1,681 research outputs found
Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019
No full textAn 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:
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.
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
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.
The data are in standard GeoTIFF file format organized by island.
These data files will be updated as further improvements are made
Global Airborne Observatory: Hawaiian Islands Bathymetry 2019+2020
No full textSummary
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.
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 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.
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.
Methods
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 Asner et al. (2020) and Asner et al. (2021). 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
Using an airborne hyperspectral and LiDAR integrated sensor approach to spectrally discriminate and map savanna bush encroaching species in the Greater Kruger National Park region
No full textIncludes abstract.Includes bibliographical references (leaves 105-113).Bush encroachment is an environmental phenomenon which affects arid and semi-arid savanna rangelands across the world. Bush encroachment has numerous negative and positive impacts on these savanna ecosystems depending on the land use practices and associated rangeland management regimes
Global Airborne Observatory: Plot-level Forest Canopy Properties in Sabah, Malaysia
No full textPlot-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:
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. Communications Earth & Environment.
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]. Zenodo. https://doi.org/10.5281/zenodo.7051897
Data details:
(1) Data cover the Danum and Sepilok sites as described in Ordway et al. (2022) Communication Earth and Environment. All data layers are presented in GeoTIFF format.
(2) ACD = Aboveground carbon density in units of Mg C per hectare at 30 meter spatial resolution, as described in https://www.sciencedirect.com/science/article/pii/S0006320717310790
(3) LAD = Leaf area density in units of m2 per m3 at 50 meter spatial resolution.
(4) chems = Leaf chemical traits at 4 meter spatial resolution, as described in https://www.mdpi.com/2072-4292/10/2/199
(5) TCH = top-of-canopy height in units of meters, as described in https://www.sciencedirect.com/science/article/pii/S000632071731079
Global Airborne Observatory: Forest Functional Diversity of Peru
No full textNational-scale maps of forest functional diversity at two levels, as published by Asner et al. Science 2017: (1) Forest functional groups in 6 classes; (2) Forest functional class in 36 classes. These data are provided as standard GeoTIFF format at 1-hectare (100m x 100m) resolution as mapped by airborne laser-guided imaging spectroscopy.
Use of these data requires citation of this Zenodo data source plus citation of the original journal paper as follows
Asner, G.P., R.E. Martin, D.E. Knapp, R. Tupayachi, C.B. Anderson, F. Sinca, N.R. Vaughn, and W. Llactayo. 2017. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355:385-389
Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data
No full textTropical forests are a key component of the global carbon cycle, and mapping their carbon density is essential for understanding human influences on climate and for ecosystem-service-based payments for forest protection. Discrete-return airborne laser scanning (ALS) is increasingly recognised as a high-quality technology for mapping tropical forest carbon, because it generates 3D point clouds of forest structure from which aboveground carbon density (ACD) can be estimated. Area-based models are state of the art when it comes to estimating ACD from ALS data, but discard tree-level information contained within the ALS point cloud. This paper compares area-based and tree-centric models for estimating ACD in lowland old-growth forests in Sabah, Malaysia. These forests are challenging to map because of their immense height. We compare the performance of (a) an area-based model developed by Asner and Mascaro (2014), and used primarily in the neotropics hitherto, with (b) a tree-centric approach that uses a new algorithm (itcSegment) to locate trees within the ALS canopy height model, measures their heights and crown widths, and calculates biomass from these dimensions. We find that Asner and Mascaro’s model needed regional calibration, reflecting the distinctive structure of Southeast Asian forests. We also discover that forest basal area is closely related to canopy gap fraction measured by ALS, and use this finding to refine Asner and Mascaro’s model. Finally, we show that our tree-centric approach is less accurate at estimating ACD than the best-performing area-based model (RMSE 18% vs 13%). Tree-centric modelling is appealing because it is based on summing the biomass of individual trees, but until algorithms can detect understory trees reliably and estimate biomass from crown dimensions precisely, areas-based modelling will remain the method of choice
Global Airborne Observatory: Forest Canopy Height and Carbon Stocks of Panama
No full textTwo maps are provided from a study of the Republic of Panama. The maps are based on airborne light detection and ranging (lidar) data, combined with satellite-based maps of forest cover and properties, acquired in 2012. The resulting maps are: (1) top of canopy height or TCH; and (2) aboveground carbon density or ACD. Units for TCH are meters above ground. Units for ACD are Mg C per hectare. Maps are provided at 1.0 ha spatial resolution. File format is GeoTIFF.
Use of these data require citation of this dataset and the original journal paper that delivered the mapping method. These citations are as follows:
Asner, G.P., J. Mascaro, C. Anderson, D.E. Knapp, R.E. Martin, T. Kennedy-Bowdoin, M. van Breugel, S. Davies, J.S. Hall, H.C. Muller-Landau, C. Potvin, W. Sousa, J. Wright and E. Bermingham. 2013. High-fidelity national carbon mapping for resource management and REDD+. Carbon Balance and Management 8:7 (doi:10.1186/1750-0680-8-7)
Asner, G.P., J. Mascaro, C. Anderson, D.E. Knapp, and R.E. Martin. 2021. Global Airborne Observatory: Forest canopy height and carbon stocks of Panama (Version 1.0) [Data set]. Zenodo http://doi.org/10.5281/zenodo.462424
Electron transport through single donors in silicon
No full text-Kavli Institute of Nanoscience DelftApplied Science
Spectral signatures of macroalgae on Hawaiian reefs
No full text<p>Spectral reflectance of benthic macroalgae in Hawaii. Dataset accompanies this paper:</p>
<div>Fuller, K., R.E. Martin, and G.P. Asner. 2024. Spectral signatures of macroalgae of Hawaiian reefs. <em>Remote Sensing</em> in review (will be updated shortly)</div>
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<div>Please cite this paper when using these data or any derivative of these data.</div>
<p>The CSV file lists each spectral filename, species, and other information as referenced in the original journal paper. The ZIP file contains each spectral file in Analytical Spectral Devices (ASD) format. Each binary ASD spectral file can be read using code provided here: https://github.com/ASU-GDCS/ASDtoCSV</p>
Global Airborne Observatory: Forest Carbon Stocks of the Hawaiian Islands
No full textForest aboveground carbon density (ACD) for the main eight Hawaiian Islands in 2015-2016. The data are in 30 meter resolution format with the units of Mg C per hectare. The file is a standard GeoTIFF.
Use of these data requires citation of this dataset plus citation of the source study as follows:
Asner, G.P., S. Sousan, D.E. Knapp, P.C. Selmants, R.E. Martin, R.F. Hughes, and C.P. Giardina. 2016. Rapid forest carbon assessments of oceanic islands: a case study of the Hawaiian archipelago. Carbon Balance and Management 11, doi:10.1186/s13021-015-0043-
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