Landsat color composite image draped on a Digital Elevation Model

Size: px
Start display at page:

Download "Landsat color composite image draped on a Digital Elevation Model"

Transcription

1 SFR406 Spring 2015 Formation and Interpretation of Color Composite Images Introduction One sees color images collected by earth orbiting satellites in popular magazines, in movies and television shows; however, few viewers have any understanding of what they are looking at. Many of these images were recorded by Landsat or other multispectral scanners. These images are usually color composite images (as described in the following sections). Color composite images are often interpreted visually, sometimes to compliment data analysis in remote sensing studies. For example, one can learn how to interpret general vegetation types from a summer color composite image to determine where to go to visit or sample cover type attributes in the field. Color composite images combined with a digital elevation model (DEM) can depict some interesting perspectives of the landscape (Figure). Visual interpretation of time-series Landsat color composites has been used as a method to support accuracy assessment of forest or land cover change images (Cohen et al., 1998; Sader et al., 2003). In regions where vegetation or land cover maps do not exist or are out of date, visual interpretation of Landsat color composites has been employed as the method to develop the vegetation maps for large mapping areas (FAO, 1993). Landsat color composite image draped on a Digital Elevation Model Digital and color composite images can be interpreted visually as in aerial photo interpretation or digitally as in digital image processing performed with computer

2 programs. Color composite images can be interpreted visually using many of the same PI principles used in aerial photo interpretation. Students well- grounded in PI principles and related concepts will be able to adapt their interpretation skills to digital images, particularly the interpretation of satellite color composite images formed on a computer monitor or printed/plotted on paper. Fundamentals Concepts The art of interpreting a 3 band color composite image is aided by the following: 1) Field knowledge and distribution of cover types throughout the study area (e.g., forest types and plant ecology, topography, and terrain analysis. 2) Familiarity with the multispectral reflectance characteristics and DN levels for different forest and land cover type. 3) Familiarity with additive color theory. 4) Knowledge of PI principles and PI experience. Mechanics of a Color Composite Image If three different wavebands are displayed simultaneously on the computer monitor, a color composite image will result. The color write functions or color guns in the monitor are represented by primary colors - red, green and blue (RGB). Two colors in combination form complementary colors (R + G = yellow; R + B = magenta; G + B = cyan, all 3 primaries combined are white and lack of all three form no color (black). Each image represents 8 bits and three 8 bit images in the composite represents over 16 million possible colors. The additive primaries: red, green, and blue Characteristics: Equal proportions of the three additive primaries combine to form white light. The strength of the composite colors formed are controlled by the relative brightness or DN in each of the 3 wavebands coupled with R, G, and B. For example, a blue pixel represents high reflectance and thus a high DN coupled with the B color plane, and the wavebands coupled with R and G have very low, or no DN values. For example deep, clear water may

3 appear blue on a true color composite because there might be higher reflectance in the shortest waveband (visible blue) and most of the energy at the other wavelengths are mostly absorbed, thus little to none reflected (and hence low DN for wavelength couple with R and G color gun in the computer monitor). Clouds that are not moisture laden are white on all color composites, because they reflect highly in all visible and reflected infrared bands on any composite image combination. The figure above depicts a generic color table interpretation chart to understand the relation of waveband DN and additive color theory (primary and complementary colors that result). Once the student understands the principles of wavelength dependent reflectance of earth surface features, image interpretation principles, and additive color theory, one can develop the skill to interpret a color composite image collected by satellites or other sensors. With these skills and understanding, one should be able to view a satellite color composite image collected virtually anywhere in the world and know what the general cover types are, without ever being there. One must keep in mind that all remote sensing work needs to be supported by field observations, but where it is not possible, these interpretation skills can be invaluable for a general overview of the area. References Cohen, W.B., M. Fiorella, J. Gray, E. Helmer, and K. Anderson An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery. Photogrammetric Engineering and Remote Sensing 64(4): [FAO] Food and Agricultural Organization Forest Resources Assessment, 1990, Tropical Countries. FAO Forestry Paper 112. Rome: Food and Agricultural Organization of the United Nations. Sader, S.A., Bertrand, M., & Wilson, E.H Satellite change detection of forest harvest patterns on an industrial forest landscape, Forest Science, 49(3),

4 True Color, CIR and False Color Composite Images using Landsat 8 OLI Interpreting a True Color Composite To create a true color composite, the three visible bands available on Landsat are coupled with the primary colors in the computer monitor (R = visible red, G = visible green, and B = visible blue). Other names for this composite are normal or natural color. Landsat -8 OLI RGB 432 true color composite Vegetation types are variations of green; urban and inert surfaces are white; water is dark blue; bog/wetlands are brownish. There is a cloud in the upper left corner of the image with thin clouds and haze trending in the southeast direction from the main cloud. This composite image will have similar color to true or normal color aerial photos and the way humans see color. Healthy vegetation is green, water is dark blue to black (depending on depth, turbidity, algae content, etc). Landsat is capable of displaying a true color composite but most other medium spatial resolution, multispectral scanners on orbiting commercial satellites cannot, because they do not contain all three visible wavebands. Many interpreters prefer true color, because colors look natural to our eyes. Perhaps one disadvantage of true color is that some different earth surface features have similar low reflectance in the visible wavelengths making it difficult to interpret subtle difference among vegetation types, for example.

