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8 results for “Global Airborne Observatory”
Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019
<p>An airborne mapping approach combining laser-guided imaging spectroscopy and deep learning models was used to quantify the geographic distribution of live corals to 16 m water depth throughout the eight main Hawaiian Islands. Full metadata and methods are provided in:</p> <p>Asner, G.P., N.R. Vaughn, J. Heckler, D.E. Knapp, C. Balzotti, E. Shafron, R.E. Martin, B.J. Neilson, J.M. Gove. 2020. Large-scale mapping of live corals to guide reef conservation. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.2017628117.</p> <p>Asner, G.P., N.R. Vaughn, J. Heckler. 2020. Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019 (Version 3.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4292660</p> <p>Use of our data requires that you cite both of these sources together. In addition, we ask that these data be used to make the world a better place.</p> <p>The data are in standard GeoTIFF file format organized by island.</p> <p>These data files will be updated as further improvements are made.</p>
Global Airborne Observatory: Hawaiian Islands Reef Rugosity 2019+2020
<p><strong>Summary</strong></p> <p>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.</p> <p><strong>Data Use Requirements</strong></p> <p>Use of these data must acknowledge the source its funders as:</p> <p>“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.”</p> <p>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:</p> <p>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. <em>Remote Sensing</em> 12:310 (doi:10.3390/rs12020310)</p> <p>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. <em>Frontiers in Marine Science </em>8:631842. (doi: 10.3389/fmars.2021.631842)</p> <p><strong>Map Properties</strong></p> <p>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.</p> <p>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).</p> <p><strong>Methods</strong></p> <p>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. <a href="http://doi.org/10.5281/zenodo.4292660">http://doi.org/10.5281/zenodo.4292660</a>). 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.</p>
Global Airborne Observatory: Hawaiian Islands Bathymetry 2019+2020
<p><strong>Summary</strong></p> <p>Bathymetry 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 benthic depth, derived from airborne imaging spectroscopy data collected by the GAO in January 2019 and January 2020.</p> <p><strong>Data Use Requirements</strong></p> <p>Use of these data must acknowledge the source its funders as:</p> <p>“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.”</p> <p>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:</p> <p>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. <em>Remote Sensing</em> 12:310 (doi:10.3390/rs12020310)</p> <p>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. <em>Frontiers in Marine Science </em>8:631842. (doi: 10.3389/fmars.2021.631842)</p> <p><strong>Map Properties</strong></p> <p>There are multiple map products available as part of this collection. 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. 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.</p> <p>The blended bathymetry maps give modeled depth as a floating-point values 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.</p> <p><strong>Methods</strong></p> <p>GAO spectrometer data for Hawaii were collected in 1.3 km wide flight line strips and the flights were planned such that individual flight lines overlap each other by about 50%, giving at least two passes of coverage per year of collection. Thus, we have two or more passes of data over most of the Hawaiian coastlines. Details of data collection protocols can be found in <strong><em>Asner et al. (2020) and Asner et al. (2021)</em></strong>. To build the blended bathymetry maps, we identified areas with sufficient sunlight and low surface glint for each flight line, and then applied GAO-created a neural network model to derive estimated depth of each pixel in such areas.</p>
Global Airborne Observatory: Plot-level Forest Canopy Properties in Sabah, Malaysia
<p>Plot-level mapping data derived from the Global Airborne Observatory mission in Sabah (Borneo), Malaysia in 2016. Use of these data requires citation of this publication as well as this dataset as follows:</p> <p>Ordway E.M., G.P. Asner, D. Burslem, S. Lewis, R. Martin, R. Nilus, M.J. O’Brien, O. Phillips, L. Qie, N.R. Vaughn, and P.R. Moorcroft. 2022. Mapping tropical forest functional variation at satellite remote sensing resolutions depends on key traits. <em>Communications Earth & Environment.</em></p> <p>Asner, G.P., E. Ordway, J. Heckler, and N.R. Vaughn. 2022. Global Airborne Observatory: Plot-level Forest Canopy Properties in Sabah, Malaysia (1.0) [Data set]. <em>Zenodo</em>. https://doi.org/10.5281/zenodo.7051897</p> <p>Data details:</p> <p>(1) Data cover the Danum and Sepilok sites as described in Ordway et al. (2022) <em>Communication Earth and Environment. </em>All data layers are presented in GeoTIFF format.</p> <p>(2) ACD = Aboveground carbon density in units of Mg C per hectare at 30 meter spatial resolution, as described in <em>https://www.sciencedirect.com/science/article/pii/S0006320717310790</em></p> <p>(3) LAD = Leaf area density in units of m2 per m3 at 50 meter spatial resolution.</p> <p>(4) chems = Leaf chemical traits at 4 meter spatial resolution, as described in <em>https://www.mdpi.com/2072-4292/10/2/199</em></p> <p>(5) TCH = top-of-canopy height in units of meters, as described in <em>https://www.sciencedirect.com/science/article/pii/S0006320717310790</em></p> <p> </p>
