Precision Remote Sensing and Image Processing for Precision Agriculture (PA)

Similar documents
Summary. Introduction. Remote Sensing Basics. Selecting a Remote Sensing Product

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

Lecture 6: Multispectral Earth Resource Satellites. The University at Albany Fall 2018 Geography and Planning

REMOTE SENSING INTERPRETATION

Introduction of Satellite Remote Sensing

Remote Sensing for Rangeland Applications

An Introduction to Remote Sensing & GIS. Introduction

An Introduction to Geomatics. Prepared by: Dr. Maher A. El-Hallaq خاص بطلبة مساق مقدمة في علم. Associate Professor of Surveying IUG

Satellite Remote Sensing: Earth System Observations

Remote Sensing and GIS

Remote Sensing Platforms

Introduction to Remote Sensing

Blacksburg, VA July 24 th 30 th, 2010 Remote Sensing Page 1. A condensed overview. For our purposes

The studies began when the Tiros satellites (1960) provided man s first synoptic view of the Earth s weather systems.

Introduction to Remote Sensing Fundamentals of Satellite Remote Sensing. Mads Olander Rasmussen

IKONOS High Resolution Multispectral Scanner Sensor Characteristics

9/12/2011. Training Course Remote Sensing Basic Theory & Image Processing Methods September 2011

Remote Sensing Platforms

Introduction to Remote Sensing Part 1

GIS Data Collection. Remote Sensing

Interpreting land surface features. SWAC module 3

The studies began when the Tiros satellites (1960) provided man s first synoptic view of the Earth s weather systems.

Lecture 13: Remotely Sensed Geospatial Data

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

remote sensing? What are the remote sensing principles behind these Definition

Remote Sensing. Measuring an object from a distance. For GIS, that means using photographic or satellite images to gather spatial data

Introduction to Remote Sensing

Remote sensing in archaeology from optical to lidar. Krištof Oštir ModeLTER Scientific Research Centre of the Slovenian Academy of Sciences and Arts

Using Freely Available. Remote Sensing to Create a More Powerful GIS

Image transformations

Environmental and Natural Resources Issues in Minnesota. A Remote Sensing Overview: Principles and Fundamentals. Outline. Challenges.

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

FAQs by Jack F Tutorials about Remote Sensing Science and Geospatial Information Technologies

Image Band Transformations

CHARACTERISTICS OF REMOTELY SENSED IMAGERY. Spatial Resolution

Satellite Imagery and Remote Sensing. DeeDee Whitaker SW Guilford High EES & Chemistry

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

The New Rig Camera Process in TNTmips Pro 2018

Some Basic Concepts of Remote Sensing. Lecture 2 August 31, 2005

A (very) brief introduction to Remote Sensing: From satellites to maps!

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

REMOTE SENSING. Topic 10 Fundamentals of Digital Multispectral Remote Sensing MULTISPECTRAL SCANNERS MULTISPECTRAL SCANNERS

to Geospatial Technologies

REMOTE SENSING FOR FLOOD HAZARD STUDIES.

NORMALIZING ASTER DATA USING MODIS PRODUCTS FOR LAND COVER CLASSIFICATION

typical spectral signatures of photosynthetically active and non-photosynthetically active vegetation (Beeri et al., 2007)

RGB colours: Display onscreen = RGB

Introduction to Remote Sensing

Module 3 Introduction to GIS. Lecture 8 GIS data acquisition

Course overview; Remote sensing introduction; Basics of image processing & Color theory

Enhancement of Multispectral Images and Vegetation Indices

Geo/SAT 2 INTRODUCTION TO REMOTE SENSING

1. Theory of remote sensing and spectrum

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

Image interpretation I and II

Remote Sensing of Environment (RSE)

Center for Advanced Land Management Information Technologies (CALMIT), School of Natural Resources, University of Nebraska-Lincoln

CHARACTERISTICS OF REMOTELY SENSED IMAGERY. Radiometric Resolution

Introduction. Introduction. Introduction. Introduction. Introduction

CHAPTER 7: Multispectral Remote Sensing

Remote Sensing in Daily Life. What Is Remote Sensing?

