scholarly journals Reef Cover, a coral reef classification for global habitat mapping from remote sensing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Emma V. Kennedy ◽  
Chris M. Roelfsema ◽  
Mitchell B. Lyons ◽  
Eva M. Kovacs ◽  
Rodney Borrego-Acevedo ◽  
...  
Keyword(s):  
Coral Reef ◽  
Large Scale ◽  
Global Scale ◽  
End Users ◽  
Ecological Knowledge ◽  
Observation Data ◽  
Reef Management ◽  
Global Mapping ◽  
Earth Observation Data ◽  
Coral Reef Classification

AbstractCoral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale reef mapping to date are typically poorly defined, not shared or region-specific, limiting end-users’ ability to interpret outputs. Here we present Reef Cover, a coral reef geomorphic zone classification, developed to support both producers and end-users of global-scale coral reef habitat maps, in a transparent and version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge of reef form and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. Two case studies demonstrate application of the Reef Cover classification scheme and its scientific and conservation benefits: 1) detailed mapping of the Cairns Management Region of the Great Barrier Reef to support management and 2) mapping of the Caroline and Mariana Island chains in the Pacific for conservation purposes.

scholarly journals Reef Cover: a coral reef classification to guide global habitat mapping from remote sensing

2020 ◽  
Author(s):  
Emma Kennedy ◽  
Chris Roelfsema ◽  
Mitchell Lyons ◽  
Eva Kovacs ◽  
Rodney Borrego-Acevedo ◽  
...  
Keyword(s):  
Coral Reef ◽  
Large Scale ◽  
Spatial Scales ◽  
Global Scale ◽  
Habitat Mapping ◽  
Ecological Knowledge ◽  
Observation Data ◽  
Reef Management ◽  
Global Mapping ◽  
Earth Observation Data

AbstractCoral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications employed in large-scale reef mapping to date are typically poorly defined, not shared or region-specific. Here we present Reef Cover, a new coral reef geomorphic zone classification, developed to support global-scale coral reef habitat mapping in a transparent and version-based framework. We developed scalable classes by focusing on attributes that can be observed remotely, but whose membership rules also reflect knowledge of reef formation, growth and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. We use the Caroline and Mariana Island chains in the Pacific as a case study to demonstrate use of the classification scheme and its scientific and conservation applications. The primary application of Reef Cover is the Allen Coral Atlas global coral reef mapping project, but the system will support bespoke reef mapping conducted at a variety of spatial scales.

scholarly journals FREE GLOBAL DSM ASSESSMENT ON LARGE SCALE AREAS EXPLOITING THE POTENTIALITIES OF THE INNOVATIVE GOOGLE EARTH ENGINE PLATFORM

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 627-633 ◽  
Author(s):  
A. Nascetti ◽  
M. Di Rita ◽  
R. Ravanelli ◽  
M. Amicuzi ◽  
S. Esposito ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Global Scale ◽  
Google Earth ◽  
Observation Data ◽  
Geometric Accuracy ◽  
Computing Platform ◽  
Aster Gdem ◽  
Google Earth Engine ◽  
Earth Observation Data

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters.<br><br> In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

Analyzing large-scale Earth Observation data repositories made simple with OpenEO Platform

2021 ◽  
Author(s):  
Edzer Pebesma ◽  
Patrick Griffiths ◽  
Christian Briese ◽  
Alexander Jacob ◽  
Anze Skerlevaj ◽  
...  
Keyword(s):  
Large Scale ◽  
Virtual Machines ◽  
Google Earth ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Data Repositories ◽  
Software Ecosystem ◽  
Wide Range ◽  
Earth Observation Data

&lt;p&gt;The OpenEO API allows the analysis of large amounts of Earth Observation data using a high-level abstraction of data and processes. Rather than focusing on the management of virtual machines and millions of imagery files, it allows to create jobs that take a spatio-temporal section of an image collection (such as Sentinel L2A), and treat it as a data cube. Processes iterate or aggregate over pixels, spatial areas, spectral bands, or time series, while working at arbitrary spatial resolution. This pattern, pioneered by Google Earth Engine&amp;#8482; (GEE), lets the user focus on the science rather than on data management.&lt;/p&gt;&lt;p&gt;The openEO H2020 project (2017-2020) has developed the API as well as an ecosystem of software around it, including clients (JavaScript, Python, R, QGIS, browser-based), back-ends that translate API calls into existing image analysis or GIS software or services (for Sentinel Hub, WCPS, Open Data Cube, GRASS GIS, GeoTrellis/GeoPySpark, and GEE) as well as a hub that allows querying and searching openEO providers for their capabilities and datasets. The project demonstrated this software in a number of use cases, where identical processing instructions were sent to different implementations, allowing comparison of returned results.&lt;/p&gt;&lt;p&gt;A follow-up, ESA-funded project &amp;#8220;openEO Platform&amp;#8221; realizes the API and progresses the software ecosystem into operational services and applications that are accessible to everyone, that involve federated deployment (using the clouds managed by EODC, Terrascope, CreoDIAS and EuroDataCube), that will provide payment models (&amp;#8220;pay per compute job&amp;#8221;) conceived and implemented following the user community needs and that will use the EOSC (European Open Science Cloud) marketplace for dissemination and authentication. A wide range of large-scale cases studies will demonstrate the ability of the openEO Platform to scale to large data volumes.&amp;#160; The case studies to be addressed include on-demand ARD generation for SAR and multi-spectral data, agricultural demonstrators like crop type and condition monitoring, forestry services like near real time forest damage assessment as well as canopy cover mapping, environmental hazard monitoring of floods and air pollution as well as security applications in terms of vessel detection in the mediterranean sea.&lt;/p&gt;&lt;p&gt;While the landscape of cloud-based EO platforms and services has matured and diversified over the past decade, we believe there are strong advantages for scientists and government agencies to adopt the openEO approach. Beyond the absence of vendor/platform lock-in or EULA&amp;#8217;s we mention the abilities to (i) run arbitrary user code (e.g. written in R or Python) close to the data, (ii) carry out scientific computations on an entirely open source software stack, (iii) integrate different platforms (e.g., different cloud providers offering different datasets), and (iv) help create and extend this software ecosystem. openEO uses the OpenAPI standard, aligns with modern OGC API standards, and uses the STAC (SpatioTemporal Asset Catalog) to describe image collections and image tiles.&lt;/p&gt;

