scholarly journals Mangrove Vegetation Mapping Using Sentinel-2A Imagery Based on Google Earth Engine Cloud Computing Platform

2021 ◽  
Vol 6 (1) ◽  
pp. 249-255
Author(s):  
Luhur Moekti Prayogo
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Google Earth ◽  
Mangrove Forests ◽  
Application Development ◽  
Computing Platform ◽  
Mapping Technology ◽  
Google Earth Engine ◽  
Sentinel 2A

Mangroves are trees whose habitat is affected by tides, and their presence has decreased from year to year. Today, mapping technology has undergone many developments, including the availability of images of various resolutions and cloud-based image processing. One of the popular platforms today is the Google Earth Engine. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for extensive processing. The advantage of using Google Earth Engine is that users do not have to be IT experts without experts in application development, WEB programming, and HTML. This study aims to conduct a study on mangrove mapping in Gili Genting District with Sentinel-2A imagery using a Google Earth Engine. This location was chosen since there are still many mangroves, especially on the Gili Raja and Gili Genting Islands. From this research, it can be concluded that cloud computing-based Sentinel-2A image processing shows that the vegetation value of NDVI results ranges from -0.923208 to 0.75579. The classification results show that mangrove forests' overall presence on Gili Genting Island is more expansive than Gili Raja Island with 16.74 ha and 14.75 ha. The use of the Google Earth Engine platform simplifies the analysis process because image processing can be done once with various scripts so that analysis becomes faster.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

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.

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

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.

scholarly journals Google Earth Engine Applications

2019 ◽  
Vol 11 (5) ◽  
pp. 591 ◽  
Author(s):  
Onisimo Mutanga ◽  
Lalit Kumar
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Google Earth ◽  
Data Sets ◽  
Computing Platform ◽  
Huge Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

The Google Earth Engine (GEE) is a cloud computing platform designed to store and process huge data sets (at petabyte-scale) for analysis and ultimate decision making [...]

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