gis rs portfolio

GIS and Remote Sensing Projects Portfolio

by

Kristen Hestir

Maps

Image Processing

Charts

Tables

Graphs

Geospatial analysis of invasive species

Posters

Geographic

Analysis

Compares

acreage of

organic

crops

to pesticide

usage in

California.

Mexico

Arizona

Texas

New Mexico

Utah Colorado

U.S. Bureau of the Census, Map of United States

New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers

Projection: UTM Zone 13N, Datum: WGS 84

Interstate

HighwayLas Cruces

MetroStudy

Area

Boundaries and Roads

0 250 500125

Kilometers

California Organic Crops (2003)

versus Pesticide Use (2007)

Source: University of California at Davis, Statistical Review of California's Organic Agriculture, 1998-2003 and Pesticide Action Network

Kristen Hestir

5/01/200

GIS and Cartography

Pounds of pesticide per crop acre

0.00 - 1.00

1.01 - 2.50

2.51 - 5.00

5.01 - 10.00

10.01 - 12.60

Pesticides types include:

insecticides

hebicides

microbiocides

fungicides

rodenticides

0 100 200 300 400 50050

Kilometers

®One dot represents 50

acres of organic crops

Crops include:

field crops

fruit and nutsl

livestock and apiary

vegetables

nursery and floriculture

Representative densities: number

of acres per 100 square kilometers

50

1000

250

Projection: California Teale Albers

Cartography

Banana

exports from

South America

to the USA.

Cartograms

use distorted

map geometry

in order to

convey

thematic

information in

a visually

stimulating

way.

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

Ecuador

Guatemala

Costa Rica

Colombia

Honduras

Mexico

Peru

Nicaragua

Brazil

PanamaVenezuela

Bolivia

Jamaica

Cartogram of Banana Exports

to the United States, 2002

±

Source: Tariff and trade data from the U.S. Department of Commerce, the U.S. Treasury, and the U.S. International Trade Commission.

Krsiten Hestir, 4/24/2009, Cartography & GIS

Exports in 1000 Kilogram Units

450225

Banana Exports in 1000 Kilogram Units

1 - 200,000

200,001 - 400,000

400,001 - 600,000

600,001 - 800,000

800,001 - 1,022,347

Includes all bananas as food either fresh or dried

900

Spatial Analysis

Viewshed

AM/FM Radio

Coverage

of

Dona Ana

County

Viewshed

illustrates an area

of land that is

“visible” from a

fixed vantage

point.

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

Top Ranked 100 Countries by

Gross Domestic Product and Quality of Life, 2005

Kristen Hestir, 4/15/2009

Source: The Economist Intelligence Unit Quality of Life Index0 1,750 3,500 5,250 7,000

Kilometers

Top 5 countries: Ireland, Switzerland, Norway, Luxemborg, Sweden

Rank by GDP per capita

Not in top 100

1 - 5

6 - 10

11 - 50

51 - 100 ³1 - 5

6 - 10

11 - 50

51 - 100

Rank (best to least)

quality of lifeMaterial wellbeing

Life expectancy

Political stability and security

Low divorce rate

Community life

Climate and geography

Job security

Political freedom

Gender equality

9 Criteria for Quality of Life

RI

VA

TX

GA

WI

AL

KY

SC

VT

WA

MTMEND

SD

WYID

MN

OR

NH

IA

MA

NE

NY

PACT

NJINNV

UT

CA

OHIL

DE

WV

MDCO

KS

MO

AZOK

NCTN

TX

NM

MS

AR

LA

FL

MI

0 500 1,000 1,500250Kilometers

±

Percent of Children

0.16 to 0.20

0.21 to 0.40

0.41 to 0.60

0.61 to 0.80

0.81 to 0.95

Wells with Unsafe Arsenic Levels

per 1,000 Square Kilometers

No unsafe wells

0.01 to 0.04

0.05 to 0.25

0.26 to 0.50

1.01 to 2.13

0.51 to 1.00

Representative Densities:

