Applied Geosolutions/USDA-ARS SWRC/MSUApplied Geosolutions/USDA-ARS SWRC/MSUApplied Geosolutions NASA Rangeland DSS ProjectApplied Geosolutions NASA Rangeland DSS Project

A Presentation of Preliminary Results for Discussion to Help Refine Our Approach

November, 2007

OverviewOverview

We are building a canopy cover dataset and decision support system (DSS) for rangeland management in Arizona.

The initial version will focus on providing interagency access to a database of grassland canopy cover estimates from 2000 to the present across Arizona:a) Allowing for a better understanding of vegetation

conditions over time and across the landscapeb) Identifying problem areas and providing timely

warning of changes in grasslandsc) Improving documentation of successful

management by land management agenciesd) Ideally, freeing up time for on-the-ground work

Product: Product: Fractional Vegetation Cover Fractional Vegetation Cover

Fractional Vegetation (Canopy) Cover Map for Southeastern Arizona

0% 65%

Privacy ConcernsPrivacy Concerns

Land cover imagery of Arizona is available at very high resolution and no cost through Google Earth or the Arizona Geodata Portal.

Nevertheless, to allay fears of “big-brother-in-the-sky”, a password system will be used to limit access to quantitative estimates of cover to the agency that manages a parcel of land, unless the agency gives permission, or as a picture at gross resolution.

StrengthsTrustworthyDocumentationProcess UnderstandingResolve DifferencesValid in court

WeaknessesExpensive

InfrequentIsolated

VariabilityMethodsObservers

InterpretationRepresentative?Trend?

The Rangeland Standard: Field DataThe Rangeland Standard: Field Data

Field Data WeaknessesExpensive

InfrequentIsolated

VariabilityMethodsObservers

InterpretationRepresentative?Trend?

RS Strengths Inexpensive?

FrequentExtensive

VariabilityNo bias, but willdepend on target

InterpretationEasier, though shallower

Remote Sensing’s Strengths Compensate Remote Sensing’s Strengths Compensate for Field Data’s Weaknessesfor Field Data’s Weaknesses

Our ChallengeOur Challenge

An approach that takes advantage of the strengths of both on-the-ground data collection and remote sensing would be best-of-both-worlds. Is remote sensing mature enough, now, to contribute to rangeland management in Arizona?

-Improved remote sensing algorithms-MODIS imagery available from mid-2000-IT tools to manage large datasets are available-Ancillary datasets (climate, GIS) are available

Clearly, remote sensing can distinguish gross differences. Our challenge is to show just what state-of-the-art remote sensing can do.

Your ChallengeYour Challenge

Canopy cover is only one characteristic of interest, and the accuracy or spatial scale of remotely sensed estimates may not be adequate for decision-making.

Your challenge is to assess the our data and tools to determine if their application would help your agency improve its land management. Are there applications where best-of-both-world benefits justify a balance of remotely sensed, and on-the-ground data collection methods?

Clearly, your agency has to determine the adequacy of our data for any particular purpose.

Very High CoverVery High Cover

Irrigated

Mountain

San Pedro Riparian Area

Very Low CoverVery Low Cover

Douglas Prison

Bisbee Mine

Douglas – A.P.

Landscape FeaturesLandscape Features

Terrace - landfill

Fencelines

Border by San Pedro

Cover Estimation – Part 1Cover Estimation – Part 1

Total Vegetation Fractional Cover is scaled fromGROUND MEASUREMENTS to LANDSAT (30 m) ….

From Marsett et al. 2006.

Cover Estimation - Part 2Cover Estimation - Part 2

…..And then from LANDSAT scale (30 m) to the MODIS scale (250-500m).

LANDSAT

•30 meter resolution•16 day repeat overpass•7 spectral bands

MODIS

•250-500 meter resolution•daily repeat overpass•7 spectral bands

Data ProductsData Products

The primary data provided is 8-day maps of canopy cover of green and senescent vegetation on grasslands, though we are optimistic about estimating cover for other vegetation types as well.

a)Updated near-real-time from NASA’s MODIS sensorb)Historic data back to 2000

Vegetation cover estimates are examined within the context of other data sets:

a)Weather (precipitation and temperature)b)Soil propertiesc)Slope and topography

Example: Santa Rita Pasture 12BExample: Santa Rita Pasture 12B

Histogram

Average 2006 Canopy Cover of LRU 41-2

Fre

qu

en

cy

0 10 20 30 40 50

02

00

04

00

06

00

08

00

0

Histogram and Density Plot

Average 2006 Canopy Cover of LRU 41-2

De

nsi

ty

0 10 20 30 40 50

0.0

00

.01

0.0

20

.03

0.0

40

.05

0.0

6

0 10 20 30 40 50 60

0.0

00

.02

0.0

40

.06

0.0

8

Percent Canopy Cover

De

nsi

ty

41.1 Oak Savannah

41.3 Semi-desert Grassland

41.2 Desert Shrub Mix

Average Canopy Cover in 2006 by LRU

The Audubon Research RanchSept 30, 2000

The Audubon Research RanchOct 1, 2006

20 30 40 50 60 70

0.00

0.20

2000

Percent CanopyCover

20 30 40 50 60 70

0.00

0.20

2001

Percent CanopyCover

20 30 40 50 60 70

0.00

0.20

2002

Percent CanopyCover

20 30 40 50 60 70

0.00

0.20

2003

Percent CanopyCover

20 30 40 50 60 70

0.00

0.20

2004

Percent CanopyCover

20 30 40 50 60 70

0.00

0.20

2005

Percent CanopyCover

20 30 40 50 60 70

0.00

0.20

2006

Telles Cover in Red vs Surrounding Area (Prelim 500m Est - Last week in Sept)

