Log in

Relevant bibliographies by topics / GEE (Google Earth Engine) / Journal articles

To see the other types of publications on this topic, follow the link: GEE (Google Earth Engine).

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

Author: Grafiati

Published: 14 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'GEE (Google Earth Engine).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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).

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

Lahay, Rakhmat Jaya, and Syahrizal Koem. "Ekstraksi Perubahan Tutupan Vegetasi Di Kabupaten Gorontalo Menggunakan Google Earth Engine." Jambura Geoscience Review 4, no. 1 (2021): 11–21. http://dx.doi.org/10.34312/jgeosrev.v4i1.12086.

Full text

Abstract:

Monitoring changes in vegetation cover is important for the restoration of ecosystems in the Gorontalo Regency area. The utilization of remote sensing technology makes it possible to detect the dynamics of changes in vegetation cover spatially and temporally. The Terra MODIS satellite image collection in the study area is available in large numbers and sizes. Therefore, cloud computing-based spatial technology support is needed. Google Earth Engine (GEE) as a geospatial computing device is an alternative to cover this shortfall. The aim of this study is to explore the condition of vegetation c

APA, Harvard, Vancouver, ISO, and other styles

12

Kumar, Lalit, and Onisimo Mutanga. "Google Earth Engine Applications Since Inception: Usage, Trends, and Potential." Remote Sensing 10, no. 10 (2018): 1509. http://dx.doi.org/10.3390/rs10101509.

Full text

Abstract:

The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a

APA, Harvard, Vancouver, ISO, and other styles

13

Hasan, Sajjad H., Amjed N. M. AL-Hameedawi, and H. S. Ismael. "Supervised Classification Model Using Google Earth Engine Development Environment for Wasit Governorate." IOP Conference Series: Earth and Environmental Science 961, no. 1 (2022): 012051. http://dx.doi.org/10.1088/1755-1315/961/1/012051.

Full text

Abstract:

Abstract As a result of the advancements that have occurred in the technical field of geomatics, particularly after the development of developmental programming environments, they have become the most important machine for conducting image analyses of satellite data, creating and modifying spatial analysis tools, and performing large data analyses at a fast rate without the need for high-end specifications on the personal computer. This study has several objectives, including the definition and popularization of the use of the power of Google Earth Engine (GEE) in the speed of conducting spati

APA, Harvard, Vancouver, ISO, and other styles

14

Montero, D., C. Aybar, M. D. Mahecha, and S. Wieneke. "SPECTRAL: AWESOME SPECTRAL INDICES DEPLOYED VIA THE GOOGLE EARTH ENGINE JAVASCRIPT API." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 6, 2022): 301–6. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-301-2022.

Full text

Abstract:

Abstract. Spectral Indices derived from Remote Sensing (RS) data are widely used for characterizing Earth System dynamics. The increasing amount of spectral indices led to the creation of spectral indices catalogues, such as the Awesome Spectral Indices (ASI) ecosystem. Google Earth Engine (GEE) is a cloud-based geospatial processing service with an Application Programming Interface (API) that is accessible through JavaScript (Code Editor) and Python. Tools for computing indices, including raster operations, normalized differences, and expression evaluation methods have been developed in the A

APA, Harvard, Vancouver, ISO, and other styles

15

Давибіда, Лідія Іванівна. "АНАЛІЗ МОЖЛИВОСТЕЙ І ДОСВІДУ ВИКОРИСТАННЯ ПЛАТФОРМИ GOOGLE EARTH ENGINE ДЛЯ ВИРІШЕННЯ ЗАДАЧ МОНІТОРИНГУ ДОВКІЛЛЯ". Ecological Safety and Balanced Use of Resources, № 2(24) (7 лютого 2022): 75–86. http://dx.doi.org/10.31471/2415-3184-2021-2(24)-75-86.

Full text

Abstract:

Метою даного дослідження є оцінка потенціалу застосування платформи Google Earth Engine (GEE) для обробки даних дистанційного зондування Землі при вирішенні різноманітних завдань моніторингу довкілля та для інших галузей прикладної геоінформатики. GEE є відкритою хмарною платформою, що дозволяє здійснювати аналіз і візуалізацію геопросторових наборів даних великого обсягу для наукових, освітніх, громадських, державних і комерційних організацій. GEE надає інструментальні програмні засоби з відкритим кодом для геопросторового аналізу, а також доступ до публічного каталогу растрових і векторних д

APA, Harvard, Vancouver, ISO, and other styles

16

ЯНЕЦ, П. К., С. А. ИВАНОВА, and Ю. Г. ДАНИЛОВ. "Using Google Earth engine (GEE) and Landsat satellite images to determine forest fires." Vestnik of North-Eastern Federal University. Series "Earth Sciences", no. 2(26) (June 30, 2022): 22–31. http://dx.doi.org/10.25587/svfu.2022.26.2.003.