Number of Superfund Sites

per 125 Square Kilometers

One dot represents 5 superfund sites

Dot placement is randomized at the state level

5 Superfund sites

30 Superfund sites

60 Superfund sites

Autism Prevalence (2006), Superfund Sites (2007) and Arsenic Groundwater Contamination (2001)

Study

Area

Maps

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

Yuma Valley,

Arizona

Mesilla Valley,

New Mexico

Texas

Utah

Arizona

California

Colorado

Nevada

New Mexico

Kansas

NebraskaWyoming

Oklahoma

MEXICO

0 10 20Kilometers ¯

Rio Grande River

Projection: Lambert Conformal Conic

Projection: UTM, WGS 84, Zone 13S

Projection: UTM, WGS 84, Zone 11S

Colo

rado

River

Yuma Valley Study Area

Yuma 1990 Metro

Yuma 2007 Metro

Las Cruces Metro

Mesilla Valley Study Area

Image

Derivative

Land Surface

Temperature

Maps

Yuma

Valley, AZ

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

0 5 10 15Kilometers ¯

Temperature

(Degrees Kelvin)

High: 329

Low : 279

Leaf-On

(Min: 300°K; Max: 329°K)

Leaf-Off

(Min: 279°K; Max: 311°K)

Image

Derivative

Normalized

Difference

Impervious

Surface

Yuma

Valley, AZ

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers


Leaf-On

(Min: 0.11; Max: 0.97)

Leaf-Off

(Min: 0.11; Max: 0.98)

NDISI

High : 0.98

Low : 0.11

Image

Derivative

Tasseled Cap

Transformation

Yuma Valley,

AZ

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers


Leaf-On

Leaf-Off

TCT

RGB

Red: Band 1

Green: Band 2

Blue: Band 3

Band 1 Min: 46 Max: 16811

Band 2 Min: -729 Max: 5877

Band 3 Min: -7101Max: 3270

Band 1 Min: 469 Max: 14905

Band 2 Min: -913 Max: 4457

Band 3 Min: -6160 Max: 3622

Digitized

Land

Cover

Change

Maps

Landcover Assessment from Landsat TM5 Image,Mesilla Valley 2009

Land Cover Classes

Residential

Industrial and Commercial

Transportation

Mixed Urban or Built-Up Land

Cropland and Pasture

Orchards, Etc.

Confined Feeding Operations

Mixed Rangeland

Streams and Canals

Reservoirs

Forested Wetland

Sandy Areas other than Beaches

Strip Mines, Quarries, Gravel Pits

Transitional Areas

Mixed Barren Land

0 7.5 15 22.5 30Kilometers /

Land

Cover

Change

Maps

and Pie

Charts

Overall Accuracy = 76%

9%

9%

66%

13%

2% 1%

Rangeland

Wetland

Water

Urban

Barren Land

Agricultural Land

1985

Land

Cover

Change

Maps

and Pie

Charts

Overall Accuracy = 83.7%

5%

10%

64%

16%

3% 2%

Rangeland

Wetland

Water

Urban

Barren Land

Agricultural Land

2009

Process Flow Chart

Stage 1: Leaf-on

Leaf-on, Leaf-

off

Leaf-off

Normalized

Difference

Impervious Surface

Leaf-off

Normalized

Difference

Impervious Surface

Leaf-on, Leaf-off

Normalized

Difference

Impervious Surface

Leaf-on

Tasseled Cap

Leaf-on

Tasseled Cap

Leaf-off

Tasseled Cap

Leaf-on, Leaf-

off

Principal

Component Analysis

Leaf-on

Principal

Component Analysis

Leaf-off

Principal

Component Analysis

Leaf-on, Leaf-off

Land Surface

Temperature Leaf-on

Land Surface

Temperature Leaf-off

Land Surface

Temperature Leaf-on,

Leaf-off

Stage 2:

Stage 3:

Select top performers and apply:

5 textures: entropy, angular second moment, homogeneity, correlation, contrast

3 x 3, 5 x 5 and 7 x 7 windows,

Classify: Maximum Likelihood

Evaluate: Confusion Matrices and McNemar tests.

