Planetary Science in Ethiopia research has been emerged in Ethiopia based on the establishment of Entoto Observatory and Research Center (EORC) as well as special commencement of special graduate in three specialized fields such as (astronomy and astrophysics, space science, remote sensing and Geodesy). The installation of the twining optical telescope and ratification of national space policy has opened many opportunities to focus of planetary science research and training in Ethiopia.  Ethiopia has progressed in research, training, and technology and infrastructure development in planetary science, space technology and astronomy. This paper will focus on planetary science and related activities, current development and future prospects.

Continuous mapping of the height and canopy cover of forests is vital for measuring forest biomass, monitoring forest degradation and restoration. In this regard, the contribution of Light Detection and Ranging (LiDAR) sensors, which were developed to obtain detailed data on forest composition across large geographical areas, is immense. Accordingly, this study aims to predict forest canopy cover and height in tropical forest areas utilizing Global Ecosystem Dynamics Investigation (GEDI) LIDAR, multisensor images, and random forest regression. To achieve this, we gathered predictor variables from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Sentinel-2 multispectral datasets, and Sentinel-1 synthetic aperture radar (SAR) backscatters. The model's accuracy was evaluated based on a validation dataset of GEDI Level 2A and Level 2B. The random forest method was used the combination of data layers from Sentinel-1, Sentinel-2, and topographic measurements to model forest canopy cover and height. The produced canopy height and cover maps had a resolution of 30m with R2 = 0.86 and an RMSE of 3.65m for forest canopy height and R2 = 0.87 and an RMSE of 0.15 for canopy cover for the year 2022. These results suggest that combining multiple variables and data sources improves canopy cover and height prediction accuracy compared to relying on a single data source. The output of this study could be helpful in creating forest management plans that support sustainable utilization of the forest resources.

BackgroundLand use/land cover change (LULCC) exacerbates the global environmental changes through affecting ecosystem services. Soil erosion, which is the most significant global environmental problems caused mainly by LULCC, negatively affects agriculture and water resources development projects by increasing siltation of a reservoirs. As such, up-to-date and reliable information on the LULCC, trends, and its impacts on soil erosion and sedimentation is highly required. However, such an integrated study that uses high spatial earth observation data is limited in most basins of Ethiopia including the Fincha'a watershed. This study was, therefore, to quantify changes in LULC and its impacts on soil erosion by integrating RUSLE model and geospatial technologies. MethodsFor this study, spatial datasets such as Landsat TM, ETM+, and OLI/TIRS satellite images, satellite rainfall, soil, and digital elevation model (DEM) datasets were employed. As such, land use/land cover maps of the 1991, 2006, and 2021 were produced using the supervised image classification techniques of maximum likelihood algorithm. In addition, the impacts of changes in LULC on soil erosion were assessed by changing the C-and P-factors and keeping the other parameters constant. ResultsThe result showed increasing cultivated land, settlement land, and waterbody at the expenses of forest land, shrub land, and grassland. In response to the changes in LULC, the mean annual soil loss increased from 34.5 t ha−1year−1 during the 1991 to 46.4 t ha−1year−1 during the 2006 and to 58.7 t ha−1year−1 during the 2021 year. The result further shows that about 29,180.1 ha (7.6 %), 40,095.6 ha (10.5 %), and 40,969.0 ha (10.7 %) of the watershed were severely eroded during the 1991, 2006, and 2021 years, respectively. Likewise, increasing sediment yield from 1991 (6.7t ha−1year−1), 2006 (8.5t ha−1year−1), and to 2021 (10.3 t ha−1year−1) were observed. ConclusionThe increased soil erosion and sedimentation negatively affects water resource availability and agricultural production. Therefore, proper land management systems should be employed in the watershed to minimize the off-site and on-site effects of soil erosion.

Understanding the variability of climate and vegetation, and their relationship in space and time are crucial for agricultural planning, flood risk assessment, and drought monitoring. However, most studies depend on scarce and unevenly distributed climate station datasets in most basins of Ethiopia like the Genale Dawa basin. Detailed climate and vegetation variabilities and their relationship have not well studied in the Genale Dawa basin, which is often affected by recurrent drought. Satellite remote sensing-derived high spatial resolution climate and vegetation data can provide detailed information on climate variability and vegetation changes. This study assessed spatiotemporal variability of temperature, rainfall, and vegetation greenness and their relationship in the Genale Dawa basin. For this study, temperature (minimum and maximum), rainfall data (4 × 4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (250 × 250 m) were used. Statistical indices like the Mann Kendall (MK) trend test, Coefficient of Variation (CV), Standard Anomaly Index (SAI), Precipitations Concentration Index (PCI), and Pearson correlation techniques were used to examine the trends and variabilities. The result generally indicated increasing trends in rainfall during the dry (“Bega”) season, and maximum and minimum temperatures during Kiremt (Wet season) and annual seasons. Moreover, high rainfall variability in the Dry and Kiremt seasons and low variability of temperature at all timescales were observed in the basin. In addition, anomalies in temperatures (minimum and maximum) and rainfall were observed in the basin during all the timescales. The result further showed a strong correlation of NDVI with rainfall at annual (r = 0.58), dry season (r = 0.9), and the short rainy season (r = 0.7) while weakly correlated at Kiremt (r = 0.31) seasons in the Genale Dawa basin. The result generally showed spatial and temporal variability of climate and vegetation greenness in the Genale Dawa basin, which negatively affects agricultural production, water resource availability, and vegetation growth. Therefore, appropriate management strategies should be implemented to minimize the negative effects.