Full text

Abstract:

Проблема лесных пожаров становится все более заметной как в глобальном, так и в местном масштабе. Пожары в Якутии являются серьезной проблемой. Бореальные леса играют важную роль в глобальном потеплении и циркуляции углекислого газа. Изменения пожарного режима и климата в этом регионе уже начались, и это оказывает влияние на углеродную динамику в региональном и глобальном масштабе. Все чаще при изучении пожаров используются спутниковые данные. В последние годы при обработке спутниковых данных используются так называемые "большие данные". Чтобы правильно оценить масштаб угрозы, необходимо разра

APA, Harvard, Vancouver, ISO, and other styles

17

Canty, Morton J., Allan A. Nielsen, Knut Conradsen, and Henning Skriver. "Statistical Analysis of Changes in Sentinel-1 Time Series on the Google Earth Engine." Remote Sensing 12, no. 1 (2019): 46. http://dx.doi.org/10.3390/rs12010046.

Full text

Abstract:

Time series analysis of Sentinel-1 SAR imagery made available by the Google Earth Engine (GEE) is described. Advantage is taken of a recent modification of a sequential complex Wishart-based algorithm which is applicable to the dual polarization intensity data archived on the GEE. Both the algorithm and a software interface to the GEE Python API for convenient data exploration and analysis are presented; the latter can be run from a platform independent Docker container and the source code is available on GitHub. Application examples are given involving the monitoring of anthropogenic activity

APA, Harvard, Vancouver, ISO, and other styles

18

Papilaya, Patrich Phill Edrich. "Aplikasi Google Earth Engine Dalam Menyediakan Citra Satelit Sumberbedaya Alam Bebas Awan." MAKILA 16, no. 2 (2022): 96–103. http://dx.doi.org/10.30598/makila.v16i2.6586.

Full text

Abstract:

    translator   Ketersediaan Citra Satelit yang berkualitas menjadi salah satu syarat keberhasilan penelitian sumberdaya alam, secara khusus dibidang kehutanan. Google Earth Engine (GEE) adalah salah satu platform berbasis awan (cloud) yang disediakan oleh Google. GEE bekerja berbasis Bahasa program Java Script. Hasil penelitian menunjukan bahwa aplikasi GEE mampu menyediakan citra satelit yang memiliki tutupan awan sangat rendah atau bebas awan (clouds free). Aplikasi GEE merupakan salah satu solusi penelitian sumberdaya alam terutama pada pulau-pula

APA, Harvard, Vancouver, ISO, and other styles

19

Ermida, Sofia L., Patrícia Soares, Vasco Mantas, Frank-M. Göttsche, and Isabel F. Trigo. "Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series." Remote Sensing 12, no. 9 (2020): 1471. http://dx.doi.org/10.3390/rs12091471.

Full text

Abstract:

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (G

APA, Harvard, Vancouver, ISO, and other styles

20

Salinero-Delgado, Matías, José Estévez, Luca Pipia, et al. "Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression." Remote Sensing 14, no. 1 (2021): 146. http://dx.doi.org/10.3390/rs14010146.

Full text

Abstract:

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) imp

APA, Harvard, Vancouver, ISO, and other styles

21

Pipia, Luca, Eatidal Amin, Santiago Belda, Matías Salinero-Delgado, and Jochem Verrelst. "Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine." Remote Sensing 13, no. 3 (2021): 403. http://dx.doi.org/10.3390/rs13030403.

Full text

Abstract:

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, n

APA, Harvard, Vancouver, ISO, and other styles

22

Roteta, Ekhi, Aitor Bastarrika, Magí Franquesa, and Emilio Chuvieco. "Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine." Remote Sensing 13, no. 4 (2021): 816. http://dx.doi.org/10.3390/rs13040816.

