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1
Mutanga, Onisimo, and Lalit Kumar. "Google Earth Engine Applications." Remote Sensing 11, no. 5 (March 12, 2019): 591. http://dx.doi.org/10.3390/rs11050591.
Повний текст джерела
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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 (September 21, 2021): 3778. http://dx.doi.org/10.3390/rs13183778.
Повний текст джерелаАнотація:
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 reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.
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.
Повний текст джерелаАнотація:
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).
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 (April 6, 2022): 74–83. http://dx.doi.org/10.14710/tataloka.24.1.74-83.
Повний текст джерелаАнотація:
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 lahan dianilisis menggunakan metode Random Forest (RF). Penilaian akurasi dihitung menggunakan confusion Matrix, sedangkan validasi lapang dilakukan dengan menggunakan metode stratified random sampling. Uji akurasi analisis tutupan lahan tahun 2020 dengan confusion Matrix menghasilkan nilai Overall Accuracy (OA) 0,94 dan nilai kappa 0,91; tahun 2015 dengan nilai OA 0,93 dan nilai Kappa 0,91; sedangkan tahun 2010 memiliki nilai OA 0.96 dan nilai Kappa 0.94. Hasil analisis ini menunjukkan bahwa luas lahan sawah Kabupaten Sukabumi mengalami penyusutan seluas 10,317.27 ha dalam kurun waktu sepuluh tahun (2010-2020). Klasifikasi penggunaan lahan menggunakan GEE dapat menghasilkan peta dengan akurasi tinggi dengan OA >85%, serta dapat mempersingkat waktu analisis.
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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 (July 1, 2020): 4574. http://dx.doi.org/10.3390/app10134574.
Повний текст джерелаАнотація:
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 remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments.
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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 (July 1, 2022): 012011. http://dx.doi.org/10.1088/1755-1315/1064/1/012011.
Повний текст джерелаАнотація:
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 GEE-based aquaculture ponds mapping studies. In general, some studies reported that GEE can extract the spatial distribution of aquaculture ponds with great overall accuracies, which are more than 90%. Difficult to detect small ponds and misclassification due to similar spectral reflectance are among the limitations reported in previous studies. Future research directions include incorporation of more aquaculture pond extraction techniques and different types of satellite images in GEE.
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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 (April 25, 2022): 2055. http://dx.doi.org/10.3390/rs14092055.
Повний текст джерелаАнотація:
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. We found that the LM method was superior to the NLS method when we compared the convergence speed, initial value stability, and the accuracy of fitted parameters; therefore, we then applied the LM method to develop a nonlinear fitting function for the GEE platform. We further tested this function by fitting a double-logistic equation with the global leaf area index (LAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data to the GEE platform. We concluded that the nonlinear fitting function we developed for the GEE platform was fast, stable, and accurate in fitting double-logistic models with remote sensing data. Given the generality of the LM algorithm, we believe that the nonlinear function can also be used to fit other types of nonlinear equations with other sorts of datasets on the GEE platform.
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 (April 25, 2022): 2055. http://dx.doi.org/10.3390/rs14092055.
Повний текст джерелаАнотація:
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. We found that the LM method was superior to the NLS method when we compared the convergence speed, initial value stability, and the accuracy of fitted parameters; therefore, we then applied the LM method to develop a nonlinear fitting function for the GEE platform. We further tested this function by fitting a double-logistic equation with the global leaf area index (LAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data to the GEE platform. We concluded that the nonlinear fitting function we developed for the GEE platform was fast, stable, and accurate in fitting double-logistic models with remote sensing data. Given the generality of the LM algorithm, we believe that the nonlinear function can also be used to fit other types of nonlinear equations with other sorts of datasets on the GEE platform.
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Campos-Taberner, Manuel, Álvaro Moreno-Martínez, Francisco García-Haro, Gustau Camps-Valls, Nathaniel Robinson, Jens Kattge, and Steven Running. "Global Estimation of Biophysical Variables from Google Earth Engine Platform." Remote Sensing 10, no. 8 (July 24, 2018): 1167. http://dx.doi.org/10.3390/rs10081167.
