk Area Using Object Based Image Analysis and RUSLE3D Model: A Case Study of Wenberta Watershed, Atsbi-Wenberta Woreda, Tigray, Ethiopia
    (Addis Ababa Universty, 2016-06) Getachew, Tewodros; Tesfaw, Binyam (PhD)
    Soil erosion is a global issue that threatens human livelihoods and civilization. The impact of soil erosion can be worst in the developing countries like Ethiopia, including Tigray Highlands, which is highly affected by the risk of desertification that is also aggravates by a combination of natural and human factors. In most areas of the country farmers are highly dependent on natural land proprieties and unable to improve soil fertility through application of purchased inputs. Therefore, the community try to protect the soil loss of the area to tackle such kind of problems. In this study, Revised Universal Soil Loss Equation (RUSLE) 3D model was used to quantify the erosion in a GIS environment in order to calculates actual average soil loss in Wenberta watershed, Tigray region, Ethiopia. RUSLE 3D model applied to produce the soil erosion risk map using ArcGIS10.3, SAGA GIS, and eCognition Essential software's. The model incorporates Rainfall , Soil, Topographic (a three dimensional modification), Land cover, and Practice Management factors. Land Cover map of the area was produced using Object Based Image Analysis (OBIA) from a very high resolution (0.25 cm spatial resolution) of aerial photographs for the entire watershed. The overall accuracy of land cover from OBIA was 85.2%. Slope and Upslope area were generated from a 10 m resolution Digital Elevation Model (DEM). The model final result shows that the actual soil loss quantity ranges from 0 to very high soil loss rates ( 127.92 t/ha/y). According to the final result the central parts of watershed have very high severity of soil erosion and most of the mountainous areas also high soil erosion risks. The study showed that stone bunds and bench terrace in Practice Management are successful management practice to conquer the soil erosion risks of the area. This study demonstrates that the RUSLE 3D model is a robust model for estimation of soil erosion studies and risk assessment mapping integrating with remote sensing products and Geographic Information Systems and can be applied in other parts of Ethiopia. As a result, the areas that have high erosion risk should be conserved in priority by the most known methods of conservation practices of the area. Key words: RUSLE3D, Soil Erosion, OBIA, GIS, Remote Sensing, Wenberta watershed, SAGA GIS
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    Assessment of the Spatial Distribution of Wild and Cultivated Ensete ventricosum in Ethiopia Using Geospatial Tools
    (2017-05) Awoke, Meron; Tesfaw, Binyam (PhD)
    Ensete ventricosum varieties are important for agricultural and economic developments of Ethiopia. However, little information is documented on the existing characteristics of ensete and distribution of different ensete verities across different agro-ecological zones. Hence, analyzing the spatial distribution of these species at spatial and temporal scale is of great importance for resource management and conservation planning. This study is attempted to identify, map and model the distribution of wild and cultivated Ensete ventricosum (ensete) with respective to land cover, climate, natural vegetation cover and agroecological zones. For this purpose, geospatial data analysis and MaxEnt modeling techniques were used to map the probabilistic distribution of both wild and cultivated verities. About 26 environmental factors were utilized as variables for distribution and modeling these includes 19 bioclimatic variables, DEM, LU/LC, vegetation, agro-ecology and soil type, and 192 cultivated and 20 wild ensete species records. Besides, Pearson correlation analyses were undertaken for 26 environmental variables to reduce highly correlated variables. The MaxEnt modeling has proven to be very effective at determining habitat use and species distributions for a variety of species and localities. The average test Area under curve (AUC) for the replicate runs was 0.842, and the standard deviation was, 0.046 and AUC 0.760 with standard deviation of 0.101 for cultivated ensete and wild ensete, respectively. Out of the determinants, the annual rainfall (15.7%) and LU/LC (53.7%) were the most important environmental variables that highly affected the distribution of cultivated and wild ensete, respectively. In addition, about 1.55% of the study area was covered by primarily hotpot areas of cultivated and 3.6% is for wild ensete. The probabilistic distribution of cultivated ensete is higher in the southern region, some part of Oromia region, and little areas of southern and eastern parts of Amhara region, whereas wild ensete is spatially highly distributed in Tigray and Benshangul Gumuz regions in addition to areas that cultivated ensete is distributed. In terms of agroecological zones, ensete is dominant to tropical sub-humid areas and tropical cool humid areas, typically at higher elevations (highlands), and areas having high rainfall and low temperature. Keywords: MaxEnt modeling, AUC, Ensete ventricosum, Geospatial data analysis