Global Airborne Observatory: Submarine Groundwater Discharge on West Hawaii Island
<p>Mapped submarine groundwater discharge (SGD) for the west coast of Hawaii Island. A full description of the data source and methodology is available at:</p> <p>Asner, G.P., N.R. Vaughn, and J. Heckler. 2024. Operational mapping of submarine groundwater discharge into coral reefs: Application to West Hawaii Island. Oceans 5, 547-559. https://doi.org/10.3390/oceans5030031</p> <p>There are two data layers:</p> <ol> <li>Estimated point sources of SGD</li> <li>Estimated dischage areas of SGD </li> </ol> <p>SGD discharge point sources as a point layer and SGD discharge areas are provided as polygon layers, both in GeoJSON format. The point source layer includes a Field LocCertainty, describing the certainty in percent that the discharge location is correctly identified. The discharge area layer includes fields InsideC and OutsideC, which average thermal sensor temperature in Celsius inside and outside, respectively, as well as fields for the temperature difference (dTemp) and the size of the dicharge area in hectares (ha). Both layers use the WGS84 coordinate system with spatial coordinates giving positions as degrees longitiude and latitude.</p>
Global Airborne Observatory: Mapped Infrastructure on Hawaii Island
<p>There are three mapping datasets showing infrastructure on Hawaii Island (Hawaii County) in the State of Hawaii. The datasets were created using a combination of a convolutional neural network (CNN) and gradient boosted trees (GBT) run on Global Airborne Observatory laser scanning and imaging spectroscopy data.. Full methodology is available in the following reference:</p> <p>Mason, R.E.; Vaughn, N.R.; Asner, G.P. Mapping Buildings across Heterogeneous Landscapes: Machine Learning and Deep Learning Applied to Multi-Modal Remote Sensing Data. <em>Remote Sensing </em><strong>2023</strong>, <em>15</em>, x. https://doi.org/10.3390/xxxxx</p>
Global Airborne Observatory: Example Rapid Water Quality Maps of Hawaiian Island Coastline
<p>Example maps from the publication:</p> <p>Nicholas R. Vaughn, Marcel König, Kelly L. Hondula, Dominica E. Harrison and Gregory P. Asner. 2024. Rapid Water Quality Mapping from Imaging Spectroscopy with a Superpixel Approach to Bio-Optical Inversion. <em>Remote Sensing</em>. (in press)</p> <h2>Data:</h2> <p>Maps of water quality and auxiliary information produced from Global Airborne Observatory (GAO) VSWIR Spectrometer data for five example sites:</p> <table> <tbody> <tr> <td>Site</td> <td>Island</td> <td>Date Collected</td> <td>Time Collected (24hr)</td> <td>Latitude</td> <td>Longitude</td> </tr> <tr> <td>1. Pelekane Bay</td> <td>Hawaiʻi</td> <td>30 October 2023</td> <td>10:02</td> <td>20.0243</td> <td>−155.8256</td> </tr> <tr> <td>2. East Kaho’olawe</td> <td>Kaho’olawe</td> <td>22 January 2024</td> <td>09:15</td> <td>20.6027</td> <td>−156.5638</td> </tr> <tr> <td>3. Hilo Bay</td> <td>Hawaiʻi</td> <td>15 January 2023</td> <td>11:30</td> <td>19.7353</td> <td>−155.0636</td> </tr> <tr> <td>4. Mā’alaea Bay</td> <td>Maui</td> <td>22 January 2024</td> <td>10:19</td> <td>20.7805</td> <td>−156.4842</td> </tr> <tr> <td>5. South Moloka’i</td> <td>Moloka’i</td> <td>7 January 2024</td> <td>11:31</td> <td>21.0866</td> <td>−157.2159</td> </tr> </tbody> </table> <div> <div> <div> <p>There are 41 maps for each example site:</p> <ol> <li>A single 3-band true-color map of the example site for reference (Site<<em>X_Name></em>_3band.tif)</li> <li>A single full-resolution water quality map produced using a bio-optical inversion model on each pixel (Site<<em>X_Name></em>_PixelbyPixel.tif)</li> <li>For each of 13 different superpixel size settings (50px, 100px, 250px, 500px, 600px, 750px, 1000px, 1250px, 1500px, 2000px, 3000px, 4000px, 5000px), a map of the superpixel ID of each pixel (Site<X_Name>_Size<####>_ClusterID.tif)</li> <li>For each of 13 different superpixel size settings, a superpixel-level map of water quality as estimated at the cluster level. (i.e., each pixel in a superpixel will have the same values, Site<X_Name>_Size<####>_Cluster_Preds.tif)</li> <li>For each of 13 different superpixel size settings, a full-resolution water quality map produced by interpolation of the superpixel level maps in 4. above (Site<X_Name>_Size<####>.tif)</li> </ol> <p>All files are in GeoTiff format using 32-bit floating point values. Most are compressed with the LZW algorithm. Maps are not georeferenced. No data value for map types 2-5 is -9999.</p> </div> </div> </div>
Global Airborne Observatory: Forest Canopy Height of Peruvian Amazon and Andes
<p>This dataset contains a forest canopy height map of the Peruvian Amazon and Andes region. The map is based on airborne light detection and ranging (lidar) data, combined with satellite-based maps, acquired in 2012. Units for the map are meters above ground level. The map is provided at 1.0 ha spatial resolution.</p> <p>Use of these data require citation of this dataset and the original journal paper that delivered the mapping method. These citations are as follows:</p> <p>Asner, G.P., D.E. Knapp, R.E. Martin, R. Tupayachi, C.B. Anderson, J. Mascaro, F. Sinca, K.D. Chadwick, M. Higgins, W. Farfan, W. Llactayo, and M.R. Silman. 2014. Targeted carbon conservation at national scales with high-resolution monitoring. <em>Proceedings of the National Academy of Sciences</em> 111(47):E5016-E5022 doi:10.1073/pnas.1419550111</p>
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