Land Cover Mapping Background: Training Data and Classification Methods

Southwest Regional GAP ProjectArizona, Colorado, Nevada, New Mexico, Utah

US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

John Lowry, Douglas Ramsey, Jessica Kirby, Lisa Langs and Wendy Rieth

Remote Sensing/GIS Laboratory

Utah State University

Logan, Utah

Presentation Overview

I. Project Background & Objectives

II. Mapping Methodology

III. Training Data Collection Approach

IV. Summary

• Earlier GAP efforts:– State-based vegetation

classification systems

– State-based mapping methods

– State-based mapping area

• Project Objectives:– Regionally consistent product

– Improvements in Land Cover representation

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

Mapping Zone Identification Began by Refining Bailey’s Ecoregions over a Color Shaded Relief Map

Mapping Zone Identification Began by Refining Bailey’s Ecoregions over a Color Shaded Relief Map

• 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

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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

2) 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

Imagine CART Module (USGS Eros Data Center)—C5-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

X

THE FIELD SITE POLYGON IS DRAWN ONLY AROUND THE GENERAL AREA OF THE PERSON RECORDING FIELD DATA. THE SITE SHOULD BE AT 90 METERS SQUARED (3X3 PIXEL

AREA) OR LARGER

Sub-sampling to account for positional error for point samples, and minimize size bias for polygon samples

IV. Summary

• Challenge to assure to regional consistency

• Challenge of developing tools & methods to be used by multiple analysts/teams

• Importance of training sample collection (quantity and quality)

• Primarily product oriented

• Many research questions…

Acknowledgements