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Dr 

Phinzi, Kwanele

Faculty: Faculty of Science, Agriculture and Engineering
Department: Geography and Environmental Studies
ORCID: https://orcid.org/0000-0003-1865-7011
Research Interest(s): Geographic Information System (GIS), Remote Sensing.
Biography: Kwanele Phinzi obtained his PhD in Earth Sciences, specializing in Remote Sensing, from the University of Debrecen in Hungary. He has expertise in applying GIS and Remote Sensing methodologies to address various environmental challenges, with a solid track record of publications in these areas. In March 2023, he joined the University of Zululand as a lecturer. Currently, he teaches undergraduate modules in Physical Geography and supervises postgraduate students.
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20231
Subject: Land-use/land-cover ×
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    PublicationJournal Article
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    Understanding the role of training sample size in the uncertainty of high-resolution LULC mapping using random forest
    2023
     | Springer Nature
    High-resolution sensors onboard satellites are generally reputed for rapidly producing land-use/land-cover (LULC) maps with improved spatial detail. However, such maps are subject to uncertainties due to several factors, including the training sample size. We investigated the effects of different training sample sizes (from 1000 to 12,000 pixels) on LULC classification accuracy using the random forest (RF) classifier. Then, we analyzed classification uncertainties by determining the median and the interquartile range (IQR) of the overall accuracy (OA) values through repeated k-fold cross-validation. Results showed that increasing training pixels significantly improved OA while minimizing model uncertainty. Specifically, larger training samples, ranging from 9000 to 12,000 pixels, exhibited narrower IQRs than smaller samples (1000–2000 pixels). Furthermore, there was a significant variation (Chi2 = 85.073; df = 11; p < 0.001) and a significant trend (J-T = 4641, p < 0.001) in OA values across various training sample sizes. Although larger training samples generally yielded high accuracies, this trend was not always consistent, as the lowest accuracy did not necessarily correspond to the smallest training sample. Nevertheless, models using 9000–11,000 pixels were effective (OA > 96%) and provided an accurate visual representation of LULC. Our findings emphasize the importance of selecting an appropriate training sample size to reduce uncertainties in high-resolution LULC classification.
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