Select top performers and apply:

Combined feature stacks: textures, derivatives etc.

Classify with: Maximum Likelihood, Support Vector Machine, Artificial Neural Network

Evaluate: Confusion Matrices

Classify: Maximum Likelihood Evaluate: Confusion Matrices and McNemar tests.

Matrices - Error Assessment and Statistical Test of

Significance

Confusion

Matrix:

McNemarMatrix:

Accuracy

MeasuresOverall accuracy, Kappa coefficient

Ground Reference Data (Pixels)

Map Data Agriculture Barren Rangeland Urban Water Wetland Total

Agriculture 45 0 0 1 1 0 47

Barren 5 28 14 7 1 1 56

Rangeland 61 5 308 7 1 4 386

Urban 9 21 33 155 1 5 224

Water 27 0 5 21 51 0 104

Wetland 64 3 55 11 7 43 183

Total 211 57 415 202 62 53 1000

Map 1

wrong correct

Map 2 wrong sum both wrong M

₂₁

total wrong Map 2

correct M

₁₂

sum both right total right Map 2

total wrong Map 1 total right Map 1

Confusion Matrices Results

58

63

68

73

78

83

No

Derivative

LST NDISI PCA TCT

Ov

era

ll A

ccu

racy

(%

)

Feature Stacks

L-On

L-Off

12B

58

63

68

73

78

83

No

Derivatives

LST NDISI TCT PCA

Feature Stack

Yuma ValleyMesilla Valley

Comparative Analysis – Bar Chart

12B TCT

L-OnL-On LST

L-On TCT

L-On NDISI

L-On PCA

12B LST

12B PCA

L-Off NDISI

L-Off

L-Off LST

L-Off PCA

L-Off TCT

150

200

250

300

350

400

450

500

550

600

0 3 6 9 12 15

High ------- Overall Accuracy Rank ------ Low

McNemar Tests Results

Statistically

similar

12B TCT

12B LST

12B

12B PCA

6B-Off6B OFF LST

6B Off PCA

12B NDISI

6B Off NDISI

25

75

125

175

225

0 3 6 9 12 15

High ------- Overall Accuracy Rank ------Low

Mesilla Valley Yuma Valley

Comparative Analysis - Scattergram

McN

ema

r S

um

s

60

62

64

66

68

70

72

74

Over

all

Acc

ura

cy (

%)

Feature Stacks from Stage 1

Mesilla Valley

overall

accuracy

Yuma Valley

overall

accuracy


Comparative Analysis – Line Chart

Comparative Analysis – Line Chart


26

36

46

56

66

76

86

1 3 5 7 9 11 13 15

Ov

erall

Acc

ura

cy (

%)

High ------------ Overall Accuracy Rank ------------ Low

Mesilla Valley

Stage 1

Mesilla Valley

Stage 2

Mesilla Valley

Stage 3

Yuma Valley

Stage 1

Yuma Valley

Stage 2

Yuma Valley

Stage 3

An

Assessment

Using

Remote

Sensing and

GIS

Salt Cedar

Dynamics in

Northern

Doña Ana

County, N

M

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

Salt Cedar

Dynamics in

Study Areas

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

Site 1

Site 2

Site 3

Site 4

0 10 205Kilometers

New Mexico

¯

Las Cruces

M. Smith, T. Jones, V. Prileson, and K. Hestir, 2010/04/11

Projection: UTM Zone 13N, NAD 83

1936

Land

Cover

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

0 500 1,000250

Meters

¯Projection: UTM, Zone 13N, NAD83

Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)Data source: NAIP 2009 Natural Color Aerial Photography

