Due Date: September 22

Size: px
Start display at page:

Download "Due Date: September 22"

Transcription

1 Geography 309 Lab 1 Page 1 LAB 1: INTRODUCTION TO REMOTE SENSING Due Date: September 22 Objectives To familiarize yourself with: o remote sensing resources on the Internet o some remote sensing sensors and their uses o the course textbook o the Geomatica image analysis software Part A: Remote Sensing Resources There is a wealth of remote sensing information and data on the Internet. In order to help you become acquainted with some of the available resources on-line, I would like you to find and document some of them. Two effective ways of looking for information on the Internet are: Procedure use a search engine (e.g. Google); and look for links to related sites (once you have found one site dealing with your topic area). A1. Find and visit 5 different sites on the Internet that deal with remote sensing. None of your 5 sites can come from the same umbrella organization. For example, you would not be able to count the Departments of Geography and Environmental Systems Engineering at UofR as two separate sites. You cannot include the Canada Centre for Remote Sensing (CCRS) as one of your sites (since it has already been described for you, below). You are certainly encouraged to explore the abundance of information available at CCRS, however. Create a table with the following entries for each site: Real-World Location URL Brief Description Identify the real-word name for this site. Give the Web location of the site's home page. Provide a short description of the data and information available at this site. You may provide this in point form, but you must be reasonably descriptive (e.g., don't just say "imagery", say "Landsat imagery of Western Africa from 1985 to the present"). Question 1: Submit your table of web sites. (2 marks)

2 Geography 309 Lab 1 Page 2 For example: Real-World Location URL Brief Description Canada Centre for Remote Sensing g.pl?e An extensive site containing information on all aspects of remote sensing from a Canadian perspective. On-line information includes: a comprehensive summary of remote sensing satellites & sensors, including RADARSAT; current research issues; education al resources, including an on-line tutorial, remote sensing glossary, and FAQ; a remote sensing image "tour" of Canada A2. The text chosen for this course also contains a lot of useful information. Scan through the text and identify 5 different remote sensing instruments (sensors). For each sensor, list one application for which the authors suggest it can be used. Create a table with the following entries for each sensor: Sensor Satellite Application Remote sensors are frequently identified with both their satellite name and the sensor name, e.g. Landsat 7 (satellite) Enhanced Thematic Mapper (sensor) - try to separate the sensor name from its satellite name. sometimes called spacecraft or platform The images in the book are a good source of example applications. Question 2: Submit your table of Sensors and Satellites. (2 marks) For example: Sensor Satellite Application Enhanced Thematic Mapper Landsat 7 monitoring forest re-growth after logging (TM) or burns

3 Geography 309 Lab 1 Page 3 Part B: Selecting an Image for Class Presentation One of the requirements for this course is to find a remote sensing image on the Web and present it to the class. A "suitable" image is one which: you personally find interesting; (mostly) fits on one screen without scrolling; does not originate on the Geography 309 web site; is not from Google Earth; you are able to provide the following details: earth location URL satellite name sensor name sensor bands or channels shown in the image image acquisition date If you are unclear about the suitability of an image, feel free to check with me. Question 3: (5 marks) Present your image to the class at your scheduled time. Limit your presentation to about 5 minutes. Important:check the on-line course schedule to find out when you are presenting no marks will be given if you miss your presentation date or fail to hand in your presentation description. You do not need to hand in your presentation description until your actual presentation date. Image presentations will take place at some time during the lecture period. Presentation marks will be assigned as follows: 1 mark for having a "suitable" image this includes providing the requested details (as defined above) about the image 3 marks for a description of the "significance" of the image for: physical geography; and human geography. For your "significance" discussion, you could address such topics as: Did you learn anything new? Or, How can these data be used by decision-makers? Include at least 3 points for each of the human and physical geography descriptions. 1 mark for your oral presentation: did you speak loudly, clearly, enthusiastically, and succinctly? did you try to engage the class in discussion? Procedure B1. Search the web and find a remote sensing image suitable for presenting to the class. Be sure that you can also provide the required details for the image. B2. Click on the image with the right mouse button and select Save Picture As... from the popup menu. Save the image to your local disk.

4 Geography 309 Lab 1 Page 4 B3. Create a description page for your image. Be sure your page includes: the image earth location URL satellite name sensor name sensor bands or channels shown in the image image acquisition date your name, address, and ID number the descriptions of the "significance" of the image for physical geography and human geography. You may use point form. B4. At least ONE DAY before you are scheduled to present your image (sooner, if you wish), e- mail a copy of the image (just your image, not your description page) to me. I will post it on the course web site for you to access during your presentation. B5. At the start of class on your presentation day, please hand in a printed copy of your description page for marking.

5 Geography 309 Lab 1 Page 5 Example Description Earth Location URL Satellite Landsat 5 Sensor Sensor Channels/Bands Shown in Image Hamilton, Ontario, Canada Thematic Mapper 2, 3, 4 Image Acquisition Date September 20, 1985 Geographic Significance (Human) Geographic Significance (Physical) This is a medium-size city (by Canadian standards). A large industrial complex is evident by infilling along the south shore of the harbour, as well as piers for large ships. Shipping might be an important mode of transportation although no ships are visible, a canal can be seen joining the harbour to the lake. There does not appear to be much agriculture. There are 2 bays/harbours separated from the main lake by natural barriers. The innermost bay seems to have a lot of suspended sediment (lighter blue colour). The Niagara Escarpment is visible as a band of trees (shown in red) cutting horizontally across the city. The land appears to be relatively flat, given the grid layout of the city streets The strong NIR reflectance suggests that this area receives adequate rainfall to support photosynthetically active plant growth.

6 Geography 309 Lab 1 Page 6 Part C: Visual Interpretation of Colour Composite Imagery You will now examine a Landsat Thematic Mapper image of Regina using the PCI Geomatica Image Analysis System. Notes Procedure A course disk has been set up where I can put data sets for your use. On the University computer systems, you will find this disk on the T:\ drive in the folder \Class\Geography\geog309. Note that it is not necessary to copy the file(s) from this disk unless I give you explicit instructions to do so. Tables 1, 2, and 3 (referred to in Questions 3, 4, and 5) are listed at the end of the lab. You may use Geomatica FreeView for this lab see the link at the bottom of the course homepage. C1. From one of the systems in CL 109 click on the PCI Geomatica icon. C2. In a few moments, the Geomatica toolbar should appear on your screen: C3. There are many functions available from the toolbar, but in this course we will be primarily using the Focus image viewer. Focus should start automatically after about 10 seconds when you start Geomatica, but if it doesn't you can start it by licking once on the left toolbar button and after a few moments, the Focus window will appear. C4. Click on the Open File button and a File Selection window will be presented. a) The image you want to use in this lab is called ReginaTM2010.pix. You will find it on the Geography 309 course disk. 1 Double-click on this file and the image should be displayed in the Focus window. C5. You will be looking at a Landsat TM image centred on the City of Regina but also showing some of the surrounding urban and agricultural areas. This image was acquired on September 7, The course disk can be found on your T: drive in the folder \Class\Geography\geog309\.

