Using Digitized Spacecraft Film and a Revised Lunar Control Network for Photogrammetric Mapping

Total Page:16

File Type:pdf, Size:1020Kb

Using Digitized Spacecraft Film and a Revised Lunar Control Network for Photogrammetric Mapping USING DIGITIZED SPACECRAFT FILM AND A REVISED LUNAR CONTROL NETWORK FOR PHOTOGRAMMETRIC MAPPING Mark R. Rosiek R.L. Kirk B.A. Archinal T.L. Becker L. Weller B. Redding E. Howington-Kraus D. Galuszka U.S. Geological Survey 2255 N. Gemini Dr Flagstaff AZ 86001 [email protected] ABSTRACT This paper reports on the results of photogrammetric mapping with Lunar Orbiter, Apollo panoramic and Apollo metric camera digitized photographs using modern softcopy digital mapping techniques and a revised lunar control network to establish control. We will report on the differences between Digital Elevation Models (DEMs) collected from Lunar Orbiter, Apollo panoramic and metric camera digitized photographs. Our test area is the Rima Hadley region, which includes the Apollo 15 landing site. This area is covered by multiple sources of data that can be used for comparison and evaluation of accuracy. The original Lunar Orbiter photographs reconstructed in the 1960s had limited utility for mapping due to a stair-step offset in the reconstructed photographs. These images have been re- cently digitized and digitally reconstructed to fit the reseaux and the fiducials to the camera calibration data. The Apollo imagery was previously used to produce topographic maps using different control networks. These previous networks have kilometer-size offsets between them and were also limited in size and accuracy. A revised lunar con- trol network, the Unified Lunar Control Network 2005, improves the accuracy and the density of control points and includes solved-for for elevations of the control points. We find that all the image sets studied provide useful DEMs with minimal requirement for interactive editing but varying tradeoffs between area covered and resolution. The Apollo metric camera data are the cleanest and would support topomapping of nearly 20% of the Moon at 50 m post spacing. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorse- ment by the U.S. Government. INTRODUCTION We are evaluating the utility of using modern softcopy digital mapping techniques for extracting digital eleva- tion models (DEMs) from Lunar Orbiter (LO) and Apollo digitized photographs. These photographs were used in the 1960s-70s for mapping, mission planning, and control purposes. Previous work with LO imagery was difficult due to image artifacts, and the mapping was not controlled to any horizontal or vertical datum (Hansen, 1970). Mapping with Apollo imagery used 2 different control networks. The hardcopy maps that resulted are the Lunar Topographic Orthophotomap (LTO) and Lunar Orthophotomap (LO) map series (Wu and Doyle, 1990). Since these maps were produced, the lunar control network has been improved (Archinal et al., 2006a, b) and the LO images are now available as digitized photographs with the artifacts greatly reduced (Gaddis et al., 2003, Weller et al., 2006). The tools available for softcopy mapping permit the generation of DEMs, from which contour maps can be made if desired, but the digital DEMs contain more detailed topographic information that can also be used for image rectification, photometric correction, slope analyses, etc. Use of the current control and datum en- sures that the topographic data can be used in conjunction with data from current and future missions. Thus the products enabled by this study supersede earlier maps and will be useful for upcoming lunar missions including SELENE – Japan, Chang'e 1 – China, Chandrayaan-1 – India, and Lunar Reconnaissance Orbiter – USA. ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006 SOURCE DATA Lunar Orbiter Imagery LO photographs were collected by five missions, LO-I through -V, during 1966–1967. Stereo models are pro- vided both by the overlap of photographs for global mapping and by deliberate targeting of specific sites of interest. LO IV coverage of the nearside includes 6°-wide bands of stereo at 0° and ±30° latitude, and from about ±60° lati- tude to the poles (>25% of the nearside in all). The resolution of the imagery is between 30 and 100 m. The other LO missions returned stereo imagery of spot areas with higher resolution (10 - 40 m) (Hansen, 1970). These images were used for Apollo landing site selection, but the full topographic information was not extracted because errors in reconstructing the photographs from sections scanned on the spacecraft produced artifacts in the form of linear "cliffs" in the stereomodels. Apollo Imagery Apollo 15, 16, and 17 photographed ~20% of the Moon immediately under their orbital tracks using both a frame mapping camera and a panoramic camera. The frame camera was a Fairchild metric camera with a 4.5 x 4.5 in film format. Stereo