A Scoping Review of Landform Classification Using Geospatial Methods - Zama Eric Mashimbye; Kyle Loggenberg (Geomatics, 2023)
Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model ( DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods.