Search Results (6)

The automation in image acquisition and processing using UAV drones has the potential to acquire terrain data that can be utilized for the accurate production of 2D and 3D digital data. In this study, the DJI Phantom 4 drone was employed for large-scale topographical mapping, and based on the photogrammetric Structure-from-Motion (SfM) algorithm, drone-derived point clouds were used to generate the terrain DSM, DEM, contours, and the orthomosaic from which the topographical map features were digitized. An evaluation of the horizontal (X, Y) and vertical (Z) coordinates of the UAV drone points and the RTK-GNSS survey data showed that the Z-coordinates had the highest MAE(_X,Y,Z), RMSE_(X,Y,Z) and Accuracy_(X,Y,Z) errors. An integrated georeferencing of the UAV drone imagery using the mobile RTK-GNSS base station improved the 2D and 3D positional accuracies with an average 2D (X, Y) accuracy of <2 mm and height accuracy of −2.324 mm, with an overall 3D accuracy of −4.022 mm. Geometrically, the average difference in the perimeter and areas of the features from the RTK-GNSS and UAV drone topographical maps were −0.26% and −0.23%, respectively. The results achieved the recommended positional accuracy standards for the production of digital geospatial data, demonstrating the cost-effectiveness of low-cost UAV drones for large-scale topographical mapping. Full article

For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression–cellular automata (LR-CA) and artificial neural network–cellular automata (ANN-CA), for the prediction of LULCC (2019–2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986–2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (−2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land–water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies. Full article

To predict the variability of dam water levels, parametric Multivariate Linear Regression (MLR), stochastic Vector AutoRegressive (VAR), Random Forest Regression (RFR) and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) models were compared based on the influences of climate factors (rainfall and temperature), climate indices (DSLP, Aridity Index (AI), SOI and Niño 3.4) and land-use land-cover (LULC) as the predictor variables. For the case study of the Gaborone dam and the Bokaa dam in the semi-arid Botswana, from 2001 to 2019, the prediction results showed that the linear MLR is not robust for predicting the complex non-linear variabilities of the dam water levels with the predictor variables. The stochastic VAR detected the relationship between LULC and the dam water levels with R² > 0.95; however, it was unable to sufficiently capture the influence of climate factors on the dam water levels. RFR and MLP-ANN showed significant correlations between the dam water levels and the climate factors and climate indices, with a higher R² value between 0.890 and 0.926, for the Gaborone dam, compared to 0.704–0.865 for the Bokaa dam. Using LULC for dam water predictions, RFR performed better than MLP-ANN, with higher accuracy results for the Bokaa dam. Based on the climate factors and climate indices, MLP-ANN provided the best prediction results for the dam water levels for both dams. To improve the prediction results, a VAR-ANN hybrid model was found to be more suitable for integrating LULC and the climate conditions and in predicting the variability of the linear and non-linear time-series components of the dam water levels for both dams. Full article

This study presents a remote sensing-based index for the prediction of soil erosion susceptibility within railway corridors. The empirically derived index, Normalized Difference Railway Erosivity Index (NDReLI), is based on the Landsat-8 SWIR spectral reflectances and takes into account the bare soil and vegetation reflectances especially in semi-arid environments. For the case study of the Botswana Railway Corridor (BRC), the NDReLI results are compared with the RUSLE and the Soil Degradation Index (SDI). The RUSLE model showed that within the BRC, the mean annual soil loss index was at 0.139 ton ha⁻¹ year⁻¹, and only about 1% of the corridor area is susceptible to high (1.423–3.053 ton ha⁻¹ year⁻¹) and very high (3.053–5.854 ton ha⁻¹ year⁻¹) soil loss, while SDI estimated 19.4% of the railway corridor as vulnerable to soil degradation. NDReLI results based on SWIR1 (1.57–1.65 μm) predicted the most vulnerable areas, with a very high erosivity index (0.36–0.95), while SWIR2 (2.11–2.29 μm) predicted the same regions at a high erosivity index (0.13–0.36). From empirical validation using previous soil erosion events within the BRC, the proposed NDReLI performed better than the RUSLE and SDI models in the prediction of the spatial locations and extents of susceptibility to soil erosion within the BRC. Full article

This study presents the estimation of reservoir storage capacity using multiresolution Real-Time Kinematic Global Positioning System (RTK-GPS) DEM, in comparison with ASTER and contour-derived DEM. Through RMSE comparisons of the elevation point uncertainty and error analysis, the results shows that the RTK-GPS DEM gave the best results for the reservoir capacity-area power curve estimation, defined by a convex slope with an exponential deterministic relationship given by $V=0.09\times A^{1.435}$ . The results further show the existence an empirical relationship between the reservoir volume certainty and the GPS point density $d_i$ as $V_e=c\times d_i^{\rho }$ . This relationship is dependent on the reservoir terrain, slope and surface area. Validation of the results with in situ data showed the differences between the simulated and observed storage volumes was less than +10%, and using the Nash-Sutcliffe coefficient of efficiency on the storage volumes, an average efficiency of +0.7 on the monthly observed and simulated reservoir storage volume was observed. Full article

This study aims at providing expertise for preparing public-based flood mapping and estimating flood risks in growing urban areas. To model and predict the magnitude of flood risk areas, an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) analysis techniques are used for the case of Eldoret Municipality in Kenya. The flood risk vulnerability mapping follows a multi-parametric approach and integrates some of the flooding causative factors such as rainfall distribution, elevation and slope, drainage network and density, land-use/land-cover and soil type. From the vulnerability mapping, urban flood risk index (UFRI) for the case study area, which is determined by the degree of vulnerability and exposure is also derived. The results are validated using flood depth measurements, with a minimum average difference of 0.01 m and a maximum average difference of 0.37 m in depth of observed flooding in the different flood prone areas. Similarly with respect to area extents, a maximum error of not more than 8% was observed in the highly vulnerable flood zones. In addition, the Consistency Ratio which shows an acceptable level of 0.09 was calculated and further validated the strength of the proposed approach. Full article