Land Cover Mapping for the Southwest Regional GAP Analysis Project Tenth Biennial Forest Service...

Land Cover Mapping for the Southwest Regional GAP Analysis Project

Tenth Biennial Forest Service Remote Sensing Applications Conference, RS-2004, Salt Lake City, Utah

John Lowry and R. Douglas Ramsey

Remote Sensing/GIS Laboratory

Utah State University

Logan, Utah

Presentation Overview

• Project Background & Objectives

• Mapping Methodology

• Training Data Collection Approach

• Current Status & Preliminary Results

• State-based vegetation classification systems (cover type legends)

• State-based mapping methods

• State-based mapping area

A R I Z O N A

1999

52 Classes

N E W M E X I C O

1996

42 Classes

U T A H

1995

36 Classes

C O L O R A D O

2000

52 Classes

N E V A D A

1997

65 Classes

I. Project Background & Objectives

• 40 Mapping zones

• Spectrally consistent

• Eco-regionally distinct

• Labor divided among 5 state teams

UTNV

CO

AZ NM

NVC Formation

NVC Alliance

NVC Association

Gap Analysis ProgramMRLC 2000

Proposal

~1,800 units

National Park Mapping

~ NVC Class/Subclass

~10units

NatureServe Ecological Systems

~5,000 units

~700 units

(Natural/Semi-natural types)

~300 units

(Slide Courtesy Pat Comer, Nature Serve)

Thematic Target LegendDeveloped with NatureServe

Groups of plant communities and sparsely vegetated habitats unified by similar ecological processes, substrates, and/or environmental gradients...and spectral characteristics.

Ecological Systems

Elevation Landform

Predictor Datasets: DEM derived

July-Aug Sept-Oct

ETM Bands 5, 4, 3 ETM Bands 5, 4, 3

Predictor Datasets: Imagery Derived

• Data-mining software for decision-making and exploratory data analysis

• Identifies complex relationships between multiple independent variables to predict a single categorical class

• Predictor variables may be categorical or continuous

• Recursively “splits” the predictor data to create prediction rules or a decision tree.

• Software packages available: See5, SPLUS, CART

II. Mapping Methods: Classification Trees

Mining the Predictor Layers

Fall Brightness

Summer NDVI

Elevation

Landform

Etc….

