Matthew J. Thoman1 and Kaitlyn McCollum1 with Ramesh Sivanpillai2

Slides:



Advertisements
Similar presentations
Remote Sensing GIS/Remote Sensing Workshop June 6, 2013.
Advertisements

Page 1 of 50 Optimization of Artificial Neural Networks in Remote Sensing Data Analysis Tiegeng Ren Dept. of Natural Resource Science in URI (401)
September 5, 2013 Tyler Jones Research Assistant Dept. of Geology & Geography Auburn University.
Use of Remote Sensing and GIS in Agriculture and Related Disciplines
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.
Introduction, Satellite Imaging. Platforms Used to Acquire Remote Sensing Data Aircraft Low, medium & high altitude Higher level of spatial detail Satellite.
Published in Remote Sensing of the Environment in May 2008.
Global Monitoring of Large Reservoir Storage from Satellite Remote Sensing Huilin Gao 1, Dennis P. Lettenmaier 1, Charon Birkett 2 1 Dept. of Civil and.
Remote Sensing Applications. Signatures – a unique identifier…
Remote Sensing 2012 SUMMER INSTITUTE. Presented by: Mark A. Van Hecke National Science Olympiad Earth-Space Science Event Chair Roy Highberg North Carolina.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
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 Applications Supporting Regional Transportation Database Development CLEM 2001 August 6, 2001 Santa Barbara, CA Chris Chiesa,
Remote Sensing. Gives us “the Big Picture” Allows us to see things from the larger perspective. Allows us to see things we otherwise might miss.
Classification & Vegetation Indices
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
Aerial Photographs and Remote Sensing Aerial Photographs For years geographers have used aerial photographs to study the Earth’s surface. In many ways.
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.
Learning Objectives Nature of Light Color & Spectroscopy ALTA Spectrophotometer Spectral Signature of Substances Interpretation of Satellite Images.
U.S. Department of the Interior U.S. Geological Survey Multispectral Remote Sensing of Benthic Environments Christopher Moses, Ph.D. Jacobs Technology.
 The textbook GIS methods section: Provides basic understanding of GIS concepts What is RS? How can we use RS for GIS, when, where and why?
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.
Mapping shorelines to subpixel accuracy using Landsat imagery Ron Abileah (1), Stefano Vignudelli (2), and Andrea Scozzari (3) (1) jOmegak, San Carlos.
Christine Urbanowicz Prepared for NC Climate Fellows Workshop June 21, 2011.
INDICES FOR INFORMATION EXTRACTION FROM SATELLITE IMAGERY Course: Introduction to RS & DIP Mirza Muhammad Waqar Contact:
Juan de Dios Barrios, M.S. Research Associate Nick J. Rahall Appalachian Transportation Institute and James O. Brumfield, Ph. D. College of Science Marshall.
Support the spread of “good practice” in generating, managing, analysing and communicating spatial information Introduction to Remote Sensing Images By:
West Hills College Farm of the Future The Precision-Farming Guide for Agriculturalists Chapter Five Remote Sensing.
Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew #, P.
EG2234: Earth Observation Introduction to RS Dr Mark Cresswell.
What is an image? What is an image and which image bands are “best” for visual interpretation?
7 elements of remote sensing process 1.Energy Source (A) 2.Radiation & Atmosphere (B) 3.Interaction with Targets (C) 4.Recording of Energy by Sensor (D)
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai JSPS.
1 October 8, 2015 GIS Day 2015 Geospatial Technologies GPS (global positioning system) –Car GPS systems, yield monitors, smart phones RS (remote sensing)
Observing Laramie Basin Grassland Phenology Using MODIS Josh Reynolds with PROPOSED RESEARCH PROJECT Acknowledgments Steven Prager, Dept. of Geography.
A Remote Sensing Sampler. Typical reflectance spectra Remote Sensing Applications Consultants -
Violet:  m Blue:  m Green:  m Yellow:  m Orange:  m Red:
Citation: Moskal., L. M. and D. M. Styers, Land use/land cover (LULC) from high-resolution near infrared aerial imagery: costs and applications.
Updated Cover Type Map of Cloquet Forestry Center For Continuous Forest Inventory.
Introduction to Aerial Stereo Photographs
Estimating intra-annual changes in the surface area of Sand Mesa Reservoir #1 using multi-temporal Landsat images Cody A. Booth 1 with Ramesh Sivanpillai.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Mapping Forest Burn Severity Using Non Anniversary Date Satellite Images By: Blake Cobb Renewable Resources Department with Dr. Ramesh Sivanpillai Department.
Remote Sensing. Content Introduction What is remote sensing? History of Remote Sensing Applications of Remote Sensing Components Advantages Disadvantages.
Christopher Steinhoff Ecosystem Science and Management, University of Wyoming Ramesh Sivanpillai Department of Botany, University of Wyoming Mapping Changes.
Effect of Sun Incidence Angle on Classifying Water Bodies in Landsat Images Ina R. Goodman, Dr. Ramesh Sivanpillai Department of Botany WyomingView.
Mapping Historic Waterbodies using Landsat and QGIS Justin Epting USFWS, Pacific Southwest Region.
Kate E. Richardson 1 with Ramesh Sivanpillai 2 1.Department of Ecosystem Science and Management 2.Department of Botany University of Wyoming.
Mapping Burn Severity of the Marking Pen Prescribed Burn in the Seminoe Mountains using pre- and post-fire Landsat Thematic Mapper images Erik Collier1.
Quantifying Analyst Bias in Mapping Flooded Areas from Landsat Images
Mapping Variations in Crop Growth Using Satellite Data
Using vegetation indices (NDVI) to study vegetation
Mapping wheat growth in dryland fields in SE Wyoming using Landsat images Matthew Thoman.
STUDY ON THE PHENOLOGY OF ASPEN
Factsheet #11 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Small Stream Mapping Method: Local.
Orin J. Hutchinson1 with Dr. Ramesh Sivanpillai2
Biomass Study Is there a correlation between annual rainfall and vegetation index using NDVI in a suburban area?
Remote Sensing What is Remote Sensing? Sample Images
Mapping shorelines to subpixel accuracy using Landsat imagery
Evaluating Land-Use Classification Methodology Using Landsat Imagery
with Ramesh Sivanpillai2 and Alexandre Latchininsky3
Introduction to Remote-Sensing
By Blake Balzan1, with Ramesh Sivanpillai PhD2
Image Information Extraction
Bailey Terry1 & Benjamin Beaman2 with Ramesh Sivanpillai3
Quantifying Producer Error in the Unsupervised Classification of Reservoirs Skye Swoboda-Colberg1, Chris Sheets2, Dr. Ramesh Sivanpillai3 1. Department.
Remote Sensing Section 3.
Remote Sensing Landscape Changes Before and After King Fire 2014
Presentation transcript:

