Abstract:
Accurate species-level mapping of mangrove forests remains a persistent challenge due to spectral
similarity among taxa and the limited spatial resolution of conventional satellite imagery. Despite
increasing recognition of Sri Lanka's mangrove biodiversity, no previous study has employed
unmanned aerial vehicle (UAV)-derived multispectral imagery integrated with machine learning for
automated species-level classification. This study aimed to discriminate four dominant mangrove
species, namely Lumnitzera racemosa, Avicennia officinalis, Excoecaria agallocha, and Rhizophora
mucronata, within an approximately 12-hectare mangrove area of Rekawa Lagoon, Sri Lanka. An
integrated framework was developed, combining UAV derived multispectral imagery, object-based
image analysis, and comparative machine learning classification. Five-band multispectral imagery was
acquired using a DJI Phantom 4 Multispectral UAV equipped with a Real-Time Kinematic Global
Navigation Satellite System, flown at an altitude of 70 m, achieving a ground sampling distance of 3.44
cm. Image objects were delineated using the mean shift segmentation algorithm. Twenty-eight features
were extracted, encompassing raw spectral bands, six vegetation indices, sixteen Haralick texture
metrics, and a canopy height model. Ground truth data were incorporated to train and validate the
classification models. Three machine learning algorithms implemented in ArcGIS Pro 3.6-Support
Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighborhood (KNN) were evaluated
across five incremental feature combinations. SVM achieved the highest overall accuracy of 82.18%
when spectral bands were combined with canopy height data. McNemar's statistical test confirmed
canopy height as the most significant contributor to classification performance (p < 0.05). Vegetation
indices yielded no statistically significant improvement over baseline spectral features, while texture
metrics provided a secondary but meaningful contribution. These findings underscore the importance
of three-dimensional structural information for resolving spectral ambiguity among mangrove species.
The proposed framework offers an operationally efficient and transferable approach for high-resolution
mangrove species mapping, biodiversity assessment, and conservation planning in Sri Lankan coastal
ecosystems.
