Masters Degrees (Geography and Environmental Studies)

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Browsing Masters Degrees (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.