scholarly journals Application of Remote Sensing Imagery and Algorithms in Google Earth Engine platform for Drought Assessment

2021 ◽  
Vol 62 (3) ◽  
pp. 53-67
Author(s):  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Google Earth ◽  
Agricultural Drought ◽  
The South ◽  
Central Coast ◽  
South Central ◽  
Drought Conditions ◽  
Google Earth Engine ◽  
Coast Of Vietnam

In Vietnam, drought is one of the natural disasters caused by high temperatures and lack of precipitation, especially with El Nino and the global warming phenomenon. It affects directly environmental, economical, social issueproblems, and the lives of humans. Many methods have been used to assess drought, andin which remote sensing indices are considered the most commonly used tool today. They are used to analyze spatio-temporal distribution of drought conditions and identify drought severity. Especially with the launch of Google Earth Engine (GEE) - a cloud-based platform for geospatial analysis, it is easy to access high-performance computing resources for processing multi-temporal satellite data online. With the GEE platform, we focus on writing and running scripts with the indicators suitable for evaluating drought phenomenon, instead of calculating on software and downloading remote sensing imagery with large size. In this study, we collected 26 Landsat 8 images in the dry season in 2019 (from April to July) in Tay Hoa district, Phu Yen – a region in the South Central Coast of Vietnam where agricultural drought occurs frequently. wWe assessed the distribution of drought conditions in the dry season in 2019 in Tay Hoa district, Phu Yen – a region in the South Central Coast of Vietnam where agricultural drought occurs frequently by using a drought index (VHI index – Vegetation Health Index) produced from Landsat satellite data in the GEE platform. The study results indicated that the drought (from mild to severe) concentrated in the North of the region, corresponding to high surface temperature and NDVI low or NDVI moderate values. VHI maps were visually compared with the drought map of the South Central Coast and the Central Highlands. In general, the results also reflect the the method’s reliability and can be used to support the managers to plan policies, making long-term plans to cope with climate change in the future at Tay Hoa in particular and other regions in general.

scholarly journals A Hybrid Approach Using GIS-Based Fuzzy AHP–TOPSIS Assessing Flood Hazards along the South-Central Coast of Vietnam

2020 ◽  
Vol 10 (20) ◽  
pp. 7142
Author(s):  
Huu Xuan Nguyen ◽  
An Thinh Nguyen ◽  
Anh Tu Ngo ◽  
Van Tho Phan ◽  
Trong Doi Nguyen ◽  
...  
Keyword(s):  
Fuzzy Ahp ◽  
High Tide ◽  
Hybrid Approach ◽  
Local Economy ◽  
Provincial Government ◽  
Flood Hazards ◽  
The South ◽  
Central Coast ◽  
South Central ◽  
Coast Of Vietnam

Flood hazards affect the local economy and the livelihood of residents along the South-Central Coast of Vietnam. Understanding the factors influencing floods’ occurrence potentially contributes to establish mitigation responses to the hazards. This paper deals with an empirical study on applying a combination of the fuzzy analytic hierarchy process (AHP), the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), and a geographic information system (GIS) to assess flood hazards along the South-Central Coast of Vietnam. Data are collected from focus group discussions (FGDs) with five communal authorities; a questionnaire completed by eight hamlet heads in the Phuoc Thang commune (Binh Dinh province); and documents, reports, and thematic maps provided from official sources. A total of 12 maps of flood factors are prepared. The results show that terrain elevation, creek-bottom terrains, high tide-induced flooding area, and distance to water body are the main factors affecting flood hazards. The An Loi hamlet faces the highest risk for floods, followed by Lac Dien, Luong Binh, and Pho Dong. The map of flood hazards indicates the western part is assessed as low hazard, whereas the eastern part is a very high hazard area. The study findings show that the hybrid approach using GIS-based fuzzy AHP–TOPSIS allows connecting decision makers with the influencing factors of flooding. To mitigate floods, both the Vietnam national government and the Binh Dinh provincial government should integrate natural hazard mitigation into socio-economic development policies.

