scholarly journals Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine

2019 ◽  
Vol 12 (1) ◽  
pp. 46 ◽  
Author(s):  
Morton J. Canty ◽  
Allan A. Nielsen ◽  
Knut Conradsen ◽  
Henning Skriver
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Data Availability ◽  
Anthropogenic Activity ◽  
Time Data ◽  
Software Interface ◽  
Recent Modification ◽  
Disaster Assessment ◽  
Google Earth Engine ◽  
Sar Imagery

Time series analysis of Sentinel-1 SAR imagery made available by the Google Earth Engine (GEE) is described. Advantage is taken of a recent modification of a sequential complex Wishart-based algorithm which is applicable to the dual polarization intensity data archived on the GEE. Both the algorithm and a software interface to the GEE Python API for convenient data exploration and analysis are presented; the latter can be run from a platform independent Docker container and the source code is available on GitHub. Application examples are given involving the monitoring of anthropogenic activity (shipping, uranium mining, deforestation) and disaster assessment (flash floods). These highlight the advantages of the good temporal resolution resulting from cloud cover independence, short revisit times and near real time data availability.

scholarly journals COPERNICUS BIG DATA AND GOOGLE EARTH ENGINE FOR GLACIER SURFACE VELOCITY FIELD MONITORING: FEASIBILITY DEMONSTRATION ON SAN RAFAEL AND SAN QUINTIN GLACIERS

2018 ◽  
Vol XLII-3 ◽  
pp. 289-294 ◽  
Author(s):  
M. Di Tullio ◽  
F. Nocchi ◽  
A. Camplani ◽  
N. Emanuelli ◽  
A. Nascetti ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Velocity Field ◽  
Field Measurements ◽  
Google Earth ◽  
Surface Velocity ◽  
Glacier Surface ◽  
Google Earth Engine ◽  
Sar Imagery ◽  
San Rafael

The glaciers are a natural global resource and one of the principal climate change indicator at global and local scale, being influenced by temperature and snow precipitation changes. Among the parameters used for glacier monitoring, the surface velocity is a key element, since it is connected to glaciers changes (mass balance, hydro balance, glaciers stability, landscape erosion). The leading idea of this work is to continuously retrieve glaciers surface velocity using free ESA Sentinel-1 SAR imagery and exploiting the potentialities of the Google Earth Engine (GEE) platform. GEE has been recently released by Google as a platform for petabyte-scale scientific analysis and visualization of geospatial datasets. The algorithm of SAR off-set tracking developed at the Geodesy and Geomatics Division of the University of Rome La Sapienza has been integrated in a cloud based platform that automatically processes large stacks of Sentinel-1 data to retrieve glacier surface velocity field time series. We processed about 600 Sentinel-1 image pairs to obtain a continuous time series of velocity field measurements over 3 years from January 2015 to January 2018 for two wide glaciers located in the Northern Patagonian Ice Field (NPIF), the San Rafael and the San Quintin glaciers. Several results related to these relevant glaciers also validated with respect already available and renown software (i.e. ESA SNAP, CIAS) and with respect optical sensor measurements (i.e. LANDSAT8), highlight the potential of the Big Data analysis to automatically monitor glacier surface velocity fields at global scale, exploiting the synergy between GEE and Sentinel-1 imagery.

scholarly journals A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine

2020 ◽  
Vol 2 ◽  
Author(s):  
Paulo Arévalo ◽  
Eric L. Bullock ◽  
Curtis E. Woodcock ◽  
Pontus Olofsson
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Land Cover Change ◽  
Google Earth ◽  
Data Availability ◽  
Ecosystem Change ◽  
Series Data ◽  
Land Change ◽  
Continuous Change ◽  
Google Earth Engine