746A27 Remote Sensing and GIS. Multi spectral, thermal and hyper spectral sensing and usage

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

Lab 1 Introduction to ENVI

Sensors and Data Interpretation II. Michael Horswell

Introduction to Remote Sensing. Electromagnetic Energy. Data From Wave Phenomena. Electromagnetic Radiation (EMR) Electromagnetic Energy

Govt. Engineering College Jhalawar Model Question Paper Subject- Remote Sensing & GIS

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

Using Color-Infrared Imagery for Impervious Surface Analysis. Chris Behee City of Bellingham Planning & Community Development

Sources of Geographic Information

Remote Sensing Exam 2 Study Guide

Atmospheric Correction (including ATCOR)

INTRODUCTION TO REMOTE SENSING AND ITS APPLICATIONS

FOR 353: Air Photo Interpretation and Photogrammetry. Lecture 2. Electromagnetic Energy/Camera and Film characteristics

First Exam: Thurs., Sept 28

University of Texas at San Antonio EES 5053 Term Project CORRELATION BETWEEN NDVI AND SURFACE TEMPERATURES USING LANDSAT ETM + IMAGERY NEWFEL MAZARI

How to Access Imagery and Carry Out Remote Sensing Analysis Using Landsat Data in a Browser

Remote Sensing 1 Principles of visible and radar remote sensing & sensors

Crop and Irrigation Water Management Using High-resolution Airborne Remote Sensing

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

Aerial photography and Remote Sensing. Bikini Atoll, 2013 (60 years after nuclear bomb testing)

MRLC 2001 IMAGE PREPROCESSING PROCEDURE

Aral Sea profile Selection of area 24 February April May 1998

Earth s Gravitational Pull

GEO 428: DEMs from GPS, Imagery, & Lidar Tuesday, September 11

Atmospheric interactions; Aerial Photography; Imaging systems; Intro to Spectroscopy Week #3: September 12, 2018

IMPROVEMENT IN THE DETECTION OF LAND COVER CLASSES USING THE WORLDVIEW-2 IMAGERY

Satellite data processing and analysis: Examples and practical considerations

Monitoring agricultural plantations with remote sensing imagery

Remote Sensing (Test) Topic: Climate Change Processes*

INTRODUCTORY REMOTE SENSING. Geob 373

29 th Annual Louisiana RS/GIS Workshop April 23, 2013 Cajundome Convention Center Lafayette, Louisiana

Files Used in This Tutorial. Background. Calibrating Images Tutorial

A broad survey of remote sensing applications for many environmental disciplines

Remote Sensing. Odyssey 7 Jun 2012 Benjamin Post

A map says to you, 'Read me carefully, follow me closely, doubt me not.' It says, 'I am the Earth in the palm of your hand. Without me, you are alone

Update on Landsat Program and Landsat Data Continuity Mission

Development of normalized vegetation, soil and water indices derived from satellite remote sensing data

HYPERSPECTRAL IMAGERY FOR SAFEGUARDS APPLICATIONS. International Atomic Energy Agency, Vienna, Austria

Overview. Introduction. Elements of Image Interpretation. LA502 Special Studies Remote Sensing

Transcription:

Precision Remote Sensing and Image Processing for Precision Agriculture (PA) Dr. Jack F. Paris Presented to Colorado State University, Fort Collins, CO October 20, 2005

Speaker s Current Activities: Consultant EarthMap Solutions, Inc., Longmont, CO: www.earthmapsolutions.com MicroImages SML Developer: www.microimages.com jparis37@msn.com 303-775-1195 (cell) Speaker s Experience & Education: DigitalGlobe, Inc.: New Product Development Scientist (2002-October 2004): www.digitalglobe.com California State University (1989-2002) Monterey Bay (1996-2002): Retired Fresno (1989-1996) NASA (1980-1989) JPL (1983-1989) Lyndon B. Johnson Space Center (1980-1983) University of Houston at Clear Lake (1975-1980) Lockheed (1971-1975): Subcontractor to NASA in Houston Ph.D. Texas A&M University 1971

