Land Cover Mapping Background: Training Data and Classification Methods
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Transcript of Land Cover Mapping Background: Training Data and Classification Methods
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
• 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
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gggggg
g
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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
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…