Publication Date:
2024-06-13
Description:
Mangrove forests provide valuable ecosystem services to coastal communities across
tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and
urge researchers to create improved monitoring methods for better environmental management.
Recent efforts that have focused on automatically quantifying the above-ground biomass using
image analysis have found some success on high resolution imagery of mangrove forests that have
sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation
consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove
species located in the remote Utría National Park in the Colombian Pacific coast. Our developed
workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests,
from which large orthophoto mosaics and digital surface models are built. We apply convolutional
neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average
precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for
semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the
Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water
land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of
trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically
segmented ground areas we interpolate their height from the digital surface model to generate a
digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we
calculate a canopy height model from the digital surface and elevation models and combine it with
the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree.
The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information,
as well as crown shape and size descriptions, enables the use of allometric equations to calculate
important monitoring metrics, such as above-ground biomass and carbon stocks.
Keywords:
mangrove forests
;
forest inventory
;
monitoring
;
habitat mapping
;
UAV
;
UAS
;
artificial
;
intelligence
;
machine learning
;
instance segmentation
;
semantic segmentation
;
above ground biomass
;
carbon stock
Repository Name:
National Museum of Natural History, Netherlands
Type:
info:eu-repo/semantics/article
Format:
application/pdf
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