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 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 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 Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1200 ◽  
Author(s):  
Xin Zhang ◽  
Bingfang Wu ◽  
Guillermo Ponce-Campos ◽  
Miao Zhang ◽  
Sheng Chang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
High Performance ◽  
Large Scale ◽  
Water Source ◽  
Google Earth ◽  
Hunan Province ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Indirect Information ◽  
Aperture Radar

Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for mono- and double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice.

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 The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

2018 ◽  
Vol 11 (1) ◽  
pp. 43 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Bahram Salehi ◽  
Fariba Mohammadimanesh ◽  
Saeid Homayouni ◽  
Eric Gill
Keyword(s):  
Large Scale ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wetland Mapping ◽  
Object Based ◽  
Wetland Inventory ◽  
Inventory Map ◽  
Google Earth Engine ◽  
Provincial Scale ◽  
Sentinel 2

Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.

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