Browsing by Author "Maponya, Mmamokoma Grace"

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    Machine learning and high spatial resolution multi-temporal Sentinel-2 imagery for crop type classification
    (Stellenbosch : Stellenbosch University, 2019-12) Maponya, Mmamokoma Grace; Van Niekerk, Adriaan; Mashimbye, Zama Eric; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH SUMMARY : Spatially-explicit crop type information is useful for estimating agricultural production areas. Such information is used for various monitoring and decision-making applications, including crop insurance, food supply-demand logistics, commodity market forecasting and environmental modelling. Traditional methods, such as ground surveys and agricultural censuses, involve high production costs and are often labour intensive, which limit their use for timely and accurate crop type data production. Remote sensing, however, offers a dependable, cost-effective and timely way of mapping crop types. Although remote sensing approaches – particularly using multitemporal techniques – have been successfully employed for producing crop type information, this information is mostly available post-harvest. Thus, researchers and decision-makers have to wait several months after harvest to have such information, which is usually too late for many applications. The availability and accessibility of imagery collected with optical sensors make such data preferable for mapping crop types. However, these sensors are subject to cloud-interference, which has been recognised as a source of error in the retrieval of surface parameters. It is therefore important to assess the strengths and weaknesses of using multi-temporal optical imagery for differentiating crop types. This study utilises Sentinel-2A and 2B imagery to perform several experiments in selected parts of the Western Cape, South Africa, to undertake this assessment. The first three experiments assessed the significance of image selection on the accuracies of crop type classification. A recommended number of Sentinel-2 images was selected, using two different methods. The first of the three experiments was conducted with uni-temporal images. Based on the performance rankings of the uni-temporal images, five images with the highest ranks were used to set up Experiment 2. The third experiment was undertaken with a handpicked set of five images, based on crop developmental stages. The two image selection methods were compared to each other and subsequently to the entire time-series, to determine the significance of selecting images for crop type mapping. These classifications were undertaken with several supervised machine learning classifiers and one parametric classifier. Results showed no significant difference in classification accuracies between the two image selection methods and the entire time-series. Overall, the support vector machine (SVM) and random forest (RF) algorithms outperformed all the other classifiers. The fourth experiment was undertaken by chronologically adding images to the classifiers. The progression of classification accuracies against time and the increase in the number of images were analysed to determine the earliest period (pre-harvest) when crops can be classified with sufficient accuracies. The highest pre-harvest accuracy achieved was then compared to that obtained at the end of the season, including images acquired post-harvest, to assess the effectiveness of machine learning classifiers for classifying crop types when only pre-harvest images are used. The results of this experiment showed that machine learning classifiers can classify crops when only preharvest images are used, with accuracies similar to those obtained when the entire time-series is used. Satisfactory classification accuracies were attainable as early as Aug/Sept (eight weeks before harvest). The fifth to tenth experiments were undertaken to assess the impact of cloud cover and image compositing on crop type classification accuracies. The fifth and sixth experiments were performed with non-composited images. Experiment Five (5) was undertaken with cloud-free images only, while the sixth experiment involved using all available images, including cloudcontaminated observations. The seventh to tenth experiments were undertaken with monthly image composites computed using four different image compositing approaches. All these experiments were undertaken using several machine learning classifiers. The results showed that machine learning classifiers performed best when all images – including cloud-contaminated images – are used as input to the classifiers. Image compositing had a detrimental effect on classification accuracies. Generally, multi-temporal Sentinel-2 data hold great potential for operational crop type map production early in the season. However, more work is needed to develop simple workflows for eliminating cloud cover, particularly for crop type mapping in areas characterised by frequent overcast conditions.