Output table

SAMPLE SITESImagery: Landsat 7 ETM (1999-2002) for spring, summer & fall

NDVI, SAVI, Brightness,Greeness, Wetness, Landsat 7 Bands

DEM: Elevation, Aspect, Slope, Landform

Vector: Geology, Soils

Meteorological : DAYMET

0.2 0.3 0.4 0.5

FALL 1999 NDVI

1500

2000

2500

3000

ELE

V

grass

wyoming

mountain

juniper

mountain

g

g

g g

gg

g

g

gg

g

g

gg

g ggg

ggg

gg

g

g

g

g

g g

gggggg

g

j

jj

j

j

jj

j jjjj

jj j

j

j

j

j

j

j

jj

jj

j

j

j

jj

j

jj

j

j

j

j

j

j

j

m

m

m

m

m

m

mm

mm

mm

m

m

mm

mm

m

m

m

m

m

m

m

m

m

mm

m

mm

mmm

m m

m

m

mm

mm

mm

m

m

m

m

m

ww

ww

www

w

w ww

wwww

w ww

ww

w

w

ww w

www

www

w

w

ww

ww ww

ww

www

ww

ww

w ww

ww

ww w

w w

w

w

w

ww

w

www www

w

ww

ww

ww w

www

www

w

ww w

w

w

w www

w ww

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w wwwww

Simplified Example: Splits on 2 variables

|FA99ND<0.24685

ELEV<1515.5

ELEV<1931.38

ELEV<1935.83

grass

wyoming mountain

juniper mountain

Simplified Example: Tree output for 2 variables

Example: Rules Output

See5 [Release 1.17] Wed Apr 23 13:42:02 2003  Options: Rule-based classifiers Class specified by attribute `dep' Read 7097 cases (10 attributes) from t3.data Rules: Rule 1: (17, lift 45.4) band01 = 1 band03 > 115 band03 <= 122 band05 <= 81 band06 <= 1419 -> class 1 [0.947] Rule 2: (9, lift 43.6) band01 = 1 band02 <= 102 band03 > 115 band03 <= 118 band04 <= 117 band06 <= 1419 -> class 1 [0.909] Rule 3: (6, lift 42.0) band01 = 13 band03 <= 110 band05 <= 73 band07 = 4

| Generated with cubistinput by EarthSat| Training samples : 10260| Validation samples: 2565| Minimum samples : 0| Sample method : Random| Output format : See5 dep. |h:/mgzn_5/trainingdata/mrgpts1.img(:Layer_1) Xcoord: ignore.Ycoord: ignore.band01: 1,2,-30 |h:/mgzn_5/img_files/sum30cl.img(:Layer_1)band02: continuous. |h:/mgzn_5/img_files/subrt.img(:Layer_1)band03: continuous. |h:/mgzn_5/img_files/sundvi.img(:Layer_1)band04: continuous. |h:/mgzn_5/img_files/fandvi.img(:Layer_1)band05: continuous. |h:/mgzn_5/img_files/fabrt.img(:Layer_1)band06: continuous. |h:/mgzn_5/img_files/elev.img(:Layer_1)band07: 0,1,2,3,4,5,6,7,8,9,10. |h:/mgzn_5/img_files/landf.img(:Layer_1) dep: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20.

|h:/mgzn_5/trainingdata/mrgpts1

Boosting (iterative tree’s try to account for previous tree’s errors)—C5

Different over-fitting issues associated with each tree tend to be averaged out.

Multiple Tree Approaches MNF1<=2

8

MNF3<=19 MNF13>5

6

MNF1>19

MNF16<=54

decid.

shrub

MNF3<=38

MNF1<=28

MNF1<=25

MNF3<=24

MNF8<=28

decid. shrub

MNF11<51

MNF17>56

shrub cedar

decid.

P. pine

cedar

MNF2<=43

cedar

cedar P. pine

MNF1<=28

MNF3<=19 MNF13>5

6

MNF1>19

MNF16<=54

decid.

shrub

MNF3<=38

MNF1<=28

MNF1<=25

MNF3<=24

MNF8<=28

decid. shrub

MNF11<51

MNF17>56

shrub cedar

decid.

P. pine

cedar

MNF2<=43

cedar

cedar P. pine

MNF1<=28

MNF3<=19 MNF13>5

6

MNF1>19

MNF16<=54

decid.

shrub

MNF3<=38

MNF1<=28

MNF1<=25

MNF3<=24

MNF8<=28

decid. shrub

MNF11<51

MNF17>56

shrub cedar

decid.

P. pine

cedar

MNF2<=43

cedar

cedar P. pine

MNF1<=28

MNF3<=19 MNF13>5

6

MNF1>19

MNF16<=54

decid.

shrub

MNF3<=38

MNF1<=28

MNF1<=25

MNF3<=24

MNF8<=28

decid. shrub

MNF11<51

MNF17>56

shrub cedar

decid.

P. pine

cedar

MNF2<=43

cedar

cedar P. pine

MNF1<=28

MNF3<=19 MNF13>5

6

MNF1>19

MNF16<=54

decid.

shrub

MNF3<=38

MNF1<=28

MNF1<=25

MNF3<=24

MNF8<=28

decid. shrub

MNF11<51

MNF17>56

shrub cedar

decid.

