scholarly journals Comment on Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine

2020 ◽  
Author(s):  
Anonymous
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Google Earth ◽  
Synthetic Aperture ◽  
Amplitude Change ◽  
Google Earth Engine ◽  
Rapid Landslide ◽  
Aperture Radar

scholarly journals Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine

2020 ◽  
Author(s):  
Alexander L. Handwerger ◽  
Shannan Y. Jones ◽  
Mong-Han Huang ◽  
Pukar Amatya ◽  
Hannah R. Kerner ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Google Earth ◽  
Disaster Mitigation ◽  
Synthetic Aperture ◽  
Detection Methods ◽  
Amplitude Change ◽  
Landslide Event ◽  
Google Earth Engine ◽  
Aperture Radar

Abstract. The rapid and accurate mapping of landslides is critical for emergency response, disaster mitigation, and improving our understanding of where landslides occur. Satellite-based synthetic aperture radar (SAR) can be used to identify landslides, often within days after triggering events, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Although there are many landslide detection methods using SAR, most require downloading a large volume of data to a local system and specialized processing software and training. Here we present a SAR-based amplitude change detection approach designed for those without SAR expertise that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to identify landslides on Google Earth Engine (GEE). We provide strategies that can aid in rapid response and event inventory mapping. We test our GEE-based approach in a ~ 277 km2 area in Hiroshima Prefecture, Japan where ~ 3,800 landslides were triggered by rainfall in July 2018. Our ability to detect landslides improves with the total number of SAR images acquired before and after the landslide event, by combining both ascending and descending acquisition geometry data, and by using topographic data to mask out flat areas unlikely to experience landslides. Importantly, our GEE approach allows the broader hazards and landslide community to utilize these state-of-the-art remote sensing data.

scholarly journals Strategies for landslide detection using open-access synthetic aperture radar backscatter change in Google Earth Engine

2021 ◽  
Author(s):  
Alexander L. Handwerger ◽  
Shannan Y. Jones ◽  
Pukar Amatya ◽  
Hannah R. Kerner ◽  
Dalia B. Kirschbaum ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Situational Awareness ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Disaster Mitigation ◽  
Synthetic Aperture ◽  
Landslide Event ◽  
Combining Data ◽  
Google Earth Engine ◽  
Aperture Radar

Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change detection approach that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to detect areas with high landslide density using the cloud-based Google Earth Engine (GEE). Importantly, our approach does not require downloading a large volume of data to a local system or specialized processing software. We provide strategies, including a landslide density heatmap approach, that can aid in rapid response and landslide detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries, and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach allows the broader hazards and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

scholarly journals Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

2019 ◽  
Vol 11 (6) ◽  
pp. 629 ◽  
Author(s):  
Fuyou Tian ◽  
Bingfang Wu ◽  
Hongwei Zeng ◽  
Xin Zhang ◽  
Jiaming Xu
Keyword(s):  
Synthetic Aperture Radar ◽  
Large Scale ◽  
Census Data ◽  
Google Earth ◽  
Synthetic Aperture ◽  
Cloud Platform ◽  
Optical Images ◽  
Google Earth Engine ◽  
Sentinel 2 ◽  
Aperture Radar

The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.

scholarly journals Quantification of Mangrove Extent using a Combination of Optical and Radar Images in Google Earth Engine Platform: The case of Anlo Beach Wetland Complex, Shama District, Western Region, Ghana

2021 ◽  
Author(s):  
Daniel Aja ◽  
Michael Miyittah ◽  
Donatus Bapentire Angnuureng
Keyword(s):  
Synthetic Aperture Radar ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Radar Images ◽  
Google Earth Engine ◽  
Sar Data ◽  
Aperture Radar

Abstract Mangrove Forest classification in tropical coastal zones based on only passive remote sensing methods is hampered by Mangrove complexities, topographic considerations and cloud cover effects among other things. This paper reports on a novel approach that combines Optical Satellite images and Synthetic Aperture Radar alongside their derived parameters to overcome the challenges of distinguishing Mangrove stand in cloud prone regions. Google Earth Engine (GEE) cloud-based geospatial processing platform was used to extract several scenes of Landsat Surface Reflectance Tier1 and synthetic aperture radar (C-band and L-band). The imageries were enhanced by creating a function that masks out clouds from the optical satellite image and by using speckle filter to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Our result show that about 16% of the mangrove extent was lost in the last decade. The accuracy was assessed based on three classification scenarios: classification of optical data only, classification of SAR data only, and combination of both optical and SAR data. The overall accuracies were 99.1% (Kappa Coefficient =0.797), 84.6% (Kappa Coefficient = 0.687) and 98.9% (Kappa Coefficient = 0.828) respectively. This case study demonstrates how mangrove mapping can help focus conservation practices locally in climate change setting, coupled with sea level rise and related threats to coastal ecosystems.

scholarly journals A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1746
Author(s):  
Salman Ahmadi ◽  
Saeid Homayouni
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contours ◽  
Training Data ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Difference Image ◽  
Proposed Model ◽  
The Difference ◽  
Aperture Radar

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.

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