Lesson 9: Multitemporal Analysis
|
|
- Arline Curtis
- 6 years ago
- Views:
Transcription
1 Lesson 9: Multitemporal Analysis Lesson Description Multitemporal change analyses require the identification of features and measurement of their change through time. In this lesson, we will examine vegetation change over time in Bale Mountain National Park using a multitemporal change detection analysis. You will learn how to calculate and quantify the difference between two images in two scenarios presented in this lesson. First, you will examine two classified forest cover layers from the dates 1987 and 2015 to measure forest cover change. Second, you will calculate the normalized difference vegetation index (NDVI) for two images and measure the differences in vegetation density and health. Objectives: The student will: 1) Calculate change between two classified images 2) Perform change analysis on two images using NDVI Keywords: Bale Mountains National Park; NDVI; Raster Calculator Resources Required: ArcMap Data Used: LC LGN00: Landsat 8 imagery near Awassa, Ethiopia Background: Given the extended history of satellite remote sensing (with the Landsat mission dating back to 1972 and aerial imagery as far back as the early 20 th century), multitemporal change analysis can be one of the most powerful applications of remote sensing. Some of the most common applications include quantification of urban sprawl, deforestation, reservoir changes, land use change, effects from a natural disaster, and more. These changes are often measured on a pixel by pixel basis where changes in values can be measured to quantify change through time and the implementation of these techniques can vary from relatively simple (basic subtraction) to more complex (e.g. Independent Component Analysis (ICA), radar change detection). The use of multitemporal change analyses allows for the possibility to solve complex problems related to Earth monitoring at a multitude of scales. 1
2 Lesson: Step 1. Postclassification Change Detection We will begin by examining two layers that have both been classified to represent forest cover, one from 1987 and the other from Copy the data folder into your local directory, then drag the files BaleMountainNP.shp, ForestCover_Bale_1987.tif and ForestCover_Bale_2015.tif into the ArcMap Table of Contents window. 1.2 The values in these rasters represent forested (value 1 or 10) or not forested (value 0). We can begin by adjusting the symbology for these layers. For each raster layer, open the Properties and on the Symbology tab, select Unique Values and set the 0 value (not forested) to white, and the 1 or 10 value (forested) to green. Set the Bale Mountain National Park boundary to a hollow fill and outline of your choice (Figure 1). Examine both the 1987 and 2015 forest cover layers. Has forest cover appeared to increase or decrease during this time? Figure 1. Bale Mountain National Park 2015 forest cover. 1.3 We will quantify the change between these two layers using a simple subtraction operation performed within the raster calculator. 1.4 Find the Raster Calculator (Spatial Analyst) tool using the Search window or use ArcToolbox and navigate to: Spatial Analyst Tools > Map Algebra > Raster Calculator 1.5 In the raster calculator, we want to subtract the 2015 layer from the 1987 layer. Double-click the 1987 layer, ForestCover_Bale_1987.tif, click or type the subtract symbol (-), then double click the 2015 layer, ForestCover_Bale_2015.tif. Your expression should appear as follows: "ForestCover_Bale_1987.tif" - "ForestCover_Bale_2015.tif" This operation will run on each individual pixel within the raster datasets. Save your output as ForestCover_Bale_Diff.tif and click OK. 1.6 Open the Properties of the new layer, ForestCover_Bale_Diff.tif, on the Symbology tab, click Unique Values (click Yes if a notification appears). Examine the values, -10, -9, 0, 1. Think about the subtraction expression performed above and what each of these resultant values represents; Answer Question 1. 2
