LAND COVER CHANGE ASSESSMENT GLCN methodological approach Antonio Di Gregorio

LAND COVER CHANGE ASSESSMENTGLCN methodological approach

Antonio Di Gregorio

Presentation contents:

•Different Land Cover Change assessment approaches (the GLCN method)

•The Kenya case study

•New perspective of the GLCN approach (CTA software)

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?

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

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.


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.


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

THE GLCN APPROACH

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.

3

A HR4/HM24

B HL4

Change in field size

A 2SOJ67

A1 2SOJ67/HR24

2

3

HR4/2SOJ67

2SOJ67/2WP6

Change in field density

Critical assesment of changes

Critical assesment of changes

IMAGE RESOLUTION

Level of change details

Actual limitation in depicting changes in heterogeneous areas

A/BMMU

Marchi, Giulio (SDRN)

Needs animated examples

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

50

7

6

6

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2

31

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37

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27

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53

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56

46

79

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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

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3

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01

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6

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5.45

1.39 1.080

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20 0 0 0 0

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3

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0 0 0 0 0 001

2

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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


Agriculture density


Meru District, Kenya – Agriculture Field Density Status 1970’s, 1980’s and 2000



Meru District, Kenya – Agriculture Field size and Density Change 1970’s – 1980’s




Meru District, Kenya – Agriculture Field size and Density Change 1980’s – 2000




Meru District, Kenya – Agriculture Field size and Density Change 1970’s – 2000




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 (80-100% polygon

area)

Medium fields (10-20% 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




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.

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


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)


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


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



•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



•Quantitative change assessment on variable dot grid

2000

Class 1 Class 2 Class 3 ….;




1990

1980

1970



•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

THANK YOU

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.


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LAND COVER CHANGE ASSESSMENT GLCN methodological approach Antonio Di Gregorio