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Journal articles on the topic 'Google Engine Earth'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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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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VILLAVICENCIO, L. M. M., D. MENDES, L. M. B. ANDRADE, and F. F. MONTEIRO. "Google Earth Engine: Mapping Changes in the Vilcanota-Peru Mountain Range." Anuário do Instituto de Geociências - UFRJ 41, no. 3 (2018): 427–33. http://dx.doi.org/10.11137/2018_3_427_433.

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Рахимов, Г., М. Б. Игемберлина, А. Ж. Жанибек та Ж. М. Батыршаева. "МОНИТОРИНГ ИЗМЕНЕНИЯ ПЛОЩАДИ ОЗЕРА БАЛХАШ С ПОМОЩЬЮ ГЕОПРОСТРАНСТВЕННОЙ ПЛАТФОРМЫ GOOGLE EARTH ENGINE". Горный журнал Казахстана, № 4(240) (2 травня 2025): 25–30. https://doi.org/10.48498/minmag.2025.240.4.008.

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В данной статье представлены результаты исследования изменения площади озера Балхаш за период с 2001 по 2023 годы с использованием спутниковых данных MODIS и платформы Google Earth Engine. Целью исследования являлось изучение динамики площади озера. Для достижения поставленной цели была разработана методология, включающая обработку спутниковых данных MODIS, расчет индекса NDWI для выделения водной поверхности, классификацию изображений, расчет площади озера и создание временного ряда. В результате исследования были получены данные о динамике площади озера Балхаш за 20-летний период. Полученные

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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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Staletović, Aleksandar. "IDENTIFIKACIJA POLJOPRIVREDNIH PARCELA UPOTREBOM GOOGLE EARTH ENGINE." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 35, no. 02 (2020): 364–67. http://dx.doi.org/10.24867/06kg03staletovic.

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U ovom radu ispitana je upotrebljivost Google Earth Engine platforme u daljinskoj detekciji kroz analizu satelitskih snimaka. Primenom satelitskih snimaka sa Sentinel-1 i Sentinel-2 platforme izvršena je identifikacija poljoprivrednih parcela.

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Yancho, J. Maxwell M., Trevor Gareth Jones, Samir R. Gandhi, Colin Ferster, Alice Lin, and Leah Glass. "The Google Earth Engine Mangrove Mapping Methodology (GEEMMM)." Remote Sensing 12, no. 22 (2020): 3758. http://dx.doi.org/10.3390/rs12223758.

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Mangroves are found globally throughout tropical and sub-tropical inter-tidal coastlines. These highly biodiverse and carbon-dense ecosystems have multi-faceted value, providing critical goods and services to millions living in coastal communities and making significant contributions to global climate change mitigation through carbon sequestration and storage. Despite their many values, mangrove loss continues to be widespread in many regions due primarily to anthropogenic activities. Accessible, intuitive tools that enable coastal managers to map and monitor mangrove cover are needed to stem

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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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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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Brooke, Sam A. S., Mitch D’Arcy, Philippa J. Mason, and Alexander C. Whittaker. "Rapid multispectral data sampling using Google Earth Engine." Computers & Geosciences 135 (February 2020): 104366. http://dx.doi.org/10.1016/j.cageo.2019.104366.

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Mateo-García, Gonzalo, Luis Gómez-Chova, Julia Amorós-López, Jordi Muñoz-Marí, and Gustau Camps-Valls. "Multitemporal Cloud Masking in the Google Earth Engine." Remote Sensing 10, no. 7 (2018): 1079. http://dx.doi.org/10.3390/rs10071079.

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Agarwal, Shivani, and Harini Nagendra. "Classification of Indian cities using Google Earth Engine." Journal of Land Use Science 14, no. 4-6 (2019): 425–39. http://dx.doi.org/10.1080/1747423x.2020.1720842.

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WANG, Xiaona, Jinyan TIAN, Xiaojuan LI, et al. "Benefits of Google Earth Engine in remote sensing." National Remote Sensing Bulletin 26, no. 2 (2022): 299–309. http://dx.doi.org/10.11834/jrs.20211317.

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13

Yogita, Gogawale, Deshpande Ankita, Gupta Pooja, and Gokhale Aditi. "Google Earth Engine based Forest Fire Detection System." International Journal of Innovative Science and Research Technology 7, no. 5 (2022): 771–74. https://doi.org/10.5281/zenodo.6626245.

