Improved machine-learning mapping of local climate zones in metropolitan areas using composite earth observation data in Google Earth Engine

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. 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.

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

10.5194/egusphere-egu21-9602 ◽

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

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&#8482; (GEE), lets the user focus on the science rather than on data management.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.A follow-up, ESA-funded project &#8220;openEO Platform&#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 (&#8220;pay per compute job&#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.&#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.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&#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.

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Application of Earth Observation Data and Google Earth Engine for Monitoring Coral Reef Exposure to Environmental Stressors

10.20944/preprints202106.0473.v1 ◽

2021 ◽

Author(s):

Michael J. Williamson ◽

Emma J. Tebbs ◽

Henry J. Thompson ◽

Terrence P. Dawson ◽

Catherine E. I. Head ◽

...

Keyword(s):

Coral Reef ◽

Google Earth ◽

Earth Observation ◽

Environmental Stressors ◽

Observation Data ◽

Stress Exposure ◽

Marine Fauna ◽

Sst Anomaly ◽

Google Earth Engine ◽

Processing Platform

Coral reefs are critical ecosystems globally for marine fauna, biodiversity and through the services they provide to humanity. However, they are significantly threatened by anthropogenic stressors, such as climate change. By combining 9 environmental variables and ecological and health-based thresholds obtained from the available literature, we develop, using fuzzy logic (discontinuous functions), a Coral Reef Stress Exposure Index (CRSEI) for remotely monitoring coral reef exposure to environmental stressors. Our approach capitalises on the abundance of readily available satellite Earth Observation (EO) data available in the Google Earth Engine (GEE) cloud-based geospatial processing platform. CRSEI values from 3157 distinct reefs were generated and mapped across 12 important coral reef ecosystem regions. Quantitative analyses indicated that the index detected significant temporal differences in stress and was, therefore, able to capture historic change at a global scale. We also applied the CRSEI to three case-study reef ecosystems, previously well-monitored for stress and disturbance using other methods. PCA analysis indicated that depth, current, sea surface temperature (SST) and SST anomaly accounted for the greatest contribution to the variance in stress in these three regions. The CRSEI corroborated temporal and spatial differences in stress exposure from known disturbances within these reference regions, in addition to identifying the potential drivers of inter- and intra-region differences in stress, namely depth, degree heating weeks and SST anomaly. We discuss how the index can be further improved in future with site-specific thresholds for each stress variable, and the incorporation of additional variables not currently available in GEE. This index provides an open access tool, built around a free and powerful processing platform, that has broad potential to assist in the regular monitoring of our increasingly imperilled coral reef ecosystems, and, in particular, those that are remote or inaccessible.

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Application of airborne remote sensing data on mapping local climate zones: Cases of three metropolitan areas of Texas, U.S.

Computers Environment and Urban Systems ◽

10.1016/j.compenvurbsys.2018.11.002 ◽

2019 ◽

Vol 74 ◽

pp. 175-193 ◽

Cited By ~ 9

Author(s):

Chunhong Zhao ◽

Jennifer Jensen ◽

Qihao Weng ◽

Nathan Currit ◽

Russell Weaver

Keyword(s):

Remote Sensing ◽

Metropolitan Areas ◽

Remote Sensing Data ◽

Airborne Remote Sensing ◽

Climate Zones ◽

Local Climate ◽

Sensing Data ◽

Local Climate Zones

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Effective, Low-cost Methods of Applying Computer Vision to Public Earth Observation Data

10.20944/preprints202005.0345.v1 ◽

2020 ◽

Author(s):

Michael Evans ◽

Taylor Minich

Keyword(s):

Wildlife Conservation ◽

Low Cost ◽

Ground Truth ◽

Google Earth ◽

Earth Observation ◽

Learning Technologies ◽

Observation Data ◽

Solar Arrays ◽

Observation Systems ◽

Earth Observation Data

We have an unprecedented ability to analyze and map the Earth’s surface, as deep learning technologies are applied to an abundance of Earth observation systems collecting images of the planet daily. In order to realize the potential of these data to improve conservation outcomes, simple, free, and effective methods are needed to enable a wide variety of stakeholders to derive actionable insights from these tools. In this paper we demonstrate simple methods and workflows using free, open computing resources to train well-studied convolutional neural networks and use these to delineate objects of interest in publicly available Earth observation images. With limited training datasets (<1000 observations), we used Google Earth Engine and Tensorflow to process Sentinel-2 and National Agricultural Imaging Program data, and use these to train U-Net and DeepLab models that delineate ground mounted solar arrays and parking lots in satellite imagery. The trained models achieved 81.5% intersection over union between predictions and ground-truth observations in validation images. These images were generated at different times and from different places from those upon which they were trained, indicating the ability of models to generalize outside of data on which they were trained. The two case studies we present illustrate how these methods can be used to inform and improve the development of renewable energy in a manner that is consistent with wildlife conservation.

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