Open-source Google Earth Engine 30-m evapotranspiration rates retrieval: The SEBALIGEE system

2020 ◽  
Vol 133 ◽  
pp. 104845 ◽  
Author(s):  
Mario Mhawej ◽  
Ghaleb Faour
Keyword(s):  
Open Source ◽  
Google Earth ◽  
Google Earth Engine

scholarly journals Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1791
Author(s):  
Carmen Fattore ◽  
Nicodemo Abate ◽  
Farid Faridani ◽  
Nicola Masini ◽  
Rosa Lasaponara
Keyword(s):  
Open Source ◽  
Archaeological Site ◽  
Unsupervised Classification ◽  
Google Earth ◽  
Heritage Sites ◽  
Cultural Properties ◽  
Google Earth Engine ◽  
Archaeological Areas ◽  
The Impact

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage.

scholarly journals OPEN FORIS AND GOOGLE EARTH ENGINE LINKING EXPERT PARTICIPATION WITH NATURAL RESOURCE MAPPING AND REMOTE SENSING TRAINING IN TANZANIA

2018 ◽  
Vol XLII-4/W8 ◽  
pp. 117-122 ◽  
Author(s):  
U. Leinonen ◽  
J. Koskinen ◽  
H. Makandi ◽  
E. Mauya ◽  
N. Käyhkö
Keyword(s):  
Remote Sensing ◽  
Open Source ◽  
Open Source Software ◽  
Google Earth ◽  
Earth Observation ◽  
Teaching Staff ◽  
Participatory Mapping ◽  
Observation Data ◽  
Data Repositories ◽  
Google Earth Engine

<p><strong>Abstract.</strong> There is an increasing amount of open Earth observation (EO) data available, offering solutions to map, assess and monitor natural resources and to obtain answers to global and local societal challenges. With the help of free and open source software (FOSS) and open access cloud computing resources, the remote sensing community can take the full advantage of these vast geospatial data repositories. To empower developing societies, support should be given to higher education institutions (HEIs) to train professionals in using the open data, software and tools. In this paper, we describe a participatory mapping methodology, which utilizes open source software Open Foris and QGIS, various open Earth observation data catalogues, and computing capacity of the free Google Earth Engine cloud platform. Using this methodology, we arranged a collaborative data collection event, Mapathon, in Tanzania, followed by a training of the related FOSS tools for HEIs’ teaching staff. We collected feedback from the Mapathon participants about their learning experiences and from teachers about the usability of the methodology in remote sensing training in Tanzania. Based on our experiences and the received feedback, using a participatory mapping campaign as a training method can offer effective learning about environmental remote sensing through a real-world example, as well as networking and knowledge sharing possibilities for the participating group.</p>

Detecting coastal change around the North Sea from open-source satellite images

2021 ◽  
Author(s):  
Freya Fenwick ◽  
Timothy Price ◽  
Gerben Ruessink
Keyword(s):  
Open Source ◽  
Satellite Imagery ◽  
North Sea ◽  
Morphological Evolution ◽  
Google Earth ◽  
Water Levels ◽  
The North ◽  
The North Sea ◽  
Google Earth Engine ◽  
Wave Conditions

&lt;p&gt;Wave-dominated sandy coastlines worldwide are susceptible to change under the expected climate-change induced developments in sea level rise, mean wave conditions and storm events. For coastal management it remains important to observe and quantify these coastal changes, especially in low-lying developed coastal areas susceptible to flooding. The beaches surrounding an ocean basin have a variety of orientations, tidal ranges and management strategies, to name a few, which will lead to a range of morphological responses to future changes in hydrodynamic conditions within the basin. In addition, the conditions under which these varied responses mainly occur (e.g., under regular conditions or only during storm conditions) is not clear. Here, we used satellite imagery to compare the morphological response of a selection of beaches surrounding the North Sea.&lt;/p&gt;&lt;p&gt;The position of the shoreline is generally considered as a key variable to monitor the morphological evolution of sandy coasts. This research used the open-source software toolkit CoastSat (Vos et al., 2019) to automatically map shorelines from publicly available satellite imagery from 1984 to present, which are retrieved via Google Earth Engine (Gorelick et al., 2017). We selected five long, sandy beaches around the North Sea with varying tidal ranges, orientations and wave exposure for our analysis: (1) Skallingen in Denmark, (2) Egmond aan Zee and (3) the barrier island of Schiermonnikoog, both in the Netherlands, (4) Groenendijk in Belgium, and (5), Theddlethorpe in the UK. Approximately 2000 images per site were used for the shoreline extraction. Offshore wave buoy measurements and numerical model output provided the tidal water levels and wave conditions for the different sites. To account for tidal correction of the shoreline to a reference elevation, we used the dataset of Athanasiou et al. (2019) to estimate characteristic beach face slopes. At the conference we will present our analysis of the shoreline responses around the North Sea over the last few decades.&lt;/p&gt;&lt;p&gt;Athanasiou, P., Van Dongeren, A., Giardino, A., Vousdoukas, M., Gaytan-Aguilar, S., &amp; Ranasinghe, R. (2019). Global distribution of nearshore slopes with implications for coastal retreat. Earth system science data, 11(4).&lt;/p&gt;&lt;p&gt;Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., &amp; Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.&lt;/p&gt;&lt;p&gt;Vos, K., Splinter, K. D., Harley, M. D., Simmons, J. A., &amp; Turner, I. L. (2019). CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling &amp; Software, 122, 104528.&lt;/p&gt;