5 Interpreting a Color Infrared Composite To create a color infrared composite image, two visible bands and the near infrared band are combined (R = NIR, G = visible red, B = visible green). This color pattern simulates the same color patterns as seen on color infrared photos although a different color process (subtractive primary colors) is involved with aerial photos and film. This is also called a false color composite (this is from a human perspective because it is not true color the way we see it). All composites that do not form true color can be categorized as false color. On the color infrared composite, healthy vegetation is reddish to magenta. Softwood are darker red-brown and hardwood are brighter red-magenta. Water is black. Landsat 8 OLI RGB- 543 Color Infrared composite image This composite simulates the color of a color infrared aerial photo and can be interpreted using the same logic. Vegetation types are variations of magenta; urban features and bare field are cyan. This composite contains the near infrared waveband and therefore vegetation types are better distinguished compared to a true color composite False Color Composite OLI RGB- 564 and OLI RGB- 654 This false color composite image is one of the best for showing color differences between vegetation types (e.g., hardwood, softwood, wetland types). The TM wavebands and primary colors in the computer monitor are coupled as follows: R = NIR, G = Mid IR, B = visible red. Hardwoods are orange to yellow (high R (NIR), mediumhigh G (Mid IR), and low B because the visible red is a chlorophyll absorption band. In the TM 654 composite, the near infrared and mid-infrared are rearranged with the red and green color guns so that vegetation appears green.

6 Landsat OLI RGB- 564 false color composite. This composite contains the visible red and both the near infrared and mid infrared bands. Although the colors are not natural to human eyes, the 564 band combination is arguable the best for distinguishing different forest and vegetation types in the northeastern U.S. S= Softwood H= Hardwood Landsat OLI RGB- 654 color composite The false color composite contains the same 3 bands as the previous example however the (near infrared) band (5) is coupled with the green color gun. Because vegetation reflects higher in the near infrared than the other two bands, the green color dominates. Some interpreters prefer this combination because it makes the vegetation look more natural.

Selecting the appropriate band combination for an RGB image using Landsat imagery

Selecting the appropriate band combination for an RGB image using Landsat imagery Selecting the appropriate band combination for an RGB image using Landsat imagery Ned Horning Version: 1.0 Creation Date: 2004-01-01 Revision Date: 2004-01-01 License: This document is licensed under a

More information

WorldView-2 Band Combinations

WorldView-2 Band Combinations WorldView-2 Band Combinations DigitalGlobe Constellation The purpose of this document is to show and describe several of the more important band combinations possible with the DigitalGlobe WorldView-2

More information

Review for Introduction to Remote Sensing: Science Concepts and Technology

Review for Introduction to Remote Sensing: Science Concepts and Technology Review for Introduction to Remote Sensing: Science Concepts and Technology Ann Johnson Associate Director ann@baremt.com Funded by National Science Foundation Advanced Technological Education program [DUE

More information

Spectral Response for DigitalGlobe Earth Imaging Instruments

Spectral Response for DigitalGlobe Earth Imaging Instruments Spectral Response for DigitalGlobe Earth Imaging Instruments IKONOS The IKONOS satellite carries a high resolution panchromatic band covering most of the silicon response and four lower resolution spectral

More information

How Landsat Images are Made

How Landsat Images are Made How Landsat Images are Made Presentation by: NASA s Landsat Education and Public Outreach team June 2006 1 More than just a pretty picture Landsat makes pretty weird looking maps, and it isn t always easy

More information

Resolutions of Remote Sensing

Resolutions of Remote Sensing Resolutions of Remote Sensing 1. Spatial (what area and how detailed) 2. Spectral (what colors bands) 3. Temporal (time of day/season/year) 4. Radiometric (color depth) Spatial Resolution describes how

More information

2.3 Spatial Resolution, Pixel Size, and Scale

2.3 Spatial Resolution, Pixel Size, and Scale Section 2.3 Spatial Resolution, Pixel Size, and Scale Page 39 2.3 Spatial Resolution, Pixel Size, and Scale For some remote sensing instruments, the distance between the target being imaged and the platform,

More information

GIS for Educators. Overview:

GIS for Educators. Overview: GIS for Educators Topic 5: Raster Data Objectives: Keywords: Understand what raster data is and how it can be used in a GIS. Raster, Pixel, Remote Sensing, Satellite, Image, Georeference Overview: In the

More information

Green = 0,255,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (43,215,35) Equal Luminance Gray for Green

Green = 0,255,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (43,215,35) Equal Luminance Gray for Green Red = 255,0,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (184,27,26) Equal Luminance Gray for Red = 255,0,0 (147,147,147) Mean of Observer Matches to Red=255

More information

See Lab 8, Natural Resource Canada RS Tutorial web pages Tues 3/24 Supervised land cover classification See Lab 9, NR Canada RS Tutorial web pages

See Lab 8, Natural Resource Canada RS Tutorial web pages Tues 3/24 Supervised land cover classification See Lab 9, NR Canada RS Tutorial web pages SFR 406 Remote Sensing, Image Interpretation and Forest Mapping EXAM # 2 (23 April 2015) REVIEW SHEET www.umaine.edu/mial/courses/sfr406/index.htm (Lecture powerpoint & notes) TOPICS COVERED ON 2 nd EXAM:

More information

Land Use/Land Cover Map of the Central Facility of ARM in the Southern Great Plains Site Using DOE s Multi-Spectral Thermal Imager Satellite Images

Land Use/Land Cover Map of the Central Facility of ARM in the Southern Great Plains Site Using DOE s Multi-Spectral Thermal Imager Satellite Images Land Use/Land Cover Map of the Central Facility of ARM in the Southern Great Plains Site Using DOE s Multi-Spectral Thermal Imager Satellite Images S. E. Báez Cazull Pre-Service Teacher Program University

