Continent-wide bimonthly mapping of Antarctic surface meltwater using Google Earth Engine

2021 ◽  
Author(s):  
Peter Tuckett ◽  
Jeremy Ely ◽  
Andrew Sole ◽  
Stephen Livingstone ◽  
James Lea
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Mass Balance ◽  
Large Scale ◽  
Ice Sheet ◽  
Google Earth ◽  
Landsat 8 ◽  
Supraglacial Lakes ◽  
Google Earth Engine ◽  
Temporal And Spatial

<p>Surface meltwater is widespread around the margin of the Antarctic Ice Sheet during the austral summer. This meltwater, typically transported via surface streams and rivers and stored in supraglacial lakes, has the potential to influence ice-sheet mass balance through ice-dynamic and albedo feedbacks. To predict the impact that surface melt will have on mass balance over coming decades, it is important to understand spatial and temporal variability in surface meltwater extent. A variety of methods have been used to detect supraglacial lakes in Antarctica, yet a multi-annual, continent-wide study of Antarctic supraglacial meltwater has yet to be conducted. Cloud-based computational platforms, such as Google Earth Engine (GEE), enable large-scale temporal and spatial analysis of remote sensing datasets at minimal time expense. Here, we implement an automated method for meltwater detection in GEE to generate continent-wide, bimonthly repeat assessments of supraglacial lake extent between 2013 and 2020. We use a band-threshold based approach to delineate surface water from Landsat-8 imagery. Furthermore, our method incorporates a novel technique for quantifying meltwater extent that accounts for variability in optical image coverage and cloud cover, enabling an upper uncertainty bound to be attached to minimum mapped lake areas. We present results from continent-wide mapping, and highlight initial findings that indicate evolution of lakes in Antarctica over the past seven years. This work demonstrates how platforms such as GEE have revolutionized our ability to undertake large-scale projects from remote sensing datasets, allowing for greater temporal and spatial analysis of cryospheric processes than previously possible.</p>

scholarly journals CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 855-862 ◽  
Author(s):  
Y. T. Guo ◽  
X. M. Zhang ◽  
T. F. Long ◽  
W. L. Jiao ◽  
G. J. He ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Forest Cover ◽  
Survey Methods ◽  
Coniferous Forest ◽  
Google Earth ◽  
Forest Area ◽  
Landsat 8 ◽  
Forest Change ◽  
Google Earth Engine

Abstract. Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

scholarly journals CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE

Irriga ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 160-169
Author(s):  
Cesar De Oliveira Ferreira Silva
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Supervised Classification ◽  
Source Code ◽  
Regression Trees ◽  
Google Earth ◽  
Landsat 8 ◽  
Classification And Regression ◽  
Google Earth Engine

CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE   CÉSAR DE OLIVEIRA FERREIRA SILVA1   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Avenida Universitária, n° 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP, Brasil, e-mail: [email protected].     1 RESUMO   Identificar áreas de irrigação usando imagens de satélite é um desafio que encontra em soluções de computação em nuvem um grande potencial, como na ferramenta Google Earth Engine (GEE), que facilita o processo de busca, filtragem e manipulação de grandes volumes de dados de sensoriamento remoto sem a necessidade de softwares pagos ou de download de imagens. O presente trabalho apresenta uma implementação de classificação supervisionada de áreas irrigadas e não-irrigadas na região de Sorriso e Lucas do Rio Verde/MT com o algoritmo Classification and Regression Trees (CART) em ambiente GEE utilizando as bandas 2-7 do satélite Landsat-8 e os índices NDVI, NDWI e SAVI. A acurácia da classificação supervisionada foi de 99,4% ao utilizar os índices NDWI, NDVI e SAVI e de 98,7% sem utilizar esses índices, todas consideradas excelentes. O tempo de processamento médio, refeito 10 vezes, foi de 52 segundos, considerando todo o código-fonte desenvolvido desde a filtragem das imagens até a conclusão da classificação. O código-fonte desenvolvido é apresentado em anexo de modo a difundir e incentivar o uso do GEE para estudos de inteligência espacial em irrigação e drenagem por sua usabilidade e fácil manipulação.   Keywords: computação em nuvem, sensoriamento remoto, hidrologia, modelagem.     SILVA, C. O .F SUPERVISED CLASSIFICATION OF IRRIGATED AREA USING SPECTRAL INDEXES FROM LANDSAT-8 IMAGES WITH GOOGLE EARTH ENGINE     2 ABSTRACT   Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat- 8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation.   Keywords: cloud computing, remote sensing, hydrology, modeling.

