Potential groundwater dependent vegetation in the Mediterranean
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| Autori principali: | , , , , |
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| Natura: | Dataset Open Access |
| Lingua: | en |
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PANGAEA
2023
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| _version_ | 1867169157004394496 |
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| author | El-Hokayem, Léonard De Vita, Pantaleone Usman, Muhammad Link, Andreas Conrad, Christopher |
| author_facet | El-Hokayem, Léonard De Vita, Pantaleone Usman, Muhammad Link, Andreas Conrad, Christopher |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | Groundwater dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat and sustaining human livelihoods. A novel multicriteria framework helps to identify areas where potential groundwater dependent vegetation (pGDV) occurs in the Mediterranean biome. Globally-available datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks are combined in a weighted, easy-to-use index, composed of eleven thematic layers. Input layers for the index calculation are available in the data collection: 1) pre-processed (rasterised and clipped to the Mediterranean) and 2) harmonised and reclassified. All input data was extracted globally. Either, directly from the respective studies or through the data catalogue in the Google Earth Engine. All datasets were acquired and processed in 2022 and 2023. Time series data for potential inflow dependency and Normalized Difference Vegetation Index (NDVI) were extracted for the period 2003-2021. Finally, the mean value was calculated over this period. All other data sets, however, mark a fixed point in time. Ground truth vegetation data was used to calculate layer weightings with a Random Forest. 10 m * 10 m vegetation plots were collected in July and August 2021 and 2022 in southern Italy (Campania region) inside the 'Cilento, Vallo di Diano and Alburni National Park'. 236 vegetation plots are available, containing general information on the vegetation (habitat, species number, stratification), mean indicator values, plant life forms, leaf anatomy as well as a calculated ecohydrological potential for the presence of GDV. The potential was calculated based on the coverage of phreatophyte species and the moisture value of non-phreatophyte species. The final pGDV maps including different weightings of the eleven thematic layers are compiled at a resolution of 500 m in WGS1984 (EPSG 4326). Finally, five pGDV classes (very low to very high potential) were derived and the share of high pGDV was calculated for level 8 HydroBASINS in the Mediterranean. Results support prioritisation of areas for essential regional high-resolution identification of GDV, to ensure sustainable groundwater management and in turn protect GDV as local biodiversity hotspots. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_961765 |
| institution | PANGAEA |
| language | en |
| publishDate | 2023 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Potential groundwater dependent vegetation in the Mediterranean El-Hokayem, Léonard De Vita, Pantaleone Usman, Muhammad Link, Andreas Conrad, Christopher Binary Object; Binary Object (File Size); Binary Object (Media Type); Biodiversity; File format; File name; Google Earth Engine; groundwater dependent vegetation; Mediterranean; NDVI; remote sensing Groundwater dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat and sustaining human livelihoods. A novel multicriteria framework helps to identify areas where potential groundwater dependent vegetation (pGDV) occurs in the Mediterranean biome. Globally-available datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks are combined in a weighted, easy-to-use index, composed of eleven thematic layers. Input layers for the index calculation are available in the data collection: 1) pre-processed (rasterised and clipped to the Mediterranean) and 2) harmonised and reclassified. All input data was extracted globally. Either, directly from the respective studies or through the data catalogue in the Google Earth Engine. All datasets were acquired and processed in 2022 and 2023. Time series data for potential inflow dependency and Normalized Difference Vegetation Index (NDVI) were extracted for the period 2003-2021. Finally, the mean value was calculated over this period. All other data sets, however, mark a fixed point in time. Ground truth vegetation data was used to calculate layer weightings with a Random Forest. 10 m * 10 m vegetation plots were collected in July and August 2021 and 2022 in southern Italy (Campania region) inside the 'Cilento, Vallo di Diano and Alburni National Park'. 236 vegetation plots are available, containing general information on the vegetation (habitat, species number, stratification), mean indicator values, plant life forms, leaf anatomy as well as a calculated ecohydrological potential for the presence of GDV. The potential was calculated based on the coverage of phreatophyte species and the moisture value of non-phreatophyte species. The final pGDV maps including different weightings of the eleven thematic layers are compiled at a resolution of 500 m in WGS1984 (EPSG 4326). Finally, five pGDV classes (very low to very high potential) were derived and the share of high pGDV was calculated for level 8 HydroBASINS in the Mediterranean. Results support prioritisation of areas for essential regional high-resolution identification of GDV, to ensure sustainable groundwater management and in turn protect GDV as local biodiversity hotspots. |
| title | Potential groundwater dependent vegetation in the Mediterranean |
| topic | Binary Object; Binary Object (File Size); Binary Object (Media Type); Biodiversity; File format; File name; Google Earth Engine; groundwater dependent vegetation; Mediterranean; NDVI; remote sensing |
| url | https://doi.org/10.1594/PANGAEA.961765 |