Assessing Transferability of Landsat-derived NDWI Values across Space and Time Matthew J. Thoman1 and Kaitlyn McCollum1 with Ramesh Sivanpillai2 1. Department of Ecosystem Science and Management and 2 Department of Botany University of Wyoming

Importance of water in Western us Driver Vegetation dynamics Wildlife habitat Agriculture Recreation Wyoming has several un-gauged reservoirs and dams Information on water that is stored is not available Remotely sensed data can be used to obtain info on the amount of water stored katie

Remote Sensing What is remote sensing? Is the art & science of collecting data without physical contact Sensors mounted in platforms (satellites, airplanes, Unmanned aerial systems, balloons) collect data Visible and infrared portions of the spectrum katie

Landsat Collection of Earth Observation Satellites Landsat 5 Landsat 1-5, 7, 8 (launched in Feb 2013) Started in 1970s Landsat 5 Launched in 1982 Collected data until Nov 2011 3 visible bands Blue, green and red 3 infrared bands Near infrared 1 & 2, shortwave infrared matt

goal To map water surface area using Landsat data Several techniques exist for mapping water Unsupervised classification Rule-based classification Compute an index from the image, and define rules for each class matt

Mapping water with Landsat data Normalized Difference Water Index (NDWI) is a widely used index Different formulas exist We used for NDWI: (B2 – B4) / (B2 + B4) Ranges between -1 and +1 Water B2 > B4 (+ve) Others B2 < B4 (-ve) katie

