Comparing Geomorphometric Pattern Recognition Methods for Semi-Automated Landform Mapping A thesis presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Master of Science Wael Hassan December 2020 ©2020 Wael Hassan. All Rights Reserved. 2 This thesis titled Comparing Geomorphometric Pattern Recognition Methods for Semi-Automated Landform Mapping by WAEL HASSAN has been approved for the Department of Geography and the College of Arts and Sciences by Gaurav Sinha Associate Professor of Geography Florenz Plassmann Dean, College of Arts and Sciences 3 Abstract HASSAN, WAEL , M.S., December 2020, Geography Comparing Geomorphometric Pattern Recognition Methods for Semi-Automated Landform Mapping Director of Thesis: Gaurav Sinha 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 4 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 5 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. 6 Dedication To the soul of my Aunts, may Allah rest their souls To my parents To my all teachers To All whom I love I dedicate this work 7 Acknowledgments I want to give thanks to the almighty God whose grace has brought me this far. Next, my deepest gratitude goes to my advisor, Dr. Gaurav Sinha, for his guidance, support, advice, encouragement, and great dedication. The successful completion of this thesis would not have been possible without his tremendous contribution. Just saying thank you is not enough to express how grateful I am. I would also like to express my gratitude to Dr. Dorothy Sack and Dr. Timothy Anderson, who sat on my committee and offered invaluable recommendations and generous encouragement. Additionally, I extend special thanks to Dr. Ryan Fogt and Ms. Ana Myers, and the entire Geography faculty for contributing to my academic life in one way or another in such a new environment. I have learned much from you all. I would also like to thank my family for all their support, endless love, and encouragement. A special thanks to my father, Ali Saleh, and my mother, Wafa Hassan, for their incredible investment in my life in general. Thanks to the Sudanese community in Columbus, Ohio, for their endless support. Your love and support are overwhelming. Finally, I would like to thank the Muslim Student Association (MSA) in Athens, Ohio University, for their supplication and spiritual support. I cannot conclude without offering appreciation for my fellow graduate students in the Department of Geography. You have been very kind and helpful throughout my study. 8 Table of Contents Page Abstract ........................................................................................................................... 3 Dedication ....................................................................................................................... 6 Acknowledgments ........................................................................................................... 7 List of Tables ................................................................................................................ 11 List of Figures ............................................................................................................... 15 Chapter 1: Conceptualization and Computational Representation of Landscape ............. 18 1.1 Mapping Landforms ........................................................................................... 18 1.2 Conceptualization of Landforms......................................................................... 22 1.3 Extraction of Terrain Features and Landform Objects from DEMs ..................... 24 1.4 Research Questions ............................................................................................ 27 1.5 Project Significance ........................................................................................... 29 Chapter 2: Geomorphometric Methods for Mapping Landforms .................................... 30 2.1 Geomorphometry Theory ................................................................................... 30 2.1.1 Digital Elevation Models (DEMs) ............................................................. 31 2.1.2 General Geomorphometry ......................................................................... 32 2.1.3 Segmentation of the Continuous Surface into Geomorphological Units ..... 35 2.1.4 Specific Geomorphometry: Features vs Landforms.................................... 39 2.2 Semi-Automated Feature Extraction and Mapping of Landforms ....................... 41 2.3 Bridging the General-Specific Geomorphometric Divide with Supervised Pattern Recognition methods for Landform Mapping ........................................................... 44 2.3.1 Selection of Pattern Recognition Methods ................................................. 45 2.3.2 Wood’s Multiscale Quadratic Polynomial Estimation and Six Morphometric Features ............................................................................................................