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Forest fires represent a constant threat to ecological systems and human lives. Past has witnessed various instances of forest and wild land fires. Especially in Amazon Forest. Fires play a remarkable role in determining landscape structure, pattern and eventually the species composition of ecosystems. Fires are considered as a significant environmental issue because they cause prominent economic and ecological damage despite endangering the human lives. So here we are going to develop the system which detects the fire on the earth by using Google earth engine and image processing algorithm wi

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Degano, María Florencia, Raúl Eduardo Rivas, and Martín Ignacio Bayala. "Determinación de la evapotranspiración con datos satelitales y de reanálisis utilizando Google Earth Engine." Tecnología y ciencias del agua 15, no. 4 (2024): 137–93. http://dx.doi.org/10.24850/j-tyca-2024-04-04.

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Los flujos verticales, dentro del ciclo hidrológico, son una de las variables de mayor relevancia en zona de llanura, dado que las pendientes varían entre 0 y 5%, y los flujos horizontales no son significativos. En este sentido, la evapotranspiración juega un rol fundamental en el manejo hídrico, ya que alrededor del 85% del agua que sale del sistema lo hace mediante este proceso, requiriendo una cuantificación precisa. El objetivo principal de este trabajo es calcular la evapotranspiración potencial y real (ETp y ETr) con datos de satélite y reanálisis mediante el uso de la plataforma Google

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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.

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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

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Johary, Rosa, Christophe Révillion, Thibault Catry, et al. "Detection of Large-Scale Floods Using Google Earth Engine and Google Colab." Remote Sensing 15, no. 22 (2023): 5368. http://dx.doi.org/10.3390/rs15225368.

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This paper presents an operational approach for detecting floods and establishing flood extent using Sentinel-1 radar imagery with Google Earth Engine. The methodology relies on change detection, comparing pre-event and post-event images. The change-detection method is based on the normalised difference ratio. Additionally, the HAND model is employed to delineate zones for processing only in flood-prone areas. The approach was tested and calibrated at a small scale to optimise parameters. In these calibration tests, an accuracy of 85% is achieved. The approach was then applied to the whole of

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Sousa, José Hugo Simplicio de, Amanda Rezende Moreira, Abdalan Andrade do Nascimento, George do Nascimento Ribeiro, Jose Nunes de Oliveira Neto, and Leonardo Souza do Prado Júnior. "Assessment of land use and cover in the Sucuru Watershed using Google Earth Engine." Revista Verde de Agroecologia e Desenvolvimento Sustentável 17, no. 4 (2022): 235–41. http://dx.doi.org/10.18378/rvads.v17i4.9621.

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Human activities modify the natural characteristics of numerous watersheds worldwide. Google Earth Engine provides tools for the analysis of land use and natural resources. In this work, we classify current land use and cover in the Sucuru watershed, Paraíba, Brazil. We compared the accuracy of five supervised classification algorithms of Google Earth Engine. Classifiers based on Decision Trees, such as the Classification and Regression Trees (CART) and Random Forest (RF), showed the best accuracy and visual inspection values. The Google Earth Engine is a powerful tool for analysis of large-sc

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Mullissa, Adugna, Andreas Vollrath, Christelle Odongo-Braun, et al. "Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine." Remote Sensing 13, no. 10 (2021): 1954. http://dx.doi.org/10.3390/rs13101954.

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Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by ap

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Prayogo, Luhur Moekti. "Mangrove Vegetation Mapping Using Sentinel-2A Imagery Based on Google Earth Engine Cloud Computing Platform." International Journal of Science, Engineering and Information Technology 6, no. 1 (2021): 249–55. http://dx.doi.org/10.21107/ijseit.v6i1.12175.

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Mangroves are trees whose habitat is affected by tides, and their presence has decreased from year to year. Today, mapping technology has undergone many developments, including the availability of images of various resolutions and cloud-based image processing. One of the popular platforms today is the Google Earth Engine. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for extensive processing. The advantage of using Google Earth Engine is that users do not have to be IT experts without experts in application development, WEB prog

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20

Guo, Y. T., X. M. Zhang, T. F. Long, et al. "CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 855–62. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-855-2020.