Site 1 Site 2

Site 3Site 4

Land Cover Type

Other vegetationWater

Salt cedar medium

Salt cedar low

Salt cedar high Row cropsBuilt-up

Barren Pecans

1955

Land

Cover

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

0 500 1,000250

Meters



Site 1 Site 2

Site 3Site 4

Land Cover Type


Salt cedar medium

Salt cedar low


Barren Pecans

1983

Land

Cover

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

0 500 1,000250

Meters



Site 1 Site 2

Site 3Site 4

Land Cover Type


Salt cedar medium

Salt cedar low


Barren Pecans

2009

Land

Cover

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

0 500 1,000250

Meters



Site 1 Site 2

Site 3Site 4

Land Cover Type


Salt cedar medium

Salt cedar low


Barren Pecans

Land

Cover

Dynamics

1936-

1955

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

Authors: K. Hestir, T. Jones, V. Prileson, and M. Smith (04/05/2010)

0 500 1,000250

Meters

Projection: UTM, Zone 13N, NAD83

Site 1

Site 4

¯Salt Cedar Dynamics

Water persistent

Other land covers persistent

Other land cover changes

Salt cedar increase

Salt cedar decrease

Salt cedar persistent

Site 3

Site 2

Land

Cover

Dynamics

1955-

1983

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers


0 500 1,000250

Meters


Site 1

Site 4


Water persistent



Salt cedar increase

Salt cedar decrease


Site 3

Site 2

Land

Cover

Dynamics

1983-

2009

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers


0 500 1,000250

Meters


Site 1

Site 4


Water persistent



Salt cedar increase

Salt cedar decrease


Site 3

Site 2

IntroductionJaguars (Panthera onca), the largest felids in the Americas, once were common in the southwestern United States.

Jaguars have been sighted in Arizona and New Mexico but with decreasing frequency in the past 100 years (McCain and Childs 2008). Only four males sighted in last 20 years.

Why try to conserve the Arizona and New Mexico part of their range? Populations that reside on the periphery of ranges can be critical for the long-term survival of the species.

AcknowledgementsI would like to thank Dr. Carol Campbell for the interesting topic.

References(*) http://www.junglewalk.com/photos/jaguar-pictures-I6147.htm Brown, D. E. 1983. On the status of the jaguar in the southwest. The Southwestern Naturalist 28 (4):459-460.Conde, D. A., F. Colchero, H. Zarza, N. L. Christenssen, J. O. Sexton, C. Manterola, C. Chávez, A. Rivera, D. Azuara, and G. Ceballos. Sex matters: modeling male and female habitat differences for jaguar conservation. Biological Conservation 143:1980-1988.Federal Register, January 13 75 (8): 1741-1744.Foster, R. J., B. J. Harmsen, and C. P. Doncaster. 2010. Habitat use by sympatric jaguars and pumas across a gradient of human disturbance in Belize. Biotropica 42 (6):724-731.Grigione, M. M., K. Menke, C. López-González, R. List, A. Banda, J. Carrera, R. Carrera, A. J. Gordano, J. Morrison, M. Sternberg, R. Thomas, and B. Van Pelt. 2009. Identifying potential conservation areas for felids in the USA and Mexico: integrating reliable knowledge

across an international border. Fauna and Flora International, Oryx 43 (1):78-86.Haag, T., A. S. Santos, D. A. Sana, R. G. Morato, L. Cullen. P. G. Crawshaw, C. De Angelo, M. S. Di Bitetti, F. M. Salzano, and E. Eizirik. 2010. The effect of habitat fragmentation on the genetic structure of a top predator: loss of diversity and high differentiation among

