Global Airborne Observatory: Hawaiian Islands Reef Rugosity 2019+2020

Summary

Coral reef rugosity maps were developed by the Global Airborne Observatory (GAO) team at the Center for Global Discovery and Conservation Science at Arizona State University. The maps show high-resolution seafloor rugosity derived from airborne imaging spectroscopy data collected by the GAO in January 2019 and January 2020.

Data Use Requirements

Use of these data must acknowledge the source its funders as:

“The bathymetry and rugosity data maps were created by the Global Airborne Observatory, Center for Global Discovery and Conservation Science, Arizona State University.  The project received financial support from the Lenfest Ocean Program, The Battery Foundation, John D. and Catherine T. MacArthur Foundation, Avatar Alliance Foundation, State of Hawaiʻi Division of Aquatic Resources, State of Hawaiʻi Department of Planning, National Oceanic and Atmospheric Administration.”

In addition, provide citations to the following two publications on any materials or presentations utilizing the data or products and results derived from the data:

Asner, G.P., N.R. Vaughn, C. Balzotti, P.G. Brodrick, and J. Heckler. 2020. High-resolution reef bathymetry and coral habitat complexity from airborne imaging spectroscopy. Remote Sensing 12:310 (doi:10.3390/rs12020310)

Asner, G.P., N.R. Vaughn, S.A. Foo, J. Heckler, and R.E. Martin. 2021. Drivers of reef habitat complexity throughout the Main Hawaiian Islands. Frontiers in Marine Science 8:631842. (doi: 10.3389/fmars.2021.631842)

Map Properties

There are two types of map products available as part of this collection: fine rugosity, and coarse rugosity. Except for Hawaii Island, there are three separate map files for each of the Main Hawaiian Islands (Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau).  Hawaii Island was large enough that it needed to be split into quarters for manageability, and each of the three maps are available for all quarters (12 maps total). Coordinates for all maps refer to the UTM Coordinate System, Zone 4 North using datum WGS-84, with the exception of those for Hawaii Island which refer to Zone 5 North.

The rugosity maps in meters at a 2-meter spatial resolution up to approximately 22 meters in depth, where data quality allowed. To minimize the effect of water properties, these maps are built using a blend of data from both the 2019 and 2020 collection periods.  Fine-scale rugosity seeks high frequency changes on the seafloor arising from coral colonies, rocks, and other bottom features that generate local habitat variability, and coarse-scale rugosity is more responsive to variations in larger terrain features resulting from geologic, reef-scale accretion, and subsidence processes. Fine-scale rugosity maps are produced at 2-meter horizontal resolution, but each pixel represents the conditions of a 6-meter square window centered at the given pixel. Similarly, coarse-scale rugosity maps are produced at 6-meter resolution, but each pixel represents the conditions of a 54-meter square window centered at the given pixel. Rugosity values in the maps are unitless and range from 0.0 (low rugosity) to 100.0 (high rugosity).

Methods

We computed island-wide maps of rugosity at two resolution using a standard planar image rugosity metric on the GAO blended bathymetry maps (Asner, Gregory. P., Vaughn, Nicholas, & Heckler, Joseph. (2020). Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019. Zenodo. http://doi.org/10.5281/zenodo.4292660). Prior to running the algorithm, missing data of less than two pixels in width were filled using an inverse-distance weighted average of the three nearest neighboring pixels. Fine-scale rugosity was computed using a 3 x 3 pixel (6 x 6 meters) moving window on the original 2-meter resolution bathymetric maps. Coarse-scale rugosity was computed by first down-sampling the 2-meter depth maps to 6-meter resolution using a mean filter. The rugosity metric was then computed using a 9 x 9 pixel (54.0 x 54.0 m) moving window on the 6-meter depth maps. The distribution of raw rugosity algorithm output values is extremely skewed and difficult to interpret. Thus, the rugosity maps contain rugosity values that are transformed in such a way that they have an approximate uniform [0,1] distribution. This both reduces the influence of noisy depth pixels and gives a more meaningful scale upon which to interpret the maps.