More information

AQUATIC VEGETATION SURVEYS USING HIGH-RESOLUTION IKONOS IMAGERY INTRODUCTION

AQUATIC VEGETATION SURVEYS USING HIGH-RESOLUTION IKONOS IMAGERY INTRODUCTION AQUATIC VEGETATION SURVEYS USING HIGH-RESOLUTION IKONOS IMAGERY Leif G. Olmanson, Marvin E. Bauer, and Patrick L. Brezonik Water Resources Center & Remote Sensing and Geospatial Analysis Laboratory University

More information

ANALYSIS OF FOREST CHANGE IN FIRE DAMAGE AREA USING SATELLITE IMAGES

ANALYSIS OF FOREST CHANGE IN FIRE DAMAGE AREA USING SATELLITE IMAGES ANALYSIS OF FOREST CHANGE IN FIRE DAMAGE AREA USING SATELLITE IMAGES Joon Mook Kang, Professor Joon Kyu Park, Ph.D Min Gyu Kim, Ph.D._Candidate Dept of Civil Engineering, Chungnam National University 220

More information

Generation of Cloud-free Imagery Using Landsat-8

Generation of Cloud-free Imagery Using Landsat-8 Generation of Cloud-free Imagery Using Landsat-8 Byeonghee Kim 1, Youkyung Han 2, Yonghyun Kim 3, Yongil Kim 4 Department of Civil and Environmental Engineering, Seoul National University (SNU), Seoul,

More information

TerraColor White Paper

TerraColor White Paper TerraColor White Paper TerraColor is a simulated true color digital earth imagery product developed by Earthstar Geographics LLC. This product was built from imagery captured by the US Landsat 7 (ETM+)

More information

SAMPLE MIDTERM QUESTIONS

SAMPLE MIDTERM QUESTIONS Geography 309 Sample MidTerm Questions Page 1 SAMPLE MIDTERM QUESTIONS Textbook Questions Chapter 1 Questions 4, 5, 6, Chapter 2 Questions 4, 7, 10 Chapter 4 Questions 8, 9 Chapter 10 Questions 1, 4, 7

More information

Night Microphysics RGB Nephanalysis in night time

Night Microphysics RGB Nephanalysis in night time Copyright, JMA Night Microphysics RGB Nephanalysis in night time Meteorological Satellite Center, JMA What s Night Microphysics RGB? R : B15(I2 12.3)-B13(IR 10.4) Range : -4 2 [K] Gamma : 1.0 G : B13(IR

More information

Remote Sensing Image Processing

Remote Sensing Image Processing Remote Sensing Image Processing -Pre-processing -Geometric Correction -Atmospheric correction -Image enhancement -Image classification Division of Spatial Information Science Graduate School Life and Environment

More information

Landsat Monitoring our Earth s Condition for over 40 years

Landsat Monitoring our Earth s Condition for over 40 years Landsat Monitoring our Earth s Condition for over 40 years Thomas Cecere Land Remote Sensing Program USGS ISPRS:Earth Observing Data and Tools for Health Studies Arlington, VA August 28, 2013 U.S. Department

More information

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS Nguyen Dinh Duong Department of Environmental Information Study and Analysis, Institute of Geography, 18 Hoang Quoc Viet Rd.,

More information

INTRODUCTION REMOTE SENSING

INTRODUCTION REMOTE SENSING INTRODUCTION REMOTE SENSING dr.ir. Jan Clevers Centre for Geo-Information Dept. Environmental Sciences Wageningen UR Remote Sensing --> RS Sensor at a distance EARTH OBSERVATION EM energy Earth RS is a

More information

Filters for Black & White Photography

Filters for Black & White Photography Filters for Black & White Photography Panchromatic Film How it works. Panchromatic film records all colors of light in the same tones of grey. Light Intensity (the number of photons per square inch) is

More information

Color image processing: pseudocolor processing

Color image processing: pseudocolor processing Color image processing: pseudocolor processing by Gleb V. Tcheslavski: gleb@ee.lamar.edu http://ee.lamar.edu/gleb/dip/index.htm Spring 2008 ELEN 4304/5365 DIP 1 Preliminaries Pseudocolor (false color)

More information

3.3 Normalised Difference Vegetation Index (NDVI) 3.3.1 NDVI: A non-technical overview

3.3 Normalised Difference Vegetation Index (NDVI) 3.3.1 NDVI: A non-technical overview Page 1 of 6 3.3 Normalised Difference Vegetation Index (NDVI) 3.3.1 NDVI: A non-technical overview The Normalised Difference Vegetation Index (NDVI) gives a measure of the vegetative cover on the land

More information

River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models

River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models Steven M. de Jong & Raymond Sluiter Utrecht University Corné van der Sande Netherlands Earth Observation

More information

Remote sensing is the collection of data without directly measuring the object it relies on the

Remote sensing is the collection of data without directly measuring the object it relies on the Chapter 8 Remote Sensing Chapter Overview Remote sensing is the collection of data without directly measuring the object it relies on the reflectance of natural or emitted electromagnetic radiation (EMR).