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Abstract. Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on

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21

Montero, David. "eemont: A Python package that extends Google Earth Engine." Journal of Open Source Software 6, no. 62 (2021): 3168. http://dx.doi.org/10.21105/joss.03168.

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22

Crego, Ramiro D., Jared A. Stabach, and Grant Connette. "Implementation of species distribution models in Google Earth Engine." Diversity and Distributions 28, no. 5 (2022): 904–16. http://dx.doi.org/10.1111/ddi.13491.

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23

Kennedy, Robert, Zhiqiang Yang, Noel Gorelick, et al. "Implementation of the LandTrendr Algorithm on Google Earth Engine." Remote Sensing 10, no. 5 (2018): 691. http://dx.doi.org/10.3390/rs10050691.

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24

Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. "Google Earth Engine: Planetary-scale geospatial analysis for everyone." Remote Sensing of Environment 202 (December 2017): 18–27. http://dx.doi.org/10.1016/j.rse.2017.06.031.

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Estrabis, Nayara, Hemerson Pistori, Wesley Gonçalves, and José Marcato Junior. "Brazilian Cerrado native vegetation mapping with Google Earth Engine." Conjecturas 22, no. 16 (2022): 565–86. http://dx.doi.org/10.53660/conj-2077-2s20.

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Cerrado is the second largest biome of South America, with the major extension located in Brazil. This biome is considered a world biodiversity hotspot due to the rich and important biodiversity contrasting with the high threat of destruction. Monitoring using remote sensing approaches is a crucial tool for maintaining and preserving this large-scale biome. Through this context, this study compared and assessed different scenarios with Landsat 8 OLI multispectral bands and Vegetation Indices (EVI, NDVI, and SAVI) for the Cerrado mapping in Mato Grosso do Sul state, Brazil. An amount of 512 sam

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Velastegui-Montoya, Andrés, Néstor Montalván-Burbano, Paúl Carrión-Mero, Hugo Rivera-Torres, Luís Sadeck, and Marcos Adami. "Google Earth Engine: A Global Analysis and Future Trends." Remote Sensing 15, no. 14 (2023): 3675. http://dx.doi.org/10.3390/rs15143675.

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The continuous increase in the volume of geospatial data has led to the creation of storage tools and the cloud to process data. Google Earth Engine (GEE) is a cloud-based platform that facilitates geoprocessing, making it a tool of great interest to the academic and research world. This article proposes a bibliometric analysis of the GEE platform to analyze its scientific production. The methodology consists of four phases. The first phase corresponds to selecting “search” criteria, followed by the second phase focused on collecting data during the 2011 and 2022 periods using Elsevier’s Scopu

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Wang, Wenliang. "Automatic extraction of coastline based on Google Earth engine." International Journal of Computer Science and Information Technology 1, no. 1 (2023): 102–11. http://dx.doi.org/10.62051/ijcsit.v1n1.14.

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Traditional remote sensing image-based coastline extraction is limited by data volume and processing speed, and the extracted coastline is susceptible to noise. Therefore, this paper proposes a method based on the Google Earth Engine geospatial platform, which combines threshold segmentation, the Otsu method, and morphological algorithms. First, remote sensing images are preprocessed on the platform, and the Normalized Difference Water Index (NDWI) is calculated. Then, the Otsu method is used to calculate the NDWI threshold for water-land segmentation, resulting in a binary water-land image. N

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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.

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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

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OLIVEIRA, W. N., F. MIZIARA, and N. C. FERREIRA. "Mapping Land Use and Land Cover of Mozambique Using Google Earth Engine Platform." Anuário do Instituto de Geociências - UFRJ 42, no. 1 (2019): 336–45. http://dx.doi.org/10.11137/2019_1_336_345.

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ERASLAN, Büşra. "Analysis of Settlement Dynamics of Central Districts of Samsun Province Using Google Earth Engine (1980-2030)." Kesit Akademi 10, no. 40 (2024): 678–703. http://dx.doi.org/10.29228/kesit.77938.