Повний текст джерелаАнотація:
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 data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R2 = 0.87, RMSE = 0.54 m2/m2 and ME = 0.03 m2/m2 in the case of LAI, and R2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 = 0.42 and R2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.
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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 (December 28, 2022): 1380–87. http://dx.doi.org/10.35414/akufemubid.1181347.
Повний текст джерелаАнотація:
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 biçimde değerlendirilmesini ve analiz edilmesini sağlar. Bu çalışmada 7 farklı istasyona ait toplamda 14 Landsat-8 uydu görüntüsü kullanılarak GEE platformunda kodlar yardımıyla 4 farklı metot ile yüzey sıcaklıkları elde edilmiştir. Elde edilen sıcaklıklar istasyondan ölçülen yakın hava sıcaklığı ile karşılaştırılarak tüm metotlar için Karesel Ortalama Hata (KOH) ve korelasyon değerleri hesaplanmıştır. Son dönemde çalışma konusu olarak artış gösteren YYS, GEE gibi kullanımı oldukça basit bir platformda ayrıca uydu görüntüsü indirmeye gerek kalmadan hızlı, kolay ve kısa bir sürede elde edilebileceği ortaya konulmuştur.
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Lahay, Rakhmat Jaya, and Syahrizal Koem. "Ekstraksi Perubahan Tutupan Vegetasi Di Kabupaten Gorontalo Menggunakan Google Earth Engine." Jambura Geoscience Review 4, no. 1 (December 30, 2021): 11–21. http://dx.doi.org/10.34312/jgeosrev.v4i1.12086.
Повний текст джерелаАнотація:
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 cover spatially and temporally using the GEE platform. A total of 43 MODIS images in the study area, recording periods 2000 and 2020, were used to quickly and effectively generate vegetation cover maps. The process of downloading, processing, and analyzing data was automated through the GEE interface. The results of the mapping in 2000 and 2020 are shown by maps of vegetation cover in two classes, namely; vegetation and non-vegetation. The accuracy of the vegetation cover map shows good results, namely an overall accuracy of 0.81 for 2000 and 0.85 for 2020. The area of the non-vegetation class increased by 2815.29 ha, and the vegetation class decreased by 2767.31 ha. The map of spatial changes in vegetation cover in the study area is classified into three classes, namely revegetation, devegetation, and unchanged. Based on these results, the extraction of vegetation cover changes in the study area using the GEE platform can be carried out well.
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Kumar, Lalit, and Onisimo Mutanga. "Google Earth Engine Applications Since Inception: Usage, Trends, and Potential." Remote Sensing 10, no. 10 (September 20, 2018): 1509. http://dx.doi.org/10.3390/rs10101509.
Повний текст джерелаАнотація:
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 total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.
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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 (January 1, 2022): 012051. http://dx.doi.org/10.1088/1755-1315/961/1/012051.
Повний текст джерелаАнотація:
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 spatial analyzes, which cite by conducting a classification at the level of a governorate and obtaining results with speed and relatively good quality. By using the Google Earth Engine (GEE) platform and through Javascript programming language, a classification of the land cover of Wasit Governorate, Iraq was created under the supervision of a satellite image (Landsat 8) by creating a training sample, Google Maps’ High Resolution basemap imagery was used to create this map to identify classes of landcover (water, bare soil, vegetation, and urban). Each source pixel is assigned to one of the previously mentioned classes. Then to create a land cover map of the region using the Statistical Machine Intelligence and Learning Engine (SMILE) classifier from the JAVA library, which is used by Google Earth Engine (GEE) to implement these algorithms. The result is an array of pixels (raster data). The pixel value represents the class that was previously determined by the samples.
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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.
Повний текст джерелаАнотація:
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 API. However, users still have to hard-code spectral indices for the JavaScript library since there are no implementations that link catalogues of spectral indices to the Code Editor. Here we present spectral, a module that links the Awesome Spectral Indices (ASI) catalogue to GEE for querying and computing spectral indices inside the Code Editor. The module allows accessing and computing spectral indices from the catalogue for multiple remote sensing products in GEE. All indices can be queried by using a key-value model and computed by using a single method. The module demonstrates that spectral indices can be