Planning a Remote Sensing Project

Slides:



Advertisements
Similar presentations
Ann Johnson Associate Director
Advertisements

Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,
Landsat Downloads & MODIS Downloads Data Sources for GIS in Water Resources by Ayse Kilic, David R. Maidment, and David G. Tarboton GIS in Water Resources.
Remote Sensing of Chlorophyll in Matagorda Bay and Surroundings Claire G. Griffin GIS in Water Resources Fall 2010.
Change Detection. Digital Change Detection Biophysical materials and human-made features are dynamic, changing rapidly. It is believed that land-use/land-cover.
Resolution.
Multispectral Remote Sensing Systems
ASTER image – one of the fastest changing places in the U.S. Where??
Remote Sensing of Our Environment Using Satellite Digital Images to Analyze the Earth’s Surface.
Remote Sensing What is Remote Sensing? What is Remote Sensing? Sample Images Sample Images What do you need for it to work? What do you need for it to.
Remote Sensing GTECH 201 Session 09. Remote Sensing.
Remote Sensing of Our Environment Using Satellite Digital Images to Analyze the Earth’s Surface.
The image characteristics are usually referred to as:
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
Remote Sensing of Our Environment Using Satellite Digital Images to Analyze the Earth’s Surface.
Introduction to Digital Data and Imagery
Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
Use of Remote Sensing Data for Delineation of Wildland Fire Effects
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
U.S. Department of the Interior U.S. Geological Survey Assessment of Conifer Health in Grand County, Colorado using Remotely Sensed Imagery Chris Cole.
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.
U.S. Department of the Interior U.S. Geological Survey Multispectral Remote Sensing of Benthic Environments Christopher Moses, Ph.D. Jacobs Technology.
Remotely Sensed Data EMP 580 Fall 2015 Dr. Jim Graham Materials from Sara Hanna.
The role of remote sensing in Climate Change Mitigation and Adaptation.
Resolution A sensor's various resolutions are very important characteristics. These resolution categories include: spatial spectral temporal radiometric.
Resolution Resolution. Landsat ETM+ image Learning Objectives Be able to name and define the four types of data resolution. Be able to calculate the.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Chapter 5 Remote Sensing Crop Science 6 Fall 2004 October 22, 2004.
West Hills College Farm of the Future. West Hills College Farm of the Future Precision Agriculture – Lesson 4 Remote Sensing A group of techniques for.
Christine Urbanowicz Prepared for NC Climate Fellows Workshop June 21, 2011.
May 16-18, 2005MultTemp 2005, Biloxi, MS1 Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data James C. Tilton Mail Code 606*
Remote Sensing. Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including.
Support the spread of “good practice” in generating, managing, analysing and communicating spatial information Introduction to Remote Sensing Images By:
 Introduction to Remote Sensing Example Applications and Principles  Exploring Images with MultiSpec User Interface and Band Combinations  Questions…
Remote Sensing Data Acquisition. 1. Major Remote Sensing Systems.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Terra Launched December 18, 1999
1 October 8, 2015 GIS Day 2015 Geospatial Technologies GPS (global positioning system) –Car GPS systems, yield monitors, smart phones RS (remote sensing)
Validation and comparison of Terra/MODIS active fire detections from INPE and UMd/NASA algorithms LBA Ecology Land Cover – 23 Jeffrey T. Morisette 1, Ivan.
SIMULATION OF ALBEDO AT A LANDSCAPE SCALE WITH THE D.A.R.T. MODEL AN EFFICIENT TOOL FOR EVALUATING COARSE SCALE SATELLITE PRODUCTS? Sylvie DUTHOIT*, Valérie.
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
Geosynchronous Orbit A satellite in geosynchronous orbit circles the earth once each day. The time it takes for a satellite to orbit the earth is called.
Landsat Satellite Data. 1 LSOS (1-ha) 9 Intensive Study Areas (1km x 1km) 3 Meso-cell Study Areas (25km x 25km) 1 Small Regional Study Area (1.5 o x 2.5.
Performance Performance is fundamentally limited by: –Size of data –Where the data is stored –Type of processing –Processing software –Hardware available.
Electro-optical systems Sensor Resolution
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Remote Sensing Basics | August, Fieldwork Review.
Remote sensing of snow in visible and near-infrared wavelengths
Hyperspectral Sensing – Imaging Spectroscopy
Satellite Image Pixel Size vs Mapping Scale
Basic Concepts of Remote Sensing
ASTER image – one of the fastest changing places in the U.S. Where??
Performance Performance is fundamentally limited by: Size of data
Remote Sensing What is Remote Sensing? Sample Images
This week’s earth observatory: false colour image
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Downloading Landsat Data
Satellite Sensors – Historical Perspectives
Atmospheric Correction
NASA alert as Russian and US satellites crash in space
EMP 580 Fall 2015 Dr. Jim Graham Materials from Sara Hanna
High Resolution Sensors – QuickBird
Resolution.
Land Change Modeler Advanced Remote Sensing
Remote Sensing Section 3.
Remote Sensing Landscape Changes Before and After King Fire 2014
Presentation transcript:

Planning a Remote Sensing Project Advanced Remote Sensing Land Use / Land Cover Change Analysis Module 1 Planning a Remote Sensing Project Stacy Bogan 2/13/2018

Planning for a land use / land cover change analysis remote sensing project Collect background information Select a classification scheme Select a remotely-sensed dataset Build a scene model Module Outline

Collect Background Information What is the phenomenon you want to study? What reflectance would you expect in the different areas of the electromagnetic spectrum? What are the other land covers that you need to differentiate? What are the physiological processes dictating the spectral reflectance patterns? Are there different patterns during different times of the year?

Select a classification scheme Different schemes require different types of input information One good web-based tool for choosing which bands to use is the USGS Spectral Characteristics viewer: http://landsat.usgs.gov/tools_spectralViewer.php

USGS Spectral Library http://speclab.cr.usgs.gov/spectral.lib06/

ASTER Spectral library http://speclib.jpl.nasa.gov/

Collect Spectra in the field

Develop training sites

Select possible datasets Name Launched Pixel size Swath Temporal Spectral NOAA/AVHRR 1978 1 Km 2600 km daily 4 - 6 bands Landsat 1970 30 m 183 km 16 days 7 bands SPOT 1998 20 m 60 Km 2-3 days ASTER 2000 15 - 30 m 14 bands MODIS 2001 250m - 1 Km 2300 Km 36 bands

example Scene model: Burn Monitoring in San Diego County, CA Environment Type Forest and shrub   Mediterranean Type Ecosystem Spatial Scale grain = smallest burned vegetation 1m - 5m extent = San Diego County 144 x 102 km Temporal Scale One image per week during May - October Spatial dimensions H resolution grain: 1m - 5m extent: 144 km x 102 km Temporal dimensions May - October Spectral dimensions blue (400-500 nm) green (500-600 nm) red (600-680 nm) NIR (750-900 nm) Radiometric dimensions 11 bit Error tolerance levels error < smallest unit of measurement, 1-5m example Scene model: Burn Monitoring in San Diego County, CA

Data Compliance Matrix Parameter Scene Model Data, IKONOS Level of Match Data, 4 bands of LANDSAT TM   Spatial pixel size 1-5m 4m suitable 30m unsuitable scene extent 144 x 102 km 11 x 11 km 185 km x 185 km H/L resolution H L Spectral No. of Bands 4 Position of Bands blue green red NIR Radiometric resolution 10 bit 11 bit 8 bit Temporal Date May - October yearly Scale 1 per week every 3 days every 16 days Time + Cost imagery high* depends on free processing high** budget moderate

Want to learn more? Related Modules How to download Landsat Data Module 3: Processing Level1 Landsat Data Atmospheric Correction Module 4: Analysis with the Land Change Modeler Want to learn more? Related Modules

Thank you. Please submit feedback http://gis. harvard