Land cover has been designated by the Global Climate Observing System (GCOS) as an Essential Climate Variable due to its integral role in many climate and environmental processes. Land cover and change affect regional precipitation patterns, surface energy balance, the carbon cycle and biodiversity. Accurate information on land cover and change is essential for climate change mitigation programs such as UN-REDD+. Still, uncertainties related to land change are large, in part due to the use of traditional land cover and change mapping techniques that use one or a few remotely sensed images, preventing a comprehensive analysis of ecosystem change processes. The opening of the Landsat archive and the initiation of the Copernicus Program have enabled analyses based on time series data, allowing the scientific community to explore global land cover dynamics in ways that were previously limited by data availability. One such method is the Continuous Change Detection and Classification algorithm (CCDC), which uses all available Landsat data to model temporal-spectral features that include seasonality, trends, and spectral variability. Until recently, the CCDC algorithm was restricted to academic environments due to computational requirements and complexity, preventing its use by local practitioners. The situation has changed with the recent implementation of CCDC in the Google Earth Engine, which enables analyses at global scales. What is still missing are tools that allow users to explore, analyze and process CCDC outputs in a simplified way. In this paper, we present a suite of free tools that facilitate interaction with CCDC outputs, including: (1) time series viewers of CCDC-generated time segments; (2) a spatial data viewer to explore CCDC model coefficients and derivatives, and visualize change information; (3) tools to create land cover and land cover change maps from CCDC outputs; (4) a tool for unbiased area estimation of key climate-related variables like deforestation extent; and (5) an API for accessing the functionality underlying these tools. We illustrate the usage of these tools at different locations with examples that explore Landsat time series and CCDC coefficients, and a land cover change mapping example in the Southeastern USA that includes area and accuracy estimates.

scholarly journals EXPLOITING SENTINEL-1 SAR TIME SERIES TO DETECT GRASSLANDS IN THE NORTHERN BRAZILIAN AMAZON

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 259-265
Author(s):  
A. F. Carneiro ◽  
W. V. Oliveira ◽  
S. J. S. Sant'Anna ◽  
J. Doblas ◽  
D. V. Vaz
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Google Earth ◽  
Data Availability ◽  
Kappa Index ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Sar Imagery

Abstract. Recent advances in cloud-computing technologies and remote sensing data availability foster the development of studies based on the analysis of optical and SAR imagery time series. In this paper, we assess the potential of Sentinel-1 imagery time series for grassland detection in the northern Brazilian Amazon. We used the Google Earth Engine cloud-computing platform as an alternative to obtain and analyse Sentinel-1 imagery, acquired from 2017 to 2018 over the region of Mojuí dos Campos/PA, Brazil. We extracted several temporal metrics from the imagery time series and used the Random Forest algorithm to perform the classification. In addition, we analysed the time series considering different channels, including the VV and VH polarizations, both separately and in combination, and the CR, RGI and NL indices. We could efficiently discriminate areas of grasslands from forest and agricultural crops using either VH time features or features extracted from the combination of both VV and VH polarizations. The classification map that resulted from the combination of VV and VH data presented the highest accuracy, with an overall accuracy of 95.33% and a 0.93 kappa index. Despite simple, the approach adopted in this paper showed potential to differ grasslands from areas of agriculture and forest in the northern Brazilian Amazon.

scholarly journals Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

2021 ◽  
Vol 256 ◽  
pp. 112318
Author(s):  
Dong Liang ◽  
Huadong Guo ◽  
Lu Zhang ◽  
Yun Cheng ◽  
Qi Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Sar Images ◽  
Google Earth Engine ◽  
The Antarctic

An updated national-scale mangrove forest map in China Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine

2021 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Yan Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Mangrove Area ◽  
Google Earth Engine ◽  
Forest Maps ◽  
Sentinel 2

