Remote Sensing • Geographic Information Systems • Global Positioning Systems

Lei JiDepartment of GeographyUniversity of Nebraska-Lincoln

AVHRR-NDVI: Jan 1999

AVHRR-NDVI: July 1999

Assessing Seasonal VegetationResponse to Drought

Introduction

Drought and drought indices, e.g. Standardized PrecipitationIndex (SPI)

Use of AVHRR – NDVI in detecting vegetation vigor anddrought

Relationship between NDVI and SPI

Objectives

Determine seasonal NDVI response to moisture

Determine relationship between NDVI and SPI

Study AreaDataMethods

AVHRR–NDVI(1989–2000)StandardizedPrecipitation Index(SPI)

Time series analysis of monthly NDVI and SPI for grasslandand crop by climate division (CD)

Correlation Analysis of monthly NDVI and SPI

Regression with seasonal-effect adjustment

Northern and Central Great Pains

Results

GrasslandNebraska North-central CD

CroplandNebraska Northeastern CD

Correlation of monthly NDVI and SPI

NDVI Correlation Coefficient

Relationship between vegetation condition and moistureavailability is strong

Relationship varies with growth stage

Use of NDVI for drought monitoring requires considerationof the seasonal effect

ConclusionsConclusions

Regression Model Test

p = 0.0027

R2 = 0.123

p < 0.0001

R2 = 0.792

Cropland

northeastern CD

p = 0.0016

R2 = 0.135

p < 0.0001

R2 = 0.674

Grassland

North-central CD

Simple RegressionSeasonal Adjusted

RegressionLand Cover

Andrés Viña

CALMIT

University of Nebraska-Lincoln

December, 2002

Effects of Corn Tassel on Canopy Optical Measurements

Objective

To evaluate the effects of corn tassels (flowers)on canopy optical measurements, specifically:

• Spectral regions of maximal effect

• Vegetation indices

• Leaf Area Index estimation

Results – Reflectance

With Tassels

Without Tassels

Lup

Ednρρρρλλλλ = (L↑↑↑↑λλλλ/E↓↓↓↓λλλλ)

0

5

10

15

20

25

30

35

40

45

50

400 500 600 700 800 900

Wavelength (nm)

Ref

lect

ance

(%

)

0

5

10

15

20

25

30

Rel

ativ

e D

iffe

renc

e (%

)

With Tassel

Without Tassel

RelativeDifference

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

150 170 190 210 230 250 270 290

Day of the Year

ND

VI

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

VA

RI

VARI

NDVI

Results – Vegetation Indices/LAI

NDVI = (ρρρρRed-ρρρρNIR)/(ρρρρRed+ρρρρNIR)

VARI = (ρρρρGreen-ρρρρRed)/(ρρρρGreen+ρρρρRed-ρρρρBlue)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 1 2 3 4 5 6 7

LAI

VA

RI

After Tasseling

Tasseling

Conclusions

• The red region (around 676 nm) showed themaximum relative response to theappearance of tassels.

• NDVI showed little or no sensitivity to thetassel.

• VARI showed high sensitivity to the tassel.

• VARI is a good estimator of LAI, althoughits predictive capability is reduced whentassels appear.

•• Doug MillerDoug Miller

•• University of Nebraska - LincolnUniversity of Nebraska - Lincoln

Commercial Agricultural Applications of Remote SensingCommercial Agricultural Applications of Remote Sensing

Types of Data RequiredTypes of Data RequiredBusiness ObjectivesBusiness Objectives

IBM Joint Application Development SessionIBM Joint Application Development SessionKansas City, MissouriKansas City, Missouri

March 1994March 1994

•• Make MoneyMake Money

•• Effective SupportEffective Support

•• Speed up DecisionSpeed up DecisionMakingMaking

•• Provide Fast Accurate,Provide Fast Accurate,& User Friendly& User FriendlyInformationInformation

•• Environmental ModelingEnvironmental Modeling

•• Crop ConditionsCrop Conditions

•• Soil PropertiesSoil Properties

•• Crop Production (Yield)Crop Production (Yield)

•• WeatherWeather

•• Agronomic PracticesAgronomic Practices

•• Incidence of Disease and insectIncidence of Disease and insectdamagedamage

•• Reasonably PricedReasonably Priced

•• Timely DeliveryTimely Delivery

•• PrecisePrecise

•• Reliable AccuracyReliable Accuracy

•• Current - Updated Daily (Projected/Actual)Current - Updated Daily (Projected/Actual)

•• Fits Market StructureFits Market Structure

Product ParametersProduct Parameters

Defining Market Segments in AgricultureDefining Market Segments in Agriculture

•• Who Collects Data?Who Collects Data?•• How is it used?How is it used?•• Define information sourcesDefine information sources•• Design vendor products promoting addedDesign vendor products promoting added

value products for farmersvalue products for farmers

View GPSWeather HelpModelsTSA

Walima SystemsWalima Systems

ReportsFile Tools

EastingEasting NorthingNorthing ValueValue

711467.1 4560270.9 165.6

Buffer ZoneBuffer Zone 100 mm

AvailableAvailableAttributesAttributes

DEMDEM

SoilsSoils

InsectInsect

WeedWeed

VarietyVariety

PopulationPopulation

TillageTillageNutrientNutrientLAILAIBiomassBiomassHarvest YieldHarvest Yield

ALGORITHMS FOR REMOTE ESTIMATIONOF WATER QUALITY

Giorgio Dall’OlmoCenter for Advanced Land Management Information Technologies,

School of Natural Resource Sciences,

University of Nebraska-Lincoln

The percentage of light

reflected by a target

Water body

Ed

Lup

Spectral Reflectance

0

1

2

3

4

5

400 500 600 700 800 900

Wavelength, nm

Ref

lect

ance

, %

High[Chl-a]

Low[Chl-a]

Spectral Indices

Combinations of reflectance

values at different wavelengths

Results: Chl-a model validation

0

50

100

150

0 50 100 150

OBSERVED, mg/m3

PR

ED

ICT

ED

, mg

/m3

1:1

STE = 9 mg/m 3

Geospatial Technologies for Homeland Security

Featured in PE&RS September 2002, Volume 68, Number 9

Jeff Arnold

Emergency Services,Notification andEvacuation Mapping

Critical and SensitiveFacilities Inventory

Special NeedsPopulationDetermination

Risk Assessment and Preparation

Prioritization of medical response efforts

Response and Recovery

On-line maps with query capabilities

Information Distribution

Modeling & Analysis

Lake McConaughy

North Platte

Digital elevation model indicating areas flooded

Remote Sensing • Geographic Information Systems • Global Positioning Systems