High accuracy mass spectrometry comparison of Conus bandanus and Conus marmoreus venoms from the South Central Coast of Vietnam

Toxicon ◽  
2013 ◽  
Vol 75 ◽  
pp. 148-159 ◽  
Author(s):  
Bao Nguyen ◽  
Jordi Molgó ◽  
Hung Lamthanh ◽  
Evelyne Benoit ◽  
Thi An Khuc ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Accuracy ◽  
The South ◽  
Central Coast ◽  
South Central ◽  
Coast Of Vietnam

scholarly journals SUSTAINABLE DEVELOPMENT OF THE SEA-ISLAND TOURISM IN SOUTH CENTRAL COAST OF VIETNAM

2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Nguyen Thi Quyet
Keyword(s):  
Sustainable Development ◽  
Economic Development ◽  
Economic Sector ◽  
The South ◽  
Central Coast ◽  
The Sustainable Development ◽  
South Central ◽  
Socio Economic Development ◽  
Coast Of Vietnam ◽  
Coast Region

<p>Located in the middle position of the country, all provinces/cities in the South Central Coast border the sea. With a total coastline length of 1,430 km, accounting for 43.8% of the whole country's coastline (3,260 km), the sea and island tourism in the South Central Coast is now considered a spearhead economic sector, which plays an important role in socio-economic development of this area. In this article, the authors have deeply analyzed the situation and the issues in developing the sea and island tourism in the South Central Coast region from 2010 up to now. Thence, findings lead to important practical suggestions to promote the sustainable development of the sea and island tourism in the South Central Coast in the coming time.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0616/a.php" alt="Hit counter" /></p>

scholarly journals Orchidantha anthracina (Lowiaceae), a new species from Vietnam

2020 ◽  
Vol 65 (1) ◽  
pp. 90-93
Author(s):  
H.Đ. Trân ◽  
H.T. Lu'u ◽  
J. Leong-Škorničková
Keyword(s):  
New Species ◽  
The South ◽  
Conservation Assessment ◽  
Central Coast ◽  
Number Of Species ◽  
South Central ◽  
The Family ◽  
Coast Of Vietnam ◽  
A New Species

Orchidantha anthracina (Lowiaceae), discovered at the south central coast of Vietnam, is described and illustrated, bringing the total number of species in the family to 26, of which four occur in Vietnam. The notes on distribution, habitat and etymology are given and a preliminary conservation assessment is provided. The species is compared with O. vietnamica, with which it shares flowers of similar size and colours, but from which it is readily distinguished by a narrow and strongly reflexed dorsal sepal and spreading lateral sepals, not supporting the labellum. Notes with additional comparison to all species with a similar arrangement of lateral sepals are also provided.

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.

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

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.

Rapid detection of changes in the riverbanks of Laigiang river of the South Central Coast Vietnam: A methodology based on Digital Shoreline Analysis System and Sentinel-2

2021 ◽  
Vol 14 (11) ◽  
pp. 13-24
Author(s):  
Anh Tu Ngo ◽  
Stéphane Grivel ◽  
Thai Le Phan ◽  
Huu Xuan Nguyen ◽  
Trong Doi Nguyen
Keyword(s):  
Linear Regression ◽  
Shoreline Change ◽  
The South ◽  
Central Coast ◽  
Shoreline Changes ◽  
South Central ◽  
Digital Shoreline Analysis System ◽  
Analysis System ◽  
End Point Rate ◽  
Sentinel 2

The research focuses on using Sentinel-2 that can be integrated with the Digital Shoreline Analysis System (DSAS) as an effective tool for the determination of changes in the riverbanks and using linear regression to predict shoreline changes. The research applied the assessment of shoreline changes in the period of 2015- 2020 and forecast to 2025 in Laigiang river of the South Central Coast region of Vietnam. Based on the DSAS tool, parameters such as Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR) and Linear Regression Rate (LRR) were determined. The analysis results show that the accretion process in the Laigiang river in the period of 2015-2020 with the accretion area ranges from 81.47 ha. Meanwhile, the area of shoreline erosion only fluctuates around 54.42 ha. The rhythm of evolution is a determinant element for this transitional system.

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