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    Cellular Automata Model Based Urban Sprawl Mapping: A Case of Mekelle City, Ethiopia
    (Addis Ababa Universty, 2017-06) Kidane, Berhanu; Tesfaw, Binyam (PhD)
    In Mekelle city, urban growth and rapid urbanization are recognized facts for urban sprawl. This study aims to study the urban land-use change and modeling for the city. Coupling remote sensing, GIS and Cellular Automata Markov Chain model were applied to analyze the urban land-use urban sprawl, predict its magnitude, trends and spatial pattern; on the city of Mekelle. The data were i) the United States Geological Survey Landsat 4/5 TM and acquired in January 1989 ii) Quick bird image acquired in January 2005 from INSA and iii) Aerial photograph acquired in January 2016 from Mekelle city Integrated Land Urban Development (ILUD). The supervised classification algorithm of pixel-based approach was applied to identify the major urban land-use types in the study area. These were categorized in to 5 classes: built-up, agriculture, vegetation, water bodies and open area. Shannon's entropy approach was used to know the degree of dispersion, direction of built-up area's development and urban sprawl. In addition, Cellular Automata Markov modeling approach was applied to predict urban land-use change between 1989 and 2016. Land-use maps of 1989 and 2005 were used to produce a transition probability matrix for predicated urban land-use maps of 2016. The observed spatial extent increases from 1989 to 2016. That is, built-up, agriculture, vegetation and open area was increased by 17247.1 ha (64.11%), 1107.38 ha (41.29%), 2238.34 ha (8.32%) and 1384.03 ha (5.15%), respectively, while water bodies were declined by 4302.72 ha (-15.99%).This shows that urban sprawl is highest with built-up area. In the analysis that was made to the year 2016, the pattern and tendency of urban growth change for the year 2026 was also predicted. Therefore, the predicted land-use results for the year 2026, shows that the amount of urban land-use will increase by 12% from 2016 to 2026 at the expense of other land-use types. The results also shows the location and growth area for urban expansion of Mekelle which is towards in all directions. The urban sprawl phenomenon under these study areas was of a variable irregular pattern showing the combination of three sprawl patterns: expansion clusters, leapfrog and linear strip or horizontal along highways. This information is expected to support in determining the possible future directions of the city for the next 10 years. The city administration, Mekelle Municipality, urban planner and decision makers give the best solution for rapid expansion of urban sprawl by the help of RS and GIS. Key words: Mekelle city, Urban sprawl, Remote Sensing, GIS, Cellular Automata Markov.
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    Crop Field Classification Using Fusion Approach of Unmanned Aerial Vehicle (UAV) and Sentinel 2A Satellite Data the Case of Oda Dhawata Kebele Cluster Farmland Oromia Region Ethiopia
    (Addis Ababa University, 2021-06-05) Demelash, Melkamu; Tesfaw, Binyam (PhD)
    Remote sensing technology has played a significant role in the dynamic information extraction of crop information and mapping. The accuracy of crop type information using this technology needed ground truth data and high-resolution data set. However, accurate crop classification remains challenging in both the same crop with different spectral and different crops with same spectrum phenomenon in the field. Now days, Unmanned Aerial Vehicle (UAV)-based high-resolution images gets the popularity for its high spatial resolution and applicability to solve scientific problems. Therefore, this study aims to evaluate the potential of UAV images for crop field classification blending with Sentinel 2A satellite images. In this study, Crop types was identified such as Teff, Wheat, Faba bean, Barley and Sorghum. UAV data, sentinel 2A and fieldwork data were acquired. The UAV data was preprocessed like camera calibration, photo alignment, dense point cloud generation based on the estimated camera positioning of scouting crop types. Then, orthomosaic UAV image was generated from single dense point cloud. UAV data was fused with Sentinel 2A (the medium resolution) satellite data using Gram Schmidt pan sharpening method to improve the spectral variability and evaluating the accuracy of crop type classification. For crop classification, machine-learning algorithm on R software was applied using the Random forest (RF) and Maximum likelihood. The vegetation indices of the NDVI for UAV and S2A was carried out and correlation was performed. The results show that RF classifier algorithm classifies the crop types with 94% overall accuracy whereas the Maximum likelihood classified with 90% overall accuracy. The correlation between the vegetation indices shows the fusion of UAV and S2A for crop type classification was 0.57. This indicates that how much the fusion of the sensors was fine for classifications. It is mostly significant that if the area of interest enhanced then easily detect the regions stressed, monitor and crop type mapping of Ethiopian Agricultural practices using blending of UAV and newly satellite launched by Ethiopian.
