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\begin{document}

\title{A Dynamic, Cloud-based LULC Mapping Methodology Using Sentinel-2 to
Support Climate-Smart Landscape Management in Vulnerable Fijian
Communities.}



\author[1]{Kevin Davies}%
\author[2]{John Duncan}%
\author[3]{Renata Varea}%
\author[4]{Viliame Tupua}%
\author[1]{Eleanor Bruce}%
\author[2]{Bryan Boruff}%
\author[3]{Nathan Wales}%
\affil[1]{University of Sydney}%
\affil[2]{The University of Western Australia}%
\affil[3]{University of the South Pacific Laucala}%
\affil[4]{Ministry of Forestry}%


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  \date{May 30, 2024}


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\begin{abstract}
Communities in Fiji rely on provisioning services from landscape
resources such as agricultural and forestry-related production, and
climate regulation determined by the mix of landscape resources across
space. Accurate mapping and monitoring of patterns of land use and land
cover (LULC) over time at scales relevant to livelihood processes is
important for informing landscape management, land use policies, and
climate-smart sustainable development. A methodology developed
collaboratively with landscape stakeholders to produce an inter-annual
LULC map that addresses natural resource, agricultural, and forestry
management use cases is presented here. Key requirements identified by
stakeholders were that the LULC methodology was robust, relatively easy
to reproduce, and could be applied to other Fijian landscapes with
different dynamics. Using publicly available remotely sensed data and
geospatial tools, we applied the LULC methodology for two locations in
the Ba Catchment, Fiji. Field orientation and key validation data were
collected using the QField open-source mobile GIS, and labelled training
and accuracy assessment data were collected in Google Earth. Annual
median multispectral surface reflectance and seasonal NDVI-based
phenology metrics derived from Sentinel-2, and topographic variation
from SRTM DEM provided the best discrimination between vegetation
classes across the catchment from low-lying coastal areas to the
highlands (\textgreater{} 1000 m ASL). A random forest model was trained
and validated in Google Earth Engine to produce an inter-annual LULC map
with a 10m spatial resolution. An important outcome from our work was
the transfer of skills and building of local stakeholder capacity to
continue to update the LULC map, and to expand the map to include other
communities, catchments and forestry areas across Fiji. This capacity
building included iterative stakeholder consultation, co-development of
online training materials, workshops, and collaborative fieldwork.%
\end{abstract}%



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