Water reflectance NDWI However reflection changes with location Previous studies have shown that water has a value of >0 This is used as a threshold for identifying water However reflection changes with location Topography Water characteristics water depth, presence of biological materials, and turbidity katie (HANQIU XU, 2006) (Lei Ji & Wylie, 2009)

Research Questions: Does the threshold value of >0 holds true for some lakes in North Central Wyoming? If we adjust this threshold for one lake, can this range be transferred to adjacent lakes? Alternatively we have to develop a range for each lake for a lake based on one year, can we transfer that range to other years? Alternatively we have to develop ranges for each year matt

Study Area North Central Wyoming Lakes studied: Bull Lake Sand Mesa Reservoir Lake Cameahwait Middle Reservoir Ocean Lake Boulder Lake Extend over space (1994) Extend over Time (1994, 2006, 2009) matt

Lakes studied matt Courtesy of Google Maps 2013

Data Landsat Digital Ortho Quarter Quads (DOQQs) 1994, 2006, 2009 (August) Digital Ortho Quarter Quads (DOQQs) USDA and other federal and state agencies 1994, 2002, 2006, 2009 and 2012 Summer images of 1994, 2006, and 2009 were used 1994 – B&W (single band); 2006 – true color; 2009 – color infrared katie

Data processing Pseudo ground truth data were collected Used high resolution images-DOQQ images Collected Coordinates of what and where Linked DOQQ images to Landsat Images Extracted reflectance values off Landsat based on independent pseudo classification collected from DOQQ images Water Edge Vegetation & Bare ground Katie

Results Water X Y B1 B2 B3 B4 B5 B7 NDWI 43 12 17.8925 N 37 38 25 8 1 43 9 55.2236 N 108 35 51.6178 W 34 36 20 7 43 10 52.2412 N 108 37 35.9570 W 32 31 17 5 43 12 15.4087 N 108 35 53.6123 W 43 11 5.4298 N 108 35 58.0231 W 19 6 43 10 48.9160 N 108 37 51.9055 W 18 43 11 17.8888 N 108 34 44.2554 W 35 2 43 10 12.6443 N 108 38 5.7953 W 43 11 32.1828 N 108 34 28.7001 W 22 43 11 51.4012 N 108 37 38.6603 W 16 43 11 30.5496 N 108 36 33.6799 W 33 43 10 3.7659 N 108 36 50.3860 W 21 6.8 1.3 0.8 katie NDWI = (B2 – B4) / (B2 + B4)

water & edge confusion Edge (defining edge was not easy) Shoreline boundary - bare ground and vegetation Landsat pixels are bigger (30m x 30m) than DOQQ (1m x 1m) matt

matt Courtesy of Google Maps 2013

Results: Threshold>0 Vegetation Vegetation Bare Ground Bare Ground Edge Edge matt Water Water

Results: Transfer to other lakes Matt Yes, you can transfer the values from one lake to another Edges are a proble

Results: Transfer to other years Vegetation Bare Ground Edge katie Water

Water & edge confusion Time of imagery Human interpretation Landsat – acquired in August DOQQ – exact dates are not known Seasonal differences Human interpretation 1994 were B&W – difficult to interpret Katie and matt

results Rule-based classification of NDWI can be used to map water bodies in NC Wyoming Adjusting the NDWI threshold to >0.2 might reduce misclassification of edges as water Reduce the possibility of edge misclassified as water. Future studies Use high-resolution satellite data instead of DOQQ (exact date will be known) Include additional lakes with different characteristics (depth, color, vegetation) Repeat for other years Matt-a and katie

Acknowledgement WyomingView Internship opportunity AmericaView for funding WyomingView USGS for Landsat images WyGISC for DOQQ Images Ramesh Sivanpillai, UW Botany, WyGISC

Works Cited HANQIU XU. (2006). Modification of normalized difference water index (NDWI) to enhance. International Journal of Remote Sensing, 3025–3033. Lei Ji, L. Z., & Wylie, B. (2009). Analysis of Dynamic Thresholds for. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1307- 1317.

Questions?