3 1.7 The following table shows the description of each value: Value Expression Description Forest Increase No change forested No change not forested Forest decrease We can now visualize where the most change has occurred and also quantify how much area has been affected in each of the categories. Open the Properties of the difference layer and go to the Symbology tab, select Unique Values. Set the color of each value to represent each respective description. I ve selected dark green to represent forest increase, light green for no change forested, tan for no change forested, and red for forest decrease (Figure 2). Also, note the Count column values, which store the number of pixels each value appears. 1.8 The count values should appear as follows: Value Count Switch to the Source tab, we can see the cell size for this raster is 30 by 30 meters. Figure 2. Forest cover change between 1987 and To calculate area we simply take the number of cells multiplied by the cell size (30x30 m 2 ). The area of forest increase (-10) is thus: * 30 2 = 38,860,200 m 2 = km Calculate the area for the remaining values to Answer Questions 2 and 3. 3
4 Step 2. NDVI Differencing We will now use Landsat imagery from the same dates, 1987 and 2015, to calculate the Normalized Difference Vegetation Index (NDVI) for each period in time, then measure the difference between the NDVI layers to assess changes in vegetation density. 2.1 Add the layers LT5_Bale_1987.tif and L08_Bale_2015.tif. These are top-of-atmosphere reflectance images of the park collected by Landsat 5 (1987) and Landsat 8 (2015). 2.2 We will begin by calculating NDVI for each layer. Recall from Lesson 6: Spectral Indices, NDVI is calculated as follows: NDVI = NIR - Red NIR + Red Also recall that Landsat 8 has the additional Coastal Blue band (band 1) not present on Landsat 5, therefore on Landsat 8, NIR is band 5 and Red is band 4; on Landsat 5, NIR is band 4 and Red is band In the Catalog window, click the + symbol to the left of each Landsat file to view the individual bands. For the Landsat 8 image, add bands 5 and 4, and for Landsat 5 image, add bands 4 and 3 (Figure 3). 2.4 We will calculate NDVI using the Raster Calculator. Find the Raster Calculator (Spatial Analyst) tool using the Search or use ArcToolbox and navigate to: window Spatial Analyst Tools > Map Algebra > Raster Calculator 2.5 First, calculate 2015 Landsat 8 NDVI, as follows: Figure 3. Landsat 8 and 5 bands in Catalog window ("L08_Bale_2015.tif - Band_5" - "L08_Bale_2015.tif - Band_4") / ("L08_Bale_2015.tif - Band_5" + "L08_Bale_2015.tif - Band_4") Save the output as NDVI_Bale_2015.tif. 4
5 2.6 Next, calculate 1987 Landsat 5 NDVI, as follows: ("LT5_Bale_1987.tif - Band_4" - "LT5_Bale_1987.tif - Band_3") / ("LT5_Bale_1987.tif - Band_4" + "LT5_Bale_1987.tif - Band_3") Save the output as NDVI_Bale_1987.tif. 2.7 Now that we have the NDVI layers, set a new color ramp in the Symbology tabs to better visualize the layers. Color ramps with a neutral zero color are best for visualizing these type of data (e.g. ). 2.8 We will use the Raster Calculator again to calculate the change between the NDVI layers. Open the Raster Calculator. Subtract the NDVI_Bale_2015.tif from NDVI_Bale_1987.tif as we did in step 1.6. Save the new layer as NDVI_Bale_Diff.tif. 2.9 In the new layer, higher values represent locations where vegetation density has decreased, while low values are locations where vegetation has increased. Set a color ramp with a neutral zero color to better visualize these changes (Figure 4) Examine this layer and compare it with the forest cover difference map we created in Step 1; Answer Question 4. Figure 4. NDVI Difference layer between 1987 and Any use of trade, products, or firm names is for descriptive purposes only and does not imply endorsement by Colorado State University or any other collaborating individuals or agency. This tutorial was created for educational purposes and the data presented in these lessons may be incomplete or inaccurate. 5
6 Exercise Questions 1. What do each of the values (-10, -9, 0, 1) in the forest cover difference layer represent (e.g., forest increase, forest decrease, no change in forested, no change in nonforested)? How much area falls within each forest cover class (m 2 or km 2 )? What is the net decrease of forest cover for Bale Mountain National Park between 1987 and 2015 (in km 2 )? 4. How well do these data align within forested areas? Are locations where we found forest decreased represented in the difference NDVI image? 5. Aside from assessing vegetation change, what other applications could a multitemporal analysis be useful for? Are these applications relevant to your research or projects? 6
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 informationSpatial Analyst is an extension in ArcGIS specially designed for working with raster data.
