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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?
Jan. 5-9, 2005 CLIP Meeting 3/20
APPROACH
LULC BiophysicalAttributes
RegionalClimate
LCLUAlbedo
LAIfPAR
TsPrecip.
etc.LUTLEAF2
Simulation
RegionalClimateModel
(RAMS)
RSEOS Data Products
GLC2000Africover
IGBPMODIS
Simulation
Jan. 5-9, 2005 CLIP Meeting 4/20
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
Jan. 5-9, 2005 CLIP Meeting 5/20
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?
Jan. 5-9, 2005 CLIP Meeting 6/20
Comparing OGE (RAMS), GLC2000, Africover for selected region
Jan. 5-9, 2005 CLIP Meeting 7/20
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
Jan. 5-9, 2005 CLIP Meeting 10/20
RAMS Inner Domain
No Africover dataset for:• Mozambique
• Zambia
• Central African Republic
• Malawi
• Ethiopia
Reference with GLC2000
Jan. 5-9, 2005 CLIP Meeting 11/20
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
Jan. 5-9, 2005 CLIP Meeting 12/20
Calculate Field Of View
Assumptions• Flying altitude• Tilt & Rotation• Zoom, Pan, Camera changes
Jan. 5-9, 2005 CLIP Meeting 13/20
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
Jan. 5-9, 2005 CLIP Meeting 14/20
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.
Jan. 5-9, 2005 CLIP Meeting 15/20
Example
Red = Ambo Flight line FOV
Green = 2 sample points
Africover LU/C database
Each point
• Lat / long coordinate
• Land use/cover type
Jan. 5-9, 2005 CLIP Meeting 16/20
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
Jan. 5-9, 2005 CLIP Meeting 18/20
December 2000 LAI & fPAR IllustrationSource: MODIS Science Team, NASA, BU
Jan. 5-9, 2005 CLIP Meeting 19/20
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
Jan. 5-9, 2005 CLIP Meeting 21/20
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
Jan. 5-9, 2005 CLIP Meeting 22/20
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
Jan. 5-9, 2005 CLIP Meeting 23/20
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
Jan. 5-9, 2005 CLIP Meeting 24/20
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
Jan. 5-9, 2005 CLIP Meeting 25/20
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
Jan. 5-9, 2005 CLIP Meeting 26/20
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).
Jan. 5-9, 2005 CLIP Meeting 27/20
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.
Jan. 5-9, 2005 CLIP Meeting 28/20
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
Jan. 5-9, 2005 CLIP Meeting 29/20
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
Jan. 5-9, 2005 CLIP Meeting 30/20
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
Jan. 5-9, 2005 CLIP Meeting 31/20
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
Jan. 5-9, 2005 CLIP Meeting 32/20
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
Jan. 5-9, 2005 CLIP Meeting 33/20
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
Jan. 5-9, 2005 CLIP Meeting 34/20
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
Jan. 5-9, 2005 CLIP Meeting 35/20
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
Jan. 5-9, 2005 CLIP Meeting 36/20
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
Jan. 5-9, 2005 CLIP Meeting 37/20
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
Jan. 5-9, 2005 CLIP Meeting 38/20
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
Jan. 5-9, 2005 CLIP Meeting 39/20
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
Jan. 5-9, 2005 CLIP Meeting 40/20
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
Jan. 5-9, 2005 CLIP Meeting 41/20
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
Jan. 5-9, 2005 CLIP Meeting 42/20
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
Jan. 5-9, 2005 CLIP Meeting 43/20
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
Jan. 5-9, 2005 CLIP Meeting 44/20
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
Jan. 5-9, 2005 CLIP Meeting 45/20
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)
Jan. 5-9, 2005 CLIP Meeting 46/20
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)
Jan. 5-9, 2005 CLIP Meeting 47/20
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
Jan. 5-9, 2005 CLIP Meeting 48/20
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)