Digital Elevation Model (DEM) Resolution and Stream Extraction Using Terrain Openness Josh Page and Dr. Wei Luo, Northern Illinois University, Department.

Slides:



Advertisements
Similar presentations
Poster template by ResearchPosters.co.za Effect of Topography in Satellite Rainfall Estimation Errors: Observational Evidence across Contrasting Elevation.
Advertisements

Spatial Analysis with ArcView: 2-D. –Calculating viewshed –Calculating line of sight –Add x and y coordinates –Deriving slope from surface data –Deriving.
Robust statistical method for background extraction in image segmentation Doug Keen March 29, 2001.
Major Operations of Digital Image Processing (DIP) Image Quality Assessment Radiometric Correction Geometric Correction Image Classification Introduction.
a ridge a valley going uphill
The Global Digital Elevation Model (GTOPO30) of Great Basin Location: latitude 38  15’ to 42  N, longitude 118  30’ to 115  30’ W Grid size: 925 m.
Geographic Information Systems Applications in Natural Resource Management Chapter 13 Raster GIS Database Analysis Michael G. Wing & Pete Bettinger.
Maps.
3D and Surface/Terrain Analysis
For the Lesson: Eta Characteristics, Biases, and Usage December 1998 ETA-32 MODEL CHARACTERISTICS.
From Topographic Maps to Digital Elevation Models Daniel Sheehan DUE Office of Educational Innovation & Technology Anne Graham MIT Libraries.
WFM 6202: Remote Sensing and GIS in Water Management
More Raster and Surface Analysis in Spatial Analyst
CURVE NO. DEVELOPMENT STEP 8 Soils data, land use data, watershed data, and CN lookup table are used to develop curve numbers for use in the SCS Curve.
The hydrograph for Ten Mile Creek is shown to the right. Hydrographs are calculated for each of the 30 Subcatchments. Radar data is used to create rainfall.
Colorado 14ers, pixel by pixel: An exercise in mountain geography Brandon J. Vogt, PhD Department of Geography and Environmental Studies University of.
Some Potential Terrain Analysis Tools for ArcGIS David G. Tarboton
Data Input How do I transfer the paper map data and attribute data to a format that is usable by the GIS software? Data input involves both locational.
Digital Elevation Models And Relief Models 1DEM. Part 1: The Underlying Elevation Data 2DEM.
Airborne LIDAR The Technology Slides adapted from a talk given by Mike Renslow - Spencer B. Gross, Inc. Frank L.Scarpace Professor Environmental Remote.
Lecture 07: Terrain Analysis Geography 128 Analytical and Computer Cartography Spring 2007 Department of Geography University of California, Santa Barbara.
From Topographic Maps to Digital Elevation Models Daniel Sheehan IS&T Academic Computing Anne Graham MIT Libraries.
The Global Digital Elevation Model (GTOPO30) of Great Basin Location: latitude 38  15’ to 42  N, longitude 118  30’ to 115  30’ W Grid size: 925 m.
Comparison of LIDAR Derived Data to Traditional Photogrammetric Mapping David Veneziano Dr. Reginald Souleyrette Dr. Shauna Hallmark GIS-T 2002 August.
GI Systems and Science January 23, Points to Cover  What is spatial data modeling?  Entity definition  Topology  Spatial data models Raster.
9. GIS Data Collection.
Raster and Vector 2 Major GIS Data Models. Raster and Vector 2 Major GIS Data Models.
Terrain Mapping and Analysis
Spatial data models (types)
Department of Geography, University at Buffalo—The State University of New York, 105 Wilkeson Quad, Buffalo, NY , USA UNCERTAINTY IN DIGITAL.
FNR 402 – Forest Watershed Management
Image Registration January 2001 Gaia3D Inc. Sanghee Gaia3D Seminar Material.
ESRM 250 & CFR 520: Introduction to GIS © Phil Hurvitz, KEEP THIS TEXT BOX this slide includes some ESRI fonts. when you save this presentation,
Understanding maps Geographical Data Skills (Part 1)
CS 376b Introduction to Computer Vision 04 / 29 / 2008 Instructor: Michael Eckmann.
Map Scale, Resolution and Data Models. Components of a GIS Map Maps can be displayed at various scales –Scale - the relationship between the size of features.
Sample size vs. Error A tutorial By Bill Thomas, Colby-Sawyer College.
Orthorectification using
GIS Data Structure: an Introduction
Controlling the Photographic Process. With today’s modern digital cameras you can have as much or as little control over the picture taking process as.
What is a map? A Map is a two or three-dimensional model or representation of the Earth’s surface. 2-Dimensional map.
Raster Data Model.
May 4 th (4:00pm) Multiple choice (50 points) Short answer (50 points)
A project for GY320 – Surface Processes and Geomorphology Eric Leonard Colorado College Sangre de Cristo Range from the San Luis Valley.
The Semivariogram in Remote Sensing: An Introduction P. J. Curran, Remote Sensing of Environment 24: (1988). Presented by Dahl Winters Geog 577,
7 elements of remote sensing process 1.Energy Source (A) 2.Radiation & Atmosphere (B) 3.Interaction with Targets (C) 4.Recording of Energy by Sensor (D)
UNDERSTANDING MAPS Geographical Data Skills (Part 1)
Lecture 20: GIS Analytical Functionality (IV)
Wenqi Zhu 3D Reconstruction From Multiple Views Based on Scale-Invariant Feature Transform.
During the 20 th century, thematic maps have been an ever useful tool for correlating data sets and representing relevant information. Recent technological.
1 Overview Importing data from generic raster files Creating surfaces from point samples Mapping contours Calculating summary attributes for polygon features.
L7 - Raster Algorithms L7 – Raster Algorithms NGEN06(TEK230) – Algorithms in Geographical Information Systems.
INTRODUCTION TO GIS  Used to describe computer facilities which are used to handle data referenced to the spatial domain.  Has the ability to inter-
Assignment: Use these raw TMI data from near Yellowstone Lakeraw TMI data clean them up separate sources make professional final images Write a report.
SWOT Hydrology Workshop Ka-band Radar Scattering From Water and Layover Issues Delwyn Moller Ernesto Rodriguez Contributions from Daniel Esteban-Fernandez.
R I T Rochester Institute of Technology Geometric Scene Reconstruction Using 3-D Point Cloud Data Feng Li and Steve Lach Advanced Digital Image Processing.
Copyright © Cengage Learning. All rights reserved. 16 Quality Control Methods.
Environmental and Exploration Geophysics I tom.h.wilson Department of Geology and Geography West Virginia University Morgantown,
Water Availability Modeling in the State of Texas CE 394 K.2 - Surface Water Hydrology University of Texas at Austin David Mason.
Micro-terrain feature identification and processing: An overview with practical implementations along with discussion of a potential application area of.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Viewshed Analysis A viewshed refers to the portion of the land surface that is visible from one or more viewpoints. The process for deriving viewsheds.
CCIT 1/36 National Defense University Morphometric Parameterisation of Mount Washington Terrain Jason Wang 6/22/2005 To establish project-based collaborations.
Definition In scientific literature there is no universal agreement about the usage of the terms: digital elevation model (DEM) digital terrain model (DTM)
INTRODUCTION TO GEOGRAPHICAL INFORMATION SYSTEM
Spatial Models – Raster Stacy Bogan
Spatial Data Models Raster uses individual cells in a matrix, or grid, format to represent real world entities Vector uses coordinates to store the shape.
Statistical surfaces: DEM’s
Data Queries Raster & Vector Data Models
Terrain Analysis Using Digital Elevation Models
Presentation transcript:

Digital Elevation Model (DEM) Resolution and Stream Extraction Using Terrain Openness Josh Page and Dr. Wei Luo, Northern Illinois University, Department of Geography, 2010 Introduction In the past, traditional flow direction-based methods for extracting streams from DEM data often fell short of accurately representing the spatial variation of the degree of surface dissection. Newer morphology-based algorithms use appropriate terrain attributes to capture these variations by extracting stream systems as the parts of the surface which contain concave upward morphology. The purpose of this study is to examine one such attribute (terrain openness) for stream extraction and the effect of DEM resolution. Openness Terrain Openness is defined as the average of zenith angles along the eight cardinal directions within a specified neighborhood[Figure 1]. The size of the neighborhood provides a natural built-in length scale, as the number of cells, denoted as the radial limit ‘L’ and once the maximum azimuth angle ‘D’ is chosen then the positive openness can be determined at point ‘A’[Figure 2]. Because positive openness, or ‘above ground’ openness, at a certain grid point is confined by its surrounding topography, it thus in essence represents the terrain curvature at that point. All of the calculated data is constrained to the radial limit ‘L’, which tends to emphasize topography differently. Smaller features are highlighted more with small radial limits, and larger features are shown more easily when larger radial limits are used. Process Using Geographic Information Systems software, the DEM maps of the different resolutions were used as the basis for the openness calculations. This raster data was exported as an ASCII text file of unique coordinates and values. This ASCII file was then put through a FORTRAN program that calculated the resulting openness. Figure 3a shows a computed openness image of the DEM of the study area[Figure 3b]. Note in Figure 3a, the light values represents valleys with angles ≥ 90 ⁰ whereas the darker parts are ridges. The openness greater than a threshold value, along with surface flow direction, can then be used to extract stream systems. Multiple openness calculations were conducted with different resolutions and radial limits to examine their effects on the final product of the extracted streams. Results DEM data ranged from coarse resolution(1km) to finer resolution(37m). The radial limits(L in number of cells) for different resolution DEM(R in meters) were set so that L×R is the about same for easy comparison. E.g., for 1km resolution DEM, L is 5 cells (L×R =5000m) and for 37 m resolution DEM, L is 135 cells (L×R=4995m). Initially, the threshold for openness was set as various multiples of the standard deviation above the mean for the entire study area. This resulted in an unrealistic increase in the amount of streams that were extracted for finer resolution DEMs[Figure 4]. To correct this, the threshold was set as multiples of standard deviation above the mean for only the area with openness > 90 ⁰. In other words, the mean and standard deviation were obtained after a conditional procedure was used to mask out openness values ≤ 90 ⁰. With this correction, more accurate and realistic results of streams were extracted[Figure 5]. All these new extractions are very similar due to the radial limits being multiples of each other. When the radial limit is lowered to a few meters for the finer resolutions, the data is flooded with noise. Figure 6a shows the difference between a large radial limit and a shorter one. This important fact shows that as the radial limits increase in length, the detail in the streams is quickly lost. Figure 6b shows the four different resolutions (yellow-coarse, purple-fine) at the radial limit of a multiple of 5 cells. This shows a good representation of how similar the detail of the extracted streams can be with the right type of parameters. Summary In general, finer resolution DEMs will generate more detailed streams. However, the coarser DEM data can be partially compensated by using smaller radial limits in calculating the openness. Also, the finer DEMs’ streams can get overbearing and create noise which can be compensated by using larger radial limits. References Mary Brandel (July 6, 1999). 1963: The Debut of ASCII: CNN. Accessed Moore, J.G., and R.K. Mark (1992) Morphology of the island of Hawaii, GSA Today, 2(12): and 262. Ryuzo Yokoyama, Mlchlo Shlrasawa, and Richard J. Pike (2002) American Society for Photogrammetry and Remote Sensing, Vol. 68, No. 3, March 2002, W. Luo and T.F. Stepinski (2008) Identification of Geologic Contrast from Landscape Dissection Pattern: An Application to the Cascade Range, Oregon, USA. Geomorphology, 90, p90-98