Spatial Analyst is an extension in ArcGIS specially designed for working with raster data. 1 Do you remember the difference between vector and raster data in GIS? 2 In Lesson 2 you learned about the difference
More informationRemote Sensing in an
Chapter 6: Displaying Data Remote Sensing in an ArcMap Environment Remote Sensing Analysis in an ArcMap Environment Tammy E. Parece Image source: landsat.usgs.gov Tammy Parece James Campbell John McGee
More informationRemote Sensing in an
Chapter 20: Accuracy Assessment Remote Sensing in an ArcMap Environment Remote Sensing Analysis in an ArcMap Environment Tammy E. Parece Image source: landsat.usgs.gov Tammy Parece James Campbell John
More informationUsing Soil Productivity to Assess Agricultural Land Values in North Dakota
Using Soil Productivity to Assess Agricultural Land Values in North Dakota STUDENT HANDOUT Overview Why is assigning a true and full value to agricultural land parcels important? Agricultural production
More informationF2 - Fire 2 module: Remote Sensing Data Classification
F2 - Fire 2 module: Remote Sensing Data Classification F2.1 Task_1: Supervised and Unsupervised classification examples of a Landsat 5 TM image from the Center of Portugal, year 2005 F2.1 Task_2: Burnt
More informationEnhancement 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 informationQuantifying 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 informationSoftware requirements * : Part I: 1 hr. Part III: 2 hrs.
Title: Product Type: Developer: Target audience: Format: Software requirements * : Data: Estimated time to complete: Using MODIS to Analyze the Seasonal Growing Cycle of Crops Part I: Understand and locate
More informationHow to Access Imagery and Carry Out Remote Sensing Analysis Using Landsat Data in a Browser
How to Access Imagery and Carry Out Remote Sensing Analysis Using Landsat Data in a Browser Including Introduction to Remote Sensing Concepts Based on: igett Remote Sensing Concept Modules and GeoTech
More informationMorphology Change Procedure using Satellite Derived Bathymetry
Morphology Change Procedure using Satellite Derived Bathymetry Brian Madore December 23, 2014 To monitor the morphology of a region it is important to have imagery which is taken consistently and can cover
More information8th 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 informationLand 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 informationLand 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 informationRaster 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 informationDownloading 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 informationA Little Spare Change
A Little Spare Change Monitoring land-cover change by satellite by Introduction Problem Can city utility services use remote satellite data, processed with geographic information systems (GIS), to help
More informationAssessment 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 informationPart 1 Using GIS for Tsunami Disaster Assessment
Tsunami_Hood_SG_June_2009 Learning Unit Student Guide Outline Name of Creator: Scott Hood Institution: Kennebec Valley Community College Email contact for more information: shood@kvcc.me.edu Title: Tsunami
More informationRemote Sensing in an
Chapter 15: Spatial Enhancement of Landsat Imagery Remote Sensing in an ArcMap Environment Remote Sensing Analysis in an ArcMap Environment Tammy E. Parece Image source: landsat.usgs.gov Tammy Parece James
More informationRemote 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 informationLecture 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 informationLAND SURFACE TEMPERATURE MONITORING THROUGH GIS TECHNOLOGY USING SATELLITE LANDSAT IMAGES
Abstract LAND SURFACE TEMPERATURE MONITORING THROUGH GIS TECHNOLOGY USING SATELLITE LANDSAT IMAGES Aurelian Stelian HILA, Zoltán FERENCZ, Sorin Mihai CIMPEANU University of Agronomic Sciences and Veterinary
