1 CLIP - Land Cover and Land Use Group Qi, Lusch, Reid, Maitima, Olson, Palm, Campbell Jiagun Ge,...

1

CLIP -Land Cover and Land Use Group

Qi, Lusch, Reid, Maitima, Olson, Palm, CampbellJiagun Ge, Nate Torbick

Jan. 5-9, 2005 CLIP Meeting 2/20

How do land cover changes impact regional climate and vice versa?


APPROACH

LULC BiophysicalAttributes

RegionalClimate

LCLUAlbedo

LAIfPAR

TsPrecip.

etc.LUTLEAF2

Simulation

RegionalClimateModel

(RAMS)

RSEOS Data Products

GLC2000Africover

IGBPMODIS

Simulation


Datasets:• GLC2000 (Global Land Cover) • OGE (Olson Global Ecosystem)• LEAF2(RAMS) • Africover • IGBP (International Geosphere Biosphere Program)

Data sources included:• AVHRR, MODIS, Landsat ETM+ & TM, SPOT VGT, NDVI

Issues:• creation dates • source data (resolutions)• objectives (use vs. cover) • formats• classification regimes • standards• scales (level of details)

Datasets and Issues


Science Questions1. How to determine the “best ” dataset for CLIP studies?

– Part 1. ‘Categorical Assessment’• Qualitative, visual inspection, examine class system• Quantitative statistical assessment, compare datasets directly,

crosswalk • Videography, spectral signatures, MODIS, ASTER, ETM+

– Part 2. ‘Biophysical Assessment’• LAI, Albedo, fPAR• Statistical measures

2. What is the “best ” LULC dataset for CLIP studies?• Objectives, various datasets, formats, models (RAMS, LTM…)

3. How changes in land cover are going to affect regional climate?


Comparing OGE (RAMS), GLC2000, Africover for selected region


Answer 3rd Question:Land Climate Impacts

GLC2000

Africover

OGE

IGBPIntegrate for CLIP hybrid

CrosswalkAssessmentComparison

LEAF2 characteristics

RAMSAnswer Questions # 1 and 2:

• How to determine the best LULC?• Which LULC is the best?

CLIP – LC Analysis

GLC Africover21 CLIP Land AggregateClosed evergreen lowland forest Closed trees Closed treesDegraded evergreen lowland forest Closed trees Closed treesSubmontane forest (900 -1500 m) Closed trees Closed treesMontane forest (>1500 m) Closed trees Closed treesSwamp forest Aquatic closed to open trees, shrubs and woody vegetation (brackish water) (mangroves)Aquatic trees and shrubsMangrove Aquatic closed to open trees, shrubs and woody vegetation (brackish water) (mangroves)Aquatic trees and shrubsMosaic Forest / CroplandsMosaic Forest / Savanna Tree and shrub savannah Tree and shrub savannahClosed deciduous forest Open to very open trees Open to very open treesDeciduous woodland Open to very open trees Open to very open treesDeciduous shrubland with sparse trees Open to very open trees Open to very open treesOpen deciduous shrubland Rainfed herbaceous crops (large to medium, continuous fields)Rainfed herbaceous cropClosed grassland Open to closed grassland Open to closed grasslandOpen grassland with sparse shrubs Open to closed grassland Open to closed grasslandOpen grassland Open to closed grassland Open to closed grasslandSparse grassland Open to closed grassland Open to closed grasslandSwamp bushland and grassland Aquatic closed to open grass incl. Sparse trees and shrubs (fresh water, permanently and temporarily flooded)Aquatic trees and shrubsCroplands (>50%) Rainfed herbaceous crops (large to medium, continuous fields)Rainfed herbaceous cropCroplands with open woody vegetation Closed to open shrubs and woody vegetation Closed to open shrubs and woody vegetationIrrigated croplands Irrigated and postflooding herbaceous crops Irrigated Agriculture

Rainfed shrub crops, tree crops, forest plantations Rainfed shrub cropTree crops Rainfed tree crop, forest plantationSandy desert and dunes Loose and shifting sands, bare soil, dunes, sand banks and beachesDesertStony desert Loose and shifting sands, bare soil, dunes, sand banks and beachesDesertBare rock Bare rock, bare rock with shallow sand and tidal areas, salt flats and very stony and stony soilBare RockSalt hardpans Bare rock, bare rock with shallow sand and tidal areas, salt flats and very stony and stony soilBare RockWaterbodies Water (natural and artificial) WaterCities Urban areas Urban