Full text

Abstract:

Four burned area tools were implemented in Google Earth Engine (GEE), to obtain regular processes related to burned area (BA) mapping, using medium spatial resolution sensors (Landsat and Sentinel-2). The four tools are (i) the BA Cartography tool for supervised burned area over the user-selected extent and period, (ii) two tools implementing a BA stratified random sampling to select the scenes and dates for validation, and (iii) the BA Reference Perimeter tool to obtain highly accurate BA maps that focus on validating coarser BA products. Burned Area Mapping Tools (BAMTs) go beyond the previo

APA, Harvard, Vancouver, ISO, and other styles

23

Hamunyela, Eliakim, Sabina Rosca, Andrei Mirt, et al. "Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data." Remote Sensing 12, no. 18 (2020): 2953. http://dx.doi.org/10.3390/rs12182953.

Full text

Abstract:

Monitoring of abnormal changes on the earth’s surface (e.g., forest disturbance) has improved greatly in recent years because of satellite remote sensing. However, high computational costs inherently associated with processing and analysis of satellite data often inhibit large-area and sub-annual monitoring. Normal seasonal variations also complicate the detection of abnormal changes at sub-annual scale in the time series of satellite data. Recently, however, computationally powerful platforms, such as the Google Earth Engine (GEE), have been launched to support large-area analysis of satellit

APA, Harvard, Vancouver, ISO, and other styles

24

Clemente, J. P., G. Fontanelli, G. G. Ovando, Y. L. B. Roa, A. Lapini, and E. Santi. "GOOGLE EARTH ENGINE: APPLICATION OF ALGORITHMS FOR REMOTE SENSING OF CROPS IN TUSCANY (ITALY)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 291–96. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-291-2020.

Full text

Abstract:

Abstract. Remote sensing has become an important mean to assess crop areas, specially for the identification of crop types. Google Earth Engine (GEE) is a free platform that provides a large number of satellite images from different constellations. Moreover, GEE provides pixel-based classifiers, which are used for mapping agricultural areas. The objective of this work is to evaluate the performance of different classification algorithms such as Minimum Distance (MD), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Na¨ıve Bayes (NB) on an agricul

APA, Harvard, Vancouver, ISO, and other styles

25

Maúre, Elígio de Raús, Simon Ilyushchenko, and Genki Terauchi. "A Simple Procedure to Preprocess and Ingest Level-2 Ocean Color Data into Google Earth Engine." Remote Sensing 14, no. 19 (2022): 4906. http://dx.doi.org/10.3390/rs14194906.

Full text

Abstract:

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an essential climate variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more rec

APA, Harvard, Vancouver, ISO, and other styles

26

Aghlmand, Majid, and Gordana Kaplan. "Monitoring Urban Expansion Using Remote-Sensing Data Aided by Google Earth Engine." European Journal of Geosciences 3, no. 1 (2021): 1–8. http://dx.doi.org/10.34154/2021-ejgs-0012/euraass.

Full text

Abstract:

Urbanizationis accompanied by rapid social and economic development, while the process of urbanization causes the degradation of the natural ecology. Direct loss in vegetation biomass from areas with a high probability of urban expansion can contribute to the total emissions from tropical deforestation and land-use change. Monitoring of urban expansion is essential for more efficient urban planning, protecting the ecosystem and the environment. In this paper, we use remote sensing data aided by Google Earth Engine (GEE) to evaluate the urban expansion of the city of Isfahan in the last thirty

APA, Harvard, Vancouver, ISO, and other styles

27

Sazib, Nazmus, Iliana Mladenova, and John Bolten. "Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data." Remote Sensing 10, no. 8 (2018): 1265. http://dx.doi.org/10.3390/rs10081265.

Full text

Abstract:

Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SM

APA, Harvard, Vancouver, ISO, and other styles

28

Mehmood, Hamid, Crystal Conway, and Duminda Perera. "Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform." Atmosphere 12, no. 7 (2021): 866. http://dx.doi.org/10.3390/atmos12070866.

Full text

Abstract:

The Earth Observation (EO) domain can provide valuable information products that can significantly reduce the cost of mapping flood extent and improve the accuracy of mapping and monitoring systems. In this study, Landsat 5, 7, and 8 were utilized to map flood inundation areas. Google Earth Engine (GEE) was used to implement Flood Mapping Algorithm (FMA) and process the Landsat data. FMA relies on developing a “data cube”, which is spatially overlapped pixels of Landsat 5, 7, and 8 imagery captured over a period of time. This data cube is used to identify temporary and permanent water bodies u

APA, Harvard, Vancouver, ISO, and other styles

29

Estrabis, N. V., L. Osco, A. P. Ramos, et al. "BRAZILIAN MIDWEST NATIVE VEGETATION MAPPING BASED ON GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 303–8. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-303-2020.

