Using Google Earth Engine development environment for remote sensing image analysis, Al Shuwija marsh case study

<p><strong>Abstract.</strong> Tibetan Plateau (TP) is the most abundant area of water resources and water energy resources in China. It is also the birthplace of the main rivers in Southeast Asia and plays an important strategic role. However, due to its remote location and complex topography, the observation of surface hydrometeorological elements is extremely scarce, which seriously restricts the understanding of the water cycle in this area. Using remote sensing images to extract rivers and lakes on TP can obtain a lot of valuable water resources information. However, the downloading and processing of remote sensing images is very time-consuming, especially the processing of remote sensing images with large-scale and long time series often involves hundreds of gigabytes of data, which requires a high level of personal computers and is inefficient. As a cloud platform dedicated to data processing and analysis of geoscience, Google Earth Engine(GEE) integrates many excellent remote sensing image processing algorithms. It does not need to download images and supports online remote sensing image processing, which greatly improves the output efficiency. Based on GEE, the monthly data of Yarlung Zangbo River at Nuxia Hydrological Station and the annual data of typical lakes were extracted and vectorized from the pre-processed Landsat series images. It was found that the area of Yarlung Zangbo River at Nuxia Hydrological Station varies periodically. The changing trend of typical lakes is also revealed.</p>

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A Case Study of Advancing Remote Sensing Image Analysis

Acta Cybernetica ◽

10.14232/actacyb.22.1.2015.5 ◽

2015 ◽

Vol 22 (1) ◽

pp. 57-79 ◽

Cited By ~ 2

Author(s):

Roberto Giachetta ◽

István Fekete

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Remote Sensing Image

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Mapping and Change Detection of Mangroves Using Remote Sensing and Google Earth Engine: A Case Study

ICT Systems and Sustainability - Lecture Notes in Networks and Systems ◽

10.1007/978-981-16-5987-4_20 ◽

2022 ◽

pp. 187-195

Author(s):

Geetanjali S. Mahamunkar ◽

Arvind W. Kiwelekar ◽

Laxman D. Netak

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Google Earth ◽

Google Earth Engine

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Geographic unit sequences based remote sensing image analysis: A case study of Sichuan, China

2011 19th International Conference on Geoinformatics ◽

10.1109/geoinformatics.2011.5980756 ◽

2011 ◽

Author(s):

Chunxiao Zhang ◽

Hui Lin ◽

Xuejun Lu

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Remote Sensing Image

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CROP CLASSIFICATION IN KARSHI STEPPE USING REMOTE SENSING INFORMATION AND GOOGLE EARTH ENGINE TOOL

European Science Review ◽

10.29013/esr-18-9.10.1-130-132 ◽

2018 ◽

pp. 130-132

Author(s):

Z. A. Gafurov ◽

S. B. Eltazarov ◽

T. A. Akhmedova

Keyword(s):

Remote Sensing ◽

Google Earth ◽

Google Earth Engine ◽

Crop Classification

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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

Remote Sensing ◽

10.3390/rs13081433 ◽

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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Machine Learning Comparison and Parameter Setting Methods for the Detection of Dump Sites for Construction and Demolition Waste Using the Google Earth Engine

Remote Sensing ◽

10.3390/rs13040787 ◽

2021 ◽

Vol 13 (4) ◽

pp. 787

Author(s):

Lei Zhou ◽

Ting Luo ◽

Mingyi Du ◽

Qiang Chen ◽

Yang Liu ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Google Earth ◽

Construction And Demolition Waste ◽

Parameterization Scheme ◽

Classification Methods ◽

Demolition Waste ◽

Optimal Method ◽

Identification Method ◽

Google Earth Engine

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

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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 ◽

10.3390/s21051791 ◽

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.

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