7 Geography 309 Lab 1 Page 7 C6. Use the zoom and pan tools (and scrollbars) to move around the image to get a feel for the software. C7. Now position your cursor in the middle of the UofR campus and click 2 or 3 times on the zoom in tool. You should see the campus zoom up in the middle of your screen. although it may be difficult to distinguish very much, especially if you get too close. C8. Image displays commonly have 3 basic colours: red, green and blue. Remotely sensed images may have many more channels. We are interested in exploring different ways of showing the remote sensing data channels on an image display. Of particular note is that we can show data acquired in spectral bands which the human eye cannot see in display colours that we can see.

8 Geography 309 Lab 1 Page 8 Athough there are many different band combinations possible, the two most common are: For a true colour composite image, display the: Sensor's Blue band in display Blue Sensor's Green band in display Green Sensor's Red band in display Red. For a standard false-colour composite image, display the: Sensor's Green band in display Blue Sensor's Red band in display Green Sensor's Near IR band in display Red. Lets change the colour assignments used for the display in order to make the image a little clearer. Select RGB Mapper from the Layers menu. In the RGB Mapping window you will notice 3 columns labeled Red, Green, and Blue corresponding to the three principal additive colours used by computer monitors to portray colour. Also notice that there is a red check next to the image layer TM 1, a green check next to the image layer TM 2, and a blue check next to the TM 3 layer. This is the default method that Focus uses to display colour imagery. As it turns out, this isn't a very useful combination. Change the check marks so that TM 4 is shown in red, TM 3 in green, and TM 2 in blue, as shown in the graphic, below. This, standard false-colour composite, is a common way of displaying remote sensing imagery. Since we are showing the near infrared band (TM 4) in red, healthy green vegetation appears red in this image. Close the RGB Mapping window. C9. The next step is to brighten the image up a bit by applying a contrast enhancement. You will learn more about this technique in a later lab, but for now click on the enhancements button (click on the main part of the button, not on the little downward arrow along its right edge).

9 Geography 309 Lab 1 Page 9 C10. Study the satellite image. You should be able to identify Wascana Lake, the Campus Green between the Library and the Education buildings, and with some imagination - several of the buildings on campus. C11. Now lets have a look at a true colour composite image of the same data. Create a new view of the image by clicking on the Launch the Add Layer Wizard button. C12. When the Add Layer Wizard appears specify that you wish to add an RGB layer and then click Next >. C13. Now select the channels you wish to display in Red, Green, and Blue to create a True Colour Composite image (refer to the table above): a) Click on the Red button and then select the image channel you wish to show in red; b) Click on the Green button and then select the image channel you wish to show in green; c) Click on the Blue button and then select the image channel you wish to show in blue; d) Click Finish to display the image. Note that the new layer is drawn on top of your original view thereby obscuring it. You can toggle back and forth between your colour composites by turning the RGB Layer off and on (off to show the lower true colour composite; on to overlay the false colour composite). You change a layers visibility by clicking on its visibility check box: Further, if you zoom or pan around the image in one colour mode, your actions are mirrored in the other colour composite. Question 4: (3 marks) (a) Complete Table 1 for the Landsat TM. (b) Is it possible to create a true colour composite display of SPOT HRV data? Why/Why not? (c) Is it possible to create a standard false colour composite of SPOT HRV data? Why/Why not? Clearly, it is not possible to observe everything about Regina in a Landsat TM image. Table 2 is a list of the typical types of surface features that we can identify in Landsat imagery (see

10 Geography 309 Lab 1 Page 10 for a description of the feature types). You might also find the 2010 Field Photographs link on the course website useful for viewing some of the crop types in Google Earth or Google Maps. Question 5: (1 mark) Locate and identify good examples of the land covers and land uses listed in Table 2 and describe their tone/colour in each composite image. You may not find examples of every land cover / land use class listed in Table 2 in the image. Only complete the parts of the table that you can see in the image. Refer to for a description of each land use / land cover type class. Question 6: (2 marks) You were able to identify the land use / land cover classes in Table 2 largely because you know Regina and you knew where to find them. How do you think you could find these classes in an area that you were not familiar with? Question 7: (1 mark) For each Task listed in Table 3, which colour composite image do you think provides the most information? Why do you think so? Note the annotation along the bottom of the Focus window: display scale display magnification cursor location coordinates pixel values at the cursor for the Red, Green and Blue bands In particular, the cursor location coordinates let you know the current location of your cursor and the pixel values tell you the pixel data for the channels currently in the display. Note how these values change as you move your cursor across the screen. The pixel values readings can be quite useful, but you need to remember which image bands you have loaded into the corresponding red, green, and blue display channels. If the displayed image in the above example was a true colour composite Landsat TM scene, then I could read that at the pixel location (UTM E N) TM 1 has a value of 15, TM 2 has a value of 12, and TM 3's value is 12. If you wanted to navigate to a particular location on your image, you could use the cursor control button to open a window where you can enter your cursor coordinates directly. Since the image you are working with has been georeferenced to the Universal Transverse Mercator (UTM) projection, you can enter locations in UTM coordinates by typing them into the Geocoded section of the window:

11 Geography 309 Lab 1 Page 11 Question 8: Use the cursor control panel to position your cursor to the following UTM locations: (a) What is the dark feature at E N? (b) What is the dark feature at E N? (c) What is the dark feature at E N? (d) What is the dark feature at E N? (4 marks)

12 Geography 309 Lab 1 Page 12 NAME: ID # LAB 1: INTRODUCTION TO REMOTE SENSING Answer Sheets Due Date: September 22 Question 4: (3 marks) (a) Complete Table 1 for the Landsat TM. (b) Is it possible to create a true colour composite display of SPOT HRV data? Why/Why not? (c) Is it possible to create a standard false colour composite of SPOT HRV data? Why/Why not? Table 1: Common Colour Combinations for Composite Displays Band Spectral Name [blue, green, red, near IR, middle IR, thermal IR] True Colour Composite Display Colour [red, green, blue] Standard False Colour Composite Display Colour [red, green, blue] TM 1 TM 2 TM 3 TM 4 (b) Is it possible to create a true colour composite display of SPOT HRV data? Why/Why not? (c) Is it possible to create a standard false colour composite of SPOT HRV data? Why/Why not?

