Co-aligned Hyperspectral and LiDAR Data from Drought-Stressed Beech Forest

Co-aligned hyperspectral and LiDAR data covers four sites of European beech forest in Germany's Rhӧn Biosphere Reserve. Data was collected via UAV in September 2020 to assess canopy damage from a 2018/2019 extreme drought event. Hyperspectral images have approximately 5cm pixel resolution.

Use Cases

• Classify individual tree canopy damage levels using hyperspectral pixel data for defoliation and discoloration analysis.
• Fuse LiDAR and hyperspectral data to model 3D forest structure and identify drought-induced mortality patterns.
• Train segmentation models on high-resolution (5cm) hyperspectral imagery to delineate individual European beech trees.
• Analyze spectral signatures from hyperspectral data to correlate with specific drought stress responses in beech stands.

Strengths

• Data is co-aligned, providing spatially matched hyperspectral and LiDAR observations.
• Hyperspectral imagery has high spatial resolution of approximately 5cm per pixel.
• Covers four distinct forest sites, each approximately 8 hectares in size.
• Focuses on a specific ecological event: forest response to the 2018/2019 central European drought.

Limitations

• Dataset scope is limited to four sites within a single biosphere reserve, reducing geographic generalizability.
• Temporal coverage is a single snapshot from September 2020, lacking time-series data on drought recovery.
• Data availability and specific file structures (e.g., spectral bands, LiDAR point density) are not detailed in the input.

Provenance

Source

Environmental Information Data Centre

Collection Method

Collected using a Headwall Hyperspec Nano sensor flown from an unmanned aerial vehicle (UAV).

Time Range

September 2020

Freshness

null

Geography

Core protected areas of the UNESCO Rhӧn Biosphere Reserve, Germany.