AGENDA LECTURE Precision Agriculture Need for Information When Making Ag Management Decisions Precision Remote Sensing (RS) Multispectral RS Precision Vegetation Index Maps AgroWatch Products Temporal Changes Using Precision Vegetation Index EarthMap Solutions, Inc. LAB Installing TNTlite About the MS Images & AgroWatch Products About Files & TNT Objects & Subobjects Displaying a MS Image Contrast Enhancement True & False Color Making a Zone Map Displaying an AgroWatch Product Making Cluster Maps

Digital Multispectral Image Reference Digital Orthophoto Quad Biomass Cover Map FROM FROM FROM FROM IMAGE IMAGE IMAGE IMAGE TO INFORMATION INFORMATION TO DECISIONS TO INFORMATION TO BETTER BETTER TO INFORMATION TO BETTER Ag Fields Near Salinas, CA Digital Multispectral Image Vegetation Condition Class Map Biomass Cover Map

Precision Remote Sensing

Remote Sensing Applications to Ag: 80 Years of History and Counting The Camera and Film First Aerial Photos For Ag 1930s Soil Mapping Human Eyes Pigeons Aircraft Rockets Satellites Digital Cameras

Extending Human Vision Visible Light Before Technology There Was Only Human Vision: Light & Color Mid-1800s: Photography UVBlue (as B&W) 1930s: Pan Airphotos of Ag Land (Soil Maps) 1940s: True Color Film BL GL RL After 2005: Super Multispectral CB BL GL YL RL RE Invisible Light 1940s: Color IR (CIR) Film GL RL NIR 1950s: Multispectral Scanners (MS) 1960s: NASA Remote Sensing (RS) 1970-90s+: Satellite MS Landsat: 3 to 7 Bands (Plus Pan for L # 7) After 2000: Color RADAR Hi-Res MS RS Hyperspectral RS

Natural (Scanners) Artificial (RADAR & LIDAR) Many Kinds of Remote Sensors 24 New Imagers Coming in the Next Decade

P h o t o UV B G R Pan & IR Color Color IR NIR PHOTOGRAPHIC FILM & CCDs

P h o t o UV B G R NIR Mid IR Pan & IR Color Color IR Thermal IR > Microwave / Radar > Scanners (Multispectral & Hyperspectral)

Abbreviations CB: Coastal Blue Light BL: Blue Light (a.k.a., Cyan Light ) GL: Green Light YL: Yellow Light RL: Red Light RE: Red Edge NA: Near-Infrared Radiation Band A MIR: Middle-Infrared Radiation (a.k.a., SWIR) TIR: Thermal-Infrared Radiation

Spacecraft-Based Imagers Current or Archive Only (Not Current, But Can Get Data) Ranked from High Spatial Resolution to Low Spatial Resolution Current 1. QuickBird Multispectral (MS, 2.4-m) and Panchromatic (PAN, 0.6-m) 2. IKONOS MS (4-m) and PAN (1-m) 3. OrbView 3 MS (4-m) and PAN (1-m) 4. SPOT 5 MS (10-m) and PAN (5-m or 2.5-m possible from 2 images) 5. SPOT 4 MS (20-m) and PAN (10-m) 6. SPOT 2 MS (20-m) and PAN (10-m) 7. Indian Remote Sensing System (IRS) MS (23.5-m) and PAN (5-m) 8. Landsat 7 Enhanced Thematic Mapper Plus (ETM+, 30-m) and PAN (15-m): Scan Line Correction (SLC) System Broke in May 2003 9. Landsat 5 Thematic Mapper (TM, 30-m) 10. Terra ASTER MS (30-m) 11. DMC MS (31.5-m) 12. Terra & Aqua MODIS RL NIR (250-m), BL, GL, 3 Mid-IR (500-m) 13. SPOT VEGETATION & NOAA AVHRR MS (1000-m) MANY MORE ARRIVING EVERY MONTH

