Browsing by Author "Higgs, Caley"

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    Discriminating between forest plantation genera using remote sensing and machine learning algorithms
    (Stellenbosch : Stellenbosch University, 2021-12) Higgs, Caley; van Niekerk, A.; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH ABSTRACT: Forest inventories are constructed on a compartmental level and contain information such as forest age, species/genus, location, and extent. An up-to-date forest inventory is critical for monitoring harvests, assessing the production of timber, planning, maximising production, assessing water use, and assessing timber quality. On a national scale, forest inventories are used for monitoring the impact forests have on the climate and stream flow, assessing the contribution forests have on alleviating poverty, monitoring forest trends, and supporting policy and trade decisions. Conventional methods for obtaining forest inventory information, such as plantation genus/species, is done in-field, which is time-consuming and costly. Remote sensing is a more efficient way to capture forest genus information. Very high-resolution, hyperspectral, and unmanned aerial vehicle (UAV) imagery have been shown to contain suitable spectral and spatial information for machine learning algorithms to differentiate between forest species. However, such data requires extensive processing and is expensive to acquire, making it unsuitable for mapping over larger areas. High-resolution imagery, such as Sentinel-2, combined with textural measures and vegetation indices as features in machine learning algorithms, have shown potential to differentiate between spectrally similar classes. However, it is not known what impact training sample configuration and size have on classification accuracies when classifying acacia, eucalyptus, and pinus (pine) genera. It is also not known whether signature extension is a viable method for reducing the time and effort spent on obtaining in situ training data when mapping forest plantations over a large and complex area. This research set out two main experiments. The first experiment evaluated the impact of using an even, uneven, or an area-proportionate training sample configuration and size in a random forest machine learning model for classifying acacia, eucalyptus, and pine compartments. It was found that the study area that contained an uneven area planted with acacia, eucalyptus, and pine trees was classified more accurately using a balanced training sample configuration, compared to using an unbalanced and area-proportionate training sample configuration. It was also found that a saturation point exists where adding more training samples adds little value to the overall accuracy (OA). The saturation point was found to be ~ 57n, where n is the number of features used in the classification. The second set of experiments was set out to test the viability of training data signature extension for constructing random forest machine learning models to differentiate between acacia, eucalyptus, and pine trees using Sentinel-2 imagery as input. The study area was split into 19 Sentinel-2 tiles spanning the Mpumalanga, KwaZulu-Natal, Eastern Cape, and Western Cape provinces. Three separate random forest models were built using training data collected in one tile located in Mpumalanga, one tile located in KwaZulu-Natal, and one tile located in the Eastern Cape. A fourth model was built using training data from all three source tiles. The four models were applied to all 19 Sentinel-2 tiles to map forest plantation genera. The results show that a ~70% OA can be achieved if the training data is collected in areas with similar climates (rainfall seasonality) to the areas that are being mapped. In addition, it was found that signature extension distance (i.e. distance between the training data and the area being classified) should not exceed 500 km.