<p>A high resolution mangrove map (e.g., 10-m), which can identify mangrove patches with small size (< 1 ha), is a central component to quantify ecosystem functions and help government take effective steps to protect mangroves, because the increasing small mangrove patches, due to artificial destruction and plantation of new mangrove trees, are vulnerable to climate change and sea level rise, and important for estimating mangrove habitat connectivity with adjacent coastal ecosystems as well as reducing the uncertainty of carbon storage estimation. However, latest national scale mangrove forest maps mainly derived from Landsat imagery with 30-m resolution are relatively coarse to accurately characterize the distribution of mangrove forests, especially those of small size (area < 1 ha). Sentinel imagery with 10-m resolution provide the opportunity for identifying these small mangrove patches and generating high-resolution mangrove forest maps. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features for random forest to classify mangroves in China. We found that Sentinel-2 imagery is more effective than Sentinel-1 in mangrove extraction, and a combination of SAR and MSI imagery can get a better accuracy (F1-score of 0.94) than using them separately (F1-score of 0.88 using Sentinel-1 only and 0.895 using Sentinel-2 only). The 10-m mangrove map derived by combining SAR and MSI data identified 20,003 ha mangroves in China and the areas of small mangrove patches (< 1 ha) was 1741 ha, occupying 8.7% of the whole mangrove area. The largest area (819 ha) of small mangrove patches is located in Guangdong Province, and in Fujian the percentage of small mangrove patches in total mangrove area is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest maps are expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of mangrove forest.</p>

Sentinel-1 Time-Series Analysis for Fires Monitoring using Google Earth Engine Tools

2021 ◽  
Author(s):  
Massimiliano Gargiulo ◽  
Antonio Iodice ◽  
Daniele Riccio ◽  
Giuseppe Ruello
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Google Earth ◽  
Series Analysis ◽  
Google Earth Engine

scholarly journals Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

2020 ◽  
pp. 100458
Author(s):  
Michelle Li Ern Ang ◽  
Dirk Arts ◽  
Danielle Crawford ◽  
Bonifacio V. Labatos ◽  
Khanh Duc Ngo ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Analysis ◽  
Google Earth ◽  
Cover Time ◽  
Web Based ◽  
Series Analysis ◽  
Google Earth Engine

Monitoring of water table dynamics in peatlands with OPTRAM: Towards globally applicable algorithms in Google Earth Engine using Landsat and Sentinel-2

2021 ◽  
Author(s):  
Iuliia Burdun ◽  
Michel Bechtold ◽  
Viacheslav Komisarenko ◽  
Annalea Lohila ◽  
Elyn Humphreys ◽  
...  
Keyword(s):  
Time Series ◽  
Water Table ◽  
Input Data ◽  
Short Wave ◽  
Google Earth ◽  
Google Earth Engine ◽  
Short Wave Infrared ◽  
Northern Peatlands ◽  
Sentinel 2

<p>Fluctuations of water table depth (WTD) affect many processes in peatlands, such as vegetation development and emissions of greenhouse gases. Here, we present the OPtical TRApezoid Model (OPTRAM) as a new method for satellite-based monitoring of the temporal variation of WTD in peatlands. OPTRAM is based on the response of short-wave infrared reflectance to the vegetation water status. For five northern peatlands with long-term in-situ WTD records, and with diverse vegetation cover and hydrological regimes, we generate a suite of OPTRAM index time series using (a) different procedures to parametrise OPTRAM (peatland-specific manual vs. globally applicable automatic parametrisation in Google Earth Engine), and (b) different satellite input data (Landsat vs. Sentinel-2). The results based on the manual parametrisation of OPTRAM indicate a high correlation with in-situ WTD time-series for pixels with most suitable vegetation for OPTRAM application (mean Pearson correlation of 0.7 across sites), and we will present the performance differences when moving from a manual to an automatic procedure. Furthermore, for the overlap period of Landsat and Sentinel-2, which have different ranges and widths of short-wave infrared bands used for OPTRAM calculation, the impact of the satellite input data to OPTRAM will be analysed. Eventually, the challenge of merging different satellite missions in the derivation of OPTRAM time series will be explored as an important step towards a global application of OPTRAM for the monitoring of WTD dynamics in northern peatlands.</p>

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