Unmanned Aerial Vehicle Applications in Forest Remote Sensing

Unmanned aerial vehicles (UAVs), commonly known as drones, have revolutionised forest remote sensing by offering high‐resolution, flexible platforms for acquiring data on canopy structure, species composition, biomass and ecosystem health. Equipped with sensors such as LiDAR, multispectral and hyperspectral cameras, and thermal imagers, UAVs enable rapid mapping of forest attributes across heterogeneous terrain. Their low operational cost, ease of deployment and capacity for repeat flights facilitate near‐real–time monitoring of forest dynamics, from growth rates and carbon stock estimation to early detection of pest outbreaks and post‐disturbance recovery. Integration of advanced data‐processing techniques, including machine learning and three‐dimensional point‐cloud analysis, has further enhanced the extraction of ecologically meaningful variables, supporting both local management and global carbon accounting frameworks. The growing synergy between UAV hardware miniaturisation and cloud‐based analytical pipelines promises to extend applications into understudied biomes and developing regions, thereby strengthening efforts in conservation, sustainable forestry and climate mitigation.

Research from Nature Portfolio

Recent studies have demonstrated the potential of deep learning applied to UAV‐borne LiDAR point clouds for forest biomass estimation. In one work, convolutional neural networks trained on three‐dimensional canopy metrics delivered carbon stock estimates that closely matched ground‐truth inventories, offering a scalable approach for national carbon reporting. Another investigation combined UAV‐mounted hyperspectral imaging with radiative transfer models to detect early signs of insect infestation in broadleaf stands, achieving species‐level discrimination and stress detection weeks before visible symptoms emerged. A third study introduced autonomous flight planning algorithms that adapt in real time to topographic complexity, optimising data coverage and reducing mission time by up to 30 per cent, thus improving the efficiency of repeated monitoring campaigns in montane forests.

Unmanned Aerial Vehicle Applications in Forest Remote Sensing publication trend

The graph below shows the total number of articles in unmanned aerial vehicle applications in forest remote sensing across all publications each year (not limited to Nature Index journals).

Technical terms

LiDAR (Light Detection and Ranging): A remote‐sensing technique that measures distances by illuminating targets with laser pulses and recording the reflected signals to produce three‐dimensional point clouds.

Multispectral Imaging: Acquisition of imagery at a few discrete wavelength bands, typically spanning visible and near‐infrared spectra, used to assess vegetation indices and health.

Hyperspectral Imaging: Capture of imagery across hundreds of narrow, contiguous spectral bands, enabling fine‐scale discrimination of species and stress signals.

Structure‐from‐Motion Photogrammetry: A method for reconstructing three‐dimensional structures from overlapping two‐dimensional images captured from different viewpoints.

Canopy Height Model (CHM): A surface model representing vegetation height above ground level, derived by subtracting a digital terrain model from a digital surface model.

Normalised Difference Vegetation Index (NDVI): A ratio of near‐infrared and red reflectance used as an indicator of vegetation vigour and density.

Point Cloud: A collection of spatial coordinates (x, y, z) representing the external surfaces of objects, obtained via LiDAR or photogrammetric methods.

References

1. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sensing (2016).