More informationUrban 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 informationLand 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 informationUsing 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 informationThis 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 informationWhite 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 informationDirty REMOTE SENSING Lecture 3: First Steps in classifying Stuart Green Earthobservation.wordpress.com
Dirty REMOTE SENSING Lecture 3: First Steps in classifying Stuart Green Earthobservation.wordpress.com Stuart.Green@Teagasc.ie You have your image, but is it any good? Is it full of cloud? Is it the right
More informationMonitoring land-cover change by satellite
Change in the Right Direction Monitoring land-cover change by satellite by Introduction Problem Can city utility services use remote satellite data, processed with geographic information systems (GIS),
More informationSeasonal Progression of the Normalized Difference Vegetation Index (NDVI)
Seasonal Progression of the Normalized Difference Vegetation Index (NDVI) For this exercise you will be using a series of six SPOT 4 images to look at the phenological cycle of a crop. The images are SPOT
More informationAPCAS/10/21 April 2010 ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS TWENTY-THIRD SESSION. Siem Reap, Cambodia, April 2010
APCAS/10/21 April 2010 Agenda Item 8 ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS TWENTY-THIRD SESSION Siem Reap, Cambodia, 26-30 April 2010 The Use of Remote Sensing for Area Estimation by Robert
More informationGEOG432: 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 informationSupervised 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 informationQuantifying Change in. Quality Effects on a. Wetland Extent & Wetland. Western and Clark s Grebe Breeding Population
Quantifying Change in Wetland Extent & Wetland Quality Effects on a Western and Clark s Grebe Breeding Population Eagle Lake, CA: 1998-2010 Renée E. Robison 1, Daniel W. Anderson 2,3, and Kristofer M.
More informationigett Cohort 2, June 2008 Learning Unit Student Guide Template Stream_Quality_Perkins_SG_February2009
igett Cohort 2, June 2008 Learning Unit Student Guide Template Stream_Quality_Perkins_SG_February2009 Name of Creator: Reed Perkins Institution: Queens University of Charlotte Email contact for more information:
More informationExercise 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 informationThe (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 informationGeography 281 Map Making with GIS Project Ten: Mapping and Spatial Analysis
Geography 281 Map Making with GIS Project Ten: Mapping and Spatial Analysis This project introduces three techniques that enable you to manipulate the spatial boundaries of geographic features: Clipping
More informationRemote 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 informationInter-Calibration of the RapidEye Sensors with Landsat 8, Sentinel and SPOT
Inter-Calibration of the RapidEye Sensors with Landsat 8, Sentinel and SPOT Dr. Andreas Brunn, Dr. Horst Weichelt, Dr. Rene Griesbach, Dr. Pablo Rosso Content About Planet Project Context (Purpose and
More informationGEOG432: 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 informationImage Change Tutorial
Image Change Tutorial In this tutorial, you will use the Image Change workflow to compare two images of an area over Indonesia that was impacted by the December 26, 2004 tsunami. The first image is a before
More informationIn late April of 1986 a nuclear accident damaged a reactor at the Chernobyl nuclear
CHERNOBYL NUCLEAR POWER PLANT ACCIDENT Long Term Effects on Land Use Patterns Project Introduction: In late April of 1986 a nuclear accident damaged a reactor at the Chernobyl nuclear power plant in Ukraine.