In this paper, we undertake a unified study of background dynamics and cosmological perturbations in the presence of the Chaplygin gas (CG). This is done by first constraining the background cosmological parameters of different Chaplygin gas models with SNeIa and [Formula: see text] data for detailed statistical analysis of the CG models. Based on the statistical criteria we followed, none of the models has substantial observational support, but we show that the so-called “original” and “extended/generalised” Chaplygin gas models have some observational support and less observational support, respectively, whereas the “modified” and “modified generalised” Chaplygin gas models miss out on the category less observational support, but cannot be ruled out. The so-called “generalised” cosmic Chaplygin gas model, on the other hand, falls under the no observational support category of the statistical criterion and can be ruled out. The models which are statistically accepted are considered for perturbation level in both theoretical and observational aspects. We also apply the [Formula: see text] covariant formalism of perturbation theory and derive the evolution equations of the fluctuations in the matter density contrast of the matter–Chaplygin gas system for the models with some or less statistical support. The solutions to these coupled systems of equations are then computed in both short-wavelength and long-wavelength modes. Then we feed these observationally restricted parameters into the analysis of cosmological perturbations to address the growth of density contrast through redshift. Using the most recent linear growth of the data [Formula: see text], CG models are considered to study the linear growth of the structure.

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

Isolation of climate and Land Use and Land Cover (LULC) changes and their compounded effect is complicated since the two influence each other through forcing-feedback cycles. This study investigates the changes in LULC and its contribution to the change in surface and air temperature in the Lake Tana Basin using data obtained from MODIS remote sensing satellites, ground observation and reanalysis model. Observation minus reanalysis (OMR) method and various analysis tools are used to estimate the impact of LULC changes on temperature. The analysis of LULC change indicates that an increase of Savanna and other type of forest cover by about 519 km2 from year 2001 to year 2020. Wetland increased by about 83.7 km2 during the said period. During the period 2001–2020, about 56.5% baren-land areas are being converted into wetland. The conversion of water bodies and baren land to wetland is likely due to the increase of water hyacinth on both the water body and nearby baren-land whereas the increase in Savanna is linked to an on-going eucalyptus tree plantation. The negative correlation between LST and NDVI (reaching a value of −0.60) is noticed over the Basin throughout the year on inter-annual time scale. An increase in vegetation cover such as Savanna and broad leaf forest coincides with the decreasing LST with an average rate of 0.08%/decade. Also, a reduction in temperature, derived from OMR, at an average cooling rate of about 0.87%/decade is observed in the study period. However, an increase in the LST change rate over built-up and grassland is about 0.03%/decade. Therefore, we conclude that the consistent associations of the increase in areal coverage of the some LULC types with lowest LSTs, 2m-temperature and temperature trend derived from NDVI-LST and LULC-LST associations as well as OMR temperature and LST are unambiguous signatures of the effect of LULC change on temperature over Lake Tana Basin, distinct from that arising due to climate change.

Fit-for-purpose land administration (FFPLA) seeks to simplify cadastral mapping via lowering the costs and time associated with conventional surveying methods. This approach can be applied to both the initial establishment and on-going maintenance of the system. In Ethiopia, cadastral maintenance remains an on-going challenge, especially in rapidly urbanizing peri-urban areas, where farmers’ land rights and tenure security are often jeopardized. Automatic Feature Extraction (AFE) is an emerging FFPLA approach, proposed as an alternative for mapping and updating cadastral boundaries. This study explores the role of the AFE approach for updating cadastral boundaries in the vibrant peri-urban areas of Addis Ababa. Open-source software solutions were utilized to assess the (semi-) automatic extraction of cadastral boundaries from orthophotos (segmentation), designation of “boundary” and “non-boundary” outlines (classification), and delimitation of cadastral boundaries (interactive delineation). Both qualitative and quantitative assessments of the achieved results (validation) were undertaken. A high-resolution orthophoto of the study area and a reference cadastral boundary shape file were used, respectively, for extracting the parcel boundaries and validating the interactive delineation results. Qualitative (visual) assessment verified the completed extraction of newly constructed cadastral boundaries in the study area, although non-boundary outlines such as footpaths and artifacts were also retrieved. For the buffer overlay analysis, the interactively delineated boundary lines and the reference cadastre were buffered within the spatial accuracy limits for urban and rural cadastres. As a result, the quantitative assessment delivered 52% correctness and 32% completeness for a buffer width of 0.4 m and 0.6 m, respectively, for the interactively delineated and reference boundaries. The study proposed publicly available software solutions and outlined a workflow to (semi-) automatically extract cadastral boundaries from aerial/satellite images. It further demonstrated the potentially significant role AFE could play in delivering fast, affordable, and reliable cadastral mapping. Further investigation, based on user input and expertise evaluation, could help to improve the approach and apply it to a real-world setting.