Spatio‐temporal and time‐series analysis of bluetongue outbreaks with environmental factors from Google Earth Engine (gee) in Andhra pradesh, india

2021 ◽  
Author(s):  
R Karthikeyan ◽  
Ramkumar N Rupner ◽  
Shiva Reddy Koti ◽  
Nagaraj Jaganathasamy ◽  
Y.S. Malik ◽  
...  
Keyword(s):  
Time Series ◽  
Environmental Factors ◽  
Time Series Analysis ◽  
Andhra Pradesh ◽  
Google Earth ◽  
Series Analysis ◽  
Google Earth Engine ◽  
Spatio Temporal

Sentinel-1 Time-Series Analysis for Fires Monitoring using Google Earth Engine Tools

2021 ◽  
Author(s):  
Massimiliano Gargiulo ◽  
Antonio Iodice ◽  
Daniele Riccio ◽  
Giuseppe Ruello
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Google Earth ◽  
Series Analysis ◽  
Google Earth Engine

scholarly journals Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

2020 ◽  
pp. 100458
Author(s):  
Michelle Li Ern Ang ◽  
Dirk Arts ◽  
Danielle Crawford ◽  
Bonifacio V. Labatos ◽  
Khanh Duc Ngo ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Analysis ◽  
Google Earth ◽  
Cover Time ◽  
Web Based ◽  
Series Analysis ◽  
Google Earth Engine

scholarly journals Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis

2021 ◽  
Vol 265 ◽  
pp. 112648
Author(s):  
Shijuan Chen ◽  
Curtis E. Woodcock ◽  
Eric L. Bullock ◽  
Paulo Arévalo ◽  
Paata Torchinava ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Temperate Forest ◽  
Forest Degradation ◽  
Google Earth ◽  
Series Analysis ◽  
Google Earth Engine

Landslides in Central Italy identified from Sentinel 2 multispectral imaging time series analysis with Google Earth Engine

2020 ◽  
Author(s):  
Marco Bartola ◽  
Carla Braitenberg ◽  
Carlo Bisci
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Multispectral Imaging ◽  
Spectral Response ◽  
Central Italy ◽  
Slope Angle ◽  
Google Earth ◽  
Series Analysis ◽  
Google Earth Engine ◽  
Sentinel 2

<p>In 2016, Central Italy was hit by a months-lasting earthquake sequence that started off in August 24<sup>th</sup> 2016 with a Mw 6.2 earthquake which provoked severe damage to the towns of Accumoli (RI) and Amatrice (RI). The following October 30<sup>th</sup> 2016 earthquake (Mw 6.5), with epicenter in Norcia (PG) about 20 km NW of the first shock, triggered landslides in the area of Visso (MC), as reported by local newspapers.</p><p>The purpose of this work is to individuate the areas affected by such landslides using the radiance variation recorded by multispectral images acquired by Sentinel 2. The time series analysis of the images has been carried out in Google Earth Engine environment, that allows access to the entire suite of available images. Due to the steep terrain, the shadowing effect of the hills was taken into account and comparison of images have been made only for those taken in the same seasonal moment of different years, thus guaranteeing the same solar elevation.</p><p>It was found that the band of red was instrumental in identifying landslides along slopes made up of limestone, which is the typical outcrop of the area. Due to the extended time period between the images (July 2015 and July 2017), anthropogenic changes in land-use were present and had to be distinguished from landslides. A criterion involving the slope angle was developed, maintaining only the changes that had occurred on slopes steeper than 25°, since man-made interventions giving similar spectral response are hardly done in steep areas. The slope analysis and correlation study with the extension and location of landslides was carried out using a Geographic Information System. (ESRI ArcGIS 10.5) The total extent of the area affected by the surveyed landslides is very large, having  been estimated to be more than 200 000 m<sup>2</sup>.</p>

scholarly journals Time series analysis of vegetation-cover response to environmental factors and residential development in a dryland region

2018 ◽  
Vol 56 (3) ◽  
pp. 362-387 ◽  
Author(s):  
Noa Ohana-Levi ◽  
Tarin Paz-Kagan ◽  
Natalya Panov ◽  
Aviva Peeters ◽  
Asaf Tsoar ◽  
...  
Keyword(s):  
Time Series ◽  
Environmental Factors ◽  
Time Series Analysis ◽  
Vegetation Cover ◽  
Residential Development ◽  
Series Analysis

Weather Forecasting in Coastal Districts of Odisha and Andhra Pradesh by Using Time Series Analysis

2018 ◽  
Vol 6 (8) ◽  
pp. 85
Author(s):  
Naresh Patnaik ◽  
F Baliarsingh
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Time Series Analysis ◽  
Andhra Pradesh ◽  
Maximum Temperature ◽  
Climatic Parameters ◽  
Series Analysis ◽  
Annual Maximum Temperature

Climate change in world is always one of the most important topics in Water Resources. Now the issue is so predominant that it is gradually restricting out social life, peace and harmony. Climate change is a change in the statistical distribution of weather pattern of an area, when such changes occur for a long period of time. Weather is the state of atmosphere at a particular place and time. Climate is the long term statistical expression of short term weather. This study presents a comprehensive assessment of the future climate pattern/weather prediction by taking different climatic parameters such as temperature, precipitation, solar radiation, wind speed and relative humidity by using time series analysis. The study area of research work covers the coastal districts of Odisha and some parts of Andhra Pradesh. The climatic parameters are collected over last 20 years (1993-2013) from the selected 10 stations and the prediction is made using Time Series Analysis (ARIMA Model). The annual maximum temperature, solar radiation of all districts indicates a statistically significant increase in trend, whereas in the case of wind speed and relative humidity indicates significant deceasing trend. The annual rain fall shows an increasing trend of 2.69 mm/year in all station except Srikakulam, Khordha, Jagatsinghpur and Balasore which shows a decreasing trend of 1.94, 1.29, 0.56 and 1.18 mm/year respectively. As a whole the annual maximum temperature and solar radiation shows an increase trend of 0.16 ⁰C and 0.073 MJ/m² per year respectively. Further the wind speed and relative humidity of all stations indicates a decreasing trend of 0.056 m/s and 0.003(Units in fraction) per year respectively.

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