The Swiss data cube, analysis ready data archive using earth observations of Switzerland

AbstractSince the opening of Earth Observation (EO) archives (USGS/NASA Landsat and EC/ESA Sentinels), large collections of EO data are freely available, offering scientists new possibilities to better understand and quantify environmental changes. Fully exploiting these satellite EO data will require new approaches for their acquisition, management, distribution, and analysis. Given rapid environmental changes and the emergence of big data, innovative solutions are needed to support policy frameworks and related actions toward sustainable development. Here we present the Swiss Data Cube (SDC), unleashing the information power of Big Earth Data for monitoring the environment, providing Analysis Ready Data over the geographic extent of Switzerland since 1984, which is updated on a daily basis. Based on a cloud-computing platform allowing to access, visualize and analyse optical (Sentinel-2; Landsat 5, 7, 8) and radar (Sentinel-1) imagery, the SDC minimizes the time and knowledge required for environmental analyses, by offering consistent calibrated and spatially co-registered satellite observations. SDC derived analysis ready data supports generation of environmental information, allowing to inform a variety of environmental policies with unprecedented timeliness and quality.

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Paving the Way towards an Armenian Data Cube

Data ◽

10.3390/data4030117 ◽

2019 ◽

Vol 4 (3) ◽

pp. 117 ◽

Cited By ~ 8

Author(s):

Shushanik Asmaryan ◽

Vahagn Muradyan ◽

Garegin Tepanosyan ◽

Azatuhi Hovsepyan ◽

Armen Saghatelyan ◽

...

Keyword(s):

Environmental Issues ◽

Data Cube ◽

New Paradigm ◽

Band Ratio ◽

Earth Observations ◽

In Situ Data ◽

Global Concern ◽

Sentinel 2 ◽

The Way

Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

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Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Remote Sensing ◽

10.3390/rs12010186 ◽

2020 ◽

Vol 12 (1) ◽

pp. 186 ◽

Cited By ~ 3

Author(s):

Yang Hu ◽

Xuelei Xu ◽

Fayun Wu ◽

Zhongqiu Sun ◽

Haoming Xia ◽

...

Keyword(s):

Machine Learning ◽

Test Phase ◽

Hunan Province ◽

Training Phase ◽

Computing Platform ◽

Cloud Computing Platform ◽

Stock Volume ◽

Sentinel 2 ◽

Mlr Model

The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R2 = 0.91, RMSE = 35.13 m3 ha−1, n = 321) and in the test phase (i.e., R2 = 0.58, RMSE = 65.03 m3 ha−1, and n = 138). This model was followed by the SVR model (R2 = 0.54, RMSE = 65.60 m3 ha−1, n = 321 in training; R2 = 0.54, RMSE = 66.00 m3 ha−1, n = 138 in testing), which was slightly better than the MLR model (R2 = 0.38, RMSE = 75.74 m3 ha−1, and n = 321 in training; R2 = 0.49, RMSE = 70.22 m3 ha−1, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 108 m3) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 108 m3). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.

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Identification of Short-Rotation Eucalyptus Plantation at Large Scale Using Multi-Satellite Imageries and Cloud Computing Platform

Remote Sensing ◽

10.3390/rs12132153 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2153 ◽

Cited By ~ 1

Author(s):

Xinping Deng ◽

Shanxin Guo ◽

Luyi Sun ◽

Jinsong Chen

Keyword(s):

Time Series ◽

Cloud Computing ◽

Statistical Hypothesis ◽

Statistical Hypothesis Testing ◽

Computing Platform ◽

Short Rotation ◽

Red Edge ◽

Eucalyptus Plantations ◽

Cloud Computing Platform ◽

Sentinel 2

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer’s accuracy of 63.85% and user’s accuracy of 66.89% for eucalyptus plantations.

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Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2019.1711366 ◽

2020 ◽

Vol 46 (1) ◽

pp. 15-33 ◽

Cited By ~ 9

Author(s):

Masoud Mahdianpari ◽

Bahram Salehi ◽

Fariba Mohammadimanesh ◽

Brian Brisco ◽

Saeid Homayouni ◽

...

Keyword(s):

Cloud Computing ◽

Big Data ◽

First Generation ◽

Google Earth ◽

Computing Platform ◽

Wetland Inventory ◽

Inventory Map ◽

Cloud Computing Platform ◽

Google Earth Engine ◽

Sentinel 2

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Design Research in Motion Cloud Computing Platform Based on a Training Aid System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.1551 ◽

2014 ◽

Vol 635-637 ◽

pp. 1551-1554

Author(s):

Quan Chang Wang ◽

Hong Jiang Wu

Keyword(s):

Cloud Computing ◽

Design Research ◽

Economic Benefits ◽

Sports Training ◽

Adverse Conditions ◽

Computing Platform ◽

Training Aids ◽

Cloud Computing Platform ◽

Acquisition Management ◽

Effectiveness Data

With cloud computing robust operating platform, build effective sports training aids. In terms of its economic benefits, training effectiveness, data transmission and other processing for scientific and rational exercise training has an important role in promoting. Adverse conditions in the region can effectively improve the uneven development of sports resources and information obstruction. Cloud computing platform sports training aids including architecture, data acquisition, management and service platform, a team of experts in coordination aspects.

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WATER QUALITY MONITORING OVER FINGER LAKES REGION USING SENTINEL-2 IMAGERY ON GOOGLE EARTH ENGINE CLOUD COMPUTING PLATFORM

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2021-279-2021 ◽

2021 ◽

Vol V-3-2021 ◽

pp. 279-283

Author(s):

R. M. Khan ◽

B. Salehi ◽

M. Mahdianpari ◽

F. Mohammadimanesh

Keyword(s):

Water Quality ◽

Cloud Computing ◽

Agricultural Runoff ◽

Google Earth ◽

Computing Platform ◽

Finger Lakes ◽

Cloud Computing Platform ◽

Google Earth Engine ◽

Sentinel 2 ◽

Finger Lakes Region

Abstract. Surface water quality is degrading continuously both due to natural and anthropogenic causes. There are several indicators of water quality, among which sediment loading is mainly determined by turbidity. Normalized Difference Water Index (NDWI) is one indirect measure of sediments present in water. This study focuses on detecting and monitoring sediments through NDWI over the Finger Lakes region, New York. Time series analysis is performed using Sentinel 2 imagery on the Google Earth Engine (GEE) platform. Finger Lakes region holds high socio-economic value because of tourism, water-based recreation, industry, and agriculture sector. The deteriorating water quality within the Finger Lake region has been reported based on ground sampling techniques. This study takes advantage of a cloud computing platform and medium resolution atmospherically corrected satellite imagery to detect and analyse water quality through sediment detection. In addition, precipitation data is used to understand the underlying cause of sediment increase. The results demonstrate the amount of sediments is greater in the early spring and summer months compared to other seasons. This can be due to the agricultural runoff from the nearing areas as a result of high precipitation. The results confirm the necessity for monitoring the quality of these lakes and understanding the underlying causes, which are beneficial for all the stakeholders to devise appropriate policies and strategies for timely preservation of the water quality.

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