Landscape regions and hydrological features such as wetlands, rivers, and lakes are frequently mapped and stored digitally as features. Their boundary can be mapped and identified at the physically observable wetland-dryland interface. However, landforms such as mountains, hills, mesas, valleys, which are cognized as component features of or objects attached to the terrestrial surface are not easily delineated due to the lack of clear or unambiguous criteria for defining their boundaries. It is quite challenging to determine where the boundary of the mountain, hill, or valley starts and ends because terrain type, culture, language, and other subjective factors greatly affect how the same portion of the terrestrial surface maybe discretized, classified, labeled, and characterized by people. Cartographers have traditionally used point and line symbols as labels to describe landforms in a map, but this approach ignores the problem of representing the possible physical shape and extension of landforms. This thesis advanced prior work in the fields of geomorphometry and geographic information science to test the viability of existing semi-automated terrain analysis methods for mesoscale landforms that are easily recognized by people because of local topographic and cultural salience. The focus was on finding methods that can help automate the extraction of three broad categories of landforms: non-linear eminences (e.g., peak, mount, pillar, mountain, hill, mesa, butte), linear eminences (e.g., ridge and spur) and linear depressions (e.g., channel, valley, and hollow). Three methods proposed by Wood (1996), Jasiewicz and Stepinski (2013), and Weiss (2001) were selected because they are popular in terrain characterization, have shown promising results for mapping discrete terrain features that are intended to resemble landforms recognized intuitively by people, and because they are easily available for experimentation in freely available software. These methods require only an elevation raster as input, and then users must modify a few parameters to derive classified rasters reflecting discrete morphometric features or landform objects. The three methods were first independently tested by varying their parameters and creating many classified rasters for each method for three study areas in the continental US (Great Smoky Mountains (NC-TN), White Mountains (NH), and Colorado Plateau (NM)). These experimental results were then compared in 2D and 3D map views in GIS software, followed by quantitative comparative analysis of a subset of the rasters to answer questions about the impact of input parameters and the terrain type on quality of results. Additional comparative analysis of the methods also helped answer questions about the relative strengths and weaknesses of the methods and the semantic similarity between some of the landform classes recognized in the unique classification system used by each method. The major finding from this thesis was that only smaller neighborhood scales between 300 to 400 meters are the optimum scales for extracting landform objects that correspond well to expected shapes and extents. Other parameters have similarly specifically narrow ranges for which cognitively plausible results can be obtained. Identifying these ranges of parameters is the major contribution of this thesis. The impact of terrain type is not as critical as initially assumed, but more careful analysis is warranted for low relief areas which make it harder to detect and delineate landform boundaries. Despite differences, all three (Wood, Geomorphon and TPI) methods are worthy candidates for mapping all three types of landform categories, with the TPI method producing the most realistic, narrower linear polygons for non-linear eminences and depressions. However, the TPI method lacks a dedicated class for mapping non-linear eminences, leaving only the Wood and Geomorphon methods as candidates for mapping non-linear eminences. The semantic analysis of the classification systems is complicated and preliminary analysis suggests that a much more carefully planned and detailed analysis will be needed. This thesis clearly shows the potential for automated mapping of landforms, but also raises enough questions that further research must be conducted on parameterization impacts for each method, feasibility of extending GNIS feature representing beyond points to polygons, and creating an automation workflow based on a combination of methods, instead of hoping to rely on one method exclusively. A comprehensive set of findings for each research question and important limitations and recommendations for future research are provided in the concluding chapter.