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    Detection of Geothermal Anomalies and Evaluating Land Surface Temperature in Northern Abaya Geothermal Field, Main Ethiopian Rift
    (Addis Ababa University, 2020-09-02) Taye, Tsion; Tesfaw, Binyam (PhD)
    Land Surface Temperature (LST), which is a radiative skin temperature of a given area, depends on several factors and physical parameters indicating that a onetime satellite image analysis will not be enough to identify areas with geothermal potential. Given the difficulty associated with surface temperature over wider areas, its characterization, distribution and temporal evolution, therefore, necessitates measurements with thorough spatial and temporal samplings. In the present thesis, a multi temporal Thermal infrared (TIR) remote sensing data (2000-2019 in two years interval) from the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Landsat has been used to detect geothermal areas and evaluate the LST of the northern Lake Abaya geothermal prospect of the southern Main Ethiopian Rift (MER). The study uses the single channel algorithm to derive LSTs and compare the result with borehole data from different literatures. The result shows that the mean LST is highest in 2003 (320.1 K) and lowest in 2019 (303.1 K). The change in mean LST was between –9 K to 13 K. This LST results from ASTER images were validated with ASTER LST products and show more than 70% correlations. The derived LSTs from the nine images were compared with the borehole data. LSTs of the year 2003 have been much closer to the actual temperature value from borehole data. The geothermal anomaly areas were validated with the existed field data (19 sites). Fifteen sites fall in the identified geothermal anomaly areas, which is 79 % of the total. The result of correlation between time and LST values in known geothermal activity sites shows no correlation (less than 0.5) except one site, which is Boramitta (0.54). This shows that the LST is time invariant which goes well with the fact that there shouldn‘t be any significant change of LST in geothermal anomalous areas.
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    Flood Detection and Mapping Using Microwave Remote Sensing; A Case Study on Lake Koka Cachment, Awash River Basin, Ethiopia
    (Addis Ababa Universty, 2017-07) Tessema, Getu; Tesfaw, Binyam (PhD)
    Sentinel-1 is a microwave remote sensing mission providing continuous all-weather and day-night time radar data. The main goal of the present study is to evaluate microwave remote sensing data for flood detection and to develop the flood extent map from a series of radar SAR images. The study area is on Lake Koka catchment, Awash River basin which has an increased agricultural investment interests. This area was frequently affected by flood during the “belg” and summer seasons in 2016 caused by the over flow of Awash River and the flash flood of the surrounding tributary streams. For the present study, Sentinel-1 SAR time series images, covering the same scene but at different times were utilized in order to achieve the research objectives. These images were: i) before flooding i.e. acquired on 22 March, 2016 and ii) after the flood event; acquired on 15 April and 09 May, 2016. The images were de-speckled using various filtering algorisms. After comparison of the image quality based on the algorisms, the gamma map 77 kernel size speckle filtering method was selected and used as speckle removal for the study. The backscatter properties of five different feature classes in the context of flood extent extraction were derived from time series SAR images. These feature classes were open water, flooded area, agriculture, vegetation and bare soil. From such backscatter properties of test class features on the SAR image, appropriate change detection threshold value was set by visual interpretation and image histogram analysis. Based on the threshold value the changed and unchanged areas were identified for inundated area delineation. Change detection algorithms were applied to extract the flood extent from the processed SAR images. Of all other change detection methods, the band subtraction, band ratioing and principal component differencing (PCD) techniques were utilized. The results of each technique was compared with one another. The band subtraction and band rationg algorithms showed similar flood extent map. The flood extent extracted from band subtraction method was 24.12 km2 for 15 April, 2016 and 17.63 km2 for 09 May, 2016 flood events. The band ratio method has resulted 23.22 km2 and 17.3 km2 flood extent of 15 April, 2016 and 09 May, 2016 respectively. The other method, the PCD accounted for 20.67 km2 area of 15 April, 2016 and 15.7 km2 of 09 May, 2016 flood extent. The flood extent maps were presented separately for each flood detection method. The SAR images was also used for land-use/land-cover classification with Landsat 8 optical sensor image. Based on these stacked different sensor images, the land-use/land-cover of the study area was classified in to six classes. Theses six land-cover classes were; 1) agriculture field, 2) bare land, 3) irrigated land, 4) water body, 5) settlement and 6) vegetation. The overall classification accuracy was 90% with 0.86 kappa statistics value. Generally, this research has observed that space-born SAR satellite data is an outstanding technology for near real time flood detection and mapping. It provided promising flood extent map that could help in the preparation of flood monitoring and management processes. Keywords: Microwave remote sensing, Sentinel-1, SAR, Awash River, Change detection, Flood, Backscatter analysis, Speckle filtering
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    A Fuzzy Approach for Modeling Potential Wind Farm Areas: A Case of Hitosa Woreda, Oromia Region, Ethiopia
    (Addis Ababa Universty, 2016-06) Tesfaye, Ebisa; Tesfaw, Binyam (PhD)
    Being cleaner and climate friendly, wind energy has been increasingly utilized to meet the evergrowing global energy demands. In Ethiopia, a wide gap exists between wind resources and actual energy production, and it is imperative to expand the wind energy production. This study was conducted in Hitosa Woreda, which is located in East Showa Zone of Oromia Region, in the Rift valley area of the country. The main objective of this study was to identify potential wind farm sites in the study area using fuzzy approach. The development of new wind farm energy project requires studying of many parameters to achieve maximum benefits at the cost of minimum environmental impacts. While site selection, there is a problem comes with prioritizing criteria that determine the best location. Dealing with real life situation and experts' judgments involves uncertainty. To solve this problem, a model containing Multi-Criteria Decision Making (MCDM) technique that is Analytical Hierarchy Process (AHP) with fuzzy theory was designed to handle the uncertainty situations. Ten criteria were adopted in this method, including wind speed, distance to roads, to rivers, from towns, from faults, closeness to power line, slope, lithology, elevation, slope and exclusionary areas. The weights of the criteria of the site were obtained through application of developed FAHP idea. Geographic Information System (GIS) was used to overlay and generate criteria maps, and IDRISI 17.0 was used for fuzzy aggregation and development of suitability map. The study ends with an assessment of proposed sites to the generated suitability map. The results of the assessment showed that the northern zones of the investigated region have high wind energy potentials. Such zones are appropriate for setting up electricity generating wind turbines. From total investigated area of 1260sq. km. the amount of extremely suitable zone was 96.902 sq. km, highly suitable zones was 152.194 sq. km, moderately suitable zones is 179.11 sq. km, less suitable zones was 311.159 sq. km. The suggested model may serve as a useful decision making tool for the energy planners and decision makers, intending to develop wind farm energy in the present study area. This model is accepted to help to identify suitable wind farm locations in other areas with a similar geographic back ground. Key words: Crisp, Fuzzy, FAHP, GIS, wind farm
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    Groundwater potential and Recharge zone mapping by using GIS and Remote sensing Techniques in the case of Middle Awash River Basins, Ethiopia
    (Addis Ababa Universty, 2017-05) Lammesa, Bane; Tesfaw, Binyam (PhD)
    Groundwater is a valuable and important natural resource in the world and it is the most fundamental for the growth and development of one country. However estimating the potential of groundwater potential and its recharge zone has still uncertainty due to the nature of groundwater. Therefore, this study aims to use the timely and cost effective remote sensing and geographical information system (GIS) methods for delineating, classify groundwater potential, and recharge zone in the Middle Awash River basin. Nine factors as a thematic map derived from Landsat 8 Operational Land Imager (OLI) Satellite image of the year 2016, Digital Elevation Models (DEM) with 30 m and 1km spatial resolution and secondary sources were utilized in