13 Geography 309 Lab 1 Page 13 Question 5: (1 mark) Locate and identify good examples of the land covers and land uses listed in Table 2 and describe their tone/colour in each composite image. You may not find examples of every land cover / land use class listed in Table 2 in the image. Only complete the parts of the table that you can see in the image. Refer to for a description of each land use / land cover type class. Table 2: Surface Feature Appearance in Two Common Colour Composite Displays Land Cover / Land Use 1 Water 11 Open Water True Colour Composite Standard False Colour Composite 2 Developed 12 Perennial Ice/Snow 21 Low Intensity Residential 22 High Intensity Residential 23 Commercial/Industrial/Transportation 3 Barren 31 Bare Rock/Sand/Clay 32 Quarries/Strip Mines/Gravel Pits 4 Forested Upland 33 Transitional 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest

14 Geography 309 Lab 1 Page 14 5 Shrubland 6 Non-Natural Woody 51 Shrubland 61 Orchards/Vineyards/Other 7 Herbaceous Upland Natural/Semi-natural Vegetation 71 Grasslands/Herbaceous 8 Herbaceous Planted/Cultivated 81 Pasture/Hay 82 Row Crops 83 Small Grains 84 Fallow 9 Wetlands 85 Urban/Recreational Grasses 91 Woody Wetlands 92 Emergent Herbaceous Wetlands Question 6. (2 marks) You were able to identify the land use / land cover classes in Table 2 largely because you know Regina and you knew where to find them. How do you think you could find these classes in an area that you were not familiar with?

15 Geography 309 Lab 1 Page 15 Question 7: (1 mark) For each Task listed in Table 3, which colour composite image do you think provides the most information? Why do you think so? Table 3: Applications of Colour Composite Displays Task differentiating water from land delineating the residential street network differentiating the built environment from the natural environment Which Composite Image is Better? Why do you Think So? Question 8: Use the cursor control panel to position your cursor to the following UTM locations: (a) What is the dark feature at E N? (b) What is the dark feature at E N? (c) What is the dark feature at E N? (d) What is the dark feature at E N? (4 marks)

1. Start a bit about Linux

1. Start a bit about Linux GEOG432/632 Fall 2017 Lab 1 Display, Digital numbers and Histograms 1. Start a bit about Linux Login to the linux environment you already have in order to view this webpage Linux enables both a command

More information

GEOG432: Remote sensing Lab 3 Unsupervised classification

GEOG432: Remote sensing Lab 3 Unsupervised classification GEOG432: Remote sensing Lab 3 Unsupervised classification Goal: This lab involves identifying land cover types by using agorithms to identify pixels with similar Digital Numbers (DN) and spectral signatures

More information

Land Cover Change Analysis An Introduction to Land Cover Change Analysis using the Multispectral Image Data Analysis System (MultiSpec )

Land Cover Change Analysis An Introduction to Land Cover Change Analysis using the Multispectral Image Data Analysis System (MultiSpec ) Land Cover Change Analysis An Introduction to Land Cover Change Analysis using the Multispectral Image Data Analysis System (MultiSpec ) Level: Grades 9 to 12 Macintosh version Earth Observation Day Tutorial

More information

GEOG432: Remote sensing Lab 3 Unsupervised classification

GEOG432: Remote sensing Lab 3 Unsupervised classification GEOG432: Remote sensing Lab 3 Unsupervised classification Goal: This lab involves identifying land cover types by using agorithms to identify pixels with similar Digital Numbers (DN) and spectral signatures

More information

Exercise 4-1 Image Exploration

Exercise 4-1 Image Exploration Exercise 4-1 Image Exploration With this exercise, we begin an extensive exploration of remotely sensed imagery and image processing techniques. Because remotely sensed imagery is a common source of data

More information

AmericaView EOD 2016 page 1 of 16

AmericaView EOD 2016 page 1 of 16 Remote Sensing Flood Analysis Lesson Using MultiSpec Online By Larry Biehl Systems Manager, Purdue Terrestrial Observatory (biehl@purdue.edu) v Objective The objective of these exercises is to analyze

More information

Module 11 Digital image processing

Module 11 Digital image processing Introduction Geo-Information Science Practical Manual Module 11 Digital image processing 11. INTRODUCTION 11-1 START THE PROGRAM ERDAS IMAGINE 11-2 PART 1: DISPLAYING AN IMAGE DATA FILE 11-3 Display of

More information

Supervised Land Cover Classification An introduction to digital image classification using the Multispectral Image Data Analysis System (MultiSpec )

Supervised Land Cover Classification An introduction to digital image classification using the Multispectral Image Data Analysis System (MultiSpec ) Supervised Land Cover Classification An introduction to digital image classification using the Multispectral Image Data Analysis System (MultiSpec ) Level: Grades 9 to 12 Windows version With Teacher Notes

More information

Land Cover Change Analysis An Introduction to Land Cover Change Analysis using the Multispectral Image Data Analysis System (MultiSpec )

Land Cover Change Analysis An Introduction to Land Cover Change Analysis using the Multispectral Image Data Analysis System (MultiSpec ) Land Cover Change Analysis An Introduction to Land Cover Change Analysis using the Multispectral Image Data Analysis System (MultiSpec ) Level: Grades 9 to 12 Windows version With Teacher Notes Earth Observation

More information

The (False) Color World

The (False) Color World There s more to the world than meets the eye In this activity, your group will explore: The Value of False Color Images Different Types of Color Images The Use of Contextual Clues for Feature Identification

More information

Viewing Landsat TM images with Adobe Photoshop

Viewing Landsat TM images with Adobe Photoshop Viewing Landsat TM images with Adobe Photoshop Reformatting images into GeoTIFF format Of the several formats in which Landsat TM data are available, only a few formats (primarily TIFF or GeoTIFF) can

More information

8. EDITING AND VIEWING COORDINATES, CREATING SCATTERGRAMS AND PRINCIPAL COMPONENTS ANALYSIS

8. EDITING AND VIEWING COORDINATES, CREATING SCATTERGRAMS AND PRINCIPAL COMPONENTS ANALYSIS Editing and viewing coordinates, scattergrams and PCA 8. EDITING AND VIEWING COORDINATES, CREATING SCATTERGRAMS AND PRINCIPAL COMPONENTS ANALYSIS Aim: To introduce you to (i) how you can apply a geographical

More information

Unsupervised Classification

Unsupervised Classification Unsupervised Classification Using SAGA Tutorial ID: IGET_RS_007 This tutorial has been developed by BVIEER as part of the IGET web portal intended to provide easy access to geospatial education. This tutorial

More information

Enhancement of Multispectral Images and Vegetation Indices

Enhancement of Multispectral Images and Vegetation Indices Enhancement of Multispectral Images and Vegetation Indices ERDAS Imagine 2016 Description: We will use ERDAS Imagine with multispectral images to learn how an image can be enhanced for better interpretation.

