scholarly journals Brief communication: Detection of glacier surge activity using cloud computing of Sentinel-1 radar data

2021 ◽  
Vol 15 (10) ◽  
pp. 4901-4907
Author(s):  
Paul Willem Leclercq ◽  
Andreas Kääb ◽  
Bas Altena
Keyword(s):  
Climate Change ◽  
Cloud Computing ◽  
Radar Data ◽  
Google Earth ◽  
Snow Avalanches ◽  
Glacier Surface ◽  
Operational Monitoring ◽  
Special Events ◽  
Glacier Surges ◽  
Google Earth Engine

Abstract. For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges and some other special events such as large rock and snow avalanches.

scholarly journals Brief Communication: Detection of glacier surge activity using cloud computing of Sentinel-1 radar data

2021 ◽  
Author(s):  
Paul Willem Leclercq ◽  
Andreas Kääb ◽  
Bas Altena
Keyword(s):  
Climate Change ◽  
Cloud Computing ◽  
Radar Data ◽  
Google Earth ◽  
Snow Avalanches ◽  
Glacier Surface ◽  
Operational Monitoring ◽  
Special Events ◽  
Glacier Surges ◽  
Google Earth Engine

Abstract. For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges, and some other special events such as large rock and snow avalanches.

scholarly journals Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

2018 ◽  
Vol 10 (6) ◽  
pp. 909 ◽  
Author(s):  
Kel Markert ◽  
Calla Schmidt ◽  
Robert Griffin ◽  
Africa Flores ◽  
Ate Poortinga ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Surface Sediments ◽  
Google Earth ◽  
Operational Monitoring ◽  
Mekong Basin ◽  
Lower Mekong Basin ◽  
Google Earth Engine

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

scholarly journals Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

2017 ◽  
Vol 126 ◽  
pp. 225-244 ◽  
Author(s):  
Jun Xiong ◽  
Prasad S. Thenkabail ◽  
Murali K. Gumma ◽  
Pardhasaradhi Teluguntla ◽  
Justin Poehnelt ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Google Earth ◽  
Google Earth Engine ◽  
Cropland Mapping

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals DETECTION OF FLOOD IMPACTED AREAS IN EAST NUSA TENGGARA USING SENTINEL-1 IMAGERY

CI-TECH ◽  
2021 ◽  
Vol 2 (01) ◽  
pp. 37-41
Author(s):  
Bagas Aryaseta
Keyword(s):  
Cloud Computing ◽  
Tropical Cyclone ◽  
Data Processing ◽  
High Intensity ◽  
Flash Floods ◽  
Google Earth ◽  
Classification Method ◽  
Small Signal ◽  
Pixel Value ◽  
Google Earth Engine

Flash Floods in East Nusa Tenggara occured on April 4th, 2021. These Flash Floods are scattered from East Flores Regency, Lembata Regency, Alor Regency, Malacca Regency, Sabu Raijua Regency, Kupang City, Kupang Regency, and Ende Regency. The cause of these Flash Floods is the high intensity of rain caused by the tropical cyclone Seroja. Mapping of flood locations plays an important role in prevention and mitigation efforts. In this study, InSAR data processing was carried out from the Sentinel 1A satellite to find flood-affected locations in East Nusa Tenggara. 32 images of Sentinel-1 were processed before and 31 images after the Flash Floods incident. The method used is the classification method using cloud computing, Google Earth Engine. The results show that the flood-affected areas can be detected based on a lower pixel value (indicating a very small signal backscatter value), then compared to the conditions before the flood. The four sample points identified, namely points A, B, C, and D each have pixel values ​​of -8.58, -9.99, -12.43, and -9.29 for the VV polarized image, respectively. For VH polarized image is -17.35, -17.96, -17.84, and -14.22, respectively.

Sign in / Sign up

Export Citation Format

Share Document