scholarly journals A Review of the Sustainability Concept and the State of SDG Monitoring Using Remote Sensing

2020 ◽  
Vol 12 (11) ◽  
pp. 1770 ◽  
Author(s):  
Ronald Estoque
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Global Scale ◽  
Full Potential ◽  
Observation Data ◽  
The Sustainable Development ◽  
Sustainability Concept ◽  
Development Goals ◽  
Earth Observation Data

The formulation of the 17 sustainable development goals (SDGs) was a major leap forward in humankind’s quest for a sustainable future, which likely began in the 17th century, when declining forest resources in Europe led to proposals for the re-establishment and conservation of forests, a strategy that embodies the great idea that the current generation bears responsibility for future generations. Global progress toward SDG fulfillment is monitored by 231 unique social-ecological indicators spread across 169 targets, and remote sensing (RS) provides Earth observation data, directly or indirectly, for 30 (18%) of these indicators. Unfortunately, the UN Global Sustainable Development Report 2019—The Future is Now: Science for Achieving Sustainable Development concluded that, despite initial efforts, the world is not yet on track for achieving most of the SDG targets. Meanwhile, through the EO4SDG initiative by the Group on Earth Observations, the full potential of RS for SDG monitoring is now being explored at a global scale. As of April 2020, preliminary statistical data were available for 21 (70%) of the 30 RS-based SDG indicators, according to the Global SDG Indicators Database. Ten (33%) of the RS-based SDG indicators have also been included in the SDG Index and Dashboards found in the Sustainable Development Report 2019—Transformations to Achieve the Sustainable Development Goals. These statistics, however, do not necessarily reflect the actual status and availability of raw and processed geospatial data for the RS-based indicators, which remains an important issue. Nevertheless, various initiatives have been started to address the need for open access data. RS data can also help in the development of other potentially relevant complementary indicators or sub-indicators. By doing so, they can help meet one of the current challenges of SDG monitoring, which is how best to operationalize the SDG indicators.

scholarly journals A novel low-cost, high-precision sea temperature sensor for coral reef monitoring

2020 ◽  
Vol 96 (1) ◽  
pp. 97-110
Author(s):  
James Hendee ◽  
Natchanon Amornthammarong ◽  
Lewis Gramer ◽  
Andrea Gomez
Keyword(s):  
Coral Reef ◽  
High Precision ◽  
Temperature Sensor ◽  
Large Scale ◽  
Low Cost ◽  
Global Scale ◽  
Temperature Sensors ◽  
Sea Temperature ◽  
Temperature Regimes ◽  
Reef Monitoring

The role of elevated sea temperatures in coral bleaching has been well documented. Many of the sea temperature records utilized for purposes of widespread, multi-species bleaching predictions in recent publications have been acquired through satellite remote sensing. Satellites estimate sea temperatures at only a narrow range of depths near the surface of the ocean and may therefore not adequately represent the true temperatures endured by the world's coral ecosystems. To better characterize sea temperature regimes that coral reef ecosystems experience, as well as better define the individual thresholds for each species that bleaches, in situ sea temperature sensors are required. Commercial sensors are expensive in large quantities, however, reducing the capacity to conduct large- scale research programs to elucidate the range of significant scales of temperature variability. At the National Oceanic and Atmospheric Administration's (NOAA) Atlantic Oceanographic and Meteorological Laboratory (AOML), we designed a low-cost (roughly US$9 in parts) and high- precision sea temperature sensor that uses an Arduino microprocessor board and a high accuracy thermistor. This new temperature sensor autonomously records temperatures onto a memory chip and provides better accuracy (+0.05 °C) than a comparable commercial sensor (+0.2 °C). Moreover, it is not difficult to build; anyone who knows how to solder can build the temperature sensor. In March 2019, students at middle and high schools in Broward County, Florida, built close to 60 temperature sensors. During 2019, these sensors will be deployed by Reef Check, a global-scale coral reef monitoring organization, as well as by other programs to determine worldwide sea temperature regimes through the Opuhala Project (https://www. coral. noaa. gov/opuhala). This paper chronicles results from the initial proof-of-concept deployments for these AOML-designed sensors.