More information

Digital image processing

Digital image processing 746A27 Remote Sensing and GIS Lecture 4 Digital image processing Chandan Roy Guest Lecturer Department of Computer and Information Science Linköping University Digital Image Processing Most of the common

More information

The Utilization of Satellite Images to Identify Tree Endangering Transmission Lines

The Utilization of Satellite Images to Identify Tree Endangering Transmission Lines The Utilization of Satellite Images to Identify Tree Endangering Transmission Lines Y. Kobayashi M. S. Moeller G. G. Karady G. T. Heydt R. G. Olsen Project Tele-Seminar March 18, 2008 3/10/2008 1 Introduction

More information

Name Class Date. spectrum. White is not a color, but is a combination of all colors. Black is not a color; it is the absence of all light.

Name Class Date. spectrum. White is not a color, but is a combination of all colors. Black is not a color; it is the absence of all light. Exercises 28.1 The Spectrum (pages 555 556) 1. Isaac Newton was the first person to do a systematic study of color. 2. Circle the letter of each statement that is true about Newton s study of color. a.

More information

Remote Sensing of Global Climate Change

Remote Sensing of Global Climate Change of Global Climate Change 2009 TECHNICAL STUFF... WHERE ON THE EARTH IS THAT??? Students should understand various geographic coordinate systems used to locate images, and how to translate these into distances

More information

Finding and Downloading Landsat Data from the U.S. Geological Survey s Global Visualization Viewer Website

Finding and Downloading Landsat Data from the U.S. Geological Survey s Global Visualization Viewer Website January 1, 2013 Finding and Downloading Landsat Data from the U.S. Geological Survey s Global Visualization Viewer Website All Landsat data are available to the public at no cost from U.S. Geological Survey

More information

High Resolution Information from Seven Years of ASTER Data

High Resolution Information from Seven Years of ASTER Data High Resolution Information from Seven Years of ASTER Data Anna Colvin Michigan Technological University Department of Geological and Mining Engineering and Sciences Outline Part I ASTER mission Terra

More information

Preface. Ko Ko Lwin Division of Spatial Information Science University of Tsukuba 2008

Preface. Ko Ko Lwin Division of Spatial Information Science University of Tsukuba 2008 1 Preface Remote Sensing data is one of the primary data sources in GIS analysis. The objective of this material is to provide fundamentals of Remote Sensing technology and its applications in Geographical

More information

Supervised Classification workflow in ENVI 4.8 using WorldView-2 imagery

Supervised Classification workflow in ENVI 4.8 using WorldView-2 imagery Supervised Classification workflow in ENVI 4.8 using WorldView-2 imagery WorldView-2 is the first commercial high-resolution satellite to provide eight spectral sensors in the visible to near-infrared

More information

Color Theory The art and science of color interaction.

Color Theory The art and science of color interaction. Color Theory The art and science of color interaction. Like a physicist, artists use color wavelengths to create visual effects. Like a chemist, artists are aware of safety and permanence of dyes and pigments.

More information

GIS Lesson 6 MAPS WITH RASTER IMAGES III: SATELLITE IMAGERY TEACHER INFORMATION

GIS Lesson 6 MAPS WITH RASTER IMAGES III: SATELLITE IMAGERY TEACHER INFORMATION GIS Lesson 6 MAPS WITH RASTER IMAGES III: SATELLITE IMAGERY TEACHER INFORMATION Lesson Summary: During this lesson students use GIS to load and view truecolor and enhanced satellite images of Alaska. Based

More information

RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY

RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY M. Erdogan, H.H. Maras, A. Yilmaz, Ö.T. Özerbil General Command of Mapping 06100 Dikimevi, Ankara, TURKEY - (mustafa.erdogan;

More information

Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California

Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California Graham Emde GEOG 3230 Advanced Remote Sensing February 22, 2013 Lab #1 Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California Introduction Wildfires are a common disturbance

More information

CLIENT GUIDE TO DIGITAL ORTHO- PHOTOGRAPHY

CLIENT GUIDE TO DIGITAL ORTHO- PHOTOGRAPHY ISSUE 2 SEPTEMBER 2014 TSA Endorsed by: CLIENT GUIDE TO DIGITAL ORTHO- PHOTOGRAPHY The Survey Association s Client Guides are primarily aimed at other professionals such as engineers, architects, planners

More information

Electromagnetic Radiation (including visible light)

Electromagnetic Radiation (including visible light) An expert is a man who has made all the mistakes, which can be made in a narrow field. Neils Bohr Electromagnetic Radiation (including visible light) Behaves like a particle. light particles are called

More information

Measuring Length and Area of Objects in Digital Images Using AnalyzingDigitalImages Software. John Pickle, Concord Academy, March 19, 2008

Measuring Length and Area of Objects in Digital Images Using AnalyzingDigitalImages Software. John Pickle, Concord Academy, March 19, 2008 Measuring Length and Area of Objects in Digital Images Using AnalyzingDigitalImages Software John Pickle, Concord Academy, March 19, 2008 The AnalyzingDigitalImages software, available free at the Digital

More information

Overview. What is EMR? Electromagnetic Radiation (EMR) LA502 Special Studies Remote Sensing

Overview. What is EMR? Electromagnetic Radiation (EMR) LA502 Special Studies Remote Sensing LA502 Special Studies Remote Sensing Electromagnetic Radiation (EMR) Dr. Ragab Khalil Department of Landscape Architecture Faculty of Environmental Design King AbdulAziz University Room 103 Overview What

More information

AERIAL PHOTOGRAPHS. For a map of this information, in paper or digital format, contact the Tompkins County Planning Department.