More information

Land Remote Sensing Lab 4: Classication and Change Detection Assigned: October 15, 2017 Due: October 27, Classication

Land Remote Sensing Lab 4: Classication and Change Detection Assigned: October 15, 2017 Due: October 27, Classication Name: Land Remote Sensing Lab 4: Classication and Change Detection Assigned: October 15, 2017 Due: October 27, 2017 In this lab, you will generate several gures. Please sensibly name these images, save

More information

Land Cover Analysis to Determine Areas of Clear-cut and Forest Cover in Olney, Montana. Geob 373 Remote Sensing. Dr Andreas Varhola, Kathry De Rego

Land Cover Analysis to Determine Areas of Clear-cut and Forest Cover in Olney, Montana. Geob 373 Remote Sensing. Dr Andreas Varhola, Kathry De Rego 1 Land Cover Analysis to Determine Areas of Clear-cut and Forest Cover in Olney, Montana Geob 373 Remote Sensing Dr Andreas Varhola, Kathry De Rego Zhu an Lim (14292149) L2B 17 Apr 2016 2 Abstract Montana

More information

EE/GP140-The Earth From Space- Winter 2008 Handout #16 Lab Exercise #3

EE/GP140-The Earth From Space- Winter 2008 Handout #16 Lab Exercise #3 EE/GP140-The Earth From Space- Winter 2008 Handout #16 Lab Exercise #3 Topic 1: Color Combination. We will see how all colors can be produced by combining red, green, and blue in different proportions.

More information

Interpreting land surface features. SWAC module 3

Interpreting land surface features. SWAC module 3 Interpreting land surface features SWAC module 3 Interpreting land surface features SWAC module 3 Different kinds of image Panchromatic image True-color image False-color image EMR : NASA Echo the bat

More information

Lab 3: Image Acquisition and Geometric Correction

Lab 3: Image Acquisition and Geometric Correction Geography 309 Lab 3 Answer Page 1 Objectives Preparation Lab 3: Image Acquisition and Geometric Correction Due Date: October 22 to introduce you to digital imagery and how it can be displayed and manipulated

More information

An Introduction to Remote Sensing & GIS. Introduction

An Introduction to Remote Sensing & GIS. Introduction An Introduction to Remote Sensing & GIS Introduction Remote sensing is the measurement of object properties on Earth s surface using data acquired from aircraft and satellites. It attempts to measure something

More information

Laboratory Exercise 1

Laboratory Exercise 1 Page 1 Laboratory Exercise 1 GEOG*2420 The Earth From Space University of Guelph, Department of Geography Prof. John Lindsay Fall 2013 Total of 32 marks Learning objectives The intention of this lab exercise

More information

This week we will work with your Landsat images and classify them using supervised classification.

This week we will work with your Landsat images and classify them using supervised classification. GEPL 4500/5500 Lab 4: Supervised Classification: Part I: Selecting Training Sets Due: 4/6/04 This week we will work with your Landsat images and classify them using supervised classification. There are

More information

REMOTE SENSING INTERPRETATION

REMOTE SENSING INTERPRETATION REMOTE SENSING INTERPRETATION Jan Clevers Centre for Geo-Information - WU Remote Sensing --> RS Sensor at a distance EARTH OBSERVATION EM energy Earth RS is a tool; one of the sources of information! 1

More information

Important Missions. weather forecasting and monitoring communication navigation military earth resource observation LANDSAT SEASAT SPOT IRS

Important Missions. weather forecasting and monitoring communication navigation military earth resource observation LANDSAT SEASAT SPOT IRS Fundamentals of Remote Sensing Pranjit Kr. Sarma, Ph.D. Assistant Professor Department of Geography Mangaldai College Email: prangis@gmail.com Ph. No +91 94357 04398 Remote Sensing Remote sensing is defined

More information

Lab 3: Image Enhancements I 65 pts Due > Canvas by 10pm

Lab 3: Image Enhancements I 65 pts Due > Canvas by 10pm Geo 448/548 Spring 2016 Lab 3: Image Enhancements I 65 pts Due > Canvas by 3/11 @ 10pm For this lab, you will learn different ways to calculate spectral vegetation indices (SVIs). These are one category

More information

Basic Hyperspectral Analysis Tutorial

Basic Hyperspectral Analysis Tutorial Basic Hyperspectral Analysis Tutorial This tutorial introduces you to visualization and interactive analysis tools for working with hyperspectral data. In this tutorial, you will: Analyze spectral profiles

More information

Using the Chip Database

Using the Chip Database Using the Chip Database TUTORIAL A chip database is a collection of image chips or subsetted images where each image has a GCP associated with it. A chip database can be useful when orthorectifying different

More information

TimeSync V3 User Manual. January Introduction

TimeSync V3 User Manual. January Introduction TimeSync V3 User Manual January 2017 Introduction TimeSync is an application that allows researchers and managers to characterize and quantify disturbance and landscape change by facilitating plot-level

More information

Landsat 8 Pansharpen and Mosaic Geomatica 2015 Tutorial

Landsat 8 Pansharpen and Mosaic Geomatica 2015 Tutorial Landsat 8 Pansharpen and Mosaic Geomatica 2015 Tutorial On February 11, 2013, Landsat 8 was launched adding to the constellation of Earth imaging satellites. It is the seventh satellite to reach orbit

More information

RGB colours: Display onscreen = RGB

RGB colours:  Display onscreen = RGB RGB colours: http://www.colorspire.com/rgb-color-wheel/ Display onscreen = RGB DIGITAL DATA and DISPLAY Myth: Most satellite images are not photos Photographs are also 'images', but digital images are

More information

Lab 1 Introduction to ENVI

Lab 1 Introduction to ENVI Remote sensing for agricultural applications: principles and methods (2013-2014) Instructor: Prof. Tao Cheng (tcheng@njau.edu.cn) Nanjing Agricultural University Lab 1 Introduction to ENVI April 1 st,

More information

Image interpretation I and II

Image interpretation I and II Image interpretation I and II Looking at satellite image, identifying different objects, according to scale and associated information and to communicate this information to others is what we call as IMAGE

More information

Apply Colour Sequences to Enhance Filter Results. Operations. What Do I Need? Filter

Apply Colour Sequences to Enhance Filter Results. Operations. What Do I Need? Filter Apply Colour Sequences to Enhance Filter Results Operations What Do I Need? Filter Single band images from the SPOT and Landsat platforms can sometimes appear flat (i.e., they are low contrast images).