this research. These were rainfall, slope, geomorphology, geology, lineament density, drainage density, surface runoff, and Land-use/land-cover and soil texture. The methods to assess the potential and recharge zone was using weight overlay analysis and Analytical Hierarchy Process (AHP) algorithm. All thematic layers were reclassified based knowledge based analysis that was reviewed from different kinds of literature. Then the weight for each factor was assigned according to their relative importance as per suitable based on Saatty's scale of AHP. The important factors result show that rainfall and slope have a higher weight and lithology has the lowest weight for identifying the potential of groundwater potential and recharge zone in the study area respectively. The resulting map of groundwater potential and recharge shows that 34 % and 32% of the area has very high potential, respectively. The produced map of groundwater recharge zone reveals that the northwestern and southeastern highlands of the study area are the most suitable areas. The result also exhibits very high groundwater potential areas have a very low slope, alluvial plains, with high lineament density and sandy loam soil textures. On the other hand, very low groundwater potential corresponding to barrier landforms, structural hills, and high slope areas. High recharge areas characterized by high rainfall, dense forest, and high drainage density. This result of groundwater potential and recharge was validated and shows 0.88 correlations of high and very high areas with that of existing water source and spring data. Therefore, this research demonstrates a robust method of using GIS and remote sensing techniques, which is efficient and useful in delineating and mapping groundwater potential and recharge zone. Keywords: Groundwater potential, Groundwater Recharge, GIS, Remote Sensing, AHP
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    The Impacts of Vegetation Cover Change on Rainfall and Land Surface Temperature Using Remote Sensing: A Case Study of North Gondar Zone, Ethiopia
    (Addis Ababa University, 2018-06-03) Nega, Worku; Tesfaw, Binyam (PhD)
    Remote Sensing has a great importance for vegetation cover change analysis specifically deforestation and its impacts on climatic parameters, biodiversity, fertility of soil and land productivity. However, there is a gap on quantifying and analyzing the impact of change in vegetation cover on the change of climatic variables such as: fluctuation of rainfall and Land Surface Temperature (LST). Therefore, this study aims to assess of vegetation cover change and its impacts on rainfall and land surface temperature using remote sensing data and statistical methods. The study was conducted in North Gondar which is characterized by high deforestation, expansion of agriculture and high rate of population growth. The data were i) satellite images from Landsat 5 Thematic Mapper Sensor(TM) (1985, 2000, 2010) and Landsat 8 Operational Land Imager/ Thermal Infrared Sensor (OLI/TIRS) (2017): to analyze the vegetation cover change using NDVI threshold method by using Ground truth and google earth data, and previous studies. ii) Climate Hazard Group InfraRed Precipitation (CHIRPS) and Moderate Resolution Imaging Spectroradiometer (MODIS): to evaluate rainfall and LST using Mann-Kendall Trend Test (iii) field observation and google earth to verify remotely sensed data. Pearson Correlation coefficient was conducted to quantify and to analyze the relationship of vegetation cover with rainfall and LST. The result showed that vegetation cover occupied an area of 210,177 ha (4.7%), 173,971 ha (3.9 %), 141785 ha (3.2%) and 117,019 ha (2.6%) in 1985,2000, 2010 and 2017, respectively where 2.1% of vegetation cover has been lost in the last 32 years in in the study area. The Mann-Kendall trend test result revealed that annual and dry season rainfall increases however no significant trend in mean annual and dry season rainfall with p-value = 0.09 and 0.54 from 1981 to 2017. The trend test result also shows no significant trend in mean dry season LST with p-value= 0.35. According to the pearson correlation coefficient result, the relationship of vegetation cover with rainfall and LST was not statistically significant (p-value=0.68 and R2=0.23 with alpha 0.05) and (p-value=0.29, R2=0.80 with alpha 0.05), respectively. The findings from the zonal statistics result, this study concluded that mean annual rainfall was decreased with declined vegetation cover in central, northern and southeastern part of North Gondar zone. On the other hand, mean LST was increased with declined vegetation cover in the area. LST.
