Reusable, transparent workflows for coastal remote sensing data

Why Data Curation Matters

High-resolution remote sensing surveys, including drone-based imagery and LiDAR, are now common in coastal research. But without clear documentation and structure, even high-quality datasets can be difficult to interpret, compare, or reuse. In our lab, data curation is part of the monitoring workflow itself. We design surveys and documentation together so datasets remain useful beyond a single project or publication.

A FAIR-Aligned Approach to Remote Sensing Data

We develop and apply standardized workflows to organize, document, and share coastal remote sensing data. These practices are informed by multi-year monitoring at Sugarloaf Island, North Carolina, and are designed to support:

• repeat surveys across seasons and years
• comparison across sites and projects
• long-term public archiving
• transparent reporting of spatial accuracy and uncertainty

Our approach aligns with the FAIR data principles (Findable, Accessible, Interoperable, Reusable) by embedding documentation directly into the data structure.

Why Consistent Workflows Matter

Detecting real coastal change requires more than dense point clouds or high-resolution imagery. When surveys are repeated through time, consistency in data collection, processing, and documentation is essential for separating environmental change from workflow-related noise.

The workflow shown below provides a conceptual overview of how repeat surveys are designed and curated in our lab. Full technical details and validation are described in our data descriptor: https://doi.org/10.1038/s41597-025-06310-z.

Dataset Structure & Documentation

We use a standardized folder structure to organize raw data, processed products, and supporting documentation:

01_DroneImagery
02_RTK_GNSS
03_PointCloud
04_DEM
05_Orthomosaic
06_Metadata
07_NBS_Features

The 01_DroneImagery directory contains raw and pre-processed imagery collected from drone platforms, including imagery supporting both SfM photogrammetry and drone-based LiDAR workflows.

Each dataset also includes:

• plain-language README files
• a supporting Standard Operating Procedure (SOP)

Together, these materials allow others to understand how the data were collected, processed, and validated without needing to contact the original authors, unless they want to.

Why This Matters for Coastal Management

Well-curated remote sensing datasets support better science and better decisions. By making data easier to interpret and reuse, these workflows help coastal managers, practitioners, and researchers evaluate shoreline stabilization, habitat change, and restoration outcomes with greater confidence. Our goal is to raise the standard for how coastal remote sensing data are documented and shared, so high-resolution monitoring can meaningfully inform management and resilience planning.