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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗
NASA NTRS2020

Learning instrument invariant characteristics for generating high-resolution global coral reef maps

Coral reefs are one of the most biologically complex and diverse ecosystems within the shallow marine environment. Unfortunately, these underwater ecosystems are threatened by a number of anthropogenic challenges, including ocean acidification and warming, overfishing, and the continued increase of marine debris in oceans. This requires a comprehensive assessment of the world's coastal environments, including a quantitative analysis on the health and extent of coral reefs and other associated marine species, as a vital Earth Science measurement. However, limitations in observational and technological capabilities inhibit global sustained imaging of the marine environment. Harmonizing multimodal data sets acquired using different remote sensing instruments presents additional challenges, thereby limiting the availability of good quality labeled data for analysis. In this work, we develop a deep learning model for extracting domain invariant features from multimodal remote sensing imagery and creating high-resolution global maps of coral reefs by combining various sources of imagery and limited hand-labeled data available for certain regions. This framework allows us to generate, for the first time, coral reef segmentation maps at 2-meter resolution, which is a significant improvement over the kilometer-scale state-of-the-art maps. Additionally, this framework doubles accuracy and IoU metrics over baselines that do not account for domain invariance.

Domain Adaptation↗
NASA NTRS2022

Geophysical Observations Toolkit For Evaluating Coral Health (GOTECH) Fall 2021 Final Report

The NASA Langley Research Center (LaRC) Data Science Team (DST), under the Office of the Chief Information Officer (OCIO), is investigating the capacity of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite to infer the vitality of coral reefs. This report describes the Fall 2021 period of performance for the Geophysical Observations Toolkit for Evaluating Coral Health (GOTECH) project. During this effort, two student teams at Georgia Tech developed machine-learning models to predict the vitality of coral reefs in targeted geographic regions based on backscatter data from the CALIPSO satellite. To train these models, students fused data to form a common operating picture of how coral reefs have grown and decayed worldwide. This report describes the student assignment, background, and results of the semester's research.

Machine Learning↗
NASA NTRS1997

Calcification and photosynthesis of the coral acropora cervicornis under calcium limited conditions

Differing hypothesis about the function of calcification are based on an interesting dilemma. Is the purpose of calcification mainly a structural and protective one or does calcification serve other functions? Does photosynthesis increase carbonate ion activity and cause calcification or does calcification increase CO2 levels and stimulate photsynthesis? It is proposed that calcification in corals is not dependent upon photosynthesis but upon calcium levels in the water. Under normal ocean conditions, corals convert a certain percentage of energy to photosynthesis and respiration and another percentage to calcification. As corals become nutrient stressed, particularly calcium limited, the ratio of photosynthesis to calcification shifts towards calcification in order to generate protons. The protons generated during calcification may stimulate photosynthesis and aid in the uptake of nutrients and biocarbonates. The results of the calcification experiment show a trend towards increased calcification and decreased photosynthesis when the coral Acropora cervicornis is calcium limited, but the data are inconclusive and further research is needed.

Rathfon, Megan↗
NASA NTRS2004

Decision Support Tool Evaluation Report for Coral Reef Early Warning System (CREWS) Version 7.0

The Coral Reef Early Warning System (CREWS) is operated by NOAA's Office of Oceanic and Atmospheric Research as part of its Coral Reef Watch program in response to the deteriorating global state of coral reef and related benthic ecosystems. In addition to sea surface temperatures (SSTs), the two most important parameters used by the CREWS network in generating coral reef bleaching alerts are 1) wind speed and direction and 2) photosynthetically available radiation (PAR). NASA remote sensing products that can enhance CREWS in these areas include SST and PAR products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and wind data from the Quick Scatterometer (QuikSCAT). CREWS researchers are also interested in chlorophyll, chromophoric dissolved organic matter (CDOM), and salinity. Chlorophyll and CDOM are directly available as NASA products, while rainfall (an available NASA product) can be used as a proxy for salinity. Other potential NASA inputs include surface reflectance products from MODIS, the Advanced Spaceborne Thermal Emission and Reflection Radiometer, and Landsat. This report also identifies NASA-supported ocean circulation models and products from future satellite missions that might enchance the CREWS DST.

