Salt march Leaf Area Index determination with AI driven aerial lidar and multispectral data fusion

Salt marshes worldwide face ongoing climate change, including variations in local marine and meteorological forcing. Their resilience against relative sea level rise is partly dependent on organic soil production driven by vegetation development.

The Leaf Area Index (LAI) is a key indicator to quantify plant growth, ecosystem productivity and to characterize local vegetation distribution. However, area-wide LAI mapping from in situ measurements is challenging in inaccessible swampy and silty areas. Aerial/satellite mounted laser and imaging data have been used to augment in situ measured LAI values, but general methodology is lacking. Multi-sensor data fusion is an emerging area of research in improving LAI determination. In this abstract a novel data fusion technique is explored that uses an evolutionary AI model to map both  lidar 3D geometrical and multispectral vegetation data to LAI ground measurements.

A combined drone based survey acquiring both lidar and multispectral imagery (Green, Red, Red Edge and Near Infrared) was conducted in autumn 2025 at San Felice salt marsh in Venice Lagoon (Italy), a marsh shrinking and drowning due to microtide and reduced inorganic sediment input. Both lidar/multispectral flights were flown at around 30 meters above ground and processed into geo-referenced point clouds and multispectral orthomosaics.  Data sources were consequently merged into a multispectral point cloud by adding the nearest (in X-Y coordinate) multispectral information to each point in the point cloud. Ground based LAI in situ measurements were obtained in 40 vegetation patches spread out over the survey area.

The multispectral point cloud was subsequently divided into adjacent hexagonal cells (0.5m radius) with information per cell summarized by 19 parameters. Multispectral color (4 bands) information is reduced to a 4*4 averaged covariance matrix while a light reduction function (based on the Beer-Lambert law, 3 parameters) modeled the attenuation of Lidar returns with increasing height.

An Artificial Neural Network (ANN) model was trained using an evolutionary algorithm to find an optimized ANN model to couple multispectral point cloud parameters in the 40 ground patches to local LAI values. The model was varied in 1-3 hidden layers and 20 to 60 nodes per hidden layer.  Training data was split 80%-20% with 80% of the data used for training and the rest for prediction evaluation. The best model achieved a high prediction accuracy (R²=0.906, RMSE=0.11), but showed a tendency to underestimate LAI values possibly reflecting spectral saturation in denser vegetation. An example of a continuous salt marsh LAI map is shown in figure 1.
The data fusion approach offers a promising technique towards improved LAI mapping, contributing to a better understanding of salt marsh responses to climate change.