Swath Widths EO-1 s ALI and Hyperion can be pointed sideways a distance of one Landsat Width

Elements of Image Interpretation High-Resolution Panchromatic Images Shape Size (Relative and Absolute) Pattern (Regular Variations) Texture (Irregular Variations) Shadows (Sun Angle, 3-D, Profiles) Tone (Black & Whiteness or Grayness) Site & Association (Context) Temporal Pattern Low-Res MS Images Shape Size Not Used Not Used Not Used Color / MS / Radar Context Temporal Pattern

QuickBird Multispectral (MS) Images: Ft. Collins, CO Natural Color: 4/23/2002 Color Infrared (CIR): 4/23/2002

QuickBird MS Images: Ft. Collins, CO Natural Color: 9/14/2002 CIR: 9/14/2002

Visual Interpretation of CIR Image is Interesting But is not as Precise as Information Extraction Via Image Processing Software. Dry Beans R&D Corn Corn Corn?? Mature Wheat Corn Wheat Yuma, CO DigitalGlobe, Inc. QuickBird MS 8-ft Resolution CIR Image July 2, 2003 Bare 1 Mile

Multispectral Images for Agricultural Mapping & Monitoring with Special Attention to: Red Light (RL) and Near Infrared Band A (NA) Combinations

Reflectance of Objects Varies with Wavelength / Spectral Region Reflectance varies from one spectral band to the next. This leads to variations in image radiance (brightness) Red-Light, RL, Image Leaves are Dark; Soil is Bright. Near Infrared Band A (NA) Image Pictures that involve NIR show what is invisible to your eyes. Leaves are Bright; Soil is Dark. NOTE: NIR involves reflected sunlight. Thermal Infrared (not shown here) involves emitted heat radiation. Don t confuse these two IR types!

2-Space Plot Spectral Mixing Causes Curving Triangle Zone Called the TASSELED CAP QuickBird MS, Yuma, CO, July 2, 2003 NA Brightness End Members Dense Veg. Bright Soil Dark Soil Shadows RL Brightness

Image DNs Converted to Standardized Reflectance Factor Index (SRFI) For Details about SRFI: See: Scripts by Jack www.microimages.com/freestuf/ ScriptsByJack.htm FAQs by Jack www.microimages.com/freestuf/ FAQsByJack.htm at the MicroImages, Inc., Web Site: www.microimages.com SRFI-NA 6000 5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 Corrected for Path Reflectance, Solar Irradiance, And Other Atmospheric Effects Tasseled Cap Triangle Senesced Vegetation Blob SRFI Values Relate Directly to Surface Reflectance Factors RFsfc(%) = SRFI / 100 Data- Cloud Density Color Palette: Max Min 0 500 1000 1500 2000 2500 3000 3500 SRFI-RL

Precision Vegetation-Index Maps

Precision Vegetation-Index Maps GRUVI: http://www.microimages.com/documentation/cplates/71gruvi.pdf GRand Unified Vegetation Index (GRUVI) is able to mimic any classic Vegetation Index and, more importantly, can produce the optimal VI that minimizes soil background noise & that has a good response to vegetation biomass distributions.

Classic NDVI Transformed NDVI Yuma, CO, July 2, 2003, Source: QuickBird MS Image Classic NDVI and Transformed NDVI do not account for effects of soil wetness (south slide of dark pivot); it over-estimates the biomass density in that part of the field. Same error occurs in mature fields that are wet from pivot irrigation.

Classic TSAVI Classic SAVI Yuma, CO, July 2, 2003, Source: QuickBird MS Image Classic TSAVI and Classic SAVI handle the soilwetness effect better than NDVI. However, the absolute values of SAVI do not track the effects of the specific soils present in this scene.

Optimized GRUVI WDVI Yuma, CO, July 2, 2003, Source: QuickBird MS Image Optimized GRUVI minimizes the effects of soil background wetness and tracks the effects of the specific soils in this scene. Weighted-Difference VI overcorrects for the effects of soil wetness.