Africover21 Afrciover CLIP Aggregate Number of CLIP classesRainfed herbaceous crops (large to medium, continuous fields)Rainfed herbaceous crops 1Rainfed herbaceous crops (small, continuous fields) Rainfed herbaceous crops 1Rainfed herbaceous crops (isolated fields) open to very open trees 2Rainfed shrub crops, tree crops, forest plantations Rainfed shrub crops 3

Rainfed tree crops, forest plantations 4Irrigated and postflooding herbaceous crops Irrigated Agriculture 5Irrigated and postflooding shrub crops, irrigated tree crops, irrigated forest plantationsIrrigated Agriculture 5Aquatic agriculture Aquatic agriculture 6Closed trees Closed trees 7Open to very open trees open to very open trees 2Closed to open shrubs and woody vegetation Closed shrubs and woody vegetation 8

Open shrubs and woody vegetation 9Aquatic closed to open trees, shrubs and woody vegetation (brackish water) (mangroves)Aquatic trees and shrubs 10Aquatic closed to open trees, shrubs and woody vegetation (fresh water, permanently or temporarily flooded)Aquatic trees and shrubs 10Open to closed grassland Open to closed grassland 11Tree and shrub savannah Tree and shrub savannah 12Sparse vegetation sparse vegetation 13Aquatic closed to open grass incl. Sparse trees and shrubs (fresh water, permanently and temporarily flooded)Aquatic trees and shrubs 11Aquatic floating forbs (fresh water, permanently and temporarily flooded) and closed to open herbaceous (brackish water)Aquatic trees and shrubs 11Bare rock, bare rock with shallow sand and tidal areas, salt flats and very stony and stony soilBare rock 14Loose and shifting sands, bare soil, dunes, sand banks and beachesSand 15Urban areas Urban 16Water (natural and artificial) Water 17Snow Snow 18


RAMS Inner Domain

No Africover dataset for:• Mozambique

• Zambia

• Central African Republic

• Malawi

• Ethiopia

Reference with GLC2000


Videography -Digital video flown over selected regions for assessment

Create FOV coverages

• Heads Up On-screen digitizing

• Geo-process flightline FOVs

• Area calculations, Sampling scheme

• Capture datapoints

• Compare datasets

KENYAFlight Line Locations


Calculate Field Of View

Assumptions• Flying altitude• Tilt & Rotation• Zoom, Pan, Camera changes


Area comparisonKenya country level vs. Ambo FOV

Percentages roughly equivalent

Kenya Africover 21 Ambo FOV 21 Code Hectares % Hectares %

AG-1 1597612.748 2.74 85.57 0.38

AG-2 7525063.335 12.92 3650.395 16.19

AG-3 1151333.256 1.98 78.061 0.35

AG-4 159573.716 0.27 18.156 0.08

AG-5 10961.78 0.02

AG-6 50019.14 0.09

BA-1 1138142.023 1.95

BA-2 255124.309 0.44 180.931 0.8

FR-1 1305470.845 2.24 26.841 0.12

FR-2 2086199.825 3.58 2865.69 12.71

FR-3 18672989.33 32.07 6182.295 27.42

FR-4 49276.724 0.08

FR-5 415313.738 0.71 326.63 1.45

RL-1 2998868.994 5.15 403.138 1.79

RL-2 16907198.72 29.04 6549.339 29.05

RL-3 1449646.301 2.49 298.697 1.32

RL-4 1182086.273 2.03 1416.24 6.28

UR 44290.668 0.08 344.762 1.53

WB-1 1229813.267 2.11 120.999 0.54

WB-2 102.443 0


AFRICOVER (LC21)AMBO FOV (15 classes)

Confidence 0.95

Precision 0.05 0.1

Option 1 862 216

Option 2 711 178Option 3

Confidence 0.90

Precision 0.05 0.1

Option 1 737 185

Option 2 607 152

Option 3

Sampling scheme options1. NO information = 40-50 points/class

2. # Classes & Area information exists

3. Different levels of class importance

Distribution• Stratified random

Confidence level = probability the sample size will be sufficient to perform statistically valid accuracy assessments

Precision = acceptable risk when mistaken conclusions occur regarding the accuracy of a map based on the statistical analyses.