remnant populations of Atlantic Forest jaguars (Panthera onca). Molecular Ecology. 19:4906–4921.Hamilton, S. D. 2010. Investigative Report Macho B. U.S. Fish and Wildlife Service.Hatten, J. R.., A. Averill-Murray, and W. E. Van Pelt. 2005. A spatial model of potential jaguar habitat in Arizona. Journal of Wildlife Management 69 (3):1024-2005.McCain, E. B., and J. L. Childs. 2008. Evidence of resident jaguars (Panthera onca) in the southwestern United States and the implications for conservation. Journal of Mammalogy 89 (1):1-10.Navarro-Sermentc, C., C. A. López-González, J. P. Gallo-Reynoso. 2005. Occurrence of jaguar (Panthera onca) in Sinaloa, Mexico. The Southwestern Naturalist 50 (1):102-106.Rabinowitz, A., and K. A. Zeller, 2010. A range-wide model of landscape connectivity and conservation for the jaguar, Panther onca. Biological Conservation 143 (4):939-945.Rosas-Rosas, O. C. 2006. Ecological status and conservation of jaguars (Panthera onca) in northeastern Sonora, Mexico. Dissertation, New Mexico State University, Las Cruces, New Mexico, USA.1. Spangle, S. L. 2007. Biological opinion 22410-2007-F-0416: pedestrian fence projects at Sasabe, Nogales and Naco-Douglas, Arizona. United States Fish and Wildlife Service, Phoenix, Arizona..

ConclusionsPersistence in Arizona and New Mexico depends largely upon the critical habitat proposal by the U.S. Fish and Wildlife Service and the fate of the U.S.-Mexico border fence. Jaguars have a grimprognosis for survival in the study area.

Conservation Efforts and ThreatsConservation Efforts:In 1997 the jaguar was placed on the endangered species list by the United States Department of the Interior, Fish and Wildlife Service.

In 2009, the U. S. Fish and Wildlife Service declared designation of critical habitat is necessary and is developing proposed sites.

Disagreement within the jaguar conservation community. Use time and money to save peripheral populations, essential to survival of species

ORconcentrate time and money on the more densely populated ranges.

Threats:U.S.-Mexico border fence (from 2007), partitions northern range, reduces natural prey, limits water supplies, reduces mating potential, shifts migrant traffic and law enforcement activities into mountain habitats (further degrading habitats and increasing encounters with humans).

Illegal killing continues due to cattle depredations, pelts(Figure 9) and incidental takes from traps and snares.

Loss of habitat due to urban expansion, mineral mining, increased cattle grazing, water mining.

Climate change: models predict widespread ecosystem disruptions in Mexico.

Methods The methods in this study are based on a literature review:

• General description of the species.

• Range (historical and current)habitat requirements.

• Conservation efforts and threats to survival in Arizonaand New Mexico.

WILL THE JAGUAR (Panthera onca) PERSIST IN NEW MEXICO AND

ARIZONA?Kristen Hestir Department of Geography, New Mexico State University

Study SiteStudy site located in southern portions of Arizona and New Mexico (Figure 4), bordering Mexico. Based on historical ranges and recent remote camera sightings.

Research QuestionWill jaguars persist in the New Mexico and Arizona part of their range given the current status of the species and ongoing conservation efforts?

Figure 4. Study area and locations of jaguars reported killed in Arizona and New Mexico 1900-1980 (adapted from Brown 1983).

Figure 8. Estimated historical range of jaguars based on expert opinion (Grigione et al. 2009).

Figure 7. http://www.destination360.com/south-america/brazil/images/st/amazon-animals-jaguar.jpg.

Figure 3 JungleWalk.com (*).

Figure 5. JungleWalk.com (*).

Figure 6. JungleWalk.com (*).

Figure 9. http://www.flickr.com/photos/barcdog/2409633979/.

Figure 1. JungleWalk.com (*). Figure 2. JungleWalk.com (*).

Results

Species Description:: Northern jaguars are smaller than their South American relatives. Jaguars have fur with small dots, large irregular spots and rosette markings (Figures 1-3, 5-7). No two are alike, distinctive patterns are used to identify individuals.