AgroWatch Products

AgroWatch Products: 4 Ways to Map Variability in an Ag Field 1 2 Color Infrared Reference Image 3 Soil Brightness Map Green Vegetation Map 4 NOT SHOWN HERE: QuickBird Green Veg Change Map THIS IS SIMILAR TO SPOT-BASED Green Veg Change Map has a much higher spatial resolution Comes from 2 or more QuickBird scenes Vegetation Color (Hue) Map QuickBird & Landsat Only Value of This New AgroWatch Product Identifies vegetated pixels (colored pixels). Determines calibrated hue for these pixels. Provides brightness for non-veg pixels. Shows natural hue colors of vegetation.

Consider: QuickBird Imagery, Yuma, CO?? Yuma DigitalGlobe, Inc. QuickBird Dry Beans R&D Corn Corn Corn 8-ft Resolution Multispectral CIR Image Mature Wheat Wheat July 2, 2003 Corn Bare 1 Mile

AgroWatch Soil Zone Index, Colorized (SZC) Yuma DigitalGlobe, Inc. QuickBird Dry Beans R&D Corn Corn Wet Dry Corn 8-ft Resolution Soil Zone Index, Colorized SZC July 2, 2003 Mature Wheat Wheat SZC Color Scale Corn Veg Bare 1 Mile

AgroWatch Green Vegetation Index, Colorized (GVC) Yuma DigitalGlobe, Inc. QuickBird Dry Beans R&D Corn Corn Veg Mature Wheat Veg Corn Wheat 8-ft Resolution Green Vegetation Index, Colorized GVC July 2, 2003 GVC Color Scale Corn Bare 1 Mile

AgroWatch Green Vegetation Index, Colorized (GVC) Yuma DigitalGlobe, Inc. QuickBird Dry Beans R&D Corn Corn Mature Wheat Corn Wheat 8-ft Resolution Green Vegetation Index, Colorized GVC July 7, 2003 GVC Color Scale Corn Bare 1 Mile

Precise Change Mapping Can Be Done Based on GVC Values QuickBird, Yuma, CO, Corn Fields Under Pivot Irrigation - + 1 0 0 Shown at 1X Zoom. Green Vegetation Index, Colorized (GVC). July 7, 2003. Later Date. Shown at 1X Zoom. Green Vegetation Index, Colorized (GVC). July 2, 2003. Earlier Date.

AgroWatch Change Product: Called ScoutAide QuickBird, Yuma, CO, Corn Fields Under Pivot Irrigation Re-georeference Earlier date to Later date. Resample Earlier date to match Later date. Perform raster subtraction on a pixel by pixel basis (and add 100 to result) to get SAC value. SAC Color Scale = Shown at 1X Zoom. GVC Change: Called ScoutAide, Colorized (SAC). Change from July 2 nd to July 7 th, 2003 (plus 100 to make values > 0).

Irrigation does not Affect AgroWatch s GVI, Colorized (GVC) Values Wet Soil Dry Soil AgroWatch GVC products are not affected by variations in background soil brightness, e.g. resulting from irrigation. + Other Vegetation Indexes AgroWatch GVC Other indexes erroneously indicate that 20-25% more vegetation is present when background soils are dark (e.g., when they are wet).

GVC Allows Measuring Changes in Canopy Density After Row Closure Closed Canopy Open Canopy AgroWatch GVC products are uniquely sensitive to changes in canopy density after row closure. + AgroWatch GVC Other Vegetation Indexes Other indexes stop responding to changes in crop during growth / senescence when canopy closure occurs.

AgroWatch Green Veg Index (GVI), Sharpened: GVS Urban Veg Mapping: 2-ft Res Golf Course Softball Park

AgroWatch GVS Products: Combining 2-ft Details with 8-ft GVC Colors Visible Black & White Reference Image QuickBird Only Value of This New Product Compatible with low-end GIS (or non-gis). 8-Bit, Hi-Res image (smaller file size). Looks like historic panchromatic (no NIR). 2-ft Resolution. Green Vegetation Index, Sharpened: GVS a.k.a., Canopy Greenness Map QuickBird Only Value of This New Product Compatible with low-end GIS (or non-gis). 24-Bit, Hi-Res image (smaller file size). Merges calibrated GVC colors with VPG. 2-ft Resolution.

