Exploring the scale dependence of landscape metrics when estimated by satellite imagery: An example of the SPIN EU project in Kerkini Lake, Greece.

Size: px

Start display at page:

Download "Exploring the scale dependence of landscape metrics when estimated by satellite imagery: An example of the SPIN EU project in Kerkini Lake, Greece."

Marianna Sharleen Harris
5 years ago
Views:

1 Exploring the scale dependence of landscape metrics when estimated by satellite imagery: An example MedWet/Codde Workshop EKBY, 2 April 2006

2 THE RATIONALE Pattern change affects and is affected by ecological processes PROCESSES i.e. species dispersal, water cycle PATTERN i.e of the habitat spatial distribution

THE RATIONALE Quantification of the landscape pattern

Size Subdivision Density Edge Isolation Fractal

SPLIT_01 1 0,5 0 C1.32 C1.34 C2.3 C3.2 C3.5 E3.1 E5.

3 THE RATIONALE Quantification of the landscape pattern Wetland habitats Habitat map Landscape metrics Shape Size Subdivision Density Edge Isolation Fractal dimension 4,5 4 3,5 3 2,5 2 1,5 SPLIT_84 SPLIT_92 SPLIT_01 1 0,5 0 C1.32 C1.34 C2.3 C3.2 C3.5 E3.1 E5.4 F9.3 G1.1 X G1.112 C3.2 G1.38 G1.C G1.C X C3.2 SPLIT_01 SPLIT_92 SPLIT_84

4 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

5 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. Sensors: Landsat 7 ETM+ SPOT 5 IKONOS Ground data requirements: Identification of Areas of Interest (AOIs) according to the MedWet Habitat Description System, accuracy assessment.

6 The values of landscape metrics are strongly affected by: nomenclature classification algorithm scale Methodological approach In In the the Kerkini case study MedWet MedWet Habitat Habitat Description DescriptionSystem Maximum Maximum Likelihood Likelihood pixel pixel resolution: resolution: 30m, 30m, 10m, 10m, 4m 4m area area extend: extend: constant constant spatial analysis local local variance variance in in Kernel Kernel sizes sizesof: of: Landsat Landsat 7 ETM+ ETM+ :: 3, 3, 5 pixel pixel SPOT SPOT 5 :: 3, 3, 5, 5, 7, 7, 9, 9, 11 11pixel IKONOS IKONOS :: 3, 3, 5, 5, 7, 7, 9, 9, 11, 11, 13, 13, 15 15pixel

7 OVERVIEW Methodological approach 2. Calculation of spatial correlation of wetland habitats from images (semivariogram) 3. Image model development 4. Image classification (Maximum Likelihood) 1. Selection of landscape metrics 5. Calculation of the selected landscape metrics & examination of scale dependence

8 1. Selection of Landscape metrics 52 landscape metrics were measured for the years 1984, 1992, 2001 Methodological approach To select landscape metrics that better express changes a PCA was applied

9 Methodological approach 2. Calculation of spatial correlation of wetland habitats from Landsat 7 ETM+, SPOT 5, and IKONOS 900 semivariograms were plotted Semivariance () s (sill) a (range) a (range) lag (h) 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.

10 3. Image model development Methodological approach Landsat 7ETM+ SPOT 5 IKONOS 30 m 10 m 4 m spatial analysis in different kernel sizes Total number of images: spatial analysis in different kernel sizes spatial analysis in different kernel sizes

4. Image Classification Methodological approach - the 175.

landscape Likelihood metrics method) & Examination of scale

in order to produce binomial classified images for the core habitat

-the selected metrics were calculated for the riparian forest from

11 4. Image Classification Methodological approach - the 175. images Calculation were classified of the selected (with the Maximum landscape Likelihood metrics method) & 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.

12 Results 10 out of 52 landscape metrics were proved to better express changes that happened in habitats spatial arrangement of Kerkini Lake Area characterization: Form Description: Subdivision: CA, NP, PD, PLAND, AREA_CV, AREA_SC PARA_MN, PARAM_AM, FRAC_SD 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

13 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. Disadvantages The total number of the calculated metric values did not permit a rigorous statistical analysis. Advantages selection of the most meaningful landscape metrics based on statistical analysis (PCA); kernel sizes were determined through semivariograms;

14 Next Steps To design the implementation of the present methodological approach to a representative sample of Mediterranean wetlands. 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.

Lab #3 Background Material Quantifying Point and Gradient Patterns

Lab #3 Background Material Quantifying Point and Gradient Patterns Dispersion metrics Dispersion indices that measure the degree of non-randomness Plot-based metrics Distance-based metrics First-order