Measuring Vegetation Health NDVI Analysis of East Sacramento 1.

Slides:



Advertisements
Similar presentations
Remote Sensing. Readings: and lecture notes Figures to Examine: to Examine the Image from IKONOS, and compare it with the others.
Advertisements

You are the owner of a acre farm, and you are growing many different crops in your farm…
Remote Sensing GIS/Remote Sensing Workshop June 6, 2013.
NDVI Anomaly, Kenya, January 2009 Vegetation Indices Enhancing green vegetation using mathematical equations and transformations.
Active Remote Sensing Systems March 2, 2005 Spectral Characteristics of Vegetation Temporal Characteristics of Agricultural Crops Vegetation Indices Biodiversity.
SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom.
ASTER image – one of the fastest changing places in the U.S. Where??
Application of Remote Sensing in Washington Wine Grapes E.M. Perry 1, Jenn Smithyman 2, Russ Smithyman 2, Kevin Corliss 2, Urs Schulthess 3 1 WSU Center.
NDVI Normalized difference vegetation index Band Ratios in Remote Sensing KEY REFERENCE: Kidwell, K.B., 1990, Global Vegetation Index User's Guide, U.S.
Unmanned Aerial Vehicle System for Remote Sensing Applications in Agriculture and Aquaculture Dr. Randy. R. Price, Goutam. J. Nistala, Dr. Steven G. Hall.
Use of remote sensing on turfgrass Soil 4213 course presentation Xi Xiong April 18, 2003.
Introduction to Digital Data and Imagery
Relationships Between NDVI and Plant Physical Measurements Beltwide Cotton Conference January 6-10, 2003 Tim Sharp.
DROUGHT MONITORING THROUGH THE USE OF MODIS SATELLITE Amy Anderson, Curt Johnson, Dave Prevedel, & Russ Reading.
Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.
Watershed Watch 2013 :: Elizabeth City State University Determination of an Empirical Model Relating Canopy Cover to NDVI Values in the Pasquotank Watershed,
Copyright © 2003 Leica Geosystems GIS & Mapping, LLC Turning Imagery into Information Suzie Noble, Product Specialist Leica Geosystems Denver, CO.
Eric Rafn and Bill Kramber Idaho Department of Water Resources
U.S. Department of the Interior U.S. Geological Survey Assessment of Conifer Health in Grand County, Colorado using Remotely Sensed Imagery Chris Cole.
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.
Spectral Characteristics
ASPRS Annual Conference 2005, Baltimore, March Utilizing Multi-Resolution Image data vs. Pansharpened Image data for Change Detection V. Vijayaraj,
Karnieli: Introduction to Remote Sensing
Mapping Tile Lines with Remote Sensing and GIS Jim Giglierano Formerly with Iowa DNR - Geological and Water Survey Iowa State University
What is an image? What is an image and which image bands are “best” for visual interpretation?
TRIVIA (?) Why are plants green in spring/summer and brown in autumn?
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)
Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.
The Use of Red and Green Reflectance in the Calculation of NDVI for Wheat, Bermudagrass, and Corn Robert W. Mullen SOIL 4213 Robert W. Mullen SOIL 4213.
Land Cover Change Monitoring change over time Ned Horning Director of Applied Biodiversity Informatics
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Land Cover Change Monitoring change over time Ned Horning Director of Applied Biodiversity Informatics
Chernobyl Nuclear Power Plant Explosion
4.3 Digital Image Processing
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
Development of a Small Remotely Piloted Vehicle for the Collection of Normalized Difference Vegetative Index Readings Dr. Randy R. Price, Goutam Nistala.
Remote Sensing of Macrocystis with SPOT Imagery
ERDAS 1: INTRODUCTION TO ERDAS IMAGINE
Measuring Vegetation Characteristics
Observing Laramie Basin Grassland Phenology Using MODIS Josh Reynolds with PROPOSED RESEARCH PROJECT Acknowledgments Steven Prager, Dept. of Geography.
Violet:  m Blue:  m Green:  m Yellow:  m Orange:  m Red:
Data Models, Pixels, and Satellite Bands. Understand the differences between raster and vector data. What are digital numbers (DNs) and what do they.
Remote Sensing Theory & Background III GEOG370 Instructor: Yang Shao.
Interactions of EMR with the Earth’s Surface
NOTE, THIS PPT LARGELY SWIPED FROM
LANDSAT EVALUATION OF TRUMPETER SWAN (CYGNUS BUCCINATOR) HISTORICAL NESTING SITES IN YELLOWSTONE NATIONAL PARK Laura Cockrell and Dr. Robert B. Frederick.
BAND RATIOS Today, we begin to speak of the relationships between two+ bands.
RESULTS Comparing NDVI values for the three years shows a distinct visual difference between the general health of urban landscapes (See Figure 2). Of.
Data compression – For image archiving (satellite data) – For image transfer over internet.
Best Practices for Managing Processed Ortho Imagery Cody A. Benkelman.
Week Fourteen Remote sensing of vegetation Remote sensing of water
Mapping Variations in Crop Growth Using Satellite Data
Using vegetation indices (NDVI) to study vegetation
Evolution of OSU Optical Sensor Based Variable Rate Applicator
Colour air photo: 15th / University Way
Mapping wheat growth in dryland fields in SE Wyoming using Landsat images Matthew Thoman.
Digital Data Format and Storage
Radiometric Theory and Vegetative Indices
Remote Sensing of Vegetation
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
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.
7 elements of remote sensing process
Today, we begin to speak of the relationships between two+ bands.
Image Information Extraction
Spectral Signatures and Their Interpretation
Remote Sensing Landscape Changes Before and After King Fire 2014
Showing Drought Stress in Sacramento Parks
Calculating land use change in west linn from
Presentation transcript:

