land cover change assessment glcn methodological approach antonio di gregorio
TRANSCRIPT
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LAND COVER CHANGE ASSESSMENTGLCN methodological approach
Antonio Di Gregorio
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Presentation contents:
•Different Land Cover Change assessment approaches (the GLCN method)
•The Kenya case study
•New perspective of the GLCN approach (CTA software)
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No internationally accepted definition exists
The definition of a change depends on the context we refer to.
To characterized a change we must first define the range (values or semantic definition) that define the limit with in which no change exist.
We must determine the time within a change/no change take place
In the present study we are considering changes-Quantitative•Based on the values/semantic definition used in LCCS
WATH IS A CHANGE?
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The selection of the different of methodological approaches is directly linked to the types of final applications desired:
Approach by sample area gives relatively fast statistical tabular information but doesn’t show the location of changes. Its use is limited to applications where a geographic location of changes is not necessary and when statistical information (obtained with standard methods) are not available.
Approach “wall to wall” has the advantage to link the tabular information with the geographic representation of the changes.
It can be executed in different ways that can be summarized in two main
approaches:
1. Automated methods
2. Visual interpretation
CHANGE DETECTION APPROACHES
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Different types of automated methods exist:
•Post classification cross tabulation•Cross correlation analysis•Neural networks•Object oriented classification
Advantages:•Under favourable conditions, faster than the visual interpretation;•Rather objective; •Detection of changes at level of pixel size.
Disadvantages:•Needs an heavy pre-processing;•Quality of results have big dependence from differences in atmospheric conditions or seasonality;•Quality of results related to the types of classes considered;•Detection of changes at level of pixel size.
CHANGE DETECTION APPROACHES
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Different types of visual approaches exist:•Map to map comparison•Image to image
Advantages:•Simple technique;•Rather independent from differences in atmospheric conditions or seasonality;•Large number of L.C. Class types can be evaluated.
Disadvantages:•Slow process;•Quality of results correlated to the skill of photo interpreters; •Quality of results related to the types of classes considered;•Detection of changes not level of pixel size.
CHANGE DETECTION APPROACHES
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THE GLCN APPROACH
It is a visual method assisted by a specific software.A GLCN software used to perform visual interpretationhas been re-adapted to perform the change detection
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THE GLCN APPROACH
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THE GLCN APPROACH
Advantages
•Compared to other visual methods easier and faster;
•Changes are critically analyzed by the expert in a multi window system, eventual mistakes in the original interpretation can be adjusted;
•Large types of classes can be analyzed;
•All the changes are spatially localized. No heavy post-processing is needed, the final result is a fully topological vector layer that allow to track back the change history of each polygon.
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3
A HR4/HM24
B HL4
Change in field size
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A 2SOJ67
A1 2SOJ67/HR24
2
3
HR4/2SOJ67
2SOJ67/2WP6
Change in field density
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Critical assesment of changes
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Critical assesment of changes
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IMAGE RESOLUTION
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Level of change details
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Actual limitation in depicting changes in heterogeneous areas
A/BMMU
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Nairo
biCentral
Coast
Eastern
North Eastern
Nyanza
Rift Valley
Western
SUB GROUP % % % % % % % %
Rainfed herbaceous crops (large to medium, continuous fields) 22.33 11.45 7.06 20.01 0.90 3.82 30.84 24.66
Rainfed herbaceous crops (small, continuous fields) 34.07 30.87 16.69 47.09 23.68 36.53 31.39 39.77
Rainfed herbaceous crops (scattered clustered or scattered isolated fields) 15.54 12.41 52.14 23.40 75.42 32.21 17.51 19.99
Rainfed shrub crops (large to medium, continuous fields) 15.35 9.06 0.00 0.29 0.00 0.67 2.10 0.11
Rainfed shrub crops (small, continuous fields) 3.07 20.05 0.15 1.68 0.00 2.12 4.25 0.26