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    Mapping Effects of Urban Blue-Green Landscapes on Land Surface Temperature Using Geo-Spatial Techniques: The Case of Addis Ababa, Ethiopia
    (Addis Ababa University, 2021-06-28) Kifle, Neway; Tesfaw, Binyam (PhD)
    Uncontrolled, unplanned, and unprecedented urbanization characterizes most African cities. Drastic changes in the urban landscape can lead to irreversible changes to the urban thermal environment, including changes in the spatiotemporal pattern of the land surface temperature (LST). Studying these variations will help us take urban climate change mitigation and adaptation measures. This study is intended to map effects of urban blue-green landscapes on LST using geo-spatial techniques in Addis Ababa, Ethiopia from 2006 to 2021. Object-based image analysis (OBIA) method was applied for land use/land cover (LULC) classification using high-resolution imagery from SPOT 5 and Sentinel 2A satellites. Moreover, LST was retrieved from the thermal imageries of Landsat 7 ETM + (band 6) and Landsat 8 TIRS (band 10) using the Mono-Window Algorithm (MWA). Furthermore, linear regression analysis was used to determine the relationship of LST with normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNWI). Five major LULC classes were identified namely, built-up, vegetation, urban farmland, bare land, and water. The result shows that the built-up area was the most dominant LULC in the city and has shown a drastic expanding trend with an annual growth rate of 4.4% at the expense of urban farmland, vegetation, and bare land in the last 15 years. The findings demonstrated 53.7% of urban farmland, 48.1% of vegetation, and 59.4% of bare land, was transformed into a built-up class from 2006 to 2021. The mean LST showed an increasing trend, from 25.8oC in 2006 to 27.2oC and 28.2oC during 2016 and 2021 respectively. It was found that LST varied among LULC classes. The highest mean LST was observed at bare land having an average LST value of 26.9oC, 28.7oC, and 30.1oC in 2006, 2016, and 2021 respectively. While the lowest mean LST was recorded at vegetation with average LST values of 24.3oC in 2006 and 26.0oC in 2021; and at water 25.5oC in 2016. The regression analysis showed a strong negative correlation between NDVI and LST, a strong positive correlation between NDBI and LST, and a weak negative correlation between MNDWI and LST. The findings of this study have indicated that LULC alteration had contributed to the modification of LST in Addis Ababa during the period. The regression analysis results further revealed that built-up area and vegetation cover plays a decisive role in the variation of LST in the city compared to urban surface water and altitude. The findings of this study will be helpful for urban planners and decision-makers while planning and designing future urban blue-green innervations in the city.
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    Modeling Forest Stand Volume and Live Aboveground Woody Biomass Using Remote Sensing and Gis: a Case Study in Chancho Eucalyptus Globulus Plantation Forest, Oromia Regional State, Ethiopia
    (Addis Ababa University, 2014-05-05) Geda, Tariku; Berhan, Getachew (PhD); Tesfaw, Binyam (PhD)
    This study presents the utility of Landsat 5 TM satellite imagery spectral and textural features for the estimation of stem volume and aboveground biomass (AGB) in Chancho Eucalyptus globulus plantation forest, Oromia Regional State, Ethiopia. Stem volume and AGB are arguably the most important variables among forest attributes since they play an important role in understanding the function of forests in the environmental and ecosystem services. Both of them can be estimated using different data and approaches, like using field observation data (classical approach), remote sensing data, and GIS data (modern approach). Even though, field-based forest surveying provides highly accurate measurements, it has limitations with regard to incurring high cost, being time consuming and having low spatial coverage and frequency. In addition to this, in some cases, destructive sampling is laborious and negatively affect environment. This makes sustaining the socio-economic and ecological benefits of forests under challenge. On the other hand, although Remote Sensing and GIS approaches overcome these limitations, they are site and species specific and are highly uncertain. In general in Ethiopian and in particular in the present study site both stem volume and AGB are estimated based on the classical approach. Thus, the present study is conducted to improve accuracy and decrease uncertainties in the modern approach in general, and replace the classical approach in the study site in particular by developing a function that estimate both attributes (dependent variables) as a function spectral and textural features (independent variables) of Landsat 5 TM image acquisition date of January 10, 2011. Based on Pearson correlation statistics test result among dependents and independents variables , Tasseled Cap brightness, GLCM Dissimilarity and GLC Variance were found as best explanatory variables for stem volume estimation. Whereas, Landsat 5 TM Band 5, GLCM Dissimilarity and GLCM Variance found to be as best explanatory variables for AGB estimation. The modeling of the stem volume and AGB equations as a function of spectral and textural independent variables were developed using Ordinary Least Square Regression method. The modern approach estimated almost similar mean stem volume and aboveground biomass abundance with field measurement data. The overall findings presented in this study are encouraging and show that Landsat 5 TM imagery was successful in predicting both attributes with reasonable accuracy (Adjusted R2 is 0.50 and 0.51 for stem volume and AGB, respectively; mean residual is 0 for both stem volume and AGB). Further research is recommended to document the performance of the Landsat 5 TM satellite data under different environmental conditions and topographical changes, as well as for other species.