Size ranges from 1.7 to 2.4 meters (nose to tail tip) in length, weighing between 45 to 113 kilograms.

Prey: cattle (57% of biomass consumed), white-tailed deer, wild pig, rabbits,jackrabbits, coatis (raccoon family), skunk, coyote, and reptiles (Rosas-Rosas 2006).

Range: Variety of habitats from rain forest to arid scrub. In the Sonoran desert they use scrub, mesquite, grassland, woodlands.

Range size varies widely, 33 km2 to 1300 km2 per individual(Figure 8).

Density 1 to 10 individuals per 100 km2. depending on resourceavailability and habitat fragmentation.

http://www.destination360.com/south-america/brazil/images/st/amazon





http://www.flickr.com/photos






• Spectral responses of bright desert

soils are often confused with the spectral

response of impervious (urban) surfaces

(Figure 3).

• Soils dominate the spectral response

the weaker signal of sparse vegetation

can be lost.

• Physiological qualities of dryland

vegetation decreases the strong red

edge and reduces absorption in the

visible bands compared to typical

non-dryland vegetation.

• Dryland vegetation is highly sensitive to resources, so the same species at different locations can have variable spectral responses

(Figure 4).

• Soils dominate spectral responses; however, they can have heterogeneous mineral content, causing variable spectral responses (Figure4).

RESULTS AND DISCUSSIONMETHODS AND ACCURACY ASSESSMENT

LAND COVER CLASSIFICATION IN AN ARID REGION: AN EVALUATION OF REMOTE SENSING APPROACHES

Kristen Hestir1 and Dr. Michaela Buenemann1

1Department of Geography, New Mexico State University

PROBLEM STATEMENT

• Human induced land cover change is occurring at unprecedented rates worldwide and is affecting an estimated 39 to 50% of Earth’s land

surface.

• Drylands are of particular concern, they cover 41% of Earth’s land surface, are home to 35% of world population and are experiencing

rapid population growth.

• Land cover change information can provide a basis for understanding what dryland areas are at risk, what this means for desert

ecosystems.

• Landsat Thematic Mapper satellite imagery can provide spatially explicit and continuous information on land cover change. By using

various classification algorithms and feature stacks, land cover types can be differentiated in the imagery based on their unique spectral

and spatial characteristics.

• There are, however, some characteristics of drylands which make land cover classification challenging.

OBJECTIVES

• Classify land cover of the Mesilla Valley (Figures 1 & 2) using two classification algorithms and various combinations of Landsat TM-

derived spectral and textural information

• Compare the land cover maps in terms of their overall accuracies.

• A leaf-on image of July 29, 2009 was georectified to a 2009 National Aerial Imagery Program Digital Ortho-Quarter Quad (DOQQ) and

radiometrically corrected using ENVI FLAASH atmospheric correction module. A leaf-off image of March 23, 2009 was georectified to

the leaf-on image and radiometrically corrected to the leaf-on image using empirical line calibration.

• 1000 GPS and DOQQ points representing 5 land covers (agriculture, barren, rangeland, water, built-up) and shadow were used to train

the two classifiers, Maximum Likelihood (MLC) and Support Vector Machine (SVM).

• Image stacks (Figure 5) included combinations of 6 bands leaf-on, 6 bands leaf-off, Principal Components Analysis (PCA), Tasseled Cap

(TC), Land Surface Temperature (LST), and Normalize Difference Impervious Surface Index (NDISI).

• Map accuracies were assessed using error (confusion) matrices based on 1000 randomly generated reference points.

CHALLENGES OF CLASSIFYING LAND COVER IN ARID REGIONS

ACKNOWLEDGMENTS

This work was supported by NSF Grant DEB-0618210, as a contribution to the Jornada Long-Term Ecological

Research (LTER) program, by the United States Department of Agriculture, Agricultural Research Service

Figure 7: Classification accuracies for Stage 1.