Rainfed shrub crops (scattered clustered or scattered isolated fields) 1.25 8.49 5.51 2.12 0.00 14.27 5.74 9.92
Rainfed tree crops (small, continuous fields) 0.00 0.05 0.32 0.56 0.00 0.00 0.05 0.00
Rainfed tree crops (scattered clustered or scattered isolated fields) 0.00 0.70 10.10 1.15 0.00 2.80 0.79 2.09
Irrigated herbaceous crops (large to medium, continuous fields) 0 0 4 3 0 5.45 1.39 1.08
Irrigated herbaceous crops (small, continuous fields) 0.00 0.76 0.00 0.00 0.00 0.30 0.59 0.00
Irrigated tree crops (large to medium, continuous fields) 0.00 0.00 1.56 0.00 0.00 0.00 0.00 0.00
Forest plantation (large to medium, continuous fields) 8.38 3.34 0.08 0.40 0.00 0.25 5.33 2.01
Aquatic agriculture (large to medium, continuous fields) 0.00 1.30 0.00 0.04 0.00 0.32 0.00 0.06
Aquatic agriculture (small, continuous fields) 0.00 1.44 2.19 0.00 0.00 1.26 0.00 0.04
Kenya case study -results
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50
7
6
6
0
2
31
7
9
19
14
37
21
27
21
53
21
23
56
46
79
42
26
24
12
8
00
0
1
2
00
23
0
2
0
1
4
0
8
10
6
3
0
17
6
11
0 00
10 0 0 00
1
11
1
0
3
1
2
0 0
4
3
0
6
1 0.95
0 0 0 0 00
0 00 00
0 0 0 0 0
1
4
0
1
00
7
3
0
2
0 0 0 0 0 00
1
2
0 0 0 0 0
0
10
20
30
40
50
60
70
80
90
Nairobi Central Coast Eastern North Eastern Nyanza Rift Valley Western
Rainfed herbaceous crops (large to medium, continuous fields) Rainfed herbaceous crops (small, continuous fields)
Rainfed herbaceous crops (scattered clustered or scattered isolated fields) Rainfed shrub crops (large to medium, continuous fields)
Rainfed shrub crops (small, continuous fields) Rainfed shrub crops (scattered clustered or scattered isolated fields)
Rainfed tree crops (small, continuous fields) Rainfed tree crops (scattered clustered or scattered isolated fields)
Irrigated herbaceous crops (large to medium, continuous fields) Irrigated herbaceous crops (small, continuous fields)
Irrigated tree crops (large to medium, continuous fields) Forest plantation (large to medium, continuous fields)
Aquatic agriculture (large to medium, continuous fields) Aquatic agriculture (small, continuous fields)
22
11
7
20
1
4
31
25
34
31
17
47
24
37
31
40
16
12
52
23
75
32
18
20
15
9
0 0 0 12
0
3
20
02
0
2
4
01
8
6
2
0
14
6
10
0 0 0 1 0 0 0 00 1
10
10
3
12
0 0
43
0
5.45
1.39 1.080
10 0 0 0 1 00 0
20 0 0 0 0
8
3
0 0 0 0
5
2
01
0 0 0 0 0 001
2
0 01
0 00
10
20
30
40
50
60
70
80
Nairobi Central Coast Eastern North Eastern Nyanza Rift Valley Western
Percentage cover
Rainfed herbaceous crops (large to medium, continuous fields) Rainfed herbaceous crops (small, continuous fields)
Rainfed herbaceous crops (scattered clustered or scattered isolated fields) Rainfed shrub crops (large to medium, continuous fields)
Rainfed shrub crops (small, continuous fields) Rainfed shrub crops (scattered clustered or scattered isolated fields)
Rainfed tree crops (small, continuous fields) Rainfed tree crops (scattered clustered or scattered isolated fields)
Irrigated herbaceous crops (large to medium, continuous fields) Irrigated herbaceous crops (small, continuous fields)
Irrigated tree crops (large to medium, continuous fields) Forest plantation (large to medium, continuous fields)
Aquatic agriculture (large to medium, continuous fields) Aquatic agriculture (small, continuous fields)
Year 2000
Year 1970
Kenya case study -results
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Agriculture density
Kenya case study -results
Meru District, Kenya – Agriculture Field Density Status 1970’s, 1980’s and 2000
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Meru District, Kenya – Agriculture Field size and Density Change 1970’s – 1980’s
Kenya case study -results
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Meru District, Kenya – Agriculture Field size and Density Change 1980’s – 2000
Kenya case study -results
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Meru District, Kenya – Agriculture Field size and Density Change 1970’s – 2000
Kenya case study -results
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Meru District, Kenya – Agriculture Field Size and Density Hectare Change
Changes in field size and field density - Meru District
0
50000
100000
150000
200000
Small f ields (10-20% polygon area)
Small f ields (20-40% polygon area)
Small f ields (40-80% polygon area)
Small f ields (80-100% polygon
area)
Medium fields (10-20% polygon area)
Medium fields (20-40% polygon area)
Medium fields (40-80% polygon area)
Medium fields (80-100% polygon
area)
Large f ields (40-80% polygon area)
Large f ields (80-100% polygon
area)
Field Size and % area covered by cultivation
He
cta
res
1970's
1980's
2000's
Kenya case study -results
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THE KENYA CASE STUDY
RESULTS CRICTICAL ANALISYS
Time/costNumber of polygons analyzed per day for the three dates80-100 –depending complexity of the features to be analyzed/speed of the expert.