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Şehirlerin gelişim dinamikleri, kentleşme süreçlerinin anlaşılması ve gelecekteki planlamalar için kritik öneme sahiptir. Samsun ili, Karadeniz Bölgesi'nde stratejik bir konuma sahip olması nedeniyle bu araştırmada örnek olarak seçilmiştir. Bu çalışma, Samsun ilinin merkez ilçeleri olan Canik, Atakum, İlkadım ve Tekkeköy'ün 1980-2030 yılları arasındaki yerleşim dinamiklerini incelemektedir. Araştırmanın temel amacı, bu ilçelerin yerleşim alanlarının nasıl ve ne şekilde genişlediğini ve bu genişlemenin gelecekte nasıl devam edeceğini anlamaktır. Çalışmada, Google Earth Engine platformu kullanıl

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Nakagawa, Koji. "Analysis Method for time-series satellite images Using Google Earth Engine and Google Colaboratory." Landscape Ecology and Management 29 (2024): 44–48. http://dx.doi.org/10.5738/jale.29.44.

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Li, Jiwei, David E. Knapp, Mitchell Lyons, et al. "Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine." Remote Sensing 13, no. 8 (2021): 1469. http://dx.doi.org/10.3390/rs13081469.

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Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm.

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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.

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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

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Asare-Ansah, A. B., Y. A. Twumasi, Z. H. Ning, et al. "TRACKING THE GODZILLA DUST PLUME USING GOOGLE EARTH ENGINE PLATFORM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-M-2-2022 (July 25, 2022): 33–38. http://dx.doi.org/10.5194/isprs-archives-xlvi-m-2-2022-33-2022.

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Abstract. As part of Earth’s nutrient cycle, a layer of air travels every summer from Africa across the Atlantic Ocean. In June 2020, the thickest and densest dust plume traveled over 5000 miles along with the Saharan Air Layer (SAL) from Africa towards the USA and the Caribbean. Due to its gravity and impact, it was nicknamed “Godzilla”. While the cause of this event remains unclear, the advantage of using remote sensing applications to monitor aerosol concentrations and movement provides future opportunities to leverage machine learning technologies to build predictive models with the goal o

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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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Schmitt, M., L. H. Hughes, C. Qiu, and X. X. Zhu. "AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 145–52. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-145-2019.

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<p><strong>Abstract.</strong> Cloud coverage is one of the biggest concerns in spaceborne optical remote sensing, because it hampers a continuous monitoring of the Earth’s surface. Based on Google Earth Engine, a web- and cloud-based platform for the analysis and visualization of large-scale geospatial data, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for user-defined areas of interest and time periods, which can be significantly shorter than the one-year time frames that are commonly used in other multi-temporal image aggregation approache

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Juan Vincent Elfonda, Vikhory Bagus Wahyu Nugroho, and Tuhu Agung Rachmanto. "Klasifikasi Tutupan Lahan di Kabupaten Trenggalek Menggunakan Google Earth Engine." Jurnal Kendali Teknik dan Sains 2, no. 3 (2024): 85–94. http://dx.doi.org/10.59581/jkts-widyakarya.v2i3.3488.

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Land cover is defined as the physical and biological cover of the earth's surface, both those formed naturally such as swamps, hills and rivers and those formed by man-made means such as rice fields, gardens, forests and buildings. As technology develops, conventional methods of satellite image processing are starting to be abandoned. This is because conventional methods require quite a long time to process satellite image data. The presence of Google Earth Engine (GEE), which is a cloud computing-based platform, makes it easier for users to process satellite image data boldly and for free. Th

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Berra, Elias Fernando, Denise Cybis Fontana, Feng Yin, and Fabio Marcelo Breunig. "Harmonized Landsat and Sentinel-2 Data with Google Earth Engine." Remote Sensing 16, no. 15 (2024): 2695. http://dx.doi.org/10.3390/rs16152695.

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Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to prod

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Benoumeldjadj, Maya, Malika Rached-Kanouni, Abdelouahab Bouchareb, and Labed Ababsa. "Quantifying LULC Changes in Constantine, Algeria Using Google Earth Engine." Indonesian Journal of Social Science Research 5, no. 1 (2024): 1–8. http://dx.doi.org/10.11594/ijssr.05.01.01.

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The objective of this research is to analyze land use changes in the municipality of Constantine over a thirty year period (1993 – 2023) using Landsat satellite data provided through the Google Earth Engine (GEE) platform and a supervised classification approach. Five land cover classes were mapped at six key dates: water bodies, green spaces, built-up areas, bare soil and agricultural land. The quantified changes highlight a considerable reduction in bare soil in favor of an increase in cultivated land, as well as an expansion of artificial surfaces during certain periods (2008 to 2023). Thes

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Tobón-Marín, Alejandro, and Julio Cañón Barriga. "Analysis of changes in rivers planforms using google earth engine." International Journal of Remote Sensing 41, no. 22 (2020): 8654–81. http://dx.doi.org/10.1080/01431161.2020.1792575.