Full text

Abstract:

Abstract. Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and non-native-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I- spectral bands (blue to shortwave infrared); II- NDVI (Normalized Difference Vegetation Index); III- mNDWI (modified Normalized Difference Water Index); IV- scenarios I and II; an

APA, Harvard, Vancouver, ISO, and other styles

30

Elnashar, Abdelrazek, Hongwei Zeng, Bingfang Wu, et al. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing." Remote Sensing 12, no. 23 (2020): 3860. http://dx.doi.org/10.3390/rs12233860.

Full text

Abstract:

Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area

APA, Harvard, Vancouver, ISO, and other styles

31

Xie, Shuai, Liangyun Liu, Xiao Zhang, Jiangning Yang, Xidong Chen, and Yuan Gao. "Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine." Remote Sensing 11, no. 24 (2019): 3023. http://dx.doi.org/10.3390/rs11243023.

Full text

Abstract:

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and

APA, Harvard, Vancouver, ISO, and other styles

32

Safanelli, José, Raul Poppiel, Luis Ruiz, et al. "Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis." ISPRS International Journal of Geo-Information 9, no. 6 (2020): 400. http://dx.doi.org/10.3390/ijgi9060400.

Full text

Abstract:

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic su

APA, Harvard, Vancouver, ISO, and other styles

33

Prikaziuk, Egor, Peiqi Yang, and Christiaan van der Tol. "Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences." Remote Sensing 13, no. 6 (2021): 1098. http://dx.doi.org/10.3390/rs13061098.

Full text

Abstract:

In this study, we demonstrate that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1μμm−1 per pixel per band. We compared GEE and DHUS single pixel time series for the period from April 2016 to September 2020 and identified two sources of this discrepancy: the ground pixel position and reprojection. The ground pixel position of OLCI product can be determined in two ways: from geo-coordinates (DHUS) or from tie-point coordinates (GEE). We recommend

APA, Harvard, Vancouver, ISO, and other styles

34

Tassi, Andrea, and Marco Vizzari. "Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms." Remote Sensing 12, no. 22 (2020): 3776. http://dx.doi.org/10.3390/rs12223776.

Full text

Abstract:

Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO approaches, including both object segmentation and object textural analysis, are still not common in the GEE environment, probably due to the difficulties existing in concatenating the proper functions, and in tuning the various parameters to overcome the GEE computational limits. In this context, thi

APA, Harvard, Vancouver, ISO, and other styles

35

Kaplan, Gordana. "Broad-Leaved and Coniferous Forest Classification in Google Earth Engine Using Sentinel Imagery." Environmental Sciences Proceedings 3, no. 1 (2020): 64. http://dx.doi.org/10.3390/iecf2020-07888.

Full text

Abstract:

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 assessme

APA, Harvard, Vancouver, ISO, and other styles

36

Liu, Yang, Junhui Liu, Yingjuan Zheng, Yulin Kang, Su Ma, and Jianan Zhou. "Tracking Changing Evidence of Water Erosion in Ordos Plateau, China Using the Google Earth Engine." Land 11, no. 12 (2022): 2309. http://dx.doi.org/10.3390/land11122309.

Full text

Abstract:

Ordos Plateau is one of the primary sources of sediment in the Yellow River, and changes in regional soil erosion directly affect the ecological status of the lower reaches of the Yellow River. Many recent studies have been published using remote sensing (RS) and geographic information systems (GIS) to evaluate soil erosion. In this study, much satellite remote sensing data in the Google Earth Engine (GEE) can better track soil erosion protection, which is significant in guiding the ecological protection and restoration of the Ordos Plateau and the Yellow River basin. In this study, we used GE

APA, Harvard, Vancouver, ISO, and other styles

37

De Oliveira Ferreira Silva, Cesar. "CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE." IRRIGA 25, no. 1 (2020): 160–69. http://dx.doi.org/10.15809/irriga.2020v25n1p160-169.

Full text

Abstract:

CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE  CÉSAR DE OLIVEIRA FERREIRA SILVA1  1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Avenida Universitária, n° 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP, Brasil, e-mail: cesaroliveira.f.silva@gmail.com.    1 RESUMO  Identificar áreas de irrigação usando imagens de satélite é um desafio que encontra em soluções de computação em nuv

APA, Harvard, Vancouver, ISO, and other styles

38

Vélez-Castaño, José Daniel, Gloria Liliana Betancurth-Montes, and Julio Eduardo Cañón-Barriga. "Erosion and progradation in the Atrato River delta: A spatiotemporal analysis with Google Earth Engine." Revista Facultad de Ingeniería Universidad de Antioquia, no. 99 (June 9, 2020): 83–98. http://dx.doi.org/10.17533/udea.redin.20200688.