More information

8th ESA ADVANCED TRAINING COURSE ON LAND REMOTE SENSING

8th ESA ADVANCED TRAINING COURSE ON LAND REMOTE SENSING Urban Mapping Practical Sebastian van der Linden, Akpona Okujeni, Franz Schug Humboldt Universität zu Berlin Instructions for practical Summary The Urban Mapping Practical introduces students to the work

More information

INTRODUCTORY REMOTE SENSING. Geob 373

INTRODUCTORY REMOTE SENSING. Geob 373 INTRODUCTORY REMOTE SENSING Geob 373 Landsat 7 15 m image highlighting the geology of Oman http://www.satimagingcorp.com/gallery-landsat.html ASTER 15 m SWIR image, Escondida Mine, Chile http://www.satimagingcorp.com/satellite-sensors/aster.html

More information

Remote Sensing Instruction Laboratory

Remote Sensing Instruction Laboratory Laboratory Session 217513 Geographic Information System and Remote Sensing - 1 - Remote Sensing Instruction Laboratory Assist.Prof.Dr. Weerakaset Suanpaga Department of Civil Engineering, Faculty of Engineering

More information

CanImage. (Landsat 7 Orthoimages at the 1: Scale) Standards and Specifications Edition 1.0

CanImage. (Landsat 7 Orthoimages at the 1: Scale) Standards and Specifications Edition 1.0 CanImage (Landsat 7 Orthoimages at the 1:50 000 Scale) Standards and Specifications Edition 1.0 Centre for Topographic Information Customer Support Group 2144 King Street West, Suite 010 Sherbrooke, QC

More information

Visualizing a Pixel. Simulate a Sensor s View from Space. In this activity, you will:

Visualizing a Pixel. Simulate a Sensor s View from Space. In this activity, you will: Simulate a Sensor s View from Space In this activity, you will: Measure and mark pixel boundaries Learn about spatial resolution, pixels, and satellite imagery Classify land cover types Gain exposure to

More information

Downloading and formatting remote sensing imagery using GLOVIS

Downloading and formatting remote sensing imagery using GLOVIS Downloading and formatting remote sensing imagery using GLOVIS Students will become familiarized with the characteristics of LandSat, Aerial Photos, and ASTER medium resolution imagery through the USGS

More information

Lesson Plan 1 Introduction to Google Earth for Middle and High School. A Google Earth Introduction to Remote Sensing

Lesson Plan 1 Introduction to Google Earth for Middle and High School. A Google Earth Introduction to Remote Sensing A Google Earth Introduction to Remote Sensing Image an image is a representation of reality. It can be a sketch, a painting, a photograph, or some other graphic representation such as satellite data. Satellites

More information

366 Glossary. Popular method for scale drawings in a computer similar to GIS but without the necessity for spatial referencing CEP

366 Glossary. Popular method for scale drawings in a computer similar to GIS but without the necessity for spatial referencing CEP 366 Glossary GISci Glossary ASCII ASTER American Standard Code for Information Interchange Advanced Spaceborne Thermal Emission and Reflection Radiometer Computer Aided Design Circular Error Probability

More information

Image interpretation. Aliens create Indian Head with an ipod? Badlands Guardian (CBC) This feature can be found 300 KMs SE of Calgary.

Image interpretation. Aliens create Indian Head with an ipod? Badlands Guardian (CBC) This feature can be found 300 KMs SE of Calgary. Image interpretation Aliens create Indian Head with an ipod? Badlands Guardian (CBC) This feature can be found 300 KMs SE of Calgary. 50 1 N 110 7 W Milestones in the History of Remote Sensing 19 th century

More information

Lecture 13: Remotely Sensed Geospatial Data

Lecture 13: Remotely Sensed Geospatial Data Lecture 13: Remotely Sensed Geospatial Data A. The Electromagnetic Spectrum: The electromagnetic spectrum (Figure 1) indicates the different forms of radiation (or simply stated light) emitted by nature.

More information

QGIS LAB SERIES GST 101: Introduction to Geospatial Technology Lab 6: Understanding Remote Sensing and Analysis

QGIS LAB SERIES GST 101: Introduction to Geospatial Technology Lab 6: Understanding Remote Sensing and Analysis QGIS LAB SERIES GST 101: Introduction to Geospatial Technology Lab 6: Understanding Remote Sensing and Analysis Objective Explore and Understand How to Display and Analyze Remotely Sensed Imagery Document

More information

Sommersemester Prof. Dr. Christoph Kleinn Institut für Waldinventur und Waldwachstum Arbeitsbereich Fernerkundung und Waldinventur.

Sommersemester Prof. Dr. Christoph Kleinn Institut für Waldinventur und Waldwachstum Arbeitsbereich Fernerkundung und Waldinventur. Basics of Remote Sensing Some literature references Franklin, SE 2001 Remote Sensing for Sustainable Forest Management Lewis Publishers 407p Lillesand, Kiefer 2000 Remote Sensing and Image Interpretation

More information

GEO/EVS 425/525 Unit 9 Aerial Photograph and Satellite Image Rectification

GEO/EVS 425/525 Unit 9 Aerial Photograph and Satellite Image Rectification GEO/EVS 425/525 Unit 9 Aerial Photograph and Satellite Image Rectification You have seen satellite imagery earlier in this course, and you have been looking at aerial photography for several years. You

More information

Introduction. Introduction. Introduction. Introduction. Introduction

Introduction. Introduction. Introduction. Introduction. Introduction Identifying habitat change and conservation threats with satellite imagery Extinction crisis Volker Radeloff Department of Forest Ecology and Management Extinction crisis Extinction crisis Conservationists

More information

GEO/EVS 425/525 Unit 3 Composite Images and The ERDAS Imagine Map Composer

GEO/EVS 425/525 Unit 3 Composite Images and The ERDAS Imagine Map Composer GEO/EVS 425/525 Unit 3 Composite Images and The ERDAS Imagine Map Composer This unit involves two parts, both of which will enable you to present data more clearly than you might have thought possible.