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    Monitoring of Rice Crop Using Sentinel 2 Optical and Sentinel 1 Radar Images in Fogereda Wereda, Ethiopia
    (Addis Ababa University, 2018-06-03) Talema, Teshome; Tesfaw, Binyam (PhD)
    Though optical remote sensing has various importance for land-cover mapping and monitoring, it is very difficult to assess and monitor rice agriculture over large areas due to cloud cover on the images and the nature of rice agriculture. Therefore, in this study, Sentinel 1 RADAR and Sentinel 2 images were integrated to alleviate this problem. Specifically, time series Sentinel 1 RADAR Interferometric Wide images were utilized to identify optimal polarization, map rice extent, and inundation in the rice fields of Fogera wereda, Ethiopia, during the 2017 growing season. To map paddy fields using Sentinel 1 RADAR, first extracting the temporal backscatter value of rice fields and background land-cover types at the vertical transmitted and vertically received (VV) and vertically transmitted and horizontal received (VH) polarizations of Sentinel 1 RADAR data. From Sentinel 1 RADAR, the temporal backscatter value of rice increased sharply at the early planting stage and decreased during the high flooding stages as well as relatively the same temporal backscatter of the other land-cover types like rice. However, the increase in rice backscatter is more sustained at the Sentinel 1 RADAR VH polarization, and two-class separability measures further showed the superiority of VH over VV in discriminating rice fields. CART model was used for the identification of optimal node of sentinel 1 RADAR VH images, that is used to different time mapping of rice. The rice extent extracted from CART optimal node was 20,911.2 ha for 14 June 2017 and 19,892.5 ha for 01 August 2017 growing seasons. Then, the temporal VH images of Sentinel 1 RADAR was combined with the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) derived from a single-date cloud-masked Sentinel 2 image (October 09, 2017). The integration of these optical indices with temporal backscatter eliminated all commission errors in the rice class and increased overall accuracy by 9.6%, demonstrating the complementary role of optical indices to Sentinel 1 RADAR data in mapping rice fields in tropical areas such as Fogera. The refined land use land-cover map identified by integrating Sentinel 1 RADAR and Sentinel 2 optical indices rice area was 19,157.8 ha with general (R² = 0.94) agreement with wereda census statistics. This study shows that the freely available Sentinel 1 RADAR images are important and applicable for assessing and monitoring paddy rice.
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    Structural Control on the Marine Incursion and Flooding History of the Dallol Depression, Northern Afar (Northeast Ethiopia)
    (Addis Ababa University, 2017-12-02) Filfilu, Ermias; Kidane, Tesfaye (professer); Tesfaw, Binyam (PhD)
    The present study area is situated in the tectonically active Dallol Depression, northern sector of the Danakil Depression, NE of Ethiopia. The main objective of the present study was to evaluate structural control on the Red Sea incursion and flooding history of the Dallol Depression along with production of geological map of the region. To accomplish the so set objective, fieldwork, remote sensing and seismic investigations (from secondary source) were carried out. Structural analysis, band combination and ratio enhancement techniques and DEM analysis enabled the discrimination of different lithologies and delineation of regional structures. Produced geologic map revealed that the northern Danakil Depression and the western margin may represent the complete sequence of rocks spanning from the Neoproterozoic to the Holocene and the dominant structural trend in the study area is NNW followed by N-S and NE trending structures. The study enabled identification of basement rocks, limestone, sandstone, basalt, gypsum and associated coral reefs, alluvial sediments, conglomerate and salt formation. Kinematic analysis of fault-slip data collected from the field indicate NE-SW extension direction coinciding with the regional extension direction in which oblique-slip sense of movement with dominant dip-slip component is exhibited. Dynamic analysis of conjugate fault sets depict that the tensional stress represented by σ3 is parallel to the overall extension direction across the Red Sea. Observation from DEM and differential GPS measurements showed that the western margin of the Depression has been affected by faulting which lead to the desiccation of the Dallol Depression from the Red Sea in combination sea level drop.