models are obtained by overlapping photographs along the flight line and between flight lines. When digitized at 10 µm, a metric camera photograph provides a useful resolution of about 15 m/pixel. The panoramic camera was an Itek panoramic camera with 45.24 x 4.5 in film format. Stereo models are obtained by using forward and aft looking photographs acquired along the same flight line. When digitized at 10 µm, a pano- ramic photograph has a resolution at image center of about 2 m/pixel and at the edge of the image the resolution is about 4 m/pixel. Additional information on the digitization process is provided below. Clementine In 1994, the Clementine spacecraft acquired digital images of the Moon at visible and near infrared wave- lengths, as well as laser altimeter data (Nozette et al., 1994). The Clementine laser altimeter (Lidar) data have a vertical accuracy of approximately 100 m, horizontal accuracy 3,000m, a surface spot size of 200m, and a spatial resolution of 2.5 . Altimetry data were collected between 79 S - 81 N (Smith et al., 1997, Neumann, 2001). The altimeter data and polar Clementine stereo data (Rosiek et al., 2002) was used to initialize the radii values in the ULCN 2005 control network described in the next paragraph. We will also use the altimeter points to analyze the DEMs that are collected from Apollo and LO images. Control We used control from two sources. First, Table 1. Lunar Horizontal Control Net Comparison. for horizontal and vertical control we used Name # points # images Horz. Acc. Vert. Acc. points from an interim solution of the Uni- ULCN 1,478 Unknown 100 m to 3 km Few km? fied Lunar control Network 2005 (ULCN Few km to CLCN 271,634 43,871 Sphere 2005). This network combines the Unified some>15 km Lunar Control Network (ULCN) and the ULCN 2005 272,931 43,871 Few km ~ 1 km or less Clementine Lunar Control Network (CLCN). Second, we also used points near the Apollo 15 landing site that were described in Davies and Colvin (2000). The ULCN was described in the last major publication about a lunar control network (Davies et al., 1994). See Table 1 for statistics on this and the other networks discussed here. Images for this network are from the Apollo, Mariner 10, and Galileo missions, and Earth-based photographs. The importance of this network is that its accuracy is relatively well quantified and published information on the network is available. The CLCN includes measurements on 43,871 Clementine 750-nm images. The purpose of this network was to determine the geometry for the Clementine Basemap Mosaic (CBM) (USGS, 1997). After the completion of the CBM, horizontal errors of 15 km or more were noticed and therefore these same errors are present in the CLCN (Malin and Ravine, 1998; Cook et al., 2000; Cook at al, 2002). The errors seem to have arisen for several reasons, including that only a few (22) near side points were fixed to ULCN positions, the camera angles were uncon- strained, and the tie points were all constrained to lie on a mass-centered sphere of radius 1736.7 km. We have merged the ULCN and CLCN and have addressed the horizontal accuracy problems of the CLCN, with the intent to create a new ULCN. Our new solution(s) include 3 changes. 1) The camera angles are con- strained to within 0.03° of their a priori Navigation and Ancillary Information Facility (NAIF) values (Acton, 1999). 2) The coordinates of all identifiable ULCN points are constrained to their reported accuracy (Davies et al., ASPRS 2006 Annual Conference Reno, Nevada May 1-5, 2006 1994). 3) Radii of all tie points are solved for. Our current results show horizontal position changes from the CLCN on average of ~7 km with some changes of dozens of km. The final results are in preparation (Archinal et al., 2006b). Site Map The site mapped in this project is the Rima Hadley re- gion, including the Apollo 15 landing site. This area has ex- cellent coverage by LO and Apollo images, and previous mapping products exist to compare with our results. We se- lected images 4102_H3 and 4103_H1 from LO IV, images 5105_MED, 5106_MED, 5107_MED, 5106_H1, 5106_H2, and 5106_H3 from LO V, images 0583, 0585, and 0587 from the Apollo 15 metric camera, and images 9809, 9811, 9814, and 9816 from the Apollo 15 panoramic camera. Figure 1 shows the image footprints. PROCEDURES We are using a commercial photogrammetric work- station with SOCET SET (® BAE Systems) software to view the images in stereo, select control and tie points, and collect Figure 1. Footprints of images used in this our DEMs. The LO and Apollo images, obtained by digitiza- study, shown on a Clementine image mosaic tion of film as described below, are imported into the work- with north at top. Blue: LO IV frames 4102_H3 station in TIFF file format. Support data were obtained from and 4103_H1; Green: LO V frames 5105_MED, the National Space Science Data Center and entered by hand.
