Daniel Aja | Addis Ababa University (original) (raw)

Papers by Daniel Aja

CRC Press eBooks, Oct 11, 2022

The lack of spatially explicit flood hazard mapping has hampered the development of appropriate f... more The lack of spatially explicit flood hazard mapping has hampered the development of appropriate flood interventions at community levels in Nigeria. This paper reports on a work conducted to develop a local government level flood hazard map to delineation flood vulnerable areas. Flood vulnerability mapping in this work was addressed from a perspective of administrative unit as the unit of investigation. The study is anchored on the Modeling Flow based on Relational Rule for flood assessment using ArcGIS in combination with modified rational model. The output of the modified rational model was integrated into the Geographic Information Systems environment by the arithmetic overlay operation methods. The results show that the delineated areas/sub-catchments however experienced the same rainfall intensity of 414.2 mm/h but the flood extents in the areas are different. For instance, the very high flood risk zone covers about 22.8% of the study area while the low risk zone covers about 44.3% and the possible areas likely to experience seasonal floods with a given rainfall input are mostly below 40 m elevation. The results of this study will be helpful to prioritize development efforts at grassroot in the study location and to formulate flood adaptation strategies.

Ecology and Evolution

Cloud cover effects make it difficult to evaluate the mangrove ecosystem in tropical locations us... more Cloud cover effects make it difficult to evaluate the mangrove ecosystem in tropical locations using solely optical satellite data. Therefore, it is essential to conduct a more precise evaluation using data from several sources and appropriate models in order to manage the mangrove ecosystem as effectively as feasible. In this study, the status of the mangrove ecosystem and its potential contribution to coastal resilience were evaluated using the Google Earth Engine (GEE) and the InVEST model. The GEE was used to map changes in mangrove and other land cover types for the years 2009 and 2019 by integrating both optical and radar data. The quantity allocation disagreement index (QADI) was used to assess the classification accuracy. Mangrove height and aboveground biomass density were estimated using GEE by extracting their values from radar image clipped with a digital elevation model and mangrove vector file. A universal allometric equation that relates canopy height to aboveground b...

Sustainability

The classification of mangrove forests in tropical coastal zones, based only on passive remote se... more The classification of mangrove forests in tropical coastal zones, based only on passive remote sensing methods, is hampered by mangrove complexities, topographical considerations, and cloud cover effects, among others. This paper reports on a study that combines optical and radar data to address the challenges of distinguishing mangrove stands in cloud-prone regions. The Google Earth Engine geospatial processing platform was used to extract multiple scenes of Landsat surface reflectance Tier 1 and synthetic aperture radar (C-band and L-band). The images were enhanced by creating a feature that removes clouds from the optical data and using speckle filters to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Classification was evaluated using three scenarios: classification of optical data only, classification of radar data only, and combination of optical and radar da...

Environmental Challenges

Abstract Metal leaching into adjacent agricultural soil from dumpsites continues to hamper the qu... more Abstract Metal leaching into adjacent agricultural soil from dumpsites continues to hamper the quality of produce around the world. Three strategically located major solid waste Dumpsites within Abakaliki metropolis were selected and assessed for potential environmental risks of heavy metals. Samples of the soils from the dumpsites were collected from 0 to 15 cm and 15 to 30 cm soil depth. Multivariate approaches, descriptive statistics and contamination indices were employed. Metal concentrations were compared with local and international standard and most were found to be within the threshold. The results further showed that phosphate (PO43−), nitrate (NO3−) sulphate (SO42−), lead (Pb), copper (Cu), cadmium (Cd), zinc (Zn) and iron (Fe) varied within depths and across dumpsites. Metal concentration was generally higher at 0–15 cm depth and decreased at 15–30 cm. The distribution trend across the studied dumpsites was Fe > Zn > Cu > Pb > Cd. Anions (NO3−, PO43− and SO42−) concentrations generally increased with depth. We used the world average elemental concentrations as a benchmark for risk assessment. The factor of contamination (CF) and the environmental risk index (RI) indicated high contamination as well as environmental risks. The factor of enrichment (EF), Geo-accumulation index (Igeo) and modified environmental risk index (MRI) indicated very high enrichment and environmental risks of Pb, Cd, and Cu. Phyto-remediation, bioremediation, perimeter fencing, and periodic soil monitoring were recommended to restore the degraded soil for improved agricultural productivity.

The study examined the influence of the two most prominent exotic species in Abakaliki, southeast... more The study examined the influence of the two most prominent exotic species in Abakaliki, southeast, Nigeria and the nutrient accumulation on the soils. The plantations species were Gmelina arborea (Gmelina) established in 1988 and Gliricidia sepium established in the same year. The treatments were: Gmelina Plantation Area (GmPA), Gmelina Free Area GmFA, Gliricidia Plantation Area (GlPA), Gliricidia Free Area GlFA. The experiment was established as a Randomized Complete Block Design (RCBD) with four (4) treatments replicated six (6) times. Ground-truthing survey was carried out using a Geographical Positioning System (GPS) and the point data were keyed in into arc GIS software to delineate the study area. The Gmelina and Gliricidia plantation areas and their respective free areas were mapped into 6 plots, and on each plot, sampling points were randomly established, soil samples were taken using soil auger within 0-20cm soil depth. The overall results of exchangeable bases of the soil ...

