Browsing by Subject "Remote Sensing"

Now showing 1 - 3 of 3
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    Open Access
    Analysing the road reserve encroachment in Maseru Lesotho using remote sensing and image analysis
    (2021) Ralitsoele, Teboho; Sithole, George
    The increasing rate of urbanization and the problem of road reserve encroachment mean that there is no space for road expansion and sometimes for maintenance and road furniture, these and other problems have exposed the problem of road reserve encroachment. The main aim of this study was to investigate methods of finding the road reserve encroachment in Maseru Lesotho using aerial photos. The study used single image analysis and multiple image analysis methods. In single image analysis, the study used three methods of image classifications to find objects that are in the road reserve. Under classification, the study used both supervised and unsupervised image classifications. For supervised classification, the study used the direct image classification method where the aim was to look for every object found in the road reserve. For the indirect approach, the study looked for the ground to find objects in the road reserve. For unsupervised image classification, the study assumed that small clusters are encroachment. In multiple images analysis, the study used the 2015 and 2017 images to determine permanent objects found to have encroached road reserves. Here the assumption was that encroachment does not change over time, which means that unchanged objects during the change detection have encroached on the road reserve. The confusion matrix was used to tell the best performing method and the results show that the indirect method, both in Qoaling and Maqalika performed best. All the methods showed that there was an encroachment on a road reserve, and found that permanent objects were; houses, shops, and shopping centers. The study recommended the use of images with higher resolution and more bands, also that images be taken frequently.
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    Open Access
    Riverine flooding using GIS and remote sensing
    (2020) Dambe, Natalia; Smit, Julian
    Floods are caused by extreme meteorological and hydrological changes that are influenced directly or indirectly by human activities within the environment. The flood trends show that floods will reoccur and shall continue to affect the livelihoods, property, agriculture and the surrounding environment. This research has analyzed the riverine flood by integrating remote sensing, Geographical Information Systems (GIS), and hydraulic and/or hydrological modeling, to develop informed flood mapping for flood risk management. The application of Hydrological Engineering Center River Analysis System (HEC RAS) and HEC HMS models, developed by the USA Hydrologic Engineering Center of the Army Corps of Engineers in a data-poor environment of a developing country were successful, as a flood modeling tools in early warning systems and land use planning. The methodology involved data collection, preparation, and model simulation using 30m Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) as a critical data input of HEC RAS model. The findings showed that modeling using HEC-RAS and HEC HMS models in a data-poor environment requires intensive data enhancements and adjustments; multiple utilization of open sources data; carrying out multiple model computation iterations and calibration; multiple field observation, which may be constrained with time and resources to get reasonable output.
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    Open Access
    Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model
    (2025) Makhaba, Limpho Mapulane; Winberg, Simon
    Over the years, the frequency and intensity of wildfires have increased due to climate change, old fire management practices, and other environmental factors [1]. These wildfires pose a threat to ecosystems, infrastructure, property, and lives, particularly in highly susceptible Mediterranean regions, with suitable climatic conditions characterized by hot, dry summers and cool, humid winters [2]. Although fire is essential to maintaining such ecosystems' health, it can destroy long-term environmental sustainability and cause devastating destruction [1, 3]. Wildfire spread prediction from the ignition point to the surrounding area is a complex phenomenon affected by multiple variables such as weather, topography, and land-cover characteristics. Traditional systems used in wildfire spread prediction are often limited in accounting for the dynamic and complex factors influencing fire behaviour. To address this challenge, this research uses a multidisciplinary approach that combines Machine Learning techniques, remote sensing data, and wildfire science to develop an advanced wildfire prediction system. This research performs a comprehensive investigation into the use of supervised classification Machine Learning(ML) models, specifically, Artificial Neural Networks(ANN), Convolutional Autoencoder, and Convolution Long Short-Term Memory(convLSTM), in predicting wildfire spread. The three ML models developed were based on domain knowledge of fire behaviour and utilised Google Earth Engine(GEE) weather data and Sentinel Hub burned scars satellite data to train a logistic method for simulating fire burn maps. The ML models were then trained, tested, and validated using this data. An analysis of the models' capabilities in predicting the propagation of wildfire spread, as well as the driving factors that affect wildfire behaviour, was performed. The following evaluation matrices: loss, precision, recall, Area Under the Curve-Precision Recall(AUC_PR) and F1 score are used to assess the performance of the models in predicting the spatial and temporal properties of wildfire spread. The results indicate that the convLSTM can predict both temporal and spatial properties of fire, while the Convolutional Autoencoder can predict only the spatial properties of fire with minimal loss. The ANN produced the least satisfying results in predicting fire's spatial properties.