Greenland ice sheet supraglacial lake drainages between 2000 and 2019

2020 ◽  
Author(s):  
Stephen Brough ◽  
James Lea
Keyword(s):  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Google Earth ◽  
Ice Dynamics ◽  
Supraglacial Lakes ◽  
Warming Climate ◽  
Google Earth Engine ◽  
Insight Into ◽  
Rapid Transfer ◽  
Fundamental Mechanism

<p>The drainage of supraglacial lakes provides a fundamental mechanism for the rapid transfer of surface meltwater to the bed of an ice sheet, impacting both subglacial hydrology and ice dynamics. As a consequence, it is crucial to understand where and when these lakes drain, and how or if this has changed through time. Given that lakes are now occurring in greater numbers and at higher elevations, identifying changing modes in behaviour will have significant implications for the future dynamics of the Greenland ice sheet. Nevertheless, previous studies of supraglacial lakes and associated drainage events have been limited in spatial and/or temporal scale relative to the entire ice sheet.</p><p>Here we use daily maps of Greenland wide supraglacial lake coverage – derived from MODIS Terra within Google Earth Engine – to investigate the style, pattern and timing of lake drainages between 2000 and 2019. Results from this study: i) add to the understanding of how supraglacial hydrology and its coupling to the bed has changed in response to more extensive supraglacial lake cover over the last 20 years; and ii) provide insight into how these lakes and associated drainage events can be expected to respond to increased surface meltwater production under a warming climate.</p>

scholarly journals Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine

2021 ◽  
Vol 13 (7) ◽  
pp. 1245
Author(s):  
Jinhuang Lin ◽  
Xiaobin Jin ◽  
Jie Ren ◽  
Jingping Liu ◽  
Xinyuan Liang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Agricultural Production ◽  
Large Scale ◽  
Texture Features ◽  
Google Earth ◽  
Distribution Data ◽  
Classification Algorithms ◽  
Google Earth Engine ◽  
Remote Sensing Mapping ◽  
Terrain Features

A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai’an County, Rudong County and Pizhou City.

scholarly journals Estimation of Total Suspended Sediment Solid in Porong River Waters Using Multitemporal Satellite Imagery

2021 ◽  
Vol 936 (1) ◽  
pp. 012006
Author(s):  
Z N Ghuvita Hadi ◽  
T Hariyanto ◽  
N Hayati
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Satellite Imagery ◽  
River Estuary ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Sample Point ◽  
Sensing Data ◽  
Google Earth Engine

Abstract Monitoring the concentration of Total Suspended Solid (TSS) is one method to determine water quality, because a high TSS value indicates a high level of pollution. Remote sensing data can be used effectively in generating suspended sediment concentrations. Nowdays, Google Earth Engine platform has provided a large collection of remote sensing data. Therefore, this study uses Google Earth Engine which is processed for free and aims to calculate the TSS value in the Kali Porong area. This research was conducted multitemporal in the last ten years, namely from 2013-2021 using multitemporal satellite imagery landsat-8 and sentinel-2 by applying empirical algorithms for calculating TSS. The results of this study are the value of TSS concentration at each sample point and a multitemporal TSS concentration distribution map. The year 2016, 2017, and 2021, the distribution of TSS concentration values was higher than in other years. At the sample point, the lowest TSS concentration value was 16.55 mg/L in 2013. Meanwhile, the highest TSS concentration value of 266.33 mg/L occurred in 2014 precisely in the Porong River estuary area which is the border area between land and water. the sea so that a lot of TSS material is concentrated in the area due to waves and ocean currents.

scholarly journals Using Landsat 8 Images Along with Google Earth Engine for Investigation the Location of the Crashed Aircrafts