More informationUnsupervised 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 informationVisualizing 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 informationRemote Sensing in an
Chapter 8. Downloading Landsat Imagery using Earth Explorer Remote Sensing in an ArcMap Environment Remote Sensing Analysis in an ArcMap Environment Tammy E. Parece Image source: landsat.usgs.gov Tammy
More informationSatellite data processing and analysis: Examples and practical considerations
Satellite data processing and analysis: Examples and practical considerations Dániel Kristóf Ottó Petrik, Róbert Pataki, András Kolesár International LCLUC Regional Science Meeting in Central Europe Sopron,
More informationREMOTE SENSING. Topic 10 Fundamentals of Digital Multispectral Remote Sensing MULTISPECTRAL SCANNERS MULTISPECTRAL SCANNERS
REMOTE SENSING Topic 10 Fundamentals of Digital Multispectral Remote Sensing Chapter 5: Lillesand and Keifer Chapter 6: Avery and Berlin MULTISPECTRAL SCANNERS Record EMR in a number of discrete portions
More informationWhite 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 informationApplication of GIS to Fast Track Planning and Monitoring of Development Agenda
Application of GIS to Fast Track Planning and Monitoring of Development Agenda Radiometric, Atmospheric & Geometric Preprocessing of Optical Remote Sensing 13 17 June 2018 Outline 1. Why pre-process remotely
More informationINTRODUCTORY 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 information1. 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 informationModule 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 informationLand Cover Type Changes Related to. Oil and Natural Gas Drill Sites in a. Selected Area of Williams County, ND
Land Cover Type Changes Related to Oil and Natural Gas Drill Sites in a Selected Area of Williams County, ND FR 3262/5262 Lab Section 2 By: Andrew Kernan Tyler Kaebisch Introduction: In recent years, there
More informationAmericaView 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 informationCHANGE 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 informationIntroduction to Remote Sensing Fundamentals of Satellite Remote Sensing. Mads Olander Rasmussen
Introduction to Remote Sensing Fundamentals of Satellite Remote Sensing Mads Olander Rasmussen (mora@dhi-gras.com) 01. Introduction to Remote Sensing DHI What is remote sensing? the art, science, and technology
More informationUSGS Welcome. 38 th CEOS Working Group on Calibration and Validation Plenary (WGCV-38)
Landsat 5 USGS Welcome Prepared for 38 th CEOS Working Group on Calibration and Validation Plenary (WGCV-38) Presenter Tom Cecere International Liaison USGS Land Remote Sensing Program Elephant Butte Reservoir
More informationSeparation of crop and vegetation based on Digital Image Processing
Separation of crop and vegetation based on Digital Image Processing Mayank Singh Sakla 1, Palak Jain 2 1 M.TECH GEOMATICS student, CEPT UNIVERSITY 2 M.TECH GEOMATICS student, CEPT UNIVERSITY Word Limit
More informationLab 3: Introduction to Image Analysis with ArcGIS 10
Lab 3: Introduction to Image Analysis with ArcGIS 10 Peter E. Price TerraView 2010 Peter E. Price All rights reserved. Revised 03/2011. Revised for Geob 373 by BK Feb 7, 2017. V9 The information contained
More informationLand cover change methods. Ned Horning
Land cover change methods Ned Horning Version: 1.0 Creation Date: 2004-01-01 Revision Date: 2004-01-01 License: This document is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License.
More informationFiles Used in this Tutorial
Burn Indices Tutorial This tutorial shows how to create various burn index images from Landsat 8 imagery, using the May 2014 San Diego County wildfires as a case study. You will learn how to perform the
More informationSatellite Imagery and Remote Sensing. DeeDee Whitaker SW Guilford High EES & Chemistry
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry whitakd@gcsnc.com Outline What is remote sensing? How does remote sensing work? What role does the electromagnetic
More informationRemote Sensing for Rangeland Applications
Remote Sensing for Rangeland Applications Jay Angerer Ecological Training June 16, 2012 Remote Sensing The term "remote sensing," first used in the United States in the 1950s by Ms. Evelyn Pruitt of the