Recommended publications
  • Digital Mapping & Spatial Analysis
    Digital Mapping & Spatial Analysis Zach Silvia Graduate Community of Learning Rachel Starry April 17, 2018 Andrew Tharler Workshop Agenda 1. Visualizing Spatial Data (Andrew) 2. Storytelling with Maps (Rachel) 3. Archaeological Application of GIS (Zach) CARTO ● Map, Interact, Analyze ● Example 1: Bryn Mawr dining options ● Example 2: Carpenter Carrel Project ● Example 3: Terracotta Altars from Morgantina Leaflet: A JavaScript Library http://leafletjs.com Storytelling with maps #1: OdysseyJS (CartoDB) Platform Germany’s way through the World Cup 2014 Tutorial Storytelling with maps #2: Story Maps (ArcGIS) Platform Indiana Limestone (example 1) Ancient Wonders (example 2) Mapping Spatial Data with ArcGIS - Mapping in GIS Basics - Archaeological Applications - Topographic Applications Mapping Spatial Data with ArcGIS What is GIS - Geographic Information System? A geographic information system (GIS) is a framework for gathering, managing, and analyzing data. Rooted in the science of geography, GIS integrates many types of data. It analyzes spatial location and organizes layers of information into visualizations using maps and 3D scenes. With this unique capability, GIS reveals deeper insights into spatial data, such as patterns, relationships, and situations - helping users make smarter decisions. - ESRI GIS dictionary. - ArcGIS by ESRI - industry standard, expensive, intuitive functionality, PC - Q-GIS - open source, industry standard, less than intuitive, Mac and PC - GRASS - developed by the US military, open source - AutoDESK - counterpart to AutoCAD for topography Types of Spatial Data in ArcGIS: Basics Every feature on the planet has its own unique latitude and longitude coordinates: Houses, trees, streets, archaeological finds, you! How do we collect this information? - Remote Sensing: Aerial photography, satellite imaging, LIDAR - On-site Observation: total station data, ground penetrating radar, GPS Types of Spatial Data in ArcGIS: Basics Raster vs.
    [Show full text]
  • Direct-To-Digital Mapping Methodology: a Hands-On Guidebook for Applying Google Earth
    Steven DeRoy / The Firelight Group / January 2016 Direct-To-Digital Mapping Methodology: A Hands-on Guidebook for Applying Google Earth Copyright © Steven DeRoy and the Firelight Group (www.thefirelightgroup.com) 1 | Page Table of Contents ACKNOWLEDGEMENTS 4 PURPOSE OF THIS GUIDE 4 PREPARE TO MAP 5 DOWNLOAD AND INSTALL GOOGLE EARTH 5 SET UP YOUR FILE FOLDERS ON YOUR COMPUTER 5 INTERVIEW ROOM SETTING 5 START THE INTERVIEW 5 INTERVIEW CHECK LIST 6 INTERVIEW KIT 6 SET UP GOOGLE EARTH 6 CHECK YOUR EQUIPMENT 6 INFORM THE PARTICIPANT AND MAKE THEM COMFORTABLE 6 BEFORE STARTING THE INTERVIEW 7 REMINDERS DURING THE INTERVIEW 7 INTERVIEW OVERVIEW 8 INTRODUCTIONS 8 MAPPING 8 INTERVIEW 8 STORAGE OF RESULTS 8 MAPPING NOTES 9 OVERVIEW OF GOOGLE EARTH 10 WHAT IS GOOGLE EARTH? 10 WHY USE GOOGLE EARTH? 10 HOW ARE GOOGLE EARTH AND MAPS DIFFERENT? 10 INTRODUCTION TO THE GOOGLE EARTH INTERFACE 11 NAVIGATING IN GOOGLE EARTH 11 SET UP YOUR ENVIRONMENT 12 THE “PLACES” PANEL 13 SET UP FOLDERS TO STORE YOUR DATA 13 ORGANIZE YOUR FOLDERS 14 UNDERSTANDING SCALE 14 Copyright © Steven DeRoy and the Firelight Group (www.thefirelightgroup.com) 2 | Page HOW TO MAP IN GOOGLE EARTH 16 WHAT TO RECORD FOR EACH SITE 16 ADDING POINTS (PLACEMARKS) 16 ADDING LINES (PATHS) 18 ADDING AREAS (POLYGONS) 19 EDITING MAPPED DATA 20 INTERVIEWS CONDUCTED WITH NO MAPPING DATA 20 OVERLAYING DATA FROM PAST STUDIES 21 CLOSING OFF THE INTERVIEW 22 SAVING KMZ FILES 22 COPY AND SAVE ALL DIGITAL AUDIO AND VIDEO FILES TO YOUR LAPTOP/COMPUTER 23 BACKUP YOUR FILES 23 USEFUL RESOURCES 24 Copyright © Steven DeRoy and the Firelight Group (www.thefirelightgroup.com) 3 | Page Acknowledgements Steven DeRoy, Director of the Firelight Group, produced this comprehensive guidebook.