D'Sa, Eurico↗
NASA NTRSDate not supplied

Hawaii Water Resources: Monitoring the Impact of Land-Based Sources of Pollution on Water Quality Along the Coast of West Maui, Hawai'i, to Assess Coral Reef Condition

West Maui is at risk of losing ecosystem services provided by coral reefs due to land-based sources of pollution (LBSP). In 2011, the US Coral Reef Task Force (USCRTF) identified the West Maui watershed as a priority watershed (along with its sub-watersheds of Wahikuli, Honokōwai, Kahana, Honokahua, and Honolua) after decades of coral decline, giving rise to the multi-agency West Maui Ridge to Reef (R2R) Initiative. The DEVELOP Hawai’i Water Resources team partnered with the R2R Initiative and the Hawai’i Department of Land and Natural Resources Division of Aquatic Resources (DLNR-DAR) to address the need for better watershed management practices. The team provided the partners with a Google Earth Engine tool that displays land use and land cover changes (LULCC) in the five watersheds and detects near-shore turbidity, chlorophyll-a (chl-a), and sea surface temperature using Landsat 4 Thematic Mapper (TM), Landsat 5 TM, Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Aqua MODIS. Team members used ancillary data provided by the R2R Initiative and the USGS Pacific Coastal and Marine Science Center (PCMSC) to validate satellite parameter values. The land cover analysis captured a general trend of increasing impervious cover and decreasing vegetated cover from 1989 to 2019; however, the extent of this change varied between each watershed. This analysis, coupled with the tool, can help project partners continually monitor terrestrial and marine patterns associated with coral decline.

Water Resources↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS1973

Helium-uranium dating of corals.

Fanale and Schaeffer (1965) determined He/U ages of corals and other aragonitic fossils, and found that the results generally agreed with independent age estimates. As a more extensive and rigorous test of the reliability of He/U ages of fossil corals, I have determined He/U ages of forty-five independently dated Cenozoic corals. Uranium and thorium isotope compositions and Rn-222 retentivities were also determined as consistency checks. The results indicate that reliable ages are obtained when certain consistency tests are met and specified corrections are made.

Bender, M. L.↗
NASA NTRS2001

Astronaut Photography of Coral Reefs

Astronaut photographs of tropical coastal areas may contain information on submerged features, including coral reefs, up to depths of about 15 m in clear waters. Previous research efforts have shown that astronaut photographs can aid in estimating coral reef locations and extent on national, regional and global scales, and allow characterization of major geomorphological rim and lagoon features (Andrefouet et al. 2000, in preparation). They can be combined with traditional satellite data to help distinguish between clouds and lagoon features such as pinnacles (Andrefouet and Robinson, in review). Furthermore, astronaut photographs may provide reef scientists and managers with information on the location and extent of river plumes and sediment run off, or facilitate identification of land cover types, including mangroves (Webb et al., in press). Photographs included in the section were selected based on several criteria. The primary consideration of the editors was that the photographs represent a worldwide distribution of coral reefs, have extremely low visual interference by cloud cover, and display a spatial scale reasonable for examining reef-related features. Once photographs were selected, they were digitized from 2nd generation copies. The color and contrast were hand corrected to an approximation of natural color (required to account for spectral differences between photographs due to the color sensitivities of films used, and differences in sun angle and exposure of the photographs). None of the photographs shown here have been georeferenced to correct them to a map projection and scale. Any distortions in features due to slightly oblique look angles when the photographs were taken through spacecraft windows remain. When feasible, near vertical photographs have been rotated so that north is toward the top. An approximate scale bar and north arrow have added using distinctive features on each photograph with reference to a 1:1,000,000 scale navigation chart. Astronaut photographs provide a unique source of moderate resolution reef remote sensing data because of their global coverage and (immediate) availability in the public domain. The database of photographs can be searched an browsed online and high-resolution digital copies of photographs in this atlas can be accessed via the Website of Earth Science and Image Analysis at NASA's Johnson Space Center:

Robinson, Julie A.↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
NASA NTRS2022

NASA Satellites and Data Fusion: A Case Study with Coral Reefs

As the world is experiencing a significant rise in both AI, Climate, and Space start-up companies, we are in a new wave of limitless innovation. NASA's statutory responsibility is to "provide for the widest practicable and appropriate dissemination of information concerning its activities and the results thereof." (51 U.S.C. § 20112) In particular, through machine learning, the public data drawn from NASA’s space assets can provide insights for addressing climate-related problems here on Earth. And many climate start-up companies can benefit from leveraging this data, either for proof of concepts or their own missions. In the Fall of 2021, several scientists from NASA and Coral Vita led a Practicum with the Georgia Institute of Technology Masters in Data Analytics program. The Practicum saw two teams of students develop and implement machine learning models to infer, from CALIPSO satellite imagery, vitality properties upon satellite pass. This kind of capability can lead to real-time mapping of coral health around the world, giving organization an understanding of where to prioritize reef reconstitution.. In this presentation, we will highlight this use case and discuss other concrete applications in which space data is used to solve problems here on Earth.