12000 polygons to be analyzed for Kenya.Total time forecast 6-7 man/month work plus final analysis
OutputsOverall detection of changes precise and rather objective.ConstrainsLevel of details in depicting and reporting changes in heterogeneousareas linked with the class and cartographic standards adopted. It couldbe ameliorated.GENERAL CONSIDERATIONIn general the method is more effective for agricultural/urban/dense natural vegetated areas respect to natural open formations or very fragmented land cover features.
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THE CTA – CHANGE TREND ANALYSIS SOFTWARE
Improvments of the present approach
•Reduction of 30-50 % of the whole work execution for the present approach
•Improvement on detail analysing heterogeneous areas
•Development of additional methods to be applied according to level of detail required, time and costs expected
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THE CTA – CHANGE TREND ANALYSIS SOFTWARE
Reduction of the execution time-
• Reduction of the GIS and results analysis work new functions that automatically generates tables and vector layersdepicting the history, intensity of changes.
• Optimization of the analysis/detection of changes itself -General improvement in the multi-window analysis functions -Pattern recognition filters to select only polygon were the change has likely occurred -Increasing efficiency in the polygonization of the change (see next slide)
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THE CTA – CHANGE TREND ANALYSIS SOFTWARE
Improvment on detail analysing eterogeneous areas
•Use of magic wand simultaneusely on the multiple windows inside a given polygon to detect percentage of different cover features
•Use of a variable dot grid to asses percentage of different cover featuresand/or substitute the polygonization
MAGIC WAND USE THE POLYGON LIMIT AS ROIThe function is activated simultaneusely onone or all the windows
•MAGIC WAND GIVE % OF THE SELECTED OBJECT INSIDE THE ROI AND MAKE AN AUTOMATIC LINK OF THIS % TO THE POLYGON CODE IN A SEPARATE COLUM
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THE CTA – CHANGE TREND ANALYSIS SOFTWARE
Development of additional change assessment methods
•Change assessment with the area frame method
Increasing confidenceLevel of the results
Independent from previous L.C. interpretation
Execution time- very fast Results- tabular data
No localization of the changes No hot spots
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THE CTA – CHANGE TREND ANALYSIS SOFTWARE
Development of additional change assessment methods
•Qualitative change assessment on variable geographic grid
Indipendent from any L.C. interpretation
Execution time- very fast Results- qualitative assessment of changes on grid
No localization of the changes Hot spots- localized by grid
CHANGE INTENSITY• LOW•MEDIUM•HIGH
DIRECTION OF CHANGES•Agriculture vs Forest•Forest vs Agriculture
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THE CTA – CHANGE TREND ANALYSIS SOFTWARE
Development of additional change assessment methods
•Quantitative change assessment on variable dot grid
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2000
Class 1 Class 2 Class 3 ….;
Class 1 Class 2 Class 3 ….;
Class 1 Class 2 Class 3 ….;
Class 1 Class 2 Class 3 ….;
1990
1980
1970
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THE CTA – CHANGE TREND ANALYSIS SOFTWARE
Development of additional change assessment methods
•Quantitative change assessment on variable dot grid
Independent from any L.C. interpretation
Execution time- fast Results- quantitative assessment of changes on dot grid
Localization of the changes according to the dot grid size
Hot spots- localized by dot grid
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THANK YOU
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2 main types of approaches exist: – By sample area: a statistically valid number of
samples is randomly chosen over the study area.
The analysis of changes is performed only in the samples areas. The results are shown in form of tabular data with a certain level of confidence.
– “Wall to wall”: the change analysis is done over the whole area. The results are shown by tabular data and by geographic location of the changes.
CHANGE DETECTION APPROACHES