    [Show full text]
  • Digital Mapping Techniques ‘02— Workshop Proceedings
    cov02 5/20/03 3:19 PM Page 1 So Digital Mapping Techniques ‘02— Workshop Proceedings May 19-22, 2002 Salt Lake City, Utah Convened by the Association of American State Geologists and the United States Geological Survey Hosted by the Utah Geological Survey U.S. GEOLOGICAL SURVEY OPEN-FILE REPORT 02-370 Printed on recycled paper CONTENTS I U.S. Department of the Interior U.S. Geological Survey Digital Mapping Techniques ʻ02— Workshop Proceedings Edited by David R. Soller May 19-22, 2002 Salt Lake City, Utah Convened by the Association of American State Geologists and the United States Geological Survey Hosted by the Utah Geological Survey U.S. GEOLOGICAL SURVEY OPEN-FILE REPORT 02-370 2002 II CONTENTS CONTENTS III This report is preliminary and has not been reviewed for conformity with the U.S. Geological Survey editorial standards. Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply endorsement by the U.S. Government or State governments II CONTENTS CONTENTS III CONTENTS Introduction By David R. Soller (U.S. Geological Survey). 1 Oral Presentations The Value of Geologic Maps and the Need for Digitally Vectorized Data By James C. Cobb (Kentucky Geological Survey) . 3 Compilation of a 1:24,000-Scale Geologic Map Database, Phoenix Metropoli- tan Area By Stephen M. Richard and Tim R. Orr (Arizona Geological Survey). 7 Distributed Spatial Databases—The MIDCARB Carbon Sequestration Project By Gerald A. Weisenfluh (Kentucky Geological Survey), Nathan K. Eaton (Indiana Geological Survey), and Ken Nelson (Kansas Geological Survey) .
    [Show full text]
  • Digital Geologic Mapping Methods
    INTERNATIONAL JOURNAL OF GEOLOGY Issue 3, Volume 2, 2008 Digital geologic mapping methods: from field to 3D model M. De Donatis, S. Susini and G. Delmonaco Abstract—Classical geologic mapping is one of the main techniques software 2DMove and 3DMove by Midland Valley Exploration Ltd. used in geology where pencils, paper base map and field book are the This paper reports a case study of digital mapping carried out in traditional tools of field geologists. In this paper, we describe a new the Southern Apennines. This work is part of an ENEA project, method of digital mapping from field work to buiding three funded by the Italian Ministry of University and Research, devoted to dimensional geologic maps, including GIS maps and geologic cross- heritage sites prone to landslide risk where local-scale geologic and sections. The project consisted of detailed geologic mapping of the geomorphic mapping is fundamental in the preliminary stage of the are of Craco village (Matera - Italy). The work started in the lab by project. implementing themes for defining a cartographic base (aerial photos, topographic and geologic maps) and for field work (developing II. Study area symbols for outcrops, dip data, boundaries, faults, and landslide The study area (Fig. 1) is located in the Southern Apennines, types). Special prompts were created ad hoc for data collection. All around Craco, a small village in the Matera Province along a ridge data were located or mapped through GPS. It was possible to easily between the Bruscata and Salandrella stream. store any types of documents (digital pictures, notes, and sketches), linked to an object or a geo-referenced point.