AgroWatch HR Sharpened Product Many other applications and opportunities Three Longmont Golf Courses Green Vegetation Index, Sharpened 2 ft resolution QuickBird Imagery Collected August 14, 2002 Longmont, CO Dense Vegetation GVI GVI Color Index 95-100 90-94 85-89 80-84 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5-9 0-4 Bare Soil

AgroWatch Green Vegetation Index for Different Imagers Mix and Match HR and MR products SPOT (MR) SPOT (MR) SPOT (MR) Asparagus Ferns Central California 40 Acre Blocks Dense Vegetation 06/06/02 06/26/02 07/21/02 AgroWatch products are calibrated with a technique that is imaging-system independent. QuickBird (HR) Users can mix and match SPOT and QuickBird imagery-based Information Products regardless of resolution. Users can quantify change and rate of change in a crop between dates Non Veg 06/22/02

Usefulness of Being Able to Track Changes in Vegetation Density from Date to Date During a Growing Season

Land-Cover Mapping Possibilities This RGB color combo of AgroWatch Green Feature (GF) rasters shows general kinds of land cover in the selected AOI. White Line outlines IL CRD 4. See next slide for fullresolution details. Dark blue areas are soybean fields. Light blue & greenish areas are corn fields. Gray areas are woodland & urban. Dark areas are open water.

Multidate Color Combo of Landsat Data R = GF_Jun05, G = GF_Jun21, B = GF_Aug24 All in 2003 Beans Corn

Damage by a Tornado is Evident in this Multidate Image that Uses Calibrated Vegetation Index Crop Insurance Implications These 3 dates in 2003 appear to be sufficient for land-cover classification. GENERAL LAND COVER TYPES: Urban Highway Woodland Spring Crop Open Water Soybeans (Blue) Corn (Greenish) Path of Damage (Hail or Tornado?): Long, Thin WNW to ESE Oriented Non- Vegetation Paths Appeared in the 06/21/03 Data and Then Became Dense Volunteer Vegetation in the 08/24/03 Data. The CIR Image was Checked for Possible Clouds; There were no Cirrus Clouds or Contrails.

EarthMap Solutions, Inc.

Irradiance / Reflectance / Radiance => Image DN Spectral irradiance of the sun BL GL RL NA Spectral radiance of the scene

REFLECTANCE FACTOR, RF (%) E SUN θ E SKY Gain, G L P L L SCENE E SKY RF (%) Image RV Raster Value Thus, RF = (RV - RV P ) m where m and RV P are the Empirical Line Method s calibration factors (each band) RV P is a RV where: E SFC = 0 (shadow) or RF = 0 (black object) RV = { (E SUN t S cos θ RF + E SKY ) t O + L P } π G

BARE SOIL LINE 60% 26% 0% Calibrate 0% 24% RL BRIGHTNESS NIR BRIGHTNESS

Calibrate RL REFLECTANCE (%) NIR REFLECTANCE (%)

Calibrate RL REFLECTANCE (%) NIR REFLECTANCE (%)

1860 Industrial Circle, Suite D, Longmont, CO 80501 Precision Agriculture AgroWatch Green Vegetation Soil Zone Map Scout Aide Canopy Density Maps Variable Rate Pix Yield Trax Grow Smarter. Manage Better. See Where you cannot Walk.

1860 Industrial Circle, Suite D, Longmont, CO 80501 Precision Agriculture AgroWatch Green Vegetation Soil Zone Map Scout Aide Canopy Density Maps Variable Rate Pix Yield Trax Grow Smarter. Manage Better. See Where you cannot Walk.

LAB FOCUS: How to Do Basic Tasks with a Free Software Package (TNTlite, from MicroImages, Inc.)

Input Multispectral Raster Set

Input AgroWatch Products

Output Vegetation Classification Map

Output Management Zones Map