Department of Geography and Environmental Studies

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Browsing Department of Geography and Environmental Studies by Subject "Aerial photography"

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    Efficacy of machine learning and lidar data for crop type mapping
    (Stellenbosch : Stellenbosch University, 2019-12) Prins, Adriaan; Van Niekerk, Adriaan; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography & Environmental Studies.
    ENGLISH ABSTRACT: Accurate crop type maps are important for obtaining agricultural statistics such as water use or harvest estimations. The traditional approach to obtaining maps of cultivated fields is by manually digitising the fields from satellite or aerial imagery. However, manual digitising is time-consuming, expensive and subject to human error. Automated remote sensing methods have been a popular alternative for crop type map creation, with machine learning classification algorithms gaining popularity for classifying crop types from satellite imagery. However, using light detection and ranging (LiDAR) data for crop type mapping has not been widely researched. This study assessed the use of LiDAR data for crop type classification, by using it on its own and in combination with Sentinel-2 and aerial imagery. The first experiment evaluated the use of LiDAR data and machine learning for classifying vineyards. The LiDAR data was obtained from a 2014 survey by the City of Cape Town. The normalised digital surface model (nDSM) and intensity raster derived from the LiDAR data were interpolated at four resolutions (1.5 m, 2 m, 2.5 m and 3 m) and then used for generating a range of texture measures. The textures measures were generated using two window sizes (3x3 and 5x5) per resolution scenario, which resulted in eight datasets. The resulting dataset was then used as input for 11 machine learning classification algorithms, which performed a binary classification of vineyards and non-vineyards. The results showed that LiDAR data are able to discriminate between vineyards and non-vineyards, with the random forest (RF) classifier obtaining the highest overall accuracy (OA) of 80.9%. Furthermore, the results showed that a significant difference in accuracy can be achieved with neural networks and distance-based classifiers when the input data are standardised. The second experiment used the methods developed for the first experiment to perform a five-class classification. The five classes consisted of maize, cotton, groundnuts, orchards and non-agriculture. Sentinel-2 and aerial imagery data were added to the analysis and were compared to LiDAR data. The LiDAR data was obtained from a 2016 survey of the Vaalharts irrigation scheme. Furthermore, the three datasets (Sentinel-2, aerial imagery and LiDAR data) were combined in order to evaluate which combination of datasets produces the highest OA. The results showed that the performance of LiDAR data was similar to that of Sentinel-2 imagery, with LiDAR data obtaining a mean OA of 84.3%, while Sentinel-2 obtained a mean OA of 83.6%. The difference between the OAs of LiDAR and Sentinel-2 were statistically insignificant. The highest OA (94.6%) was obtained with RF when the LiDAR, Sentinel-2 and aerial datasets were combined. However, a combination of LiDAR data and Sentinel-2 imagery obtained similar results to when all three datasets were used in combination, with the difference in OA being statistically insignificant. Generally, LiDAR data are suitable for classifying different crop types, with RF obtaining the highest OAs in both experiments. The combination of multispectral and LiDAR data produced the highest OA.
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    The use of Landsat and aerial photography for the assessment of coastal erosion and erosion susceptibility in False Bay, South Africa
    (CONSAS Conference, 2015-06) Callaghan, Kerry; Engelbrecht, Jeanine; Kemp, Jaco
    Coastal erosion is a worldwide hazard, the consequences of which can only be mitigated via thorough and efficient monitoring of erosion. This study aimed to employ remote sensing techniques on aerial photographs and Landsat TM/ETM+ imagery for the detection and monitoring of coastal erosion in False Bay, South Africa. Vegetation change detection as well as post-classification change detection were performed on the Landsat imagery. Furthermore, aerial photographs were analysed using the Digital Shoreline Analysis System (DSAS), which determines statistical differences in shoreline position over time. The results showed that while the resolution of the Landsat imagery was not sufficient to quantify and analyse erosion along the beach itself, the larger area covered by the satellite images enabled the identification of changes in landcover conditions leading to an increased susceptibility to erosion. Notably, the post-classification change detection indicated consistent increases in built-up areas, while sand dune, beach, and sand (not beach) decreased. NDVI differencing led to the conclusion that vegetation health was decreasing while reflective surfaces such as bare sand and roads were increasing. Both of these are indicative of an increased susceptibility to coastal erosion. Aerial photographs were used for detailed analysis of four focus areas and results indicated that coastal erosion was taking place at all four areas. The higher resolution available on the aerial photographs was vital for the quantification of erosion and sedimentation rates.