AERIAL PHOTOGRAPHS. For a map of this information, in paper or digital format, contact the Tompkins County Planning Department. AERIAL PHOTOGRAPHS What are Aerial Photographs? Aerial photographs are images of the land taken from an airplane and printed on 9 x9 photographic paper. Why are Aerial Photographs Important? Aerial photographs

More information

and CCDs Colour Imaging for Colour Imaging Author: David Bowdley

and CCDs Colour Imaging for Colour Imaging Author: David Bowdley Background Science for and CCDs Author: David Bowdley This project has been funded with support from the European Commission. This publication reflects the views only of the author, and the Commission

More information

Mapping Earth from Space Remote sensing and satellite images. Remote sensing developments from war

Mapping Earth from Space Remote sensing and satellite images. Remote sensing developments from war Mapping Earth from Space Remote sensing and satellite images Geomatics includes all the following spatial technologies: a. Cartography "The art, science and technology of making maps" b. Geographic Information

More information

The Role of SPOT Satellite Images in Mapping Air Pollution Caused by Cement Factories

The Role of SPOT Satellite Images in Mapping Air Pollution Caused by Cement Factories The Role of SPOT Satellite Images in Mapping Air Pollution Caused by Cement Factories Dr. Farrag Ali FARRAG Assistant Prof. at Civil Engineering Dept. Faculty of Engineering Assiut University Assiut, Egypt.

More information

CULTURAL HISTORY Primary Colors - Part 1 of 2 by Neal McLain

CULTURAL HISTORY Primary Colors - Part 1 of 2 by Neal McLain Ok, so what are the Primary Colors? CULTURAL HISTORY Primary Colors - Part 1 of 2 by Neal McLain Since grade school art classes, we've been taught that the primary colors are red, yellow, and blue (RYB).

More information

Design of a High Resolution Multispectral Scanner for Developing Vegetation Indexes

Design of a High Resolution Multispectral Scanner for Developing Vegetation Indexes Design of a High Resolution Multispectral Scanner for Developing Vegetation Indexes Rishitosh kumar sinha*, Roushan kumar mishra, Sam jeba kumar, Gunasekar. S Dept. of Instrumentation & Control Engg. S.R.M

More information

Lake Monitoring in Wisconsin using Satellite Remote Sensing

Lake Monitoring in Wisconsin using Satellite Remote Sensing Lake Monitoring in Wisconsin using Satellite Remote Sensing D. Gurlin and S. Greb Wisconsin Department of Natural Resources 2015 Wisconsin Lakes Partnership Convention April 23 25, 2105 Holiday Inn Convention

More information

Hue. Ten hues shown in spectral order from long wave red to short wave blue. Additive primaries for mixing color with light: red, green, blue

Hue. Ten hues shown in spectral order from long wave red to short wave blue. Additive primaries for mixing color with light: red, green, blue Hue Ten hues shown in spectral order from long wave red to short wave blue. Additive primaries for mixing color with light: red, green, blue Subtractive primaries for mixing color with ink: cyan, magenta,

More information

AN INVESTIGATION OF THE GROWTH TYPES OF VEGETATION IN THE BÜKK MOUNTAINS BY THE COMPARISON OF DIGITAL SURFACE MODELS Z. ZBORAY AND E.

AN INVESTIGATION OF THE GROWTH TYPES OF VEGETATION IN THE BÜKK MOUNTAINS BY THE COMPARISON OF DIGITAL SURFACE MODELS Z. ZBORAY AND E. ACTA CLIMATOLOGICA ET CHOROLOGICA Universitatis Szegediensis, Tom. 38-39, 2005, 163-169. AN INVESTIGATION OF THE GROWTH TYPES OF VEGETATION IN THE BÜKK MOUNTAINS BY THE COMPARISON OF DIGITAL SURFACE MODELS

More information

Opportunities for the generation of high resolution digital elevation models based on small format aerial photography

Opportunities for the generation of high resolution digital elevation models based on small format aerial photography Opportunities for the generation of high resolution digital elevation models based on small format aerial photography Boudewijn van Leeuwen 1, József Szatmári 1, Zalán Tobak 1, Csaba Németh 1, Gábor Hauberger

More information

Outline. Quantizing Intensities. Achromatic Light. Optical Illusion. Quantizing Intensities. CS 430/585 Computer Graphics I

Outline. Quantizing Intensities. Achromatic Light. Optical Illusion. Quantizing Intensities. CS 430/585 Computer Graphics I CS 430/585 Computer Graphics I Week 8, Lecture 15 Outline Light Physical Properties of Light and Color Eye Mechanism for Color Systems to Define Light and Color David Breen, William Regli and Maxim Peysakhov

More information

Recent advances in Satellite Imagery for Oil and Gas Exploration and Production. DESK AND DERRICK APRIL 2016 PRESENTED BY GARY CREWS---RETIRED

Recent advances in Satellite Imagery for Oil and Gas Exploration and Production. DESK AND DERRICK APRIL 2016 PRESENTED BY GARY CREWS---RETIRED Recent advances in Satellite Imagery for Oil and Gas Exploration and Production. DESK AND DERRICK APRIL 2016 PRESENTED BY GARY CREWS---RETIRED Agenda Brief review of state of the applications in 2010 Basics

More information

A remote sensing instrument collects information about an object or phenomenon within the

A remote sensing instrument collects information about an object or phenomenon within the Satellite Remote Sensing GE 4150- Natural Hazards Some slides taken from Ann Maclean: Introduction to Digital Image Processing Remote Sensing the art, science, and technology of obtaining reliable information

More information

Digital Image Basics. Introduction. Pixels and Bitmaps. Written by Jonathan Sachs Copyright 1996-1999 Digital Light & Color