Monitoreo de los efectos de los incendios forestales mediante el uso de sensores remotos en el Área de Conservación Guanacaste, Costa Rica

2019 ◽  
Author(s):  
Mauricio Vega-Araya
Keyword(s):  
Costa Rica ◽  
Google Earth ◽  
Google Earth Engine

La Tierra y su biosfera están cambiando constantemente, por lo tanto, es fundamental detectar los cambios con el fin de entender su impacto en los ecosistemas terrestres. Los esquemas de monitoreo de ecosistemas han evolucionado rápidamente en las ultimas décadas. En el caso del monitoreo forestal, los métodos y herramientas que facilitan la utilización de imágenes satelitales permiten realizar este monitoreo con el cual se puede detectar donde y cuando un bosque es eliminado o afectado debido a un evento de deforestación o bien de fuego, lo anterior casi en tiempo real. Estas nuevas herramientas están disponibles para su implementación, sin embargo, ningún paı́s de la región centroamericana y el Caribe ha implementado un sistema como herramienta de decisión dentro de una estructura de gobierno central o federal debido a la ausencia de programas de transferencia de tecnologı́a o programas de capacitación de talento local. Los sensores remotos proporcionan mediciones consistentes y repetibles que permiten la captura de los efectos de muchos procesos que causan el cambio, incluyendo, por ejemplo, incendios, ataques de insectos, agentes de cambio naturales y antropogénicas como por ejemplo, la deforestación, la urbanización, la agricultura, etc. Las series temporales de imágenes de satélite proporcionan maneras para detectar y vigilar cambios en el tiempo y en el espacio, esto consistentemente durante los últimos 30 años a nivel mundial. Los incendios forestales afectan el proceso de sucesión del bosque, no obstante, es muy limitada la existencia de estudios locales que relacionen el efecto de los incendios forestales con las diferencias en la información espectral a partir de sensoramiento remoto. En el presente estudio se plantea y propone la utilización y aprovechamiento de lo que se ha denominado grandes datos, especialmente con el advenimiento muchas plataformas de sensores remotos como Landsat, MODIS y recientemente Sentinel, para identificar cuál es el efecto de los incendios forestales en la sucesión y sus elementos perturbadores, como por ejemplo, la presencia de lianas. Se procesaron las series temporales se usó la plataforma digital Google Earth Engine, que permitió la selección y reducción de la información espacial de los ı́ndices de vegetación en tendencia, estacionalidad y residuos. Se analizó la respuesta de estos ı́ndices en sitios con diferente afectación por incendios forestales. Con estos índices se pretende desarrollar modelos de clasificación de series espaciales de tiempo de los ı́ndices y poder ası́ comprender los cambios en el tiempo y el espacio de los ecosistemas afectados por incendios forestales. Preliminarmente, se encontró una relación entre la incidencia de los incendios forestales y el fenómeno del Niño-Oscilación del Sur para el índice de vegetación denominado índice de área foliar. Además, la evidencia indica que el índice normalizado de vegetación si presenta diferencias respecto a los sitios que tienen un historial de fuegos diferente. El establecer esta relación implica estudiar también los regı́menes de precipitación y temperatura. El descomponer las series de tiempo facilitó la correlación con otras series de tiempo, permitiendo establecer las bases de un monitoreo y a su vez, relacionar las índices de vegetación y su variación con otros elementos climáticos, como por ejemplo, el efecto ENOS.

Phát triển thuật toán giám sát lũ lụt vùng Đồng bằng sông cửu Long dựa vào nền tảng Google Earth Engine

2018 ◽  
Vol 54(9) ◽  
pp. 29
Author(s):  
Võ Quốc Tuấn ◽  
Nguyễn Thiên Hoa ◽  
Huỳnh Thị Kim Nhân ◽  
Đặng Hoàng Khải
Keyword(s):  
Google Earth ◽  
Google Earth Engine

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

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