Full text

Abstract:

The Atrato River Delta in Northwestern Colombia has experienced notable geomorphological changes in its shoreline in recent years. We analyze these changes, associated with erosion and progradation, using Landsat imagery and Google Earth Engine (GEE) algorithms to automatically identify the changes in an annual basis over 33 years (1986–2019). We compare the results with manual delineations on the same imagery using ArcGIS, obtaining similar outcomes, although GEE is much more efficient in processing large amounts of imagery compared with handmade procedures. We identify with good accuracy tre

APA, Harvard, Vancouver, ISO, and other styles

39

Terres de Lima, Lucas, Sandra Fernández-Fernández, João Francisco Gonçalves, Luiz Magalhães Filho, and Cristina Bernardes. "Development of Tools for Coastal Management in Google Earth Engine: Uncertainty Bathtub Model and Bruun Rule." Remote Sensing 13, no. 8 (2021): 1424. http://dx.doi.org/10.3390/rs13081424.

Full text

Abstract:

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of free and open-source models to estimate the sea-level impact can contribute to improve coastal management. This study aims to develop and validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE)—a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the sea-level rise impact module of TerrSet—Geospatial Monitoring and Modeling System software. The validation pr

APA, Harvard, Vancouver, ISO, and other styles

40

Oularbi, Younes, Jamila Dahmani, and Fouad MOUNIR. "Dynamics of land-use Change using Geospatial Techniques From 1986 to 2019: A Case Study of High Oum Er-Rbia Watershed (Middle Atlas Region)." Journal of Experimental Biology and Agricultural Sciences 10, no. 2 (2022): 369–78. http://dx.doi.org/10.18006/2022.10(2).369.378.

Full text

Abstract:

This work aims to expose the contribution of the use of the cloud google earth Engine (GEE) platform, in particular the capacity of optical monitoring by remote sensing to assess the impact of environmental changes on the evolution of natural resources in the Middle Atlas region. To achieve this goal, the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform was used. The spatial resolution of the images used is 30 meters for the TM 5 sensor (Thematic Mapper) and the OLI 8 sensor (Operational Land Imager). Further, the

APA, Harvard, Vancouver, ISO, and other styles

41

Jamali, A., M. Mahdianpari, and İ. R. Karaş. "A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 313–19. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-313-2021.

Full text

Abstract:

Abstract. Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the stora

APA, Harvard, Vancouver, ISO, and other styles

42

Fariz, Trida Ridho, and Ely Nurhidayati. "Mapping Land Coverage in the Kapuas Watershed Using Machine Learning in Google Earth Engine." Journal of Applied Geospatial Information 4, no. 2 (2020): 390–95. http://dx.doi.org/10.30871/jagi.v4i2.2256.

Full text

Abstract:

Land cover information is essential data in the management of watersheds. The challenge in providing land cover information in the Kapuas watershed is the cloud cover and its significant area coverage, thus requiring a large image scene. The presence of a cloud-based spatial data processing platform that is Google Earth Engine (GEE) can be answered these challenges. Therefore this study aims to map land cover in the Kapuas watershed using machine learning-based classification on GEE. The process of mapping land cover in the Kapuas watershed requires about ten scenes of Landsat 8 satellit

APA, Harvard, Vancouver, ISO, and other styles

43

Xu, R. G., G. Qiao, Y. J. Wu, and Y. J. Cao. "EXTRACTION OF RIVERS AND LAKES ON TIBETAN PLATEAU BASED ON GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1797–801. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1797-2019.

Full text

Abstract:

<p><strong>Abstract.</strong> Tibetan Plateau (TP) is the most abundant area of water resources and water energy resources in China. It is also the birthplace of the main rivers in Southeast Asia and plays an important strategic role. However, due to its remote location and complex topography, the observation of surface hydrometeorological elements is extremely scarce, which seriously restricts the understanding of the water cycle in this area. Using remote sensing images to extract rivers and lakes on TP can obtain a lot of valuable water resources information. However, the

APA, Harvard, Vancouver, ISO, and other styles

44

Bi, L., B. L. Fu, P. Q. Lou, and T. Y. Tang. "DELINEATION WATER OF PEARL RIVER BASIN USING LANDSAT IMAGES FROM GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 5–10. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-5-2020.