Stage 3: Multiple image derivatives improved

classification accuracy even further (1.2%, 1.5%

and 1.8% improvement over stage 2 for the 3

combinations. MLC and SVM classification

algorithms performed equally well. Differences

in overall accuracy ranged from ( 0.2 % to 1.6 %)

between the two classifiers (Figure 9).

Stage 1: Initial classifications show stacking leaf-on

and leaf-off imagery gives equal or improved accuracy

over single date stacks (Figure 7).

Stage 2: The texture filters entropy and

homogeneity, with 7 by 7

window, improved stage 1 initial

accuracy by 2.5 %, 8.9% , 8.3%, 5.0 %

and 2.1% for 6

bands, PCA4, TC, LST, and NDISI

stacks respectively (Figure 8).

Figure 9: Classification accuracies for Stage 3.

Figure 3. Comparison rangeland spectra (pink) and

impervious (urban) surfaces .

Figure 4. Comparison of rangeland spectra (white)

and barren land (yellow).

Figure 1: Location of the study area.

STUDY AREA

A land cover map (Figure 6) was produced for

each classification algorithm and various

combinations of Landsat TM-derived spectral

and textural information.

Figure 6: Example classified map.

Figure 8: Classification accuracies for Stage 2 with top

two textures.

Figure 5: Flowchart of Image Processing.

Mexico

Arizona

Texas

New Mexico

Utah Colorado


New Mexico

§̈¦I-10

§̈¦I-25

Texas

Ü 0 5 10 15 202.5

Kilometers


Interstate

HighwayLas Cruces

MetroStudy

Area


0 250 500125

Kilometers

Land Covers

Rangeland

Barren

Water

Built-Up

Agriculture

Figure 2: Imagery from: USGS Global Visualization

Viewer.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1 2 3 4 5 6

Re

fle

ctan

ce x

10

0

Bands

Impervious Surface

Rangeland

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1 2 3 4 5 6

Re

fle

ctan

ce x

10

0

Bands

Barren Land

Rangeland

Methods

Stage 1: Leaf-on

Leaf-on, Leaf-off

Leaf-off

Normalized Difference

Impervious Surface

Leaf-off


Impervious Surface

Leaf-on, Leaf-off


Impervious Surface

Leaf-on

Tasseled Cap

Leaf-on

Tasseled Cap

Leaf-off

Tasseled Cap

Leaf-on, Leaf-off

Principal Component

Analysis Leaf-on

Principal Component

Analysis Leaf-off

Principal Component

Analysis Leaf-on,

Leaf-off

Land Surface

Temperature Leaf-on

Land Surface

Temperature Leaf-off

Land Surface

Temperature Leaf-on,

Leaf-off

Stage 2:

Stage 3:

Select top 5 and apply textures: 3 x 3, 5 x 5 and 7 x 7 windows5 textures

Select top 3 and apply combined feature stacks: textures, derivatives etc.

Add classification algorithm:Maximum LikelihoodSupport Vector Machine

76.00%

78.00%

80.00%

82.00%

84.00%

86.00%

88.00%

90.00%

92.00%

94.00%

6 Bands PCA 4 TC LST NDISI

Ove

rall A

ccu

racy

Initial Accuracies

Entropy

Homogeneity

50.00%

55.00%

60.00%

65.00%

70.00%

75.00%

80.00%

85.00%

90.00%

95.00%

6 bands PCA 4 TC LST NDISI

Ove

rall A

ccu

racy

Leaf-on

Leaf-off

Leaf-on Leaf-off

91.5

92

92.5

93

93.5

94

Leaf-on Leaf-off + homo + pca 4

Leaf-on Leaf-off + homo + TC

PCA 4 + homo + TC homo

Ove

rall

Acc

ura

cy

Image Stacks and Multiple Derivatives

mlc

svm

PROCESS FLOW

gis rs portfolio

Career