Measuring Vegetation Health NDVI Analysis of East Sacramento 1

Project Summary  The years of 2011 to 2014 were the driest years in recorded California history.  Reduction in annual rainfall and the Sierra snowpack has increased a need for water conservation and responsible practices.  Mandatory and voluntary cuts in water usage have been issued statewide.  Winter of 2014, with rain totals significantly below average, Governor Jerry Brown declared a State of Emergency. Locally, Sacramento is in a Stage 2 Water Shortage Contingency Plan – requiring a 20% reduction in water use. 2

Purpose  The focus of the project will be on the neighborhood of East Sacramento and the vegetation’s health in the area. Using 4 band NAIP aerial imagery and ArcMap’s image analysis tools, I will construct an NDVI comparison of the most recent available imagery from June 2014 with the year prior to the assumed beginning of the California drought, June By making 2010 a control year, I hope to be able to measure the percentage of healthy vegetation reduction in 4 years of conservation efforts and drought conditions. 3

Normalized Difference Vegetation Index  The Normalized Difference Vegetation Index (NDVI) is a standardized index allowing you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster dataset—the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared (NIR) band.  NDVI = (NIR – Red Band) / (NIR + Red Band) 4

NAIP - The National Agriculture Imagery Program The default spectral resolution is natural color (Red, Green and Blue, or RGB) but beginning in 2007, some states have been delivered with four bands of data: RGB and Near Infrared. (USDA.gov) 5

Four images for 2010 and 2014 downloaded and mosaicked together. Study area outlined. Clipped both mosaicked images within data frame properties to the shape of the study area. 6

Automatic NDVI Process Easy. Can produce NDVI image with either: A color map with values of Ratio gradient of -1 to 1. 7 Not selected because it produces a lighter image with less contrast which would skew results.

Manual NDVI Process 8

NDVI in black to white 2010 NDVI in black to white

To make it more visible the symbology was changed to green to blue and inverted.

Measuring the Difference Need to compare the two years to see a change in the amount of healthy vegetation. 11 Remote Sensing and Image Interpretation( pg. 520 ) Create two images that represent every pixel that has a value >= 0.25

Overlap Comparison

13 Set up a Raster clip to eliminate the background information hidden by the data frame clip. The result will leave only the cells in the study area.

Clip Result 14

Simple Math ,957 cells307,753 cells

16

17 References “City of Sacramento: Department of Utilities, Conservation FAQs”, “Measuring Vegetation (NDVI & EVI)”, Earth Observatory, NASA.gov easuring_vegetation_2.php “Remote Sensing and Image Interpretation 7 th Edition” Lillesand, Keifer and Chipman, Digital Image Analysis - Pg 520. “California’s Drought”, Public Policy Institute of California