scholarly journals Earth Observation for Sustainable Infrastructure: A Review

2021 ◽  
Vol 13 (8) ◽  
pp. 1528
Author(s):  
Yongze Song ◽  
Peng Wu
Keyword(s):  
Sustainable Development ◽  
Large Scale ◽  
Earth Observation ◽  
Future Research ◽  
Observation Data ◽  
Infrastructure Development ◽  
Sustainable Infrastructure ◽  
Development Goals ◽  
Future Research Directions ◽  
Earth Observation Data

Infrastructure is a fundamental sector for sustainable development and Earth observation has great potentials for sustainable infrastructure development (SID). However, implementations of the timely, large–scale and multi–source Earth observation are still limited in satisfying the huge global requirements of SID. This study presents a systematical literature review to identify trends of Earth observation for sustainable infrastructure (EOSI), investigate the relationship between EOSI and Sustainable Development Goals (SDGs), and explore challenges and future directions of EOSI. Results reveal the close associations of infrastructure, urban development, ecosystems, climate, Earth observation and GIS in EOSI, and indicate their relationships. In addition, from the perspective of EOSI–SDGs relationship, the huge potentials of EOSI are demonstrated from the 70% of the infrastructure influenced targets that can be directly or indirectly derived from Earth observation data, but have not been included in current SDG indicators. Finally, typical EOSI cases are presented to indicate challenges and future research directions. This review emphasizes the contributions and potentials of Earth observation to SID and EOSI is a powerful pathway to deliver on SDGs.

scholarly journals INTEGRATION OF SENTINEL-2 AND LANDSAT-8 DATA FOR SURFACE REFLECTANCE TIME-SERIES ANALYSIS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 205-210 ◽  
Author(s):  
G. Berdou ◽  
S. Shrestha ◽  
M. Hahn
Keyword(s):  
Atmospheric Correction ◽  
Surface Characteristics ◽  
Global Scale ◽  
Landsat 8 ◽  
Observation Data ◽  
Common Reference ◽  
Key Factor ◽  
Land Covers ◽  
Earth Observation Data ◽  
Sentinel 2

Abstract. Integration of Sentinel-2 and Landsat-8 imagery is a key factor to provide earth observation data at a global scale with higher temporal resolution. Integration of data from two sensors is possible with the consistent harmonized data framed in common reference and processing, which can be used for comparing geophysical surface characteristics. This study focuses on the analysis of the atmospheric correction methods available for both Landsat-8 and Sentinel-2 products to convert the top of the atmosphere to the bottom of atmosphere reflectance. Other investigations (De Keukelaere, 2018) carried out similar analyses focusing on data acquired over water, while this study emphasises the analyses over land covers. Two processing algorithms iCOR and Sen2COR are utilized to perform atmospheric corrections, and results are statistically and visually compared. Comparisons based on same images processed with different algorithms show very strong correlation for some classes (urban: 0.99), while correlation values around 0.85 were achieved between images from different sensors.

scholarly journals A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport models

2016 ◽  
Vol 16 (2) ◽  
pp. 907-925 ◽  
Author(s):  
R. Paugam ◽  
M. Wooster ◽  
S. Freitas ◽  
M. Val Martin
Keyword(s):  
Large Scale ◽  
Atmospheric Transport ◽  
Chemical Constituents ◽  
Source Region ◽  
Ambient Air ◽  
Earth Observation ◽  
Observation Data ◽  
Transport Models ◽  
Fire Emissions ◽  
Earth Observation Data

Abstract. Landscape fires produce smoke containing a very wide variety of chemical species, both gases and aerosols. For larger, more intense fires that produce the greatest amounts of emissions per unit time, the smoke tends initially to be transported vertically or semi-vertically close by the source region, driven by the intense heat and convective energy released by the burning vegetation. The column of hot smoke rapidly entrains cooler ambient air, forming a rising plume within which the fire emissions are transported. The characteristics of this plume, and in particular the height to which it rises before releasing the majority of the smoke burden into the wider atmosphere, are important in terms of how the fire emissions are ultimately transported, since for example winds at different altitudes may be quite different. This difference in atmospheric transport then may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents, with for example very high plume injection heights being associated with extreme long-range atmospheric transport. Here we review how such landscape-scale fire smoke plume injection heights are represented in larger-scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system. In particular we detail (i) satellite Earth observation data sets capable of being used to remotely assess wildfire plume height distributions and (ii) the driving characteristics of the causal fires. We also discuss both the physical mechanisms and dynamics taking place in fire plumes and investigate the efficiency and limitations of currently available injection height parameterizations. Finally, we conclude by suggesting some future parameterization developments and ideas on Earth observation data selection that may be relevant to the instigation of enhanced methodologies aimed at injection height representation.

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