More information

First Exam: Thurs., Sept 28

First Exam: Thurs., Sept 28 8 Geographers Tools: Gathering Information Prof. Anthony Grande Hunter College Geography Lecture design, content and presentation AFG 0917. Individual images and illustrations may be subject to prior copyright.

More information

The first part of Module three, data and tools, presents some of the resources available on the internet to get images from the satellites presented

The first part of Module three, data and tools, presents some of the resources available on the internet to get images from the satellites presented The first part of Module three, data and tools, presents some of the resources available on the internet to get images from the satellites presented in the previous module and some uses of the images,

More information

IMAGE ANALYSIS TOOLBOX AND ENHANCED SATELLITE IMAGERY INTEGRATED INTO THE MAPPLACE By Ward E. Kilby 1, Karl Kliparchuk 2 and Andrew McIntosh 2

IMAGE ANALYSIS TOOLBOX AND ENHANCED SATELLITE IMAGERY INTEGRATED INTO THE MAPPLACE By Ward E. Kilby 1, Karl Kliparchuk 2 and Andrew McIntosh 2 IMAGE ANALYSIS TOOLBOX AND ENHANCED SATELLITE IMAGERY INTEGRATED INTO THE MAPPLACE By Ward E. Kilby 1, Karl Kliparchuk 2 and Andrew McIntosh 2 KEYWORDS: MapPlace, Landsat, ASTER, Image Analysis, Structural

More information

GEO/EVS 425/525 Unit 2 Composing a Map in Final Form

GEO/EVS 425/525 Unit 2 Composing a Map in Final Form GEO/EVS 425/525 Unit 2 Composing a Map in Final Form The Map Composer is the main mechanism by which the final drafts of images are sent to the printer. Its use requires that images be readable within

More information

Spectral Signatures. Vegetation. 40 Soil. Water WAVELENGTH (microns)

Spectral Signatures. Vegetation. 40 Soil. Water WAVELENGTH (microns) Spectral Signatures % REFLECTANCE VISIBLE NEAR INFRARED Vegetation Soil Water.5. WAVELENGTH (microns). Spectral Reflectance of Urban Materials 5 Parking Lot 5 (5=5%) Reflectance 5 5 5 5 5 Wavelength (nm)

More information

Urban Classification of Metro Manila for Seismic Risk Assessment using Satellite Images

Urban Classification of Metro Manila for Seismic Risk Assessment using Satellite Images Urban Classification of Metro Manila for Seismic Risk Assessment using Satellite Images Fumio YAMAZAKI/ yamazaki@edm.bosai.go.jp Hajime MITOMI/ mitomi@edm.bosai.go.jp Yalkun YUSUF/ yalkun@edm.bosai.go.jp

More information

v Introduction Images Import images in a variety of formats and register the images to a coordinate projection WMS Tutorials Time minutes

v Introduction Images Import images in a variety of formats and register the images to a coordinate projection WMS Tutorials Time minutes v. 10.1 WMS 10.1 Tutorial Import images in a variety of formats and register the images to a coordinate projection Objectives Import various types of image files from different sources. Learn how to work

More information

Remote Sensing Platforms

Remote Sensing Platforms Types of Platforms Lighter-than-air Remote Sensing Platforms Free floating balloons Restricted by atmospheric conditions Used to acquire meteorological/atmospheric data Blimps/dirigibles Major role - news

More information

First Exam. Geographers Tools: Gathering Information. Photographs and Imagery. SPIN 2 Image of Downtown Atlanta, GA 1995 REMOTE SENSING 9/19/2016

First Exam. Geographers Tools: Gathering Information. Photographs and Imagery. SPIN 2 Image of Downtown Atlanta, GA 1995 REMOTE SENSING 9/19/2016 First Exam Geographers Tools: Gathering Information Prof. Anthony Grande Hunter College Geography Lecture design, content and presentation AFG 0616. Individual images and illustrations may be subject to

More information

Lab 1: Introduction to MODIS data and the Hydra visualization tool 21 September 2011

Lab 1: Introduction to MODIS data and the Hydra visualization tool 21 September 2011 WMO RA Regional Training Course on Satellite Applications for Meteorology Cieko, Bogor Indonesia 19-27 September 2011 Kathleen Strabala University of Wisconsin-Madison, USA kathy.strabala@ssec.wisc.edu

More information

Lesson 3: Working with Landsat Data

Lesson 3: Working with Landsat Data Lesson 3: Working with Landsat Data Lesson Description The Landsat Program is the longest-running and most extensive collection of satellite imagery for Earth. These datasets are global in scale, continuously

More information

Quick Guide for ArcReader GIS Installation & Use

Quick Guide for ArcReader GIS Installation & Use Town of Hanover Planning Department Quick Guide for ArcReader GIS Installation & Use For more information, contact the Town Planner, Andrew Port (781-826-7641) or port.planning@hanover-ma.gov System Requirements

More information

Using Multi-spectral Imagery in MapInfo Pro Advanced

Using Multi-spectral Imagery in MapInfo Pro Advanced Using Multi-spectral Imagery in MapInfo Pro Advanced MapInfo Pro Advanced Tom Probert, Global Product Manager MapInfo Pro Advanced: Intuitive interface for using multi-spectral / hyper-spectral imagery

More information

Assessment of Spatiotemporal Changes in Vegetation Cover using NDVI in The Dangs District, Gujarat

Assessment of Spatiotemporal Changes in Vegetation Cover using NDVI in The Dangs District, Gujarat Assessment of Spatiotemporal Changes in Vegetation Cover using NDVI in The Dangs District, Gujarat Using SAGA GIS and Quantum GIS Tutorial ID: IGET_CT_003 This tutorial has been developed by BVIEER as

More information

White paper brief IdahoView Imagery Services: LISA 1 Technical Report no. 2 Setup and Use Tutorial

White paper brief IdahoView Imagery Services: LISA 1 Technical Report no. 2 Setup and Use Tutorial White paper brief IdahoView Imagery Services: LISA 1 Technical Report no. 2 Setup and Use Tutorial Keith T. Weber, GISP, GIS Director, Idaho State University, 921 S. 8th Ave., stop 8104, Pocatello, ID

More information

Introduction to Remote Sensing

Introduction to Remote Sensing Introduction to Remote Sensing Spatial, spectral, temporal resolutions Image display alternatives Vegetation Indices Image classifications Image change detections Accuracy assessment Satellites & Air-Photos

More information

v WMS 10.0 Tutorial Introduction Images Read images in a variety of formats and register the images to a coordinate projection

v WMS 10.0 Tutorial Introduction Images Read images in a variety of formats and register the images to a coordinate projection v. 10.0 WMS 10.0 Tutorial Read images in a variety of formats and register the images to a coordinate projection Objectives Read various types of image files from different sources. Learn how to work with

More information

Figure 1: Percent reflectance for various features, including the five spectra from Table 1, at different wavelengths from 0.4µm to 1.4µm.