Earth Sciences↗
NASA NTRS2004

Applying Tafkaa For Atmospheric Correction of Aviris Over Coral Ecosystems In The Hawaiian Islands

Growing concern over the health of coastal ecosystems, particularly coral reefs, has produced increased interest in remote sensing as a tool for the management and monitoring of these valuable natural resources. Hyperspectral capabilities show promising results in this regard, but as yet remain somewhat hindered by the technical and physical issues concerning the intervening water layer. One such issue is the ability to atmospherically correct images over shallow aquatic areas, where complications arise due to varying effects from specular reflection, wind blown surface waves, and reflectance from the benthic substrate. Tafkaa, an atmospheric correction algorithm under development at the U.S. Naval Research Laboratory, addresses these variables and provides a viable approach to the atmospheric correction issue. Using imagery from the Advanced Visible InfraRed Imaging Spectrometer (AVIRIS) over two shallow coral ecosystems in the Hawai ian Islands, French Frigate Shoals and Kane ohe Bay, we first demonstrate how land-based atmospheric corrections can be limited in such an environment. We then discuss the input requirements and underlying algorithm concepts of Tafkaa and conclude with examples illustrating the improved performance of Tafkaa using the same AVIRIS images.

Goodman, James A.↗
NASA NTRS2007

Coral Reef Remote Sensing using Simulated VIIRS and LDCM Imagery

The Rapid Prototyping Capability (RPC) node at NASA Stennis Space Center, MS, was used to simulate NASA next-generation sensor imagery over well-known coral reef areas: Looe Key, FL, and Kaneohe Bay, HI. The objective was to assess the degree to which next-generation sensor systems the Visible/Infrared Imager/Radiometer Suite (VIIRS) and the Landsat Data Continuity Mission (LDCM) might provide key input to the National Oceanographic and Atmospheric Administration (NOAA) Integrated Coral Observing Network (ICON)/Coral Reef Early Warning System (CREWS) Decision Support Tool (DST). The DST data layers produced from the simulated imagery concerned water quality and benthic classification map layers. The water optical parameters of interest were chlorophyll (Chl) and the absorption coefficient (a). The input imagery used by the RPC for simulation included spaceborne (Hyperion) and airborne (AVIRIS) hyperspectral data. Specific field data to complement and aid in validation of the overflight data was used when available. The results of the experiment show that the next-generation sensor systems are capable of providing valuable data layer resources to NOAA's ICON/CREWS DST.

Estep, Leland↗
NASA NTRS2008

Coral Reef Remote Sensing Using Simulated VIIRS and LDCM Imagery

The Rapid Prototyping Capability (RPC) node at NASA Stennis Space Center, MS, was used to simulate NASA next-generation sensor imagery over well-known coral reef areas: Looe Key, FL, and Kaneohe Bay, HI. The objective was to assess the degree to which next-generation sensor systems-the Visible/Infrared Imager/Radiometer Suite (VIIRS) and the Landsat Data Continuity Mission (LDCM)- might provide key input to the National Oceanographic and Atmospheric Administration (NOAA) Integrated Coral Observing Network (ICON)/Coral Reef Early Warning System (CREWS) Decision Support Tool (DST). The DST data layers produced from the simulated imagery concerned water quality and benthic classification map layers. The water optical parameters of interest were chlorophyll (Chl) and the absorption coefficient (a). The input imagery used by the RPC for simulation included spaceborne (Hyperion) and airborne (AVIRIS) hyperspectral data. Specific field data to complement and aid in validation of the overflight data was used when available. The results of the experiment show that the next-generation sensor systems are capable of providing valuable data layer resources to NOAA s ICON/CREWS DST.

Estep, Leland↗
NASA NTRS2019

Coral Reef Annihilation, Persistence and Recovery at Earth’s Youngest Volcanic Island

The structure and function of coral reef ecosystems is increasingly compromised by multiple stressors, even in the most remote locations. Severe, acute disturbances such as volcanic eruptions represent extreme events that can annihilate entire reef ecosystems, but also provide unique opportunities to examine ecosystem resilience and recovery. Here, we examine the destruction, persistence and initial recovery of reefs associated with the hydromagmatic eruption that created Earth’s newest landmass, the Hunga Tonga–Hunga Ha’apai volcanic island. Despite extreme conditions associated with the eruption, impacts on nearby reefs were spatially variable. Importantly, even heavily affected reefs showed signs of rapid recovery driven by high recruitment, likely from local refuges. The remote location and corresponding lack of additional stressors likely contribute to the resilience of Hunga’s reefs, suggesting that in the absence of chronic anthropogenic stressors, coral reefs can be resilient to one of the largest physical disturbances on Earth.

Refuge population↗
NASA NTRS2020

NeMO-Net – The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%.

Chirayath, Ved↗