Digital Image Basics. Introduction. Pixels and Bitmaps. Written by Jonathan Sachs Copyright 1996-1999 Digital Light & Color Written by Jonathan Sachs Copyright 1996-1999 Digital Light & Color Introduction When using digital equipment to capture, store, modify and view photographic images, they must first be converted to a set

More information

MODIS IMAGES RESTORATION FOR VNIR BANDS ON FIRE SMOKE AFFECTED AREA

MODIS IMAGES RESTORATION FOR VNIR BANDS ON FIRE SMOKE AFFECTED AREA MODIS IMAGES RESTORATION FOR VNIR BANDS ON FIRE SMOKE AFFECTED AREA Li-Yu Chang and Chi-Farn Chen Center for Space and Remote Sensing Research, National Central University, No. 300, Zhongda Rd., Zhongli

More information

ASSESSMENT OF FOREST RECOVERY AFTER FIRE USING LANDSAT TM IMAGES AND GIS TECHNIQUES: A CASE STUDY OF MAE WONG NATIONAL PARK, THAILAND

ASSESSMENT OF FOREST RECOVERY AFTER FIRE USING LANDSAT TM IMAGES AND GIS TECHNIQUES: A CASE STUDY OF MAE WONG NATIONAL PARK, THAILAND ASSESSMENT OF FOREST RECOVERY AFTER FIRE USING LANDSAT TM IMAGES AND GIS TECHNIQUES: A CASE STUDY OF MAE WONG NATIONAL PARK, THAILAND Sunee Sriboonpong 1 Yousif Ali Hussin 2 Alfred de Gier 2 1 Forest Resource

More information

Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering

Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering Digital Image Processing Prof. P.K. Biswas Department of Electronics & Electrical Communication Engineering Indian Institute of Technology, Kharagpur Lecture - 27 Colour Image Processing II Hello, welcome

More information

1. Three-Color Light. Introduction to Three-Color Light. Chapter 1. Adding Color Pigments. Difference Between Pigments and Light. Adding Color Light

1. Three-Color Light. Introduction to Three-Color Light. Chapter 1. Adding Color Pigments. Difference Between Pigments and Light. Adding Color Light 1. Three-Color Light Chapter 1 Introduction to Three-Color Light Many of us were taught at a young age that the primary colors are red, yellow, and blue. Our early experiences with color mixing were blending

More information

Photosynthesis and Light in the Ocean Adapted from The Fluid Earth / Living Ocean Heather Spalding, UH GK-12 program

Photosynthesis and Light in the Ocean Adapted from The Fluid Earth / Living Ocean Heather Spalding, UH GK-12 program Photosynthesis and Light in the Ocean Adapted from The Fluid Earth / Living Ocean Heather Spalding, UH GK-12 program Algae, like your Halimeda, and plants live in very different environments, but they

More information

and satellite image download with the USGS GloVis portal

and satellite image download with the USGS GloVis portal Tutorial: NDVI calculation with SPRING GIS and satellite image download with the USGS GloVis portal Content overview: Downloading data from GloVis: p 2 Using SPRING GIS: p 11 This document is meant to

More information

Hyperspectral Satellite Imaging Planning a Mission

Hyperspectral Satellite Imaging Planning a Mission Hyperspectral Satellite Imaging Planning a Mission Victor Gardner University of Maryland 2007 AIAA Region 1 Mid-Atlantic Student Conference National Institute of Aerospace, Langley, VA Outline Objective

More information

Remote Sensing. Vandaag. Voordelen Remote Sensing Wat is Remote Sensing? Vier elementen Remote Sensing systeem

Remote Sensing. Vandaag. Voordelen Remote Sensing Wat is Remote Sensing? Vier elementen Remote Sensing systeem Remote Sensing 1 Vandaag Voordelen Remote Sensing Wat is Remote Sensing? Vier elementen Remote Sensing systeem 2 Nederland Vanaf 700 km hoogte Landsat TM mozaïek 3 Europa vanaf 36000 km hoogte 4 5 Mount

More information

Description of Simandou Archaeological Potential Model. 13A.1 Overview

Description of Simandou Archaeological Potential Model. 13A.1 Overview 13A Description of Simandou Archaeological Potential Model 13A.1 Overview The most accurate and reliable way of establishing archaeological baseline conditions in an area is by conventional methods of

More information

APPLICATION OF GOOGLE EARTH FOR THE DEVELOPMENT OF BASE MAP IN THE CASE OF GISH ABBAY SEKELA, AMHARA STATE, ETHIOPIA

APPLICATION OF GOOGLE EARTH FOR THE DEVELOPMENT OF BASE MAP IN THE CASE OF GISH ABBAY SEKELA, AMHARA STATE, ETHIOPIA APPLICATION OF GOOGLE EARTH FOR THE DEVELOPMENT OF BASE MAP IN THE CASE OF GISH ABBAY SEKELA, AMHARA STATE, ETHIOPIA Abineh Tilahun Department of Geography and environmental studies, Adigrat University,

More information

A KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW ABSTRACT

A KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW ABSTRACT A KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW Mingjun Song, Graduate Research Assistant Daniel L. Civco, Director Laboratory for Earth Resources Information Systems Department of Natural Resources

More information

Ellenor Brown and Cornelius Ejimofor, Georgia Institute of Technology Graduate Students. Shape

