A benchmark dataset compares the performance of five laser scanning systems and a low-cost action camera for tree detection in a mixed temperate forest. The data was collected by ENVIDAT and published via NASA's Earthdata platform in 2023. It evaluates devices across three forest plots with varying tree and understorey vegetation density.
Use Cases
- Benchmarking tree detection algorithms based on data from terrestrial, handheld, and drone-based LiDAR.
- Comparing diameter at breast height (DBH) extraction accuracy between different sensor systems.
- Evaluating the performance of low-cost Structure from Motion (SfM) photogrammetry against LiDAR for forest inventory.
- Studying the impact of vegetation density on close-range remote sensing accuracy.
Strengths
- Benchmark includes data from five distinct laser scanning systems and a low-cost photogrammetry setup.
- Data collection covers three plots with a steep gradient in tree and understorey vegetation density.
- Explicitly designed to evaluate both efficiency and accuracy of close-range remote sensing methods.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and file formats are unknown, which may limit suitability assessment.
Provenance
- Source
- ENVIDAT via NASA Earthdata
- Collection Method
- Data collected using two terrestrial laser scanners (TLS), one handheld mobile scanner (PLS), two drone-based laser scanners (UAVLS), and a GoPro camera with Structure from Motion.
- Freshness
- Last updated 2023-01-01 00:00:00; freshness should be verified.
- Geography
- Ramerenwald, a mixed temperate forest (specific location not provided).