Author
D.D. Brown
Bio: D.D. Brown is an academic researcher from United States Forest Service. The author has contributed to research in topics: Landform & Terrain. The author has an hindex of 1, co-authored 1 publications receiving 32 citations.
Papers
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TL;DR: An automated method for mapping Hammond's landforms over large landscapes using digital elevation data found general agreement in landform patterns mapped by the manual and the automated approaches, and very close agreement in characterization of local topographic relief.
Abstract: We automated a method for mapping Hammond's landforms over large landscapes using digital elevation data. We compared our results against Hammond's published landform maps, derived using manual interpretation procedures. We found general agreement in landform patterns mapped by the manual and the automated approaches, and very close agreement in characterization of local topographic relief. The two approaches produced different interpretations of intermediate landforms, which relied upon quantification of the proportion of landscape having gently sloping terrain. This type of computation is more efficiently and consistently applied by computer than human. Today's ready access to digital data and computerized geospatial technology provides a good foundation for mapping terrain features, but the mapping criteria guiding manual techniques in the past may not be appropriate for automated approaches. We suggest that future efforts center on the advantages offered by digital advancements in refining an approach to better characterize complex landforms.
32 citations
Cited by
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TL;DR: In this paper, the concept of geomorphon (geomorphologic phonotypes) is introduced for classification and mapping of landform elements from a DEM based on the principle of pattern recognition rather than differential geometry.
460 citations
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TL;DR: An improved algorithm was developed for the regional landform modeling that incorporated a profile parameter for the delineation of tablelands, and accommodated negative elevation data values, and allowed neighborhood analysis window (NAW) size to vary between parameters.
Abstract: In 1964, E.H. Hammond proposed criteria for classifying and mapping physiographic regions of the United States. Hammond produced a map entitled “Classes of Land Surface Form in the Forty-Eight States, USA”, which is regarded as a pioneering and rigorous treatment of regional physiography. Several researchers automated Hammond?s model in GIS. However, these were local or regional in application, and resulted in inadequate characterization of tablelands. We used a global 250 m DEM to produce a new characterization of global Hammond landform regions. The improved algorithm we developed for the regional landform modeling: (1) incorporated a profile parameter for the delineation of tablelands; (2) accommodated negative elevation data values; (3) allowed neighborhood analysis window (NAW) size to vary between parameters; (4) more accurately bounded plains regions; and (5) mapped landform regions as opposed to discrete landform features. The new global Hammond landform regions product builds on an existing global Hammond landform features product developed by the U.S. Geological Survey, which, while globally comprehensive, did not include tablelands, used a fixed NAW size, and essentially classified pixels rather than regions. Our algorithm also permits the disaggregation of “mixed” Hammond types (e.g. plains with high mountains) into their component parts.
59 citations
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TL;DR: In this paper, an integral image-based approach was used to measure the common relative topographic position metric deviation from mean elevation (DEV ), which was applied to a large-scale digital elevation model (DEM) of an extensive and heterogeneous region in eastern North America.
58 citations
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The Joint Commission1, University of Michigan2, Michigan Department of Community Health3, Altarum Institute4, United States Geological Survey5, Great Lakes Institute of Management6, Natural Resources Research Institute7, Michigan State University8, The Nature Conservancy9, Natural Resources Canada10, United States Environmental Protection Agency11, Michigan Technological University12
TL;DR: In this paper, the authors developed a spatial classification framework and database called the Great Lakes Aquatic Habitat Framework (GLAHF), which consists of catchments, coastal terrestrial, coastal margin, nearshore, and offshore zones that encompass the entire Great Lakes Basin.
54 citations
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TL;DR: In this article, the authors introduce landscape similarity, a numerical measure that assesses affinity between two landscapes on the basis of similarity between the patterns of their constituent landform elements, which can yield answers to a query in real time, enabling a highly effective means to explore large topographic datasets.
48 citations