    [Show full text]
  • Digital Earth Based on Metadata Electronic Maps Standard
    DIGITAL EARTH BASED ON METADATA ELECTRONIC MAPS STANDARD Alexander Martynenko 2 build., 44, Vavilova st., Moscow, 117333, Russia, Institute of Informatics Problems, Russian Academy of Sciences, [email protected] Digital Earth was at the center of attention at 19th International Cartographic Conference (Ottawa) and 1st International Symposium on Digital Earth (Beijing) in 1999 [1, 2]. The discussion raised in the papers on the theory of Metadata Standard Systems that could be used in International and National Spatial Information Infrastructures to establish Digital Earth still continues. We highly appreciate the essential contribution made by Chinese scientists: Zhao Yongping, Huang Fang, Guo Jingjun, Feng Quan, Cui Weihong. Since 1990s the Information Society is being created in Russia. Each member of this Society will be able to obtain complete and actual information, including geoinformation, in any point of geographical space, by means of Internet. Today it is obvious that this problem could be solved in most effective way by using electronic libraries of various intent, including electronic map libraries. Completeness, methods and form of geoinformation, temporal factors of the information supply can be considered as the main criteria for evaluating quality of the electronic geoinformation resources. In the process of creating the electronic geoinformation resources, standardization in the area of geoinformation technologies plays the main role. Today, at the boundary of millennia, the Global Geoinformatic Mapping (GGM) appears as the prior direction of scientific and technical progress. Its goal is the cartographic representation of the real world and creation of the global computer model of the Earth, comprised of millions of space images and electronic maps of various subjects and scales, themes and also reference information [3, 4].
    [Show full text]
  • Ogdc)—Release 6
    State of Oregon Oregon Department of Geology and Mineral Industries Ian P. Madin, Interim State Geologist README FILE FOR OREGON GEOLOGIC DATA COMPILATION (OGDC)—RELEASE 6 by Rachel L. Smith1 and Warren P. Roe1 2015 1 Formerly with Oregon Department of Geology and Mineral Industries, 800 NE Oregon St., Ste. 965, Portland, OR 97015 Oregon Geologic Data Compilation Release 6 DISCLAIMER This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information. This publication cannot substitute for site-specific investigations by qualified practitioners. Site-specific data may give results that differ from the results shown in the publication. Spatial inaccuracies exist in this data set, and some functionalities of Oregon Geologic Data Compilation have changed from previous versions due to the transition to an Esri®-formatted geodatabase. Known issues include topology errors, small spatial data gaps, typographical errors, incomplete metadata, and other minor errors. Oregon Department of Geology and Mineral Industries | Oregon Geologic Data Compilation, release 6 Published in conformance with ORS 516.030 For additional information: Administrative Offices 800 NE Oregon Street #28, Suite 965 Portland, OR 97232 Telephone (971) 673-1555 Fax (971) 673-1562 http://www.oregongeology.org http://egov.oregon.gov/DOGAMI/ Oregon Department of Geology
    [Show full text]
  • Digital Soil Mapping and GIS-Based Land Evaluation for Rice Suitability in Kilombero Valley, Tanzania
    Digital Soil Mapping and GIS-based Land Evaluation for Rice Suitability in Kilombero Valley, Tanzania DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Boniface Hussein John Massawe, M.S. Graduate Program in Environment and Natural Resources The Ohio State University 2015 Dissertation Committee: Dr. Brian Slater, Advisor Dr. Warren Dick Dr. Laura Lindsey i Copyrighted by Boniface Hussein John Massawe 2015 ii Abstract A GIS-based multi-criteria land evaluation was performed in Kilombero Valley, Tanzania in order to avail decision makers and farmers with evidence based decision support tool for improved and sustainable rice production in this important region for agricultural investment. Five most important criteria for rice production in the area were identified through literature search and discussion with local agronomists and farmers. The identified criteria were 1) soil properties, 2) surface water resources, 3) accessibility, 4) distance to markets, and 5) topography. Spatial information for these criteria for the study area was generated. To generate spatial soil information, field survey and lab analysis was conducted using base map generated from a legacy soil map and 30 m Digital Elevation Model (DEM). OSACA, a k-means based clustering application was used to perform distance metric numerical classification of soil profiles. The profiles were classified into 13 clusters. The clusters were demonstrated to be different from each other through comparison of modeled continuous vertical variability of selected attributes of modal soil cluster centroids by using equal area spline functions. Two decision tree based algorithms, J48 and Random Forest (RF) were applied to construct models to spatially predict the soil clusters using environmental correlates derived from 30 m DEM, 5 m RapidEye satellite ii image and legacy soil map using the scorpan digital soil mapping framework.