Example

Red = Ambo Flight line FOV

Green = 2 sample points

Africover LU/C database

Each point

• Lat / long coordinate

• Land use/cover type


Class code: AG-1 (Africover dataset)Class Name: Rainfed herbaceous crops (large to medium, continuous fields)

XY - Decimal Minutes

Convert systems

Check reference LU/C

Jan. 5-9, 2005 CLIP Meeting 17/20LU/C Example Flight Line Points

Bare rock Urban

Rainfed herbaceousAquatic grasslands


December 2000 LAI & fPAR IllustrationSource: MODIS Science Team, NASA, BU

http://earthobservatory.nasa.gov/Newsroom/EVI_LAI_FPAR/Images/AFRICA_LAI_FPAR_DC2000_1080.jpg


LAI Curves for selected region

2 South – 4 South

20

2 South - 4 South LAI selected classes 2003 - 8 day

0

1

2

3

4

5

6

time

inde

x

Rainfed herbaceous crop

Open to very open trees

Grassland

Closed trees

Open shrubs and woody veg

Tree and shrub savannah

Aqautic trees and shrubs


2 South - 4 South LAI selected classes 2003 - 8 day

0.75

0.95

1.15

1.35

1.55

1.75

1.95

2.15

2.35

2.55

2.75

time

inde

x

Rainfed herbaceous crop

Open to very open trees

Grassland

Open shrubs and woody veg

Tree and shrub savannah

Aqautic trees and shrubs


2 South - 4 Southselected class - LAI 2003

0.75

1

1.25

1.5

1.75

2

2.25

2.5

months

inde

x

Rainfed herbaceous cropOpen to very open treesGrasslandOpen shrubs and woody vegTree and shrub savannahAqautic trees and shrubs


Statistical Measures of Goodness• As in RAMS, study area is divided into strips according to

their latitude (LAI is estimated using Lat. Information only in RAMS)

1176

931.

... .

.

30


1 3 5 7 9

11 13 15 17 19 21 23 25 27 29 31 33 35

S1

S6

S110

200

400

600

800

1000

1200

1400

1600

1800

2000

Series1Series2Series3Series4Series5Series6Series7Series8Series9Series10Series11Series12

IGBP 9

LAI of Strip 1


Statistical Analysis

• Maps of IGBP/GLC, AFRI/GLC, OGE/GLC and LEAF/GLC are generated on every level (30, 60, 100, 200, 300, 400, 600, 1200)

• Histogram are shown for each map.• Means of normalized data (IGBP/GLC,

AFRI/GLC, OGE/GLC and LEAF/GLC) are compared again using Analysis-of-Variance method

• Some initial conclusions are drawn


Statistical Measures of Goodness• Develop an approach to measure the spread of histograms on 30km

level (strip), and add up to the whole domain. So one final value will indicate the quality of each LC.

31

1 1

12

1 1

2/12/

i

k

j m

p

nijmijmn kNLAILAIM

Error Sum of Squares

Where, i ~ strip number; j ~ land cover types in one strip; m ~ month; n ~ LAI pixel number in certain strip, certain land cover type and certain month; N ~ total pixel number in each strip, which is 1176×30 in this case; We get mean error sum of square in each strip by dividing (N – k).


Statistics and assumptions

• In each strip, each land cover, 12 months of LAI values are added up and then averaged. Seasonal LAI information will be included by this part (see histogram).

• In each strip, the smaller error sum of squares, the more converged histogram

• By mean error sum of squares, we can be more justified to compare between different LC systems.