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    Urban Sprawl Mapping and Landuse Change Detection Using Spatial Metrics Method: A Case Study of Addis Ababa City and its Surrounding Areas, Ethiopia
    (Addis Ababa Universty, 2017-06) Shiferaw, Sewunet; Tesfaw, Binyam (PhD)
    Rapid and unprecedented urban expansion is becoming the characteristics of cities in developing countries. Hence, it is customary to assess and monitor urban growth changes using remote sensing and other spatial tools to quantify urban sprawl that provide paramount information for city planners. This study was conducted on Addis Ababa metropolitan area with aim of measuring urban sprawl in four years perspective 1984, 1995, 2006 and 2016. Landsat images of each perspective year were used and pan sharpened (fused) with SPOT-5 (5m) using hyperspectral color merging algorism to get better images with 5m spatial resolution. The resulted satellite images were classified and land-use/cover maps were produced using maximum likelihood of supervised classification method. The classification process was checked by producer’s, user’s, overall accuracy and kappa statistic accuracy assessments from confusion metrics. The results show acceptable agreement between the classified maps and reference data with a producer’s accuracy value greater than 74.14%, and user’s accuracy greater than 84.09%. Post classification change detection analysis and selected spatial metric indices calculation were made to detect, assess and monitor urban growth and quantify urban sprawl in the study area. Change detection analysis results indicated that Addis Ababa is growing rapidly with an average rate of 5% per year for the past 32 years from 1984–2016. In terms of area, the expansion of the city was found to be 12,218, 15,981.58, 22,513.29 and 38,801.35 in 1984, 1995, 2006 and 2016 out of the total area in hectare, respectively. In other words, the built-up area constituted 15% for 1984, 19.6% for 1995, 27.5% for 2006 and 47.5% for the year 2016 in the study area. From spatial metrics analysis, aggregated number of built-up area patches were 621, 476, 574 and 840 for the years 1984, 1995, 2006 and 2016. The decrease in number of patches in 1995 indicates that merging of previous patches into the main built-up area forming continuous urban agglomeration. The zonal analysis of urban sprawl shows that Addis Ababa is expanding by leaps and bounds to the east, south, south west and north east directions, particularly in the past ten years consuming a large amount of agricultural and green areas. Therefore, the city planners need to plan ahead and implement plans properly to cope up with the rapid and unprecedented growth of the city in the years to come. Key Words: Spatial metrics, Zonal metrics, change detection, urban sprawl, Addis Ababa
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    Volcanic Hazard and Risk Assessment from Futureeruptions of Fentale Volcano Northern Main Ethiopian Rift (NMER)
    (Addis Ababa University, 2021-09-03) Amentie, Rahel; Yirgu, Gezahegn (Professor); Tesfaw, Binyam (PhD)
    Fentale volcano is one of the Quaternary and most active volcanoes on the axial segment of Main Ethiopian Rift (MER) with long history of both effusive and explosive eruption styles. It is located adjacent to Awash national park, Lake Beseka, Metehara sugar plantation and factory where more than 50,000 people live. The most important and busiest highway and railway route of the country crosses the lower flanks of this volcano. However, this active volcano was not studied well in terms of its hazard, the vulnerability and risk of the area. Therefore, this research aims to evaluate the spatial extent of a possible future eruption of Fentale caldera using a GIS-based volcanic hazard tool to produce the hazard map, assess the vulnerabilities and risk of the area. First, the hazard of lava flow is assessed by analyzing various data such as distance from caldera, vent, fault, elevation and slope which were considered as factors and weighted based on their respective contributions to the hazard as a result the higher weight is given for both caldera and vent. Whereas satellite image (sentinel 2A) was used for pyroclastic eruption and ash fall hazard. Vulnerability is evaluated through a multi-criteria evaluation of population, infrastructure (road and rail), different facility and land use/land cover. To evaluate the overall risk, the hazard and vulnerability maps were aggregated through pairwise comparison matrices and creating risk maps. The greater risk will be expected from pyroclastic flow hazard and it is followed by ash fall hazard and lava flow hazard. The result risk maps show that Southern part of the Caldera is subjected for a very high risk including highly vulnerable cities, such as Metehara and Adis ketema (Haro Adi), and infrastructures like road, rail, and sugar plantation are also under high risk. The methodology presented in this work allows risk analysis posed by eruptions sourced from the Fentale caldera and is especially useful in focusing mitigation strategies to reduce the loss from such hazardous events.