A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing

Amani, Meisam; Brisco, Brian; Afshar, Majid; Mirmazloumi, S. Mohammad; Mahdavi, Sahel; Mirzadeh, Sayyed Mohammad Javad; Huang, Weimin; Granger, Jean

doi:10.1080/20964471.2019.1690404

Cited by 73 publications

(51 citation statements)

References 40 publications

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“…Landsat-based datasets within GEE have been employed in various applications. For instance, Landsat data available in GEE have been widely utilized in generating Land Cover/Land Use (LCLU) maps (e.g., [14]- [16]). Moreover, urban detection and extraction is an important task in the economic investigation due to rapid population growth.…”

Section: Gee Datasetsmentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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651

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: Gee Datasetsmentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

651

216

View full text Add to dashboard Cite

show abstract

“…While the validation overall accuracy gives us the accuracy of the classification of the training samples, the kappa statistics gives us the accuracy of the classification. Half of training samples have been used for training, while half of the samples have been used for the accuracy assessment [19]. Also, the results have been visually compared with satellite image from the early spring months, where the broad-leaved forest are still leafless.…”

Section: Methodsmentioning

confidence: 99%

Broad-Leaved and Coniferous Forest Classification in Google Earth Engine Using Sentinel Imagery

Kaplan

2020

The 1st International Electronic Conference on Forests—Forests for a Better Future: Sustainability, Innovation, Inter

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Forest structures knowledge is fundamental to understanding, managing, and preserving the biodiversity of forests. With the well-established need within the remote sensing community for better understanding of canopy structure, in this paper, the effectiveness of Sentinel-2 imagery for broad-leaved and coniferous forest classification within the Google Earth Engine (GEE) platform has been assessed. Here, we used Sentinel-2 image collection from the summer period over North Macedonia, when the canopy is fully developed. For the sample collection of the coniferous areas and the accuracy assessment of the classification, we used imagery from the spring period, when the broad-leaved forests are in the early green stage. A Support Vector Machine (SVM) classifier has been used for discriminating forest cover groups, namely, broadleaved and coniferous forests. According to the results, more than 90% of the canopy in North Macedonia is broad-leaved, while less than 10% is conifers. The results in this study show that, with the use of GEE, Sentinel-2 data alone can be effectively used to obtain rapid and accurate mapping of main forest types (conifers-broadleaved) with a fine resolution.

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“…Whereas, map sample data refers to the data derived from the map being assessed. A reference sample data for assessing the accuracy of maps created from moderate-resolution satellite imagery (e.g., Landsat) can be collected from a variety of sources, including previously existing maps, aerial photography and Google Earth [1,14,21,53].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Analyzing the impacts of urbanization on runoff characteristics in Adama city, Ethiopia

Bulti

Abebe

2020

SN Appl. Sci.

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Urbanization is a global trend with a substantial environmental impacts, particularly hydrologic cycle. With this respect, storm water management has become the main target of sustainable urban development and has posed the higher demand for information related to the interaction between the urbanization process and hydrological attributes in both temporal and spatial scales, but little is understood in the context of Ethiopian urban centers. For this, the fast grown and flood vulnerable urban area, Adama city, is considered to evaluate the hydrologic influences attributable to urbanization. By preparing the land use/land cover (LULC) maps of the study area for different periods, the dynamics of LULC transformations were analyzed. The SCS-CN method was employed to determine the runoff at respective years, and the spatio-temporal changes of runoff were assessed at the city watershed and its sub-watersheds. The relation between the spatio-temporal changes of imperviousness ratio and runoff depth was explored through regression analysis. The findings show that the urban built-up area undergone about 22% expansion annually from 1995 to 2019. Likewise, the runoff depth is increased by 9.5% in the City administration and 12.9% and 6.9% within the two sub-watersheds. The results also reveal that at all spatial scales, the temporal change of runoff depth is linearly associated with the rise of imperviousness ratio. This study would help planners in the formulation of effective land use plan to manage the hydrological influences of urbanization, thereby supports the efforts being made to ensure the sustainable development of urban areas.

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A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing

Abstract: 2019) A generalized supervised classification scheme to produce provincial wetland inventory maps: an application of Google Earth Engine for big geo data processing,

Cited by 73 publications

References 40 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Broad-Leaved and Coniferous Forest Classification in Google Earth Engine Using Sentinel Imagery

Analyzing the impacts of urbanization on runoff characteristics in Adama city, Ethiopia

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