Mangrove Forest classification in tropical coastal zones based on only passive remote sensing met... more Mangrove Forest classification in tropical coastal zones based on only passive remote sensing methods is hampered by Mangrove complexities, topographic considerations and cloud cover effects among other things. This paper reports on a novel approach that combines Optical Satellite images and Synthetic Aperture Radar alongside their derived parameters to overcome the challenges of distinguishing Mangrove stand in cloud prone regions. Google Earth Engine (GEE) cloud-based geospatial processing platform was used to extract several scenes of Landsat Surface Reflectance Tier1 and synthetic aperture radar (C-band and L-band). The imageries were enhanced by creating a function that masks out clouds from the optical satellite image and by using speckle filter to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Our result show that about 16% of the mangrove extent was lost in...

To delineate flood risk zones in Abakaliki Local Government Area, a hydrological model (modified ... more To delineate flood risk zones in Abakaliki Local Government Area, a hydrological model (modified rational model) was integrated into the GIS environment by the arithmetic overlay operation method, using operators such as addition and division. The results show that the delineated areas however experienced same rainfall intensity of 414.2 mm/hr yet the flood intensities of these areas differ. For instance, the very high flood risk zone covers about 22.8 percent of the study area while the low risk zone covers about 44.3 percent. And the potential areas likely to experience periodic floods with a given input of rainfall are mostly below 40m elevation. This study analyzed time series land use/land cover imageries (1986-2016) and 30 years rainfall data to examine land use/ land cover changes and rainfall variability as underlying causes for flood risk hazards on downstream community. The LULC change detection showed that out of the four identified land use classes (Forest, Agricultural lands, Bare lands, and Settlements), only settlements changed significantly (5% to 12%). The available rainfall record for 30-year period of 1986 to 2016 was analyzed to examine the trends of rainfall in the study area and to provide evidence of climate change. Time series graph was constructed to illustrate the changing trends within the months and years. Statistical analysis was performed and the result shows low monthly precipitation changes throughout the years under study. When the standard deviation values are examined, it is observed that the standard deviation values of most months (March, April, May, June, July, August, September and October) are lower than the mean values of these months indicating that the deviation from the normal distribution is not significant. Qualitative validation of the modeling results obtained through focus group discussions (FGDs) with local communities and experts shows that the flood modeling method accurately classified most communities deemed to be highly susceptible to flood hazard.

H2Open Journal

The absence of a viable flood early warning system for the Sebeya River catchment continues to im... more The absence of a viable flood early warning system for the Sebeya River catchment continues to impede government efforts towards improving community preparedness, the reduction of flood impacts and relief. This paper reports on a recent study that used satellite data, quantitative precipitation forecasts and the rainfall–runoff model for short-term flood forecasting in the Sebeya catchment. The global precipitation measurement product was used as a satellite rainfall product for model calibration and validation and forecasted European Centre Medium-Range Weather Forecasts (ECMWF) rainfall products were evaluated to forecast flood. Model performance was evaluated by the visual examination of simulated hydrographs, observed hydrographs and a number of performance indicators. The real-time flow forecast assessment was conducted with respect to three different flood warning threshold levels for a 3–24-h lead time. The result for a 3-h lead time showed 72% of hits, 7.5% of false alarms a...

Environmental Challenges, 2021

Metal leaching into adjacent agricultural soil from dumpsites continues to hamper the quality of ... more Metal leaching into adjacent agricultural soil from dumpsites continues to hamper the quality of produce around the world. Three strategically located major solid waste Dumpsites within Abakaliki metropolis were selected and assessed for potential environmental risks of heavy metals. Samples of the soils from the dumpsites were collected from 0 to 15 cm and 15 to 30 cm soil depth. Multivariate approaches, descriptive statistics and contamination indices were employed. Metal concentrations were compared with local and international standard and most were found to be within the threshold. The results further showed that phosphate (PO 4 3 −), nitrate (NO 3 −) sulphate (SO 4 2 −), lead (Pb), copper (Cu), cadmium (Cd), zinc (Zn) and iron (Fe) varied within depths and across dumpsites. Metal concentration was generally higher at 0-15 cm depth and decreased at 15-30 cm. The distribution trend across the studied dumpsites was Fe > Zn > Cu > Pb > Cd. Anions (NO 3 − , PO 4 3 − and SO 4 2 −) concentrations generally increased with depth. We used the world average elemental concentrations as a benchmark for risk assessment. The factor of contamination (C F) and the environmental risk index (R I) indicated high contamination as well as environmental risks. The factor of enrichment (E F), Geo-accumulation index (I geo) and modified environmental risk index (MR I) indicated very high enrichment and environmental risks of Pb, Cd, and Cu. Phyto-remediation, bioremediation, perimeter fencing, and periodic soil monitoring were recommended to restore the degraded soil for improved agricultural productivity.

Geology, Ecology, and Landscapes

Geology, Ecology, and Landscapes, 2019