Abies Cephalonica Stress Monitoring in Greece

Description

This research project analyzes drought impacts on Greek mountain ecosystems, focusing on fir forest (Abies cephalonica) die-off in regions such as Chelmos, Mainalo, Taygetos, Parnonas, and Epirus. The work includes environmental monitoring, GIS mapping, and climate data analysis, under the scope of providing valuable insights for the development of resilience and restoration strategies. [source: dasarxeio.com]

The extent of Abies Cephalonica species within the Natura 2000 network is provided by FORESTLIFE in vector format.

The methodology is based on lessons learned from our work with Copernicus Emergency Management Service (EMS), in activation EMSN217 - Fire risk assessment in East Sardinia, Italy.

Results

Interactive maps are served in PersLab EODC Map.

What we found -- TBD

Notebooks of analysis results

Notebook	Description
NB1	TBD

EODC Development

Based on Open Data Cube. For the EODC, the following steps have been completed:

1. Created a database and credentials
2. Created a Python environment.yaml
3. Initialized the datacube schema
4. Generated a tiling grid schema covering the affected Natura2000 sites
5. Defined EO products
6. Verified dataset storage, indexing, and retrieval from NAS using EO3 datasets and EODC

Tiling schema

The study area is divided into 23 tiles, each measuring 48 × 48 km. Tile naming follows a x00y00 format without negative indices. In 20-m imagery this translates to time×variables×2400×2400 pixel time series. A 2400×2400 size was found appropriate for an effective processing in a 32GB - 8 CPU configuration, with an average product delivery time of 7 minutes.

Composite generation

The composite pipeline automates the creation of 20-m monthly median mosaics of Sentinel-2 L2A data using the Planetary Computer through the STAC catalog.

Key points

• Input: GeoJSON configuration files define the year–month and tile code to process. Now configured in ./src/run_composites.py#L36.
• Data access: Imagery is retrieved from the STAC catalog using odc-stac, filtered by cloud cover (≤70%) and data quality (nodata < 33%).
• Native resolutions: Sentinel-2 provides bands both at 10 m and 20 m resolutions. Since 10 m bands are not natively available at 20 m, we apply the rasterio.enums.Resampling.average method to bin the resolution to 20m.
  • Implementation note: If it is applied on a subset the values may vary (due to starting pixel and shape of the raster), but if it applied on a whole granule the values are identical. Sen2Cor uses skimage.measure.block_reduce (and from docs), as it can be found in S2 MPC L2A ATBD, to perform 2×2 mean aggregation (binning), as it can be found in the L2A_Tables.py module. This is also available in xarray's coarsen function, which is a replica of this function as stated in this issue. This ensures consistency with ESA’s Sen2Cor processing chain.
• Baseline change: Working with S2-L2A time series from MS PC requires a baseline change to Sen2Cor 4.00 . For scenes after 2022-01-05 (January 25th, 2022), an offset of -1000 is applied.
• Masking: Cloud, shadow, cirrus, and snow/ice pixels are masked using the Scene Classification Layer (SCL).
• Spectral indices: NDVI, EVI, and PSRI2 are calculated per timestamp before median temporal reduction.
• Reprojection: Before reduction, the data cubes are aligned and reprojected in ETRS89-extended / LAEA Europe (EPSG:3035) with odc-geo, using rasterio's bilinear resampling. If more than two UTM zones are included, a mosaic is generated.
• Compositing: A median temporal composite is produced per tile and month.
• Output: Results are stored as Cloud-Optimized GeoTIFFs (COGs), with metadata (dataset definitions) recorded in both EO3 YAML and STAC JSON formats for datacube indexing. The assets/measurements of the bands are recorded in paths relevant to the metadata document location. To do so on a local machine, raster information were referenced by explicitly providing geo and pixel information to the metadata preparation module. To load the relevant paths of raster images from NAS into xarray, url patching is required.

Identify Baseline and Monitoring periods

Based on expert and in-situ knowledge, as well as following visual inspection of imagery, the baseline period was defined from 2020-Q1 to 2023-Q1. The monitoring period was defined from 2023-Q2 onwards.

Time series z-normalization

In the next step, for each month from April 2023 to November 2025 (the disturbance “monitoring” period), the values of NDVI, EVI, and PSRI2 median composite images were normalized using:

$$ z = \frac{x - \mu}{\sigma} $$

Where $μ$ and $σ$ are the mean and standard deviation of the SI (NDVI, EVI, PSRI2) values from 2020-January to 2023-March and $x$ is the median SI value from each month (April 2023 onward). The time series of z-values forms the basis for the main analysis.

Reference data and Sampling strategy

Sampling Strategy -- TBD

Anciliary Data

Copernicus DEM

Water stress has been observed to deviate based on sun exposure. Therefore the Copernicus Global DEM of 30m was used to extract the aspect of the terrain. The Copernicus DEM is a Digital Surface Model (DSM) which represents the top-reflective surface of the Earth including buildings, infrastructure and vegetation. Data were acquired by the TanDEM-X mission. The xarray-spatial package was used to compute the aspect. © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved

Tree Canopy Density

To minimize the inclusion of pixels that are not cover by vegetation, or they are sparsly vegetated, we emply the Tree Cover Density 2023 (raster 10 m, 100 m), Europe, yearly dataset. The dataset provides at pan-European level in the spatial resolution of 10 m and 100 m the level of tree cover density in a range from 0% to 100% for the 2023 reference year. We use the 10m layer and apply a 30% density cover threshold to acquire the vegetated pixels. The dataset is provided by the vendor in EPSG:3035 - ETRS89-extended / LAEA Europe projection.

Classification

Classification method

OWS

To serve data indeced in the EODC as visualizations, datacube-ows provides the WMS web service endpoint to (in our case) a TerriaJS web map client, by configuring the OWS

• Styles: Styles and Layers are configured in the ows configuration module
• Update: Periodically withing the EO pipelines, or at the end of each one, the OWS database is triggered automatically for an update.
• Reading: The images are loaded from NAS by patching the URL to the mounted volume inside the Docker container.
• Vizualization: The WMS of the data indexed is provided to a TerriaJS client, and are available via a NGROK app (https://emt-datacube-viewer.ngrok.app/).

Served datasets with ODC-OWS in TerriaMap.

Product Layer	Description
Sentinel-2 L2A Composites	The median monthly composites for all bands and vegetation indices
Z-Normalized Sentinel-2 L2A Time series	The S2L2A median monthly composites normalized from 2023-April onwards, based on the mean and standard deviation from baseline 2020-01 to 2023-03
Tree Canopy Density	The HRL layer of Tree Cover Density 2023 (%, raster 10 m): https://doi.org/10.2909/e677441e-fb94-431c-b4f9-304f10e4dfd8
GLO-30	The elevation and aspect layers of GLO-30 resampled at 20 metres. https://doi.org/10.5270/ESA-c5d3d65

Implementation note:

• Dont' forget to allow file sharing in Docker
• Initialization:

1. docker exec -it drought-ows-drought_ows-1 bash
2. datacube system check - Valid connection: YES
3. datacube-ows-update --schema --write-role odc_phd --read-role odc_phd
4. datacube-ows-update --views
5. datacube-ows-update

References

Hislop, S., Stone, C., Gibson, R.K., Roff, A., Choat, B., Nolan, R.H., Nguyen, T.H. and Carnegie, A.J., 2023. Using dense Sentinel-2 time series to explore combined fire and drought impacts in eucalypt forests. Frontiers in Forests and Global Change, 6, p.1018936.