2000 2001 2002 2003 2004 2005 2006

02

04

06

08

01

00

Years

Ca

no

py

Co

ver

in %

o

*

o*

o

*o* o*

o*

o*

Telles Cover in Red vs Surrounding Area (Prelim 500m Est - Last week in Sept)

Data Delivery SystemData Delivery System

This information will be provided in 3 ways:1. A web-based, interactive environment, with no software to

download or GIS experience required2. Datalayers for use within agency information systems3. Specialized reports by allotment or land unit

Simple but comprehensive analysis and reporting tools in the web-based environment will be designed to allow end-user to:1. Compare multiple allotments2. Generate time series plots (8-day resolution)3. Generate maps (30 meter and 250 meter resolution)4. What other functions are needed?

Web Interface to DataWeb Interface to Data

Just like Google Earth, the resolution will change as the user zooms in to a particular location

1. 500 m MODIS based layer2. 250 m MODIS based layer3. 30 m Landsat based layer (if available)4. 1 m aerial photo layer (if available)

Users will be able to overlay additional GIS information to help navigate, such as grazing allotments, ownership, roads, etc.

Screenshot of Prototype Web InterfaceScreenshot of Prototype Web Interface

Interactive Report DefinitionInteractive Report Definition

The web interface will have the ability to generate reports interactively in several formats (pdf, Word, XML):

a. How well does the remotely sensed data compare to the monitoring data from the allotment?

b. What are the problem areas (map included in report)?

c. What are the trends over time (graph included in report)?

d. How do vegetation conditions compare to vegetation from areas with similar production potential?

e. How has recent precipitation affected growth?

Batch Report-WriterBatch Report-Writer

•The batch report-writer would read a report definition file, and then create the specified report for each area specified in a list of vector files. This series of reports could be revised annually to include new information.

•Similarly, an annual report summarizing the findings on all areas could also be generated.

Possible Additional ProductsPossible Additional Products

•Other data layers from MODIS such as NDVI or EVI greenness indices could be added fairly easily if they would be useful.

•We may be able to estimate height and biomass on grasslands, but that is beyond the scope of the current project.

StrengthsStrengths

• We think we can quantify cover of both green and senescent vegetation, which allows for processing a time series of cover estimates throughout the year.

•MODIS images are essentially available for anywhere in the world, so if data are useful for Arizona, other states could use a similar approach at low cost.

•Canopy cover is an important characteristic for rangelands and is influential in determining rangeland health.

LimitationsLimitations

• Need to confirm the accuracy of the algorithm, especially on shrublands. • Cannot estimate vegetation composition or production.• 500 m images are pixellated at the allotment scale.• The scale of these pixels (250 or 500m) cannot be directly compared to ecological sites, nor can they be compared directly with ground based measurements that are taken over much smaller areas. • This information can complement, but not replace, on-the-ground measurements.•At some point, your agency would have to pay for this information.

Use Scenario: Resource ManagementUse Scenario: Resource Management

Ground measurements for monitoring are a critical part of rangeland management. This will not change any time in the near future.

However, budget constraints and complicated logistics make it hard (impossible?) to visit all allotments multiple times a year.  So, to which allotments do we apply our limited ground resources? How do we make difficult prioritizing decisions?  

Use Scenario: Resource Management (cont.)Use Scenario: Resource Management (cont.)

Possible answers: 

1. Visit politically sensitive areas2. Visit known trouble spots 3. Visit areas undergoing abnormal change

 Which raises a new question: Without visiting and measuring all allotments frequently, how do we know which areas are experiencing abnormal change? Rangeland remote sensing data can help. The challenge is to develop an approach that takes advantage of the strengths of both field work and remote sensing.

Preparing for the FuturePreparing for the Future

2007 Approach to Public Land Management(X Range Cons Y million acres)

2012 Approach to Public Land Management(X-? Range Cons Y million acres)

97?% of effort on field work

3% of effort on remote sensing

90?% of effort on field work

10% of effort on remote sensing

Tentative PlanTentative Plan

2007-Mid 2008: Develop prototype Rangeland DSS tool for Arizona using existing NASA Small Business Innovative Research (SBIR) funding.

Mid 2008 to 2012: Using soft money funds yet to be found, continue to work with public land managers in Arizona and other western states to refine Rangeland DSS tools and integrate with other geospatial technologies into agency workflow to improve public land management.

2012: With mature tools and a number of years of evaluation, funds are either found to continue development/production of remotely sensed products and RDSS for public land management or not.

Discussion QuestionsDiscussion Questions

• How accurate do cover estimates have to be for your agency to use them?

• How do you see your agency using this information and set of tools (what decisions could be supported)?

• What would the system have to do before you would recommend adoption by your agency?

• What would the system have to do before your agency would pay for this information?