Full text

Abstract:

Abstract. Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In o

APA, Harvard, Vancouver, ISO, and other styles

45

Amani, Meisam, Sahel Mahdavi, Majid Afshar, et al. "Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results." Remote Sensing 11, no. 7 (2019): 842. http://dx.doi.org/10.3390/rs11070842.

Full text

Abstract:

Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and inn

APA, Harvard, Vancouver, ISO, and other styles

46

Fariz, Trida Ridho, Fitri Daeni, and Habil Sultan. "Pemetaan Perubahan Penutup Lahan Di Sub-DAS Kreo Menggunakan Machine Learning Pada Google Earth Engine." Jurnal Sumberdaya Alam dan Lingkungan 8, no. 2 (2021): 85–92. http://dx.doi.org/10.21776/ub.jsal.2021.008.02.4.

Full text

Abstract:

Informasi penutup lahan merupakan data yang sangat penting dalam pengelolaan Daerah Aliran Sungai (DAS). Tantangan dalam penyediaan informasi penutup lahan di DAS Kreo adalah tutupan awan dan cangkupan areanya yang cukup luas. Hadirnya platform pengolahan data spasial berbasis cloud yaitu Google Earth Engine (GEE) bisa menjawab tantangan tersebut. Oleh karena itu penelitian ini bertujuan untuk memetakan penutup lahan di DAS Kreo menggunakan klasifikasi berbasis machine learning pada GEE. Proses pemetaan penutup lahan di DAS Kreo menggunakan citra satelit Landsat 8 dan DEM SRTM. Input data yang

APA, Harvard, Vancouver, ISO, and other styles

47

Supe, Hitesh, Ram Avtar, Deepak Singh, et al. "Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments." Remote Sensing 12, no. 9 (2020): 1466. http://dx.doi.org/10.3390/rs12091466.

Full text

Abstract:

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE pla

APA, Harvard, Vancouver, ISO, and other styles

48

Yang, Kaixiang, Youming Luo, Mengyao Li, Shouyi Zhong, Qiang Liu, and Xiuhong Li. "Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine." Remote Sensing 14, no. 17 (2022): 4395. http://dx.doi.org/10.3390/rs14174395.

Full text

Abstract:

Sentinel-2 NDVI and surface reflectance time series have been widely used in various geoscience research, but the data is deteriorated or missing due to the cloud contamination, so it is necessary to reconstruct the Sentinel-2 NDVI and surface reflectance time series. At present, there are few studies on reconstructing the Sentinel-2 NDVI or surface reflectance time series, and these existing reconstruction methods have some shortcomings. We proposed a new method to reconstruct the Sentinel-2 NDVI and surface reflectance time series using the penalized least-square regression based on discrete

APA, Harvard, Vancouver, ISO, and other styles

49

Tew, Yi Lin, Mou Leong Tan, Narimah Samat, et al. "Comparison of Three Water Indices for Tropical Aquaculture Ponds Extraction using Google Earth Engine." Sains Malaysiana 51, no. 2 (2022): 369–78. http://dx.doi.org/10.17576/jsm-2022-5102-04.

Full text

Abstract:

Information on the spatial distribution of aquaculture ponds, especially the inland brackish aquaculture, is crucial for effective and sustainable aquaculture management. Google Earth Engine (GEE) has been utilized to quickly map aquaculture ponds in different parts of the world, but the application is still limited in tropical regions. Selection of an optimal water index is essential to accurately map the aquaculture ponds from the Landsat 8 satellite images that are available in GEE. This study aims to evaluate the capability of three different water indices, namely Normalized Difference Wat

APA, Harvard, Vancouver, ISO, and other styles

50

T. Desai, Geeta, and Abhay N. Gaikwad. "Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine." International journal of electrical and computer engineering systems 13, no. 10 (2022): 909–16. http://dx.doi.org/10.32985/ijeces.13.10.6.

Full text

Abstract:

Land cover is the most critical information required for land management and planning because human interference on land can be easily detected through it. However, mapping land cover utilizing optical remote sensing is not easy due to the acute shortage of cloud-free images. Google Earth Engine (GEE) is an efficient and effective tool for huge land cover analysis by providing access to large volumes of imagery available within a few days after acquisition in one consolidated system. This article demonstrates the use of Sentinel-1 datasets to create a land cover map of Pusad, Maharashtra using

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!