Historical Co-seismic Landslides Inventory and Analysis Using Google Earth: A Case Study of 1920 M8.5 Haiyuan Earthquake, China

2015 ◽  
pp. 709-712 ◽  
Author(s):  
Weile Li ◽  
Runqiu Huang ◽  
Xiangjun Pei ◽  
Xiaochao Zhang
Keyword(s):  
Google Earth ◽  
Seismic Landslides

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.

Study on Three-Dimensional Visual Simulation for Inter-Basin Water Transfer Project

2012 ◽  
Vol 256-259 ◽  
pp. 2523-2527
Author(s):  
Qian Wei Wang ◽  
Rui Rui Sun ◽  
Wei Ping Guo
Keyword(s):  
Three Dimensional ◽  
Google Earth ◽  
Water Transfer ◽  
Digital Model ◽  
Water Diversion ◽  
Visual Simulation ◽  
Channel Water ◽  
North Water ◽  
Basin Water

With regards to the characteristics of inter-basin water transfer projects, a 3d visual simulation (Three-Dimensional Visual Simulation, 3DVS) method for inter-basin water transfer project was proposed. A virtual reproduction of the entire project and its topography is achieved. The supplement of the three-dimensional topographic data was completed by Civil 3D combinedwith Google Earth. In this paper, the 3D digital model of inter-basin water transfer project is established using 3ds Max. Based on the established digital model, the simulation of channel water were realized .The Yuzhou section of South-to-North Water Transfer Project is taken as a case study. 3D visual simulation provides an effective way for the construction management and decision-making for inter-basin water diversion project.

Google Earth: A New Resource for Shoreline Change Estimation—Case Study from Jaffna Peninsula, Sri Lanka

2018 ◽  
Vol 41 (6) ◽  
pp. 546-580 ◽  
Author(s):  
T. W. S. Warnasuriya ◽  
Kuddithamby Gunaalan ◽  
S. S. Gunasekara
Keyword(s):  
Sri Lanka ◽  
Shoreline Change ◽  
Google Earth

scholarly journals Validação de mapa de vulnerabilidade a erosão por aprendizagem de máquina

2020 ◽  
Vol 13 (2) ◽  
pp. 564
Author(s):  
Renata Cristina Mafra ◽  
Mayara Maezano Faita Pinheiro ◽  
Rejane Ennes Cicerelli ◽  
Lucas Prado Osco ◽  
Marcelo Rodrigo Alves ◽  
...  
Keyword(s):  
Multicriteria Analysis ◽  
Weather Conditions ◽  
Analysis Data ◽  
Drainage Density ◽  
Google Earth ◽  
Natural Phenomenon ◽  
Map Validation ◽  
Vulnerability Map ◽  
Inference Methods

O processo erosivo é um fenômeno que acontece devido às condições climáticas ou uso inadequado da terra. O mapeamento dos níveis de vulnerabilidade à erosão de uma área pode ocorrer usando diferentes modelos de inferência geográfica. No entanto, definir o método apropriado é ainda uma questão a ser respondida. Este trabalho apresenta uma abordagem de validação de mapa de vulnerabilidade à erosão elaborado por diferentes métodos de inferência. Como estudo de caso, adotou-se uma bacia hidrográfica e considerou-se os seguintes critérios: geomorfologia, pedologia, declividade, densidade de drenagem e cobertura da terra. Dentre os métodos testados tem-se: Combinação Linear Ponderada (CLP) e três operadores Fuzzy: soma algébrica, produto algébrico e gamma, variando o expoente “γ” entre os valores 0,4; 0,6 e 0,8. Os pesos dos critérios foram definidos com base no Processo Analítico Hierárquico. A validação dos mapas ocorreu usando 1902 pontos, sendo 951 pontos de erosão na área, definidos com base em imagens do Google Earth Pro, e 951 pontos sem erosão, gerados aleatoriamente no QGIS 3.8. O modelo de regressão logística foi usado parar comparar o desempenho de cada mapa ao apontar as áreas com maior e menor grau de vulnerabilidade. A melhor modelagem foi alcançada com o operador Fuzzy gamma quando parametrizado com γ = 0,6. Embora o CLP seja a abordagem recorrente em estudos ambientais envolvendo inferência geográfica, nossos resultados demostram que outros operadores podem produzir resultados mais próximos aos encontrados com a realidade observada em campo.  Machine learning erosion and vulnerability map validation A B S T R A C TErosion is a natural phenomenon that happens in all ecosystems, whether due to weather conditions or inappropriate land use. Mapping the erosion vulnerability levels of an area can occur using different methods of geographic inference. However, defining the appropriate method is still a question to be answered. This paper presents an erosion vulnerability map validation approach elaborated by different inference methods. As a case study, a watershed was adopted and the following criteria were considered: geomorphology, pedology, slope, drainage density and land cover. Among the tested methods are: Weighted Linear Combination (WLC) and three Fuzzy operators: algebraic sum, algebraic product and gamma, varying the exponent “γ” between the values 0.4; 0.6 and 0.8. The weights of the criteria were defined based on the Hierarchical Analytical Process. The validation of the maps took place using 1902 points, with 951 erosion points in the area defined based on Google Earth Pro images and 951 points without erosion randomly generated in QGIS 3.8. The logistic regression model was used to compare the performance of each map by pointing out the areas with the highest and lowest degree of vulnerability. The best modeling was achieved with the Fuzzy gamma operator when parameterized with γ = 0.6. Although WLC is the recurring approach in environmental studies involving geographic inference, our results show that other operators can produce results closer to those encountered with the reality observed in the field.Keywords: Geographical inference; multicriteria analysis; data validation; environmental impact.

Google earth and the archaeology of Saudi Arabia. A case study from the Jeddah area

2011 ◽  
Vol 38 (6) ◽  
pp. 1284-1293 ◽  
Author(s):  
David Kennedy ◽  
M.C. Bishop
Keyword(s):  
Saudi Arabia ◽  
Google Earth

scholarly journals Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 500
Author(s):  
Chengjie Yang ◽  
Ruren Li ◽  
Zongyao Sha
Keyword(s):  
Urban Planning ◽  
Geographically Weighted Regression ◽  
Driving Forces ◽  
Vital Role ◽  
Google Earth ◽  
Weighted Regression ◽  
Landscape Indices ◽  
Aggregation Index ◽  
Terrain Elevation

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

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