Figure 1: Percent reflectance for various features, including the five spectra from Table 1, at different wavelengths from 0.4µm to 1.4µm. Section 1: The Electromagnetic Spectrum 1. The wavelength range that has the highest reflectance for broadleaf vegetation and needle leaf vegetation is 0.75µm to 1.05µm. 2. Dry soil can be distinguished

More information

Chapter 1 Overview of imaging GIS

Chapter 1 Overview of imaging GIS Chapter 1 Overview of imaging GIS Imaging GIS, a term used in the medical imaging community (Wang 2012), is adopted here to describe a geographic information system (GIS) that displays, enhances, and facilitates

More information

Satellite image classification

Satellite image classification Satellite image classification EG2234 Earth Observation Image Classification Exercise 29 November & 6 December 2007 Introduction to the practical This practical, which runs over two weeks, is concerned

More information

Scribble Maps Tutorial

Scribble Maps Tutorial Scribble Maps Tutorial Go to the homepage of Scribble Maps here: h t t p : / / w w w. s c r i b b l e m a p s. c o m / Getting to know the Interface Scribble Maps is a free online mapping application with

More information

Exploring the Earth with Remote Sensing: Tucson

Exploring the Earth with Remote Sensing: Tucson Exploring the Earth with Remote Sensing: Tucson Project ASTRO Chile March 2006 1. Introduction In this laboratory you will explore Tucson and its surroundings with remote sensing. Remote sensing is the

More information

White Paper. Medium Resolution Images and Clutter From Landsat 7 Sources. Pierre Missud

White Paper. Medium Resolution Images and Clutter From Landsat 7 Sources. Pierre Missud White Paper Medium Resolution Images and Clutter From Landsat 7 Sources Pierre Missud Medium Resolution Images and Clutter From Landsat7 Sources Page 2 of 5 Introduction Space technologies have long been

More information

CHANGE DETECTION USING OPTICAL DATA IN SNAP

CHANGE DETECTION USING OPTICAL DATA IN SNAP CHANGE DETECTION USING OPTICAL DATA IN SNAP EXERCISE 1 (Water change detection) Data: Sentinel-2A Level 2A: S2A_MSIL2A_20170101T082332_N0204_R121_T34HCH_20170101T084543.SAFE S2A_MSIL2A_20180116T082251_N0206_R121_T34HCH_20180116T120458.SAFE

More information

Remote Sensing. The following figure is grey scale display of SPOT Panchromatic without stretching.

Remote Sensing. The following figure is grey scale display of SPOT Panchromatic without stretching. Remote Sensing Objectives This unit will briefly explain display of remote sensing image, geometric correction, spatial enhancement, spectral enhancement and classification of remote sensing image. At

More information

Introduction to TimeSync A Tool For Landsat Time Series Visualization. Warren B Cohen, USDA Forest Service Zhiqiang Yang, Oregon State University

Introduction to TimeSync A Tool For Landsat Time Series Visualization. Warren B Cohen, USDA Forest Service Zhiqiang Yang, Oregon State University Introduction to TimeSync A Tool For Landsat Time Series Visualization Warren B Cohen, USDA Forest Service Zhiqiang Yang, Oregon State University TimeSync Introduction Landsat time series visualization

More information

Introduction to image processing for remote sensing: Practical examples

Introduction to image processing for remote sensing: Practical examples Università degli studi di Roma Tor Vergata Corso di Telerilevamento e Diagnostica Elettromagnetica Anno accademico 2010/2011 Introduction to image processing for remote sensing: Practical examples Dr.

More information

33-2 Satellite Takeoff Tutorial--Flat Roof Satellite Takeoff Tutorial--Flat Roof

33-2 Satellite Takeoff Tutorial--Flat Roof Satellite Takeoff Tutorial--Flat Roof 33-2 Satellite Takeoff Tutorial--Flat Roof Satellite Takeoff Tutorial--Flat Roof A RoofLogic Digitizer license upgrades RoofCAD so that you have the ability to digitize paper plans, electronic plans and

More information

IceTrendr - Polygon. 1 contact: Peder Nelson Anne Nolin Polygon Attribution Instructions

IceTrendr - Polygon. 1 contact: Peder Nelson Anne Nolin Polygon Attribution Instructions INTRODUCTION We want to describe the process that caused a change on the landscape (in the entire area of the polygon outlined in red in the KML on Google Earth), and we want to record as much as possible

More information

Using QuickBird Imagery in ESRI Software Products

Using QuickBird Imagery in ESRI Software Products Using QuickBird Imagery in ESRI Software Products TABLE OF CONTENTS 1. Introduction...2 Purpose Scope Image Stretching Color Guns 2. Imagery Usage Instructions...4 ArcView 3.x...4 ArcGIS...7 i Using QuickBird

More information

Digital Image Processing

Digital Image Processing Digital Image Processing 1 Patrick Olomoshola, 2 Taiwo Samuel Afolayan 1,2 Surveying & Geoinformatic Department, Faculty of Environmental Sciences, Rufus Giwa Polytechnic, Owo. Nigeria Abstract: This paper

More information

Remote Sensing in an

Remote Sensing in an Chapter 11: Creating a Composite Image from Landsat Imagery Remote Sensing in an ArcMap Environment Remote Sensing Analysis in an ArcMap Environment Tammy E. Parece Image source: landsat.usgs.gov Tammy

More information

Satellite Remote Sensing: Earth System Observations

Satellite Remote Sensing: Earth System Observations Satellite Remote Sensing: Earth System Observations Land surface Water Atmosphere Climate Ecosystems 1 EOS (Earth Observing System) Develop an understanding of the total Earth system, and the effects of

