Relevant bibliographies by topics / GEE (Google Earth Engine)

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Academic literature on the topic 'GEE (Google Earth Engine)'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "GEE (Google Earth Engine)"

1

Mutanga, Onisimo, and Lalit Kumar. "Google Earth Engine Applications." Remote Sensing 11, no. 5 (2019): 591. http://dx.doi.org/10.3390/rs11050591.

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2

Zhao, Qiang, Le Yu, Xuecao Li, Dailiang Peng, Yongguang Zhang, and Peng Gong. "Progress and Trends in the Application of Google Earth and Google Earth Engine." Remote Sensing 13, no. 18 (2021): 3778. http://dx.doi.org/10.3390/rs13183778.

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Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we re
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3

Fedotova, Elena, and Anna Gosteva. "Using of Google Earth Engine in monitoring systems." E3S Web of Conferences 333 (2021): 01013. http://dx.doi.org/10.1051/e3sconf/202133301013.

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Google Earth Engine (GEE) cloud service is a powerful tool for environmental research. An example of using GEE to solve a typical research problem is shown. The following data extraction and analysis operations were used: filtering data from sets, constructing functions, building graphs, selecting data using vector and raster masks. GEE interface in the form of JavaScript code was used. Correlation between surface runoff and precipitation and snow depth in areas with forest dieback was analysed for Krasnoyarsk region in Russia (r = 0.30 for precipitation and r = 0.57 for snow depth).
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4

Siska, Widia, Widiatmaka Widiatmaka, Yudi Setiawan, and Setyono Hari Adi. "Pemetaan Perubahan Lahan Sawah Kabupaten Sukabumi Menggunakan Google Earth Engine." TATALOKA 24, no. 1 (2022): 74–83. http://dx.doi.org/10.14710/tataloka.24.1.74-83.

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Google Earth Engine (GEE) merupakan layanan pemrosesan geospasial yang telah banyak digunakan di berbagai bidang pemetaan. Tujuan penelitian ini adalah identifikasi perubahan lahan sawah Kabupaten Sukabumi menggunakan GEE. Data citra landsat 5 dan landsat 8 yang digunakan di GEE merupakan data citra yang telah di pre-process dan terkoreksi. Klasifikasi penggunaan/tutupan lahan dibedakan menjadi 6 kelas yaitu sawah, badan air, pemukiman, bervegetasi, hutan dan tanah terbuka. Sampel acak penggunaan lahan dibuat sebanyak 394 titik di GEE menggunakan poin dan rectangular. Klasifikasi penggunaan la
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5

Ghaffarian, Saman, Ali Rezaie Farhadabad, and Norman Kerle. "Post-Disaster Recovery Monitoring with Google Earth Engine." Applied Sciences 10, no. 13 (2020): 4574. http://dx.doi.org/10.3390/app10134574.

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Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of r
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6

Rajandran, Arvinth, Mou Leong Tan, Narimah Samat, and Ngai Weng Chan. "A review of Google Earth Engine application in mapping aquaculture ponds." IOP Conference Series: Earth and Environmental Science 1064, no. 1 (2022): 012011. http://dx.doi.org/10.1088/1755-1315/1064/1/012011.

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Abstract Google Earth Engine (GEE) can effectively monitor aquaculture ponds, but it is underutilized. This paper aims to review the application of GEE in mapping aquaculture ponds around the world using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. A total of 16 journal articles have been identified since 2019 from the Scopus and Science Direct databases. Most of the studies were conducted in China and United States using the Sentinel-2, Sentinel-1 and Landsat 8 images. Random Forest and Decision Tree are commonly used machine learning classifiers in
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7

Wang, Shujian, Ming Xu, Xunhe Zhang, and Yuting Wang. "Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine." Remote Sensing 14, no. 9 (2022): 2055. http://dx.doi.org/10.3390/rs14092055.

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Google Earth Engine (GEE) has been widely used to process geospatial data in recent years. Although the current GEE platform includes functions for fitting linear regression models, it does not have the function to fit nonlinear models, limiting the GEE platform’s capacity and application. To circumvent this limitation, this work proposes a general adaptation of the Levenberg–Marquardt (LM) method for fitting nonlinear models to a parallel processing framework and its integration into GEE. We compared two commonly used nonlinear fitting methods, the LM and nonlinear least square (NLS) methods.
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8

Wang, Shujian, Ming Xu, Xunhe Zhang, and Yuting Wang. "Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine." Remote Sensing 14, no. 9 (2022): 2055. http://dx.doi.org/10.3390/rs14092055.

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Abstract:
Google Earth Engine (GEE) has been widely used to process geospatial data in recent years. Although the current GEE platform includes functions for fitting linear regression models, it does not have the function to fit nonlinear models, limiting the GEE platform’s capacity and application. To circumvent this limitation, this work proposes a general adaptation of the Levenberg–Marquardt (LM) method for fitting nonlinear models to a parallel processing framework and its integration into GEE. We compared two commonly used nonlinear fitting methods, the LM and nonlinear least square (NLS) methods.
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9

Campos-Taberner, Manuel, Álvaro Moreno-Martínez, Francisco García-Haro, et al. "Global Estimation of Biophysical Variables from Google Earth Engine Platform." Remote Sensing 10, no. 8 (2018): 1167. http://dx.doi.org/10.3390/rs10081167.

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This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate da
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10

YILDIZ, Mitat Can, and Mustafa YİLMAZ. "Yer Yüzeyi Sıcaklığının Google Earth Engine Kullanılarak Elde Edilmesi ve Değerlendirilmesi." Afyon Kocatepe University Journal of Sciences and Engineering 22, no. 6 (2022): 1380–87. http://dx.doi.org/10.35414/akufemubid.1181347.

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Günümüz temel problemlerinden biri olan küresel ısınma beraberinde iklim değişikliğini de getirmektedir. Atmosfer ve dünya arasındaki enerji değişimini etkilediği için Yer Yüzeyi Sıcaklığı (YYS) iklimin en önemli parametrelerinden birisidir. Bu nedenle büyük ve küçük ölçekli çalışmalar yapılırken YYS, göz önünde bulundurulması gerekmektedir. Uzaktan algılama verilerinin işlenmesi, analiz edilmesi ve değerlendirilmesi için birçok sistem geliştirilmiştir. Bunlardan birisi web tabanlı sistem olan Google Earth Engine (GEE)’dir. GEE arayüzü, farklı çözünürlüklere sahip uydu verilerinin hızlı bir bi
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