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Xia, Zilong, Xiaona Guo, and Ruishan Chen. "Automatic extraction of aquaculture ponds based on Google Earth Engine." Ocean & Coastal Management 198 (December 2020): 105348. http://dx.doi.org/10.1016/j.ocecoaman.2020.105348.

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Aybar, Cesar, Qiusheng Wu, Lesly Bautista, Roy Yali, and Antony Barja. "rgee: An R package for interacting with Google Earth Engine." Journal of Open Source Software 5, no. 51 (2020): 2272. http://dx.doi.org/10.21105/joss.02272.

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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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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.

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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

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Desai, Geeta T., and Abhay N. Gaikwad. "Crop classification using object-oriented method and Google Earth Engine." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1271. https://doi.org/10.11591/ijai.v14.i2.pp1271-1280.

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Agriculture crop monitoring in real-time is crucial in formulating effective agricultural practices and management policies. The primary goal of the investigation is to explore how the utilization of Sentinel-1 data and its fusion with Sentinel-2 impact crop classification accuracy in a fragmented agricultural landscape in the Yavatmal District of Maharashtra, India. Pixel based classification and object-oriented classification approaches were implemented on Google Earth Engine (GEE), and obtained results were compared for different combinations of optical and microwave features. The research

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Geeta, T. Desai, and N. Gaikwad Abhay. "Crop classification using object-oriented method and Google Earth Engine." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1271–80. https://doi.org/10.11591/ijai.v14.i2.pp1271-1280.

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Agriculture crop monitoring in real-time is crucial in formulating effective agricultural practices and management policies. The primary goal of the investigation is to explore how the utilization of Sentinel-1 data and its fusion with Sentinel-2 impact crop classification accuracy in a fragmented agricultural landscape in the Yavatmal District of Maharashtra, India. Pixel based classification and object-oriented classification approaches were implemented on Google Earth Engine (GEE), and obtained results were compared for different combinations of optical and microwave features. The research

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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.

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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

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Hermawan, Erwin, Sahid Agustian, and Ikmal Wahyudi. "Analisa Pola Perubahan Suhu Permukaan Menggunakan Google Earth Engine Berbasis Web Gis." ETNIK: Jurnal Ekonomi dan Teknik 2, no. 5 (2023): 463–76. http://dx.doi.org/10.54543/etnik.v2i5.198.

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Development in an area must pay attention to the socio-economic conditions of the community such as the level of education, health and economic facilities as well as other factors. The development of this region cannot be separated from the regional spatial planning, this is very necessary to realize a balanced development. Bogor Regency is one of the regencies whose development is currently quite developed, the increase in population causes a reduction in land with vegetation cover to become built-up areas, from the increase in built-up land it causes an increase in temperature in Bogor Regen

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Kang, Yan, and Jun Wang. "Mapping and analysis using multisource oceanic satellite data and google earth engine." SHS Web of Conferences 145 (2022): 01023. http://dx.doi.org/10.1051/shsconf/202214501023.

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Ocean satellite observation because of its large coverage area and high frequency observation become ever more important data and information source with global climate changing, ocean resources protecting and oceanic engineering projects implementing. Oceanic satellite data characteristically include multi-physical parameters, are the product of multi-level processing and are multi-sourced data. Therefore, oceanic satellite data often have different manifestations making the understanding and use of these data challenging, and there is an urgent need for a flexible platform to share the data

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Vollrath, Andreas, Adugna Mullissa, and Johannes Reiche. "Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine." Remote Sensing 12, no. 11 (2020): 1867. http://dx.doi.org/10.3390/rs12111867.

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This article provides an angular-based radiometric slope correction routine for Sentinel-1 SAR imagery on the Google Earth Engine platform. Two established physical reference models are implemented. The first model is optimised for vegetation applications by assuming volume scattering on the ground. The second model is optimised for surface scattering, and therefore targeted at urban environments or analysis of soil characteristics. The framework of both models is extended to simultaneously generate masks of invalid data in active layover and shadow affected areas. A case study, using openly a

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