A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy
The multifunctional Ecosystem Service supply analysis at the spatial level is often the output of a weighted sum of layers in a Geographic Information System (GIS). This procedure is weak in detecting and representing the relationships between the input layers. Nonetheless, composite indicators prod...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-08-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X21004234 |
| _version_ | 1831569681000955904 |
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| author | Stefano Salata Carlo Grillenzoni |
| author_facet | Stefano Salata Carlo Grillenzoni |
| author_sort | Stefano Salata |
| collection | DOAJ |
| description | The multifunctional Ecosystem Service supply analysis at the spatial level is often the output of a weighted sum of layers in a Geographic Information System (GIS). This procedure is weak in detecting and representing the relationships between the input layers. Nonetheless, composite indicators produced by overlaying techniques are quite common in applied research and their discrepancies are underestimated in the scientific community, thus affecting the quality of resulting composite maps. In this work, we empirically test the effectiveness of multivariate statistics to obtain reliable composite Ecosystem Maps in the Turin metropolitan area (north-west Italy). We apply the Principal Component Analysis (PCA, using Matlab and ESRI ArcGis) to seven Ecosystem Service models (Habitat Quality, Carbon Sequestration, Water Yield, Nutrient Retention, Sediment Retention, Crop Production and Crop Pollination) and we evaluate how much the resulting composite map differs from the traditional GIS overlay. In doing this, the spectral analysis (with eigenvectors and eigenvalues) of the covariance matrix of the normalized layers confirms the heuristic arguments about the dependence between Ecosystem Services. We show that the PCA method can provide valuable results in landscape Green Network design, avoiding the limits of standard overlaying procedures. Finally, smoothing and classification techniques, applied to PCA estimates, can further improve the approach and encourage its use in various ecological indicators. |
| first_indexed | 2024-12-17T12:37:39Z |
| format | Article |
| id | doaj.art-65a42be2f13e4f0fb5bb810c50ece834 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-12-17T12:37:39Z |
| publishDate | 2021-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-65a42be2f13e4f0fb5bb810c50ece8342022-12-21T21:48:12ZengElsevierEcological Indicators1470-160X2021-08-01127107758A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, ItalyStefano Salata0Carlo Grillenzoni1City and Regional Planning Department, Izmir Institute of Technology, 35430 Urla, Turkey; Corresponding author.Department of Design and Planning, Università IUAV di Venezia, 30135 Venezia, ItalyThe multifunctional Ecosystem Service supply analysis at the spatial level is often the output of a weighted sum of layers in a Geographic Information System (GIS). This procedure is weak in detecting and representing the relationships between the input layers. Nonetheless, composite indicators produced by overlaying techniques are quite common in applied research and their discrepancies are underestimated in the scientific community, thus affecting the quality of resulting composite maps. In this work, we empirically test the effectiveness of multivariate statistics to obtain reliable composite Ecosystem Maps in the Turin metropolitan area (north-west Italy). We apply the Principal Component Analysis (PCA, using Matlab and ESRI ArcGis) to seven Ecosystem Service models (Habitat Quality, Carbon Sequestration, Water Yield, Nutrient Retention, Sediment Retention, Crop Production and Crop Pollination) and we evaluate how much the resulting composite map differs from the traditional GIS overlay. In doing this, the spectral analysis (with eigenvectors and eigenvalues) of the covariance matrix of the normalized layers confirms the heuristic arguments about the dependence between Ecosystem Services. We show that the PCA method can provide valuable results in landscape Green Network design, avoiding the limits of standard overlaying procedures. Finally, smoothing and classification techniques, applied to PCA estimates, can further improve the approach and encourage its use in various ecological indicators.http://www.sciencedirect.com/science/article/pii/S1470160X21004234Ecosystem ServicesPrincipal Component AnalysisComposite indicatorsOverlayGeographic information systemEnvironmental indicators |
| spellingShingle | Stefano Salata Carlo Grillenzoni A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy Ecological Indicators Ecosystem Services Principal Component Analysis Composite indicators Overlay Geographic information system Environmental indicators |
| title | A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy |
| title_full | A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy |
| title_fullStr | A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy |
| title_full_unstemmed | A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy |
| title_short | A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy |
| title_sort | spatial evaluation of multifunctional ecosystem service networks using principal component analysis a case of study in turin italy |
| topic | Ecosystem Services Principal Component Analysis Composite indicators Overlay Geographic information system Environmental indicators |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X21004234 |
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