Frequency

0

5

10

15

20

25

30

0.8 1 1.2 1.4 1.6 1.8 2 More

Frequency

0

5

10

15

20

25

30

0.8 1 1.2 1.4 1.6 1.8 2 More


Frequency

0

10

20

30

40

50

0.8 1 1.2 1.4 1.6 1.8 2 More

Frequency

0

10

20

30

40

50

0.8 1 1.2 1.4 1.6 1.8 2 More


Frequency

0

10

20

30

40

50

0.81 0.99 1.18 1.37 1.55 1.74 More

Frequency

0

10

20

30

40

50

0.84 1.04 1.24 1.43 1.63 1.83 More


Frequency

0

10

20

30

40

50

0.93 1.07 1.21 1.35 1.50 1.64 More

Frequency

0

10

20

30

40

50

0.87 1.10 1.33 1.57 1.80 2.03 More


Frequency

0

20

40

60

80

100

0.63 0.90 1.16 1.43 1.70 1.97 More

Frequency

0

20

40

60

80

100

0.66 0.93 1.21 1.49 1.77 2.05 More


Frequency

0

20

40

60

80

100

0.67 1.09 1.51 1.93 2.35 2.77 More

Frequency

0

20

40

60

80

100

0.95 1.11 1.28 1.44 1.60 1.77 More


Frequency

0

40

80

120

160

200

0.55 0.76 0.97 1.19 1.40 1.61 More

Frequency

0

40

80

120

160

200

0.55 0.77 0.99 1.21 1.43 1.65 More


Frequency

0

40

80

120

160

200

0.61 0.89 1.17 1.45 1.73 2.02 More

Frequency

0

40

80

120

160

200

0.74 1.13 1.51 1.90 2.29 2.68 More


Frequency

0

50

100

150

200

250

300

0.39 0.68 0.96 1.24 1.53 1.81 More

Frequency

0

50

100

150

200

250

300

0.39 0.62 0.85 1.09 1.32 1.55 More


Frequency

0

50

100

150

200

250

300

0.59 0.87 1.15 1.42 1.70 1.97 More

Frequency

0

50

100

150

200

250

300

0.67 1.08 1.48 1.88 2.28 2.68 More


Frequency

0

100

200

300

400

500

600

0.41 0.68 0.94 1.21 1.47 1.74 More

Frequency

0

100

200

300

400

500

600

0.47 0.73 0.98 1.24 1.50 1.76 More


Frequency

0

100

200

300

400

500

600

0.58 0.93 1.29 1.64 2.00 2.35 More

Frequency

0

100

200

300

400

500

600

0.73 1.02 1.31 1.60 1.88 2.17 More


Frequency

0

200

400

600

800

1000

1200

0.42 0.83 1.24 1.65 2.06 2.47 More

Frequency

0

200

400

600

800

1000

1200

0.43 0.84 1.26 1.68 2.09 2.51 More


Frequency

0

200

400

600

800

1000

1200

0.32 0.65 0.98 1.31 1.64 1.96 More

Frequency

0

200

400

600

800

1000

1200

0.44 0.76 1.07 1.38 1.69 2.01 More


Trend of Mean M Values On Each Level

1.51.71.92.12.32.52.72.93.13.3

GLC IGBP AFRI OGE LEAF

Land Cover

Mea

n M

30601002003004006001200


Statistical Comparison Results

• GLC works better than other 4 on every level

• GLC works much better at mountain area (Kilimanjaro)

• Because GLC considers altitude factor, such as type 3 (Submontane forest 900 -1500 m) and 4 (Montane forest >1500 m)


Statistical Comparison Results

• With the increase of pixel size, the performance of Africover decreases dramatically, based on the previous mean trend curves.

• Because Africover is generated based on high resolution data source (30m)


Results• Among IGBP,

AFRI, OGE and LEAF, performance of IGBP increases dramatically with the increase of pixel size, especially from 400 to 1200

Resolution Order of Performance

30 oge > leaf > afri > igbp60 oge > leaf > afri > igbp

100 oge > leaf > afri > igbp200 oge > leaf > afri > igbp300 oge > leaf > afri > igbp400 oge > leaf > afri > igbp600 oge > igbp > afri > leaf1200 igbp > oge > leaf > afri


Summary• Cross-Walk analysis to merge various datasets for

RAMS model simulations (paper 1)• Videographic data have been extracted for

quantitative assessment of LC accuracy (paper 2)• A statistical measure (M ) was developed to

determine which classification is the “best” and to quantify uncertainty of various land use/cover maps. (paper 3)

• These results will be used in the next step to determine sensitivity of RAMS model to land cover changes (paper 4)

1 CLIP - Land Cover and Land Use Group Qi, Lusch, Reid, Maitima, Olson, Palm, Campbell Jiagun Ge,...

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