exploring the scale dependence of landscape metrics when

Exploring the scale dependence of landscape metrics when estimated by satellite imagery:

An example of the SPIN EU project in Kerkini Lake, Greece.

Eleni Fitoka / Greek Biotope Wetland Centre (EKBY)

MedWet/Codde Workshop

EKBY, 2 April 2006

MedWet/Codde Workshop

EKBY, 2 April 2006

Exploring the scale dependence of landscape metrics when estimated by satellite imagery. An example of the SPIN EU project in Kerkini Lake, Greece.Eleni Fitoka / Greek Biotope Wetland Centre (EKBY)

�� ! " ��# $ �� " ��%

THE RATIONALEPattern change affects and is affected by ecological processes

PROCESSES i.e. species dispersal, water cycle

PATTERN

i.e of the habitat spatial distribution

�� ! " ��# $ �� " ��%

Wetland habitats


Habitat map Landscape metrics

C1.32 C1.34 C2.3 C3.2 C3.5 E3.1 E5.4 F9.3 G1.1 XC3.2

G1.112 G1.38 G1.C G1.C XC3.2

SPLIT_84SPLIT_92

SPLIT_01

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

SPLIT_84SPLIT_92SPLIT_01

Size

Edge

Density

Shape

Fractal dimension

Isolation

Subdivision

THE RATIONALEQuantification of the landscape pattern

�� ! " ��# $ �� " ��%


OUTLINE OF THE CASE STUDY

• Purpose, End Users, Framework

• Wetland types and features that were inventoried

• Sensors

• Ground data requirements

• Methodological approach & Results

• Advantages & Disadvantages

• Next Steps

�� ! " ��# $ �� " ��%


• Sensors: Landsat 7 ETM+

SPOT 5

IKONOS

• Purpose: To explore the scale dependence of landscape metrics when estimated by different satellite imagery, in order to identify metrics less affected by scale, which will consequently be used as monitoring indicators.

• End User: EKBY, Kerkini Lake Information Centre.

• Framework: The SPIN FP5 EU Project.• Wetland types and features that were inventoried and monitored:The geometry of the spatial arrangement of wetland habitats classified according

to the MedWet Habitat Description System.

• Ground data requirements:Identification of Areas of Interest (AOIs) according to the MedWet Habitat

Description System, accuracy assessment.

�� ! " ��# $ �� " �%

� nomenclature

� classification algorithm

� scale

� spatial analysis

� nomenclature

� classification algorithm

� scale

� spatial analysis


The values of landscape metrics are strongly affected by:

Methodological approach

In the Kerkini case study•MedWet Habitat Description System

• Maximum Likelihood

• pixel resolution: 30m, 10m, 4m

• area extend: constant

• local variance in Kernel sizes of:

Landsat 7 ETM+ : 3, 5 pixel

SPOT 5 : 3, 5, 7, 9, 11 pixel

IKONOS : 3, 5, 7, 9, 11, 13, 15 pixel

In the Kerkini case study•MedWet Habitat Description System

• Maximum Likelihood

• pixel resolution: 30m, 10m, 4m

• area extend: constant

• local variance in Kernel sizes of:

Landsat 7 ETM+ : 3, 5 pixel

SPOT 5 : 3, 5, 7, 9, 11 pixel

IKONOS : 3, 5, 7, 9, 11, 13, 15 pixel

�� ! " ��# $ �� " ��%


1. Selection of landscape metrics

Methodological approachOVERVIEWOVERVIEW

2. Calculation of spatial correlation ofwetland habitats from images

(semivariogram)

3. Image model development

4. Image classification (Maximum Likelihood)

5. Calculation of theselected landscape metrics

& examination of scale dependence

�� ! " ��# $ �� " ��%



198419922001

1. Selection of Landscape metrics52 landscape metrics were measured for the years 1984, 1992, 2001

To select landscape metrics that better express changes a PCA was applied

�� ! " ��# $ �� " ��%



2. Calculation of spatial correlation of wetland habitats from Landsat 7 ETM+, SPOT 5, and IKONOS

900 semivariograms were plotted

a (range)a (range)

s (sill)

lag (h)S

emiv

aria

nce

(�)

The range values of the wetland habitats’ spatial correlation, estimated from the three images (for each band), were used to determine the minimum and the maximum kernel size for spatial analysis.

�� ! " ��# $ �� " ��%



Landsat 7ETM+

30 m30 m

SPOT 5

10 m10 m

IKONOS

4 m4 m

3. Image model development

spatial analysis in different kernel sizes

3 5 35 79

11

35911

7



1315

Total number of images: 1717

�� ! " ��# $ �� " ��%


Methodological approach4. Image Classification

- the 17 images were classified (with the Maximum Likelihood method)5. Calculation of the selected landscape metrics

& Examination of scale dependence- each classified image was reclassified into two classes in order to produce binomial classified images for the core habitat “riparian forest”.

-the selected metrics were calculated for the “riparian forest” from the binomial classified images (with Fragstats).

-Landscape metric values were plotted against pixel resolution and against kernel size.

�� ! " ��# $ �� " ��%


Results

• 10 out of 52 landscape metrics were proved to better express changes that happened in habitats’ spatial arrangement of Kerkini Lake

Area characterization: CA, NP, PD, PLAND, AREA_CV, AREA_SC Form Description: PARA_MN, PARAM_AM, FRAC_SD Subdivision: SPLIT

• Graphs of landscape metric values against pixel size showed that:

NP, PD, PARA_MN, PARA_AM, are not affected by pixel size (for Landsat and SPOT)

AREA_SD, FRAC_SD are affected by pixel size (for Landsat and SPOT)

• Graphs of landscape metric values against kernel size showed that:

CA, PLAND are not affected by kernel size

NP, AREA_CV, SPLIT are affected by kernel size

�� ! " ��# $ �� " ��%


Advantages

• selection of the most meaningful landscape metrics based on statistical analysis (PCA);

• kernel sizes were determined through semivariograms;

• The total number of the calculated metric values did not permit a rigorous statistical analysis.

Disadvantages

The present CASE STUDY was applied to only one wetland.

In order to be characterised as a prototype product, it should be applied to a representative sample of Mediterranean wetlands.

�� ! " ��# $ �� " ��%


• To design the implementation of the present methodological approach to a representative sample of Mediterranean wetlands.

Next Steps

• To plan the calculation of landscape metrics using the map outputs that will result from the testing of the proposed prototype products during the MedWet/CODDE project.

�� ! " ��# $ �� " ��%


exploring the scale dependence of landscape metrics when

Documents