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.

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 Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia

2020 ◽  
Vol 12 (18) ◽  
pp. 3110
Author(s):  
Manjunatha Venkatappa ◽  
Nophea Sasaki ◽  
Sutee Anantsuksomsri ◽  
Benjamin Smith
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Carbon Emissions ◽  
Carbon Stock ◽  
Forest Cover ◽  
Good Practice ◽  
Google Earth ◽  
Classification Method ◽  
Siem Reap ◽  
Google Earth Engine

Digital and scalable technologies are increasingly important for rapid and large-scale assessment and monitoring of land cover change. Until recently, little research has existed on how these technologies can be specifically applied to the monitoring of Reducing Emissions from Deforestation and Forest Degradation (REDD+) activities. Using the Google Earth Engine (GEE) cloud computing platform, we applied the recently developed phenology-based threshold classification method (PBTC) for detecting and mapping forest cover and carbon stock changes in Siem Reap province, Cambodia, between 1990 and 2018. The obtained PBTC maps were validated using Google Earth high resolution historical imagery and reference land cover maps by creating 3771 systematic 5 × 5 km spatial accuracy points. The overall cumulative accuracy of this study was 92.1% and its cumulative Kappa was 0.9, which are sufficiently high to apply the PBTC method to detect forest land cover change. Accordingly, we estimated the carbon stock changes over a 28-year period in accordance with the Good Practice Guidelines of the Intergovernmental Panel on Climate Change. We found that 322,694 ha of forest cover was lost in Siem Reap, representing an annual deforestation rate of 1.3% between 1990 and 2018. This loss of forest cover was responsible for carbon emissions of 143,729,440 MgCO2 over the same period. If REDD+ activities are implemented during the implementation period of the Paris Climate Agreement between 2020 and 2030, about 8,256,746 MgCO2 of carbon emissions could be reduced, equivalent to about USD 6-115 million annually depending on chosen carbon prices. Our case study demonstrates that the GEE and PBTC method can be used to detect and monitor forest cover change and carbon stock changes in the tropics with high accuracy.

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