Ellenor Brown and Cornelius Ejimofor, Georgia Institute of Technology Graduate Students. Shape 4-8 th Grade Math and Science Lessons Ellenor Brown and Cornelius Ejimofor, Georgia Institute of Technology Graduate Students Shape Georgia Performance Standards Grade 4 M4G1 Students will define and identify

More information

Raster Data Structures

Raster Data Structures Raster Data Structures Tessellation of Geographical Space Geographical space can be tessellated into sets of connected discrete units, which completely cover a flat surface. The units can be in any reasonable

More information

Hydrographic Surveying using High Resolution Satellite Images

Hydrographic Surveying using High Resolution Satellite Images Hydrographic Surveying using High Resolution Satellite Images Petra PHILIPSON and Frida ANDERSSON, Sweden Key words: remote sensing, high resolution, hydrographic survey, depth estimation. SUMMARY The

More information

Part 1: 2D/3D Geometry, Colour, Illumination

Part 1: 2D/3D Geometry, Colour, Illumination Part 1: 2D/3D Geometry, Colour, Illumination Colours Patrice Delmas and Georgy Gimel farb COMPSCI 373 Computer Graphics and Image Processing http://socks-studio.com/2013/... http://www.mutluduvar.com/...

More information

INVESTIGA I+D+i 2013/2014

INVESTIGA I+D+i 2013/2014 INVESTIGA I+D+i 2013/2014 SPECIFIC GUIDELINES ON AEROSPACE OBSERVATION OF EARTH Text by D. Eduardo de Miguel October, 2013 Introducction Earth observation is the use of remote sensing techniques to better

More information

Graphic Design Basics. Shannon B. Neely. Pacific Northwest National Laboratory Graphics and Multimedia Design Group

Graphic Design Basics. Shannon B. Neely. Pacific Northwest National Laboratory Graphics and Multimedia Design Group Graphic Design Basics Shannon B. Neely Pacific Northwest National Laboratory Graphics and Multimedia Design Group The Design Grid What is a Design Grid? A series of horizontal and vertical lines that evenly

More information

Color. Color Vision 1

Color. Color Vision 1 Color Color Vision 1 Review of last week Color Vision 2 Review of color Spectrum Cone sensitivity function Metamers same color, different spectrum Opponent black-white, blue-yellow, red-green Color spaces

More information

Characteristics and statistics of digital remote sensing imagery

Characteristics and statistics of digital remote sensing imagery Characteristics and statistics of digital remote sensing imagery There are two fundamental ways to obtain digital imagery: Acquire remotely sensed imagery in an analog format (often referred to as hard-copy)

More information

Information Contents of High Resolution Satellite Images

Information Contents of High Resolution Satellite Images Information Contents of High Resolution Satellite Images H. Topan, G. Büyüksalih Zonguldak Karelmas University K. Jacobsen University of Hannover, Germany Keywords: satellite images, mapping, resolution,

More information

Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021 Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class

More information

3D-Canopy Models from Photogrammetry und Radargrammetry

3D-Canopy Models from Photogrammetry und Radargrammetry 3D-Canopy Models from Photogrammetry und Radargrammetry Mathias Schardt JOANNEUM RESEARCH Research Group Remote Sensing and Geoinformation Graz Technical University Institute of Remote Sensing and Photogrammetry

More information

Outline of RGB Composite Imagery

Outline of RGB Composite Imagery Outline of RGB Composite Imagery Data Processing Division, Data Processing Department Meteorological Satellite Center (MSC) JMA Akihiro SHIMIZU 29 September, 2014 Updated 6 July, 2015 1 Contents What s

More information

Scanners and How to Use Them

Scanners and How to Use Them Written by Jonathan Sachs Copyright 1996-1999 Digital Light & Color Introduction A scanner is a device that converts images to a digital file you can use with your computer. There are many different types

More information

Introduction to Spectral Reflectance (passive sensors) Overview. Electromagnetic Radiation (light) 4/4/2014

Introduction to Spectral Reflectance (passive sensors) Overview. Electromagnetic Radiation (light) 4/4/2014 Introduction to Spectral Reflectance (passive sensors) Kelly R. Thorp Research Agricultural Engineer USDA-ARS Arid-Land Agricultural Research Center Overview Electromagnetic Radiation (light) Solar Energy

More information

W H I T E P A P E R. Volumetric Measure Using Geospatial Technology

W H I T E P A P E R. Volumetric Measure Using Geospatial Technology W H I T E P A P E R Volumetric Measure Using Geospatial Technology Contents 1. Introduction... 1 2. Project Setup/Triangulation... 1 3. Workflow One: Extract DSM Terrain File... 1 3.1. Stereo Terrain Editing...

More information

Computer Vision: Machine Vision Filters. Computer Vision. Optical Filters. 25 August 2014

Computer Vision: Machine Vision Filters. Computer Vision. Optical Filters. 25 August 2014 Computer Vision Optical Filters 25 August 2014 Copyright 2001 2014 by NHL Hogeschool, Van de Loosdrecht Machine Vision BV and Klaas Dijkstra All rights reserved j.van.de.loosdrecht@nhl.nl, jaap@vdlmv.nl,

More information

Speed Detection for Moving Objects from Digital Aerial Camera and QuickBird Sensors

Speed Detection for Moving Objects from Digital Aerial Camera and QuickBird Sensors Speed Detection for Moving Objects from Digital Aerial Camera and QuickBird Sensors September 2007 Fumio Yamazaki 1, Wen Liu 1, T. Thuy Vu 2 1. Graduate School of Engineering, Chiba University, Japan.