2018 ◽  
Author(s):  
Azad Rasul
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat Images ◽  
Green Area ◽  
Google Earth Engine ◽  
Index Value ◽  
Research Period

Remote sensing data and techniques utilized for various purposes including natural disasters such as earthquake as well as flood. The research aims to consume liberates Landsat 8 images for investigating crashed airplanes such as MH370. Overall approximately 300 Landsat images with less than 10% clouds utilized in addition processed through Google Engine Platform. Due to the materials as well as the color of airplane body different from the area which is a plane crashed there, moreover, it should be the characteristics of the plane shapefile different in terms of albedo, temperature as well as vegetation index value. The research observed Landsat 8 data as well as methods utilized in this research, especially, NDVI, albedo in addition to band 4, capable to distinguish between the plane and its surrounding green area. Therefore, our result confirms during the research period, there was no plane on the location as well as MH370 not crashed in this site.

scholarly journals A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 313-319
Author(s):  
A. Jamali ◽  
M. Mahdianpari ◽  
İ. R. Karaş
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Well Being ◽  
Google Earth ◽  
The Other ◽  
Gradient Boosting ◽  
Wetland Classification ◽  
Observation Methods ◽  
Extreme Gradient Boosting ◽  
Google Earth Engine

Abstract. Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers.

scholarly journals The Google Earth Engine Digitisation Tool (GEEDiT) and the Margin change Quantification Tool (MaQiT) – simple tools for the rapid mapping and quantification of changing Earth surface margins

2018 ◽  
Vol 6 (3) ◽  
pp. 551-561 ◽  
Author(s):  
James M. Lea
Keyword(s):  
Remote Sensing ◽  
High School Students ◽  
Large Scale ◽  
Research Question ◽  
Google Earth ◽  
Earth Surface ◽  
School Students ◽  
Glacier Terminus ◽  
Wide Range ◽  
Google Earth Engine

Abstract. Changes in margins derived from satellite imagery are quantitative indicators of the environmental processes and drivers acting on the Earth's surface, for example retreating ice margins or coastal changes with rising sea level. However, the large-scale rapid visualisation and analysis of the satellite record is often impractical due to factors such as computer processing power, software availability, internet connection speed and/or user expertise in remote sensing. Here are presented three new, freely accessible tools that together can be used to process, visualise and review data from the full Landsat 4–8 and Sentinel 1–2 satellite records in seconds, enabling efficient mapping (through manual digitisation) and automated quantification of margin changes. These tools are highly accessible for users from a range of remote-sensing expertise (from academics to high school students), with minimal computational, licensing and knowledge-based barriers to access. The Google Earth Engine Digitisation Tool (GEEDiT) allows users to define a point anywhere on the planet and filter data from each satellite for user-defined time frames, maximum acceptable cloud cover extent, and options of predefined or custom image band combinations via a simple graphical user interface (GUI). GEEDiT allows georeferenced vectors to be easily and rapidly mapped from each image, with image metadata and user notes automatically appended to each vector, which can then be exported for subsequent analysis. The GEEDiT Reviewer tool allows users to quality control their own/others' data and also filter existing datasets based on the spatial/temporal requirements for their particular research question. The Margin change Quantification Tool (MaQiT) is complementary to GEEDiT and GEEDiT Reviewer, allowing the rapid quantification of these margin changes by utilising two well-established methods that have previously been used to measure glacier margin change and two new methods via a similarly simple GUI. A case study of the lake-terminating glacier Breiðamerkurjökull, Iceland, is used to demonstrate the complementary functionality of GEEDiT, GEEDiT Reviewer and MaQiT, though it should be noted that MaQiT is also suitable for the (re-)analysis of existing datasets not generated by GEEDiT. MaQiT has been developed with the original aim of quantifying tidewater glacier terminus change, though the methods included within the tool have potential for wide applications in multiple areas of Earth surface science (e.g. coastal and vegetation extent change). It is hoped that these tools will allow a wide range of researchers and students across the geosciences to efficiently map, analyse and access volumes of data that would have previously proven prohibitive.

Sign in / Sign up

Export Citation Format

Share Document