More informationTimeSync 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 informationLab 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 informationModule 3: Introduction to QGIS and Land Cover Classification
Module 3: Introduction to QGIS and Land Cover Classification The main goals of this Module are to become familiar with QGIS, an open source GIS software; construct a single-date land cover map by classification
More informationGIS Module GMS 7.0 TUTORIALS. 1 Introduction. 1.1 Contents
GMS 7.0 TUTORIALS 1 Introduction The GIS module can be used to display data from a GIS database directly in GMS without having to convert that data to GMS data types. Native GMS data such as grids and
More informationSEN3APP Stakeholder Workshop, Helsinki Yrjö Rauste/VTT Kaj Andersson/VTT Eija Parmes/VTT
Optical Products from Sentinel-2 and Suomi- NPP/VIIRS SEN3APP Stakeholder Workshop, Helsinki 19.11.2015 Yrjö Rauste/VTT Kaj Andersson/VTT Eija Parmes/VTT Structure of Presentation High-resolution data
More informationSatellite 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 informationCombined Use of SAR and Optical Time Series Data towards Near Real-Time Forest Disturbance Mapping
Background Image Source bbc.co.uk Human Planet 2011 BBC Manuela Hirschmugl, Janik Deutscher, Karl-Heinz Gutjahr, Carina Sobe, Mathias Schardt Joanneum Research Earth Observation Services for Monitoring
More informationQGIS 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 informationNRS 415 Remote Sensing of Environment
NRS 415 Remote Sensing of Environment 1 High Oblique Perspective (Side) Low Oblique Perspective (Relief) 2 Aerial Perspective (See What s Hidden) An example of high spatial resolution true color remote
More informationv. 8.0 GMS 8.0 Tutorial GIS Module Shapefile import, display, and conversion Prerequisite Tutorials None Time minutes
v. 8.0 GMS 8.0 Tutorial Shapefile import, display, and conversion Objectives Learn how to import and display shapefiles with and without ArcObjects. Convert the shapefiles to GMS feature objects. Prerequisite
More informationLecture 6: Multispectral Earth Resource Satellites. The University at Albany Fall 2018 Geography and Planning
Lecture 6: Multispectral Earth Resource Satellites The University at Albany Fall 2018 Geography and Planning Outline SPOT program and other moderate resolution systems High resolution satellite systems
More informationFinal Examination Introduction to Remote Sensing. Time: 1.5 hrs Max. Marks: 50. Section-I (50 x 1 = 50 Marks)
Final Examination Introduction to Remote Sensing Time: 1.5 hrs Max. Marks: 50 Note: Attempt all questions. Section-I (50 x 1 = 50 Marks) 1... is the technology of acquiring information about the Earth's
More informationLandsat 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 informationComparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River
Journal of Geography and Geology; Vol. 10, No. 1; 2018 ISSN 1916-9779 E-ISSN 1916-9787 Published by Canadian Center of Science and Education Comparing of Landsat 8 and Sentinel 2A using Water Extraction
More informationINTRODUCTION TO SNAP TOOLBOX
INTRODUCTION TO SNAP TOOLBOX EXERCISE 1 (Exploring S2 data) Data: Sentinel-2A Level 1C: S2A_MSIL1C_20180303T170201_N0206_R069_T14QNG_20180303T221319.SAFE 1. Open file 1.1. File / Open Product 1.2. Browse
More informationSoftware requirements * : Part I: 1 hr. Part III: 2 hrs.
Title: Product Type: Developer: Target audience: Format: Software requirements * : Data: Estimated time to complete: Using MODIS to Analyze the Seasonal Growing Cycle of Crops Part I: Understand and locate
More informationUSDA Forest Service, Remote Sensing Applications Center,
Deriving the BARC from Satellite Imagery Demonstration Deriving the BARC from Satellite Imagery French Fire 2004 BARC Dataset Rodeo Chediski Fire 2002 Landsat 7 ETM + Imagery Step 1: Pre-processing Step
More informationIntroduction. 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 informationDIGITALGLOBE ATMOSPHERIC COMPENSATION
See a better world. DIGITALGLOBE BEFORE ACOMP PROCESSING AFTER ACOMP PROCESSING Summary KOBE, JAPAN High-quality imagery gives you answers and confidence when you face critical problems. Guided by our
