Integrating drone multispectral and LiDAR data with machine learning: toward species-level biomass estimation in heterogeneous salt marsh vegetation

Abstract

Quantitative estimation and spatial mapping of aboveground biomass (AGB) for salt marsh vegetation are crucial for modeling biogeochemical cycles and assessing wetland carbon stocks. Remote sensing offers a noninvasive method for monitoring vegetation traits over large areas. However, a single technique often cannot simultaneously capture both spectral information and the vertical structure of vegetation, greatly hindering its capabilities in classifying and estimating AGB of salt marsh vegetation. This work introduces a machine learning and heterogeneous data-based approach for AGB estimation by integrating passive multispectral two-dimensional imagery and active light detection and ranging (LiDAR) three-dimensional point clouds acquired from a drone platform. Vegetation indices along with texture features are extracted from multispectral imagery, whereas intensity values and height attributes are derived from LiDAR data. Four machine learning methods, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and light gradient boosting machine (LightGBM), are employed to classify vegetation and estimate AGB through the strategic utilization of a carefully derived multispectral-LiDAR feature set. A comprehensive case study of a coastal salt marsh in Chongming Island, China, reveals that (1) No noticeable discrepancies are found for different machine learning models in AGB estimation, with XGBoost achieving the highest accuracy (R2 = 0.9207, MAE = 0.2835 kg/m2, RMSE = 0.3229 kg/m2); (2) Feature types and sensors considerably affect AGB estimation accuracy, with height and texture features having greater influence than intensity features and vegetation indices, and LiDAR outperforms multispectral data; and (3) AGB varies remarkably among species and environments, with Spartina alterniflora being more sensitive to changes in soil moisture and nutrients, while Phragmites australis maintains higher AGB even under unfavorable conditions. The proposed approach offers an alternative and effective strategy for AGB estimation and shows strong potential in quantitatively characterizing the ecological processes of salt marsh vegetation.