The paper, titled:

“Potential effects of climate change on growth and the implications for conservation of the endangered Serbian spruce (Picea omorika (Pančić) Purk.)”,

investigates how projected climate change scenarios may influence the growth patterns of the Serbian spruce—an endemic and endangered conifer species native to the Balkans.

Through ecological modeling and climate projections, the study provides valuable insights into the species’ future distribution and offers recommendations for adaptive conservation strategies aimed at mitigating climate-related risks.

This work represents a significant contribution to the fields of forest ecology and biodiversity conservation in the context of climate change.

Read the full article here: https://doi.org/10.1016/j.fecs.2024.100287

The canopy height maps were generated using a cutting-edge deep learning model (Zhou et al., 2023, Nature Ecology & Evolution) applied to Sentinel-2 imagery with the least cloud cover from May to September each year. For each year, five images were selected, and predictions were averaged to produce an annual CHM. The maps were then masked to exclude non-vegetated areas and pixels with canopy height below 5 meters, based on the Sentinel-2 L2A Scene Classification (SCL) layers.

Biomass was estimated using an adapted allometric equation from Pilli et al. (2023, ESSD), with the following relationship:
Biomass = 0.6 × H^1.9

To assess forest disturbance, the difference between the CHM layers from 2020 and 2024 was calculated. Changes smaller than 10 meters were excluded to reduce the influence of agricultural activity and model uncertainty.

This dataset represents a major output of the ForestCO2 project, which aims to develop transparent and scientifically robust methodologies for carbon credit verification in forestry using Earth Observation technologies and validated biomass modeling approaches.

Access the dataset on Zenodo:
https://zenodo.org/records/15201316

The following species were included in the study:

• Pinus nigra
• Pinus sylvestris
• Picea abies
• Quercus petraea
• Quercus robur
• Populus spp.

Methodology:

Each site was evaluated using land index criteria and ranked as follows:

• S1 (highly suitable, score 100–75) → 2 points
• S2 (moderately suitable, score 75–50) → 1 point
• N (not suitable, score <50) → 0 points

All scores were summed and expressed as the percentage of total criteria met for each location, resulting in a spatial map showing afforestation suitability in percent [%].

The dataset includes:

• .tif raster maps showing suitability percentages for each species
• .jpeg final map illustration
• species.zip – all input raster criteria files used for scoring
• species.R – the R script used to calculate afforestation potential
• data.zip – spatial layers used in the analysis
• osm_nominatim.zip – administrative boundaries of Serbia

This dataset enables strategic planning of afforestation activities in line with local soil and climate conditions, and is a significant result of the ForestCO2 project, which focuses on the role of forests in carbon sequestration and climate change mitigation.

Access the dataset on Zenodo:
https://zenodo.org/records/15202778

The dataset includes:

• LiDAR point clouds with an average density of 800 points/m², collected using a DJI Matrice 350 RTK drone equipped with a Zenmuse L2 sensor;
• Digital Terrain Models (DTM) and Canopy Height Models (CHM) at 1-meter and 20-centimeter resolutions, created using lidR and Global Mapper software;
• Soil spectral scans (NIR 1350–2500 nm) collected with a NeoSpectra handheld scanner, with six scans per sample;
• Laboratory soil analyses from 200 samples, including humus content and pH values;
• Tree inventory with over 31,000 individually classified tree features extracted from LiDAR point clouds.

This dataset represents a major outcome of the ForestCO2 project, which aims to develop methodologies for carbon credit verification in forestry using cutting-edge EO technologies and field measurements. The data is openly available and can be used for scientific research, algorithm validation, and development of AI models for forest ecosystem monitoring.

Access the dataset on Zenodo:
https://zenodo.org/record/14555118

At this important event, the ForestCO2 project was presented through a visually striking poster, which attracted the attention of visitors.