More informationGeo/SAT 2 TROPICAL WET REALMS OF CENTRAL AFRICA, PART II
Geo/SAT 2 TROPICAL WET REALMS OF CENTRAL AFRICA, PART II Paul R. Baumann Professor of Geography (Emeritus) State University of New York College at Oneonta Oneonta, New York 13820 USA COPYRIGHT 2009 Paul
More informationARC HYDRO GROUNDWATER TUTORIALS
ARC HYDRO GROUNDWATER TUTORIALS Subsurface Analyst Creating ArcMap cross sections from existing cross section images Arc Hydro Groundwater (AHGW) is a geodatabase design for representing groundwater datasets
More informationLand 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 informationCLASSIFICATION OF HISTORIC LAKES AND WETLANDS
CLASSIFICATION OF HISTORIC LAKES AND WETLANDS Golden Valley, Minnesota Image Analysis Heather Hegi & Kerry Ritterbusch 12/13/2010 Bassett Creek and Theodore Wirth Golf Course, 1947 FR 5262 Remote Sensing
More informationThe effects of uncertainty in forest inventory plot locations. Ronald E. McRoberts, Geoffrey R. Holden, and Greg C. Liknes
The effects of uncertainty in forest inventory plot locations Ronald E. McRoberts, Geoffrey R. Holden, and Greg C. Liknes North Central Research Station, USDA Forest Service, Saint Paul, Minnesota 55108
More informationPart 1. Tracing the Dimensions of Some Common Pixel Sizes using a GPS Receiver
Field and Laboratory Exercise PIXEL DELINEATIONS 1 IMPORTING GPS DATA TO IMAGE BACKGROUND Objectives: 1. Demonstrate the differences in spatial resolution of selected remote sensing instruments. 2. Use
More informationATCOR Workflow for IMAGINE 2016
ATCOR Workflow for IMAGINE 2016 Version 1.0 Step-by-Step Guide January 2017 ATCOR Workflow for IMAGINE Page 2/24 The ATCOR trademark is owned by DLR German Aerospace Center D-82234 Wessling, Germany URL:
More informationRemote Sensing And Gis Application in Image Classification And Identification Analysis.
Quest Journals Journal of Research in Environmental and Earth Science Volume 3~ Issue 5 (2017) pp: 55-66 ISSN(Online) : 2348-2532 www.questjournals.org Research Paper Remote Sensing And Gis Application
More informationin ArcMap By Mike Price, Entrada/San Juan, Inc.
Interactively Create and Apply Logarithmic Legends in ArcMap By Mike Price, Entrada/San Juan, Inc. This exercise uses the dataset for Battle Mountain, Nevada, that was used in previous exercises. The Geochemistry
More informationPlease show the instructor your downloaded index files and orthoimages.
Student Exercise 1: Sandia Forest Infestation Acquiring Orthophotos and Satellite Imagery Please show the instructor your downloaded index files and orthoimages. Objectives: Determine appropriate imagery
More informationRemote 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 informationDetermining Flood Risk in Iowa STUDENT HANDOUT
! Determining Flood Risk in Iowa STUDENT HANDOUT 2008 Flood Water Analysis This learning unit will compare two sets of flood boundaries from the June 2008 flood in Cedar Rapids, Iowa. You will extract
More informationTry what you learned (and some new things too)
Training Try what you learned (and some new things too) PART ONE: DO SOME MATH Exercise 1: Type some simple formulas to add, subtract, multiply, and divide. 1. Click in cell A1. First you ll add two numbers.
More informationManaging and Monitoring Intertidal Oyster Reefs with Remote Sensing in Coastal South Carolina
Managing and Monitoring Intertidal Oyster Reefs with Remote Sensing in Coastal South Carolina A cooperative effort between: Coastal Services Center South Carolina Department of Natural Resources City of
More informationObjectives Learn how to import and display shapefiles with and without ArcObjects. Learn how to convert the shapefiles to GMS feature objects.
v. 10.1 GMS 10.1 Tutorial Importing, displaying, and converting shapefiles Objectives Learn how to import and display shapefiles with and without ArcObjects. Learn how to convert the shapefiles to GMS
More informationRemote sensing in archaeology from optical to lidar. Krištof Oštir ModeLTER Scientific Research Centre of the Slovenian Academy of Sciences and Arts
Remote sensing in archaeology from optical to lidar Krištof Oštir ModeLTER Scientific Research Centre of the Slovenian Academy of Sciences and Arts Introduction Optical remote sensing Systems Search for
More information