P. pine

cedar

MNF2<=43

cedar

cedar P. pine

V

O

T

E

(Slide Courtesy Bruce Wylie, USGS EDC)

Imagine CART Module (USGS Eros Data Center)—See5-Imagine Integration

Legend

Desc

cool aspect cliffs, scarps, cirques, canyons

gently sloping ridges and hills

hot aspect cliffs, scarps, cirques, canyons

moderately dry slopes

moderately moist steep slopes

nearly level plateaus or terraces

toe slopes, bottoms, and swales

valley flats

very dry steep slopes

very moist steep slopes

III. Training Data Collection

Opportunistic, ground-based sampling, stratified by digital landform model

Percent ground cover by dominant species is recorded through ocular estimation. Only the top 4 species of each of 4 life forms are recorded

~3000 Air Photo Interpretation Sites from USFS Photos

! !!! !! !!!! !!!! ! !! !! !!! !! !!! !!! !!! ! !! ! !! ! !! !!! !! ! ! !!! !!! !! !! !!! ! !! !! !!! !!! ! !!! ! !! ! ! !!! !!!! !!! !!!! !! !!!!! !!! ! !! !! ! !!!! !!!! !! !! !!!! !!! ! !! !! ! !!! !!! !! !! !!!! !!! !! !!! !!!! !!!! !!! !!!!! !! !! !!!!! !!!! ! !! !! !! ! ! !!! !!! !! !!!!! !! !!! !!!!!!! ! ! !! !! !!! !! !!!! ! ! !! !! !!!! ! !! !!! !! !! !! !! !! !! !! !!! ! !! !! !! ! !! !!!! ! !! ! !! ! !!! ! ! !! ! ! !!!!! !!! !!!! !!!! !!! ! !!!! !! ! !!!!! !!! ! !!! !!! !!! !! ! !! !! !! ! !! ! !!! ! !! ! ! !! ! !!! !! !!! !! !!! ! !! ! ! !! !! !!! !!!! ! !!! !! !! ! !!! ! ! ! !! ! !! !!!! !!! !!! !!!! !! !! !! !!!!! ! ! !!! !! ! !! !!!! ! !! !!! !!! ! ! !! !!! !!! !!!! !!!! !! !! !! ! !!! ! !! ! !! ! !! !!! !! !! !! !! ! !!! !!! !!!! !!! !!!! !! ! !!! !! !!!!!! !! ! !!! ! ! !! ! !! !! !! !! ! ! !!! !! ! !! !! !! ! ! !!! !!! !! ! !!! !! ! !! !! ! !! !!!! ! !! !!! !! !! !! !! !! !! !! !! !!! ! ! !!! !! !! !! !!! !!!!! !!!!!!!

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Regional Total ~ 93,000

IV. Current Status & Preliminary Results

Edge-matching between three mapping areas

Considered correctly classified if majority of pixels agree with sample polygon

Accuracy Assessment with 20% withheld data:

Accuracy Assessment with 20% withheld data: Southern Wasatch Range

Park ValleyPark ValleyPark Valley

AGRICULTURE

GRASSLAND

GREASEWOOD

LOWLAND RIPARIAN

PICKLEWEED BARRENS

SAGEBRUSH

SAGEBRUSH/PERENNIAL GRASS

SALT DESERT SCRUB

WATER

PERENNIAL GRASSLAND

ANNUAL GRASSLAND

PLAYA

BIG SAGEBRUSH SHRUBLAND

XERIC MIXED SAGEBRUSH

MIXED SALT DESERT SCRUB

SEMI-DESERT SHRUB STEPPE

SEMI-DESERT GRASSLAND

LOWER MONTANE RIPARIAN WOODLAND AND SHRUBLAND

GREASEWOOD FLAT COMPLEX

CULTIVATED CROPS/IRRIGATED AG

PASTURE/HAY/NON-IRRIGATED AG

ANNUAL FORBLAND

1995 GAP 30 M 2004 GAP 30 M1995 GAP Pub.1KM

Summary

• Approximately 100 Ecological Systems and 10 NLCD Land Use classes

• Generalized to 1 acre MMU

• Delivered via NBII data node

• Anticipated completion: 1 September, 2004

Acknowledgements