More information

Remote Sensing Platforms

Remote Sensing Platforms Remote Sensing Platforms Remote Sensing Platforms - Introduction Allow observer and/or sensor to be above the target/phenomena of interest Two primary categories Aircraft Spacecraft Each type offers different

More information

NON-PHOTOGRAPHIC SYSTEMS: Multispectral Scanners Medium and coarse resolution sensor comparisons: Landsat, SPOT, AVHRR and MODIS

NON-PHOTOGRAPHIC SYSTEMS: Multispectral Scanners Medium and coarse resolution sensor comparisons: Landsat, SPOT, AVHRR and MODIS NON-PHOTOGRAPHIC SYSTEMS: Multispectral Scanners Medium and coarse resolution sensor comparisons: Landsat, SPOT, AVHRR and MODIS CLASSIFICATION OF NONPHOTOGRAPHIC REMOTE SENSORS PASSIVE ACTIVE DIGITAL

More information

Sources of Geographic Information

Sources of Geographic Information Sources of Geographic Information Data properties: Spatial data, i.e. data that are associated with geographic locations Data format: digital (analog data for traditional paper maps) Data Inputs: sampled

More information

Remote Sensing Part 3 Examples & Applications

Remote Sensing Part 3 Examples & Applications Remote Sensing Part 3 Examples & Applications Review: Spectral Signatures Review: Spectral Resolution Review: Computer Display of Remote Sensing Images Individual bands of satellite data are mapped to

More information

CHAPTER 7: Multispectral Remote Sensing

CHAPTER 7: Multispectral Remote Sensing CHAPTER 7: Multispectral Remote Sensing REFERENCE: Remote Sensing of the Environment John R. Jensen (2007) Second Edition Pearson Prentice Hall Overview of How Digital Remotely Sensed Data are Transformed

More information

Geomatica I Course Guide Version 10.1

Geomatica I Course Guide Version 10.1 Geomatica I Course Guide Version 10.1 Geomatica Version 10.1 2007 PCI Geomatics Enterprises Inc.. All rights reserved. COPYRIGHT NOTICE Software copyrighted by PCI Geomatics, 50 West Wilmot St., Suite

More information

Quantifying Land Cover Changes in Maine

Quantifying Land Cover Changes in Maine Quantifying Land Cover Changes in Maine! STUDENT HANDOUT Introduction Change detection tools enable us to compare satellite data from different times to assess damage from natural disasters, characterize

More information

Existing and Design Profiles

Existing and Design Profiles NOTES Module 09 Existing and Design Profiles In this module, you learn how to work with profiles in AutoCAD Civil 3D. You create and modify profiles and profile views, edit profile geometry, and use styles

More information

Module 3 Introduction to GIS. Lecture 8 GIS data acquisition

Module 3 Introduction to GIS. Lecture 8 GIS data acquisition Module 3 Introduction to GIS Lecture 8 GIS data acquisition GIS workflow Data acquisition (geospatial data input) GPS Remote sensing (satellites, UAV s) LiDAR Digitized maps Attribute Data Management Data

More information

2017 REMOTE SENSING EVENT TRAINING STRATEGIES 2016 SCIENCE OLYMPIAD COACHING ACADEMY CENTERVILLE, OH

2017 REMOTE SENSING EVENT TRAINING STRATEGIES 2016 SCIENCE OLYMPIAD COACHING ACADEMY CENTERVILLE, OH 2017 REMOTE SENSING EVENT TRAINING STRATEGIES 2016 SCIENCE OLYMPIAD COACHING ACADEMY CENTERVILLE, OH This presentation was prepared using draft rules. There may be some changes in the final copy of the

More information

Raster is faster but vector is corrector

Raster is faster but vector is corrector Account not required Raster is faster but vector is corrector The old GIS adage raster is faster but vector is corrector comes from the two different fundamental GIS models: vector and raster. Each of

More information

Annex IV - Stencyl Tutorial

Annex IV - Stencyl Tutorial Annex IV - Stencyl Tutorial This short, hands-on tutorial will walk you through the steps needed to create a simple platformer using premade content, so that you can become familiar with the main parts

More information

First Exam: New Date. 7 Geographers Tools: Gathering Information. Photographs and Imagery REMOTE SENSING 2/23/2018. Friday, March 2, 2018.

First Exam: New Date. 7 Geographers Tools: Gathering Information. Photographs and Imagery REMOTE SENSING 2/23/2018. Friday, March 2, 2018. First Exam: New Date Friday, March 2, 2018. Combination of multiple choice questions and map interpretation. Bring a #2 pencil with eraser. Based on class lectures supplementing chapter 1. Review lecture

More information

How can we "see" using the Infrared?

How can we see using the Infrared? The Infrared Infrared light lies between the visible and microwave portions of the electromagnetic spectrum. Infrared light has a range of wavelengths, just like visible light has wavelengths that range

More information

Subdivision Cross Sections and Quantities

Subdivision Cross Sections and Quantities NOTES Module 11 Subdivision Cross Sections and Quantities Quantity calculation and cross section generation are required elements of subdivision design projects. After the design is completed and approved

More information

IMPAX 6 DISPLAY TOOL LIST

IMPAX 6 DISPLAY TOOL LIST IMPAX 6 DISPLAY TOOL LIST IMPAX 6.0 TOOLS INDEX A Advance by Image Allows you to scroll from one image or frame to the next Advance by Page Pages through images in a large series, one screen at a time

More information

Adobe Photoshop CS5 Tutorial

Adobe Photoshop CS5 Tutorial Adobe Photoshop CS5 Tutorial GETTING STARTED Adobe Photoshop CS5 is a popular image editing software that provides a work environment consistent with Adobe Illustrator, Adobe InDesign, Adobe Photoshop

More information

EXERCISE 1 - REMOTE SENSING: SENSORS WITH DIFFERENT RESOLUTION

EXERCISE 1 - REMOTE SENSING: SENSORS WITH DIFFERENT RESOLUTION EXERCISE 1 - REMOTE SENSING: SENSORS WITH DIFFERENT RESOLUTION Program: ArcView 3.x 1. Copy the folder FYS_FA with its whole contents from: Kursdata: L:\FA\FYS_FA to C:\Tempdata 2. Open the folder and

More information

Viewing New Hampshire from Space

Viewing New Hampshire from Space Viewing New Hampshire from Space A Bird s-eye View of the Granite State! Introduction Environmental changes are a major concern for researchers and policy makers today since these changes have both human

More information