Using Satellite Imagery to Using Satellite Imagery to detect changes in Vegetation detect changes in Vegetation

in Arid Environmentsin Arid Environments

Kelley Keese

GIS in Water Resources

0 210,000 420,000105,000 Meters

­87% Shrubland 2.5% Grassland

10% Bare 0.5% other

Land UseElevation

Study SiteStudy Site

Nevada

•Southern Nevada

•Mojave Desert

•130 km NW Las Vegas

•22 km x 28 km

0

10

20

30

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Water Year (October - September)

An

nu

al

Pre

cip

ita

tio

n (

cm

)

Perennials Annuals units g/m2 1995 1996 1997 1995 1996 1997

Average 28 8.0 8.5 47 0 31.2 Std Dev 10.2 4.4 2.3 12.8 0 14.0

Vegetation Production Data

(Ron Green, pers com)

AVHRRAVHRRAdvanced Very High Resolution Radiometer

•Provides 4 – 6 band multipectral data

•NOAA polar-orbiting satellite series

•Data from the University of Arizona

•Bi weekly composites of maximum NDVI; 1989 - 2003

•Tiff format : data presented 0 – 255 bit scale

•ftp://aria.arizona.edu/pub/usndvi_vc/GeoTIFFS/

Normalized Difference Vegetation Index from AVHRR

(C 2 – C 3) / (C 2 + C 3)

Channel 1 - chlorophyll causes considerable absorption

Channel 2 - spongy mesophyll leaf structure leads to considerable reflectance

(Tucker1979, Jackson et al.1983, Tucker et al. 1991).

MethodsMethods

•Crop the dataCrop the data

•Convert to NDVI scale of –1 to 1Convert to NDVI scale of –1 to 1

•Compute an average for months of Feb-JulyCompute an average for months of Feb-July

•Compute the “departure from average” Compute the “departure from average”

•Generate tables of NDVI values to compute the Generate tables of NDVI values to compute the aerial average for each month aerial average for each month

•Did this 90 times!!!! … and generated tables for 6 years Did this 90 times!!!! … and generated tables for 6 years

•More Raster calculations to compute the average for More Raster calculations to compute the average for each montheach month

•36 36 moremore Raster calculations to compute the DA Raster calculations to compute the DA (monthly – mean)(monthly – mean)

Average NDVI

0.1

0.15

0.2

0.25

0.3

February March April May June July

ND

VI

19941995199619971998

Departure Departure from from

AverageAverage

February 1995 Avg = 0.160

Departure Departure from from

AverageAverage

March 1995 Avg = 0.193

Departure Departure from from

AverageAverage

April 1995 Avg = 0.203

Departure Departure from from

AverageAverage

May 1995 Avg = 0.216

Departure Departure from from

AverageAverage

June 1995 Avg = 0.211

Departure Departure from from

AverageAverage

July 1995 Avg = 0.189

Departure Departure from from

AverageAverage

February 1998 Avg = 0.190

Departure Departure from from

AverageAverage

March 1998 Avg = 0.210

Departure Departure from from

AverageAverage

April 1998 Avg = 0.200

Departure Departure from from

AverageAverage

May 1998 Avg = 0.280

Departure Departure from from

AverageAverage

June 1998 Avg = 0.235

Departure Departure from from

AverageAverage

July 1998 Avg = 0.248

Average NDVI

0.1

0.15

0.2

0.25

0.3

February March April May June July

ND

VI

19941995199619971998

ConclusionsConclusions

Study shows that vegetation in desert systems is very responsive to water availability

1 km resolution satellite data does indeed detect changes in vegetation in arid environments

Implications:

ImplicationsImplications

Water ResourcesEvapotranspiration (ET) plays a critical role in controlling groundwater rechargeUnderstanding response of vegetation to climate forcing should help us better determine system response to climate change Need to understand climate/vegetation dynamics to estimate potential impact of land use change on water resources (e.g. brush control)

Contaminant Transport and RemediationPhytoremediation: use of plants to clean up toxic waste sitesSome engineered barrier designs for waste containment depend primarily on ET to minimize water movement into underlying waste