More information

Color quality guide. Quality menu. Color quality guide. Page 1 of 6

Color quality guide. Quality menu. Color quality guide. Page 1 of 6 Page 1 of 6 Color quality guide The Color Quality guide helps users understand how operations available on the printer can be used to adjust and customize color output. Quality menu Menu item Print Mode

More information

Introduction to Imagery and Raster Data in ArcGIS

Introduction to Imagery and Raster Data in ArcGIS Esri International User Conference San Diego, California Technical Workshops July 25, 2012 Introduction to Imagery and Raster Data in ArcGIS Simon Woo slides Cody Benkelman - demos Overview of Presentation

More information

Multinomial Logistics Regression for Digital Image Classification

Multinomial Logistics Regression for Digital Image Classification Multinomial Logistics Regression for Digital Image Classification Dr. Moe Myint, Chief Scientist, Mapping and Natural Resources Information Integration (MNRII), Switzerland maungmoe.myint@mnrii.com KEY

More information

ForeFlight Mobile Legends

ForeFlight Mobile Legends ForeFlight Mobile Legends ForeFlight, LLC 9th Edition - Covers ForeFlight Mobile v7.5 and later RADAR LEGENDS (WHEN FROM INTERNET) Snowy/Icy Precipitation Mixed Precipitation Rain Echo top (in 100 s of

More information

ArcGIS Agricultural Land Use Maps from the Mississippi Cropland Data Layer

ArcGIS Agricultural Land Use Maps from the Mississippi Cropland Data Layer ArcGIS Agricultural Land Use Maps from the Mississippi Cropland Data Layer Fred L. Shore, Ph.D. Mississippi Department of Agriculture and Commerce Jackson, MS, USA fred_shore@nass.usda.gov Rick Mueller

More information

Volcanic Ash Monitoring: Product Guide

Volcanic Ash Monitoring: Product Guide Doc.No. Issue : : EUM/TSS/MAN/15/802120 v1a EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49 6151 807 555 Date : 2 June 2015 http://www.eumetsat.int WBS/DBS : EUMETSAT

More information

Image Analysis CHAPTER 16 16.1 ANALYSIS PROCEDURES

Image Analysis CHAPTER 16 16.1 ANALYSIS PROCEDURES CHAPTER 16 Image Analysis 16.1 ANALYSIS PROCEDURES Studies for various disciplines require different technical approaches, but there is a generalized pattern for geology, soils, range, wetlands, archeology,

More information

RESULTS. that remain following use of the 3x3 and 5x5 homogeneity filters is also reported.

RESULTS. that remain following use of the 3x3 and 5x5 homogeneity filters is also reported. RESULTS Land Cover and Accuracy for Each Landsat Scene All 14 scenes were successfully classified. The following section displays the results of the land cover classification, the homogenous filtering,

More information

Open icon. The Select Layer To Add dialog opens. Click here to display

Open icon. The Select Layer To Add dialog opens. Click here to display Mosaic Introduction This tour guide gives you the steps for mosaicking two or more image files to produce one image file. The mosaicking process works with rectified and/or calibrated images. Here, you

More information

PANTONE Uncoated RGB

PANTONE Uncoated RGB PANTONE R:100 G:90 B:9 Yellow U PANTONE R:76 G:32 B:72 Purple U PANTONE R:99 G:90 B:13 Process Yellow U PANTONE R:100 G:86 B:9 Yellow 012 U PANTONE R:49 G:29 B:67 Violet U PANTONE R:86 G:29 B:49 Process

More information

STAAR Science Tutorial 30 TEK 8.8C: Electromagnetic Waves

STAAR Science Tutorial 30 TEK 8.8C: Electromagnetic Waves Name: Teacher: Pd. Date: STAAR Science Tutorial 30 TEK 8.8C: Electromagnetic Waves TEK 8.8C: Explore how different wavelengths of the electromagnetic spectrum such as light and radio waves are used to

More information

UTM: Universal Transverse Mercator Coordinate System

UTM: Universal Transverse Mercator Coordinate System Practical Cartographer s Reference #01 UTM: Universal Transverse Mercator Coordinate System 180 174w 168w 162w 156w 150w 144w 138w 132w 126w 120w 114w 108w 102w 96w 90w 84w 78w 72w 66w 60w 54w 48w 42w

More information

Focus Earth The Velingara Circular Structure A meteorite impact crater?

Focus Earth The Velingara Circular Structure A meteorite impact crater? Focus Earth The Velingara Circular Structure A meteorite impact crater? S. Wade Institut des Sciences de la Terre, Faculté des Sciences et Techniques, Université Cheikh Anta Diop, Dakar-Fann, Sénégal M.

More information

Understanding Raster Data

Understanding Raster Data Introduction The following document is intended to provide a basic understanding of raster data. Raster data layers (commonly referred to as grids) are the essential data layers used in all tools developed

More information

Methods for Monitoring Forest and Land Cover Changes and Unchanged Areas from Long Time Series

Methods for Monitoring Forest and Land Cover Changes and Unchanged Areas from Long Time Series Methods for Monitoring Forest and Land Cover Changes and Unchanged Areas from Long Time Series Project using historical satellite data from SACCESS (Swedish National Satellite Data Archive) for developing

More information

Image Draping & navigation within Virtual GIS

Image Draping & navigation within Virtual GIS Image Draping & navigation within Virtual GIS Draping of Geo Corrected data such as aerial imagery or map data enables virtual 3D field tours to be conducted in an area of interest. This document covers

More information