    [Show full text]
  • Digital Mapping of Peatlands
    Earth-Science Reviews 196 (2019) 102870 Contents lists available at ScienceDirect Earth-Science Reviews journal homepage: www.elsevier.com/locate/earscirev Digital mapping of peatlands – A critical review T ⁎ Budiman Minasnya, , Örjan Berglundb, John Connollyc, Carolyn Hedleyd, Folkert de Vriese, Alessandro Gimonaf, Bas Kempeng, Darren Kiddh, Harry Liljai, Brendan Malonea,p, Alex McBratneya, Pierre Roudierd,q, Sharon O'Rourkej, Rudiyantok, José Padariana, Laura Poggiof,g, Alexandre ten Catenl, Daniel Thompsonm, Clint Tuven, Wirastuti Widyatmantio a School of Life & Environmental Science, Sydney Institute of Agriculture, the University of Sydney, Australia b Swedish University of Agricultural Sciences, Department of Soil and Environment, Uppsala, Sweden c School of History & Geography, Dublin City University, Ireland d Manaaki Whenua - Landcare Research, Palmerston North, New Zealand e Wageningen Environmental Research, PO Box 47, Wageningen, the Netherlands f The James Hutton Institute, Scotland, United Kingdom g ISRIC – World Soil Information, PO Box 353, Wageningen, the Netherlands h Department of Primary Industries, Parks, Water, and Environment, Tasmania, Australia i Luke, Natural Resources Institute, Finland j School of Biosystems & Food Engineering, University College Dublin, Ireland k Faculty of Fisheries and Food Security, Universiti Malaysia Terengganu, Malaysia l Universidade Federal de Santa Catarina, Brazil m Natural Resources Canada, Canada n Natural Resources Conservation Service, United States Department of Agriculture, USA o Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia p CSIRO Agriculture and Food, Black Mountain ACT, Australia q Te Pūnaha Matatini, The University of Auckland, Private Bag 92019, Auckland 1010, New Zealand ABSTRACT Peatlands offer a series of ecosystem services including carbon storage, biomass production, and climate regulation.
    [Show full text]
  • Using Google's Cloud-Based Platform for Digital Soil Mapping
    Computers & Geosciences 83 (2015) 80–88 Contents lists available at ScienceDirect Computers & Geosciences journal homepage: www.elsevier.com/locate/cageo Case study Using Google's cloud-based platform for digital soil mapping J. Padarian n, B. Minasny, A.B. McBratney Faculty of Agriculture and Environment, Department of Environmental Sciences, The University of Sydney, New South Wales, Australia article info abstract Article history: A digital soil mapping exercise over a large extent and at a high resolution is a computationally expensive Received 2 January 2015 procedure. It may take days or weeks to obtain the final maps and to visually evaluate the prediction Received in revised form models when using a desktop workstation. To increase the speed of time-consuming procedures, the use 29 June 2015 of supercomputers is a common practice. GoogleTM has developed a product specifically designed for Accepted 30 June 2015 mapping purposes (Earth Engine), allowing users to take advantage of its computing power and the Available online 10 July 2015 mobility of a cloud-based solution. In this work, we explore the feasibility of using this platform for Keywords: digital soil mapping by presenting two soil mapping examples over the contiguous United States. We also Supercomputing discuss the advantages and limitations of this platform at its current development stage, and potential DSM improvements towards a fully functional cloud-based soil mapping platform. Cloud computing & 2015 Elsevier Ltd. All rights reserved. GlobalSoilMap Google Earth Engine 1. Introduction space). The so-called Scorpan factors can usually be represented in a digital form, usually as raster data. The mapping is achieved by Current soil mapping heavily depends on the use of technology building a spatial soil prediction function which relates observed and computers, starting with the use of computer-based geo- soil variables with Scorpan factors.
    [Show full text]
  • Improving the Visualization of Geospatial Data Using Google's
    Improving the Visualization of Geospatial Data Using Google’s KML THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Ebenezer Attua Odoi Jr Graduate Program in Geodetic Science and Surveying The Ohio State University 2012 Master's Examination Committee: Prof. Alan Saalfeld, Advisor Prof. Ralph Von Frese, Committee Member Copyright by Ebenezer Attua Odoi Jr 2012 Abstract The Geospatial community continues to search for effective tools that produce visualizations of the nature of the earth and its features. With the aid of geobrowsers like Google Maps and Google Earth, geoscientists can now tell their ‘tales’ in ways that nonscientists can grasp and respond to in terms of awareness, policy formulation, application development and integration in ventures that usher human existence forward. This thesis explores diverse visualization techniques using Google’s Keyhole Markup Language (KML) that will benefit the viewing of geological data. In the process the thesis will show the potential of geobrowsers and KML as a unified programming language. Though there has been a proliferation of digital map viewers like geobrowsers being developed, thematic mapping capabilities, unfortunately, has been left out. The thesis will explore how KML can be used to achieve thematic mapping, though KML itself was not specifically designed for this application. Current possibilities for making proportional symbol maps, chart maps, choropleth maps and animated maps with KML will be presented. The innovation of the thesis is the conversion of a database table into a thematic map, using proportional symbols to represent the data.
    [Show full text]
  • Mobile Mapping Mobile Mapping Mediamatters
    media Mobile Mapping matters Space, Cartography and the Digital Amsterdam University clancy wilmott Press Mobile Mapping MediaMatters MediaMatters is an international book series published by Amsterdam University Press on current debates about media technology and its extended practices (cultural, social, political, spatial, aesthetic, artistic). The series focuses on critical analysis and theory, exploring the entanglements of materiality and performativity in ‘old’ and ‘new’ media and seeks contributions that engage with today’s (digital) media culture. For more information about the series see: www.aup.nl Mobile Mapping Space, Cartography and the Digital Clancy Wilmott Amsterdam University Press The publication of this book is made possible by a grant from the European Research Council (ERC) under the European Community’s 7th Framework program (FP7/2007-2013)/ ERC Grant Number: 283464 Cover illustration: Clancy Wilmott Cover design: Suzan Beijer Lay-out: Crius Group, Hulshout isbn 978 94 6298 453 0 e-isbn 978 90 4853 521 7 doi 10.5117/9789462984530 nur 670 © C. Wilmott / Amsterdam University Press B.V., Amsterdam 2020 All rights reserved. Without limiting the rights under copyright reserved above, no part of this book may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the written permission of both the copyright owner and the author of the book. Every effort has been made to obtain permission to use all copyrighted illustrations reproduced in this book. Nonetheless, whosoever believes to have rights to this material is advised to contact the publisher. Table of Contents Acknowledgements 7 Part 1 – Maps, Mappers, Mapping 1.
    [Show full text]
  • A Method for Creating a Three Dimensional Model from Published Geologic Maps and Cross Sections
    A Method for Creating a Three Dimensional Model from Published Geologic Maps and Cross Sections By Gregory J. Walsh Prepared in cooperation with the Moroccan Ministry of Energy, Mines, Water and the Environment (Ministère de l’Énergie, des Mines, de l’Eau et de l’Environnement - MEM) Google Earth (GE) files: Please note: These files are for visualizing the geology at a regional scale only, and should not be used in any way for site-specific decisionmaking. The original published maps contain more complete geologic information. In Google Earth it is possible to view the geology at much greater scale than that intended by the authors. This gives an impression of greater accuracy than is actually present in the models shown here. [Disclaimer modified from the Google Earth version of Graymer and others (2006).] Open-File Report 2009–1229 U.S. Department of the Interior U.S. Geological Survey U.S. Department of the Interior KEN SALAZAR, Secretary U.S. Geological Survey Marcia K. McNutt, Director U.S. Geological Survey, Reston, Virginia: 2009 For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1–888–ASK–USGS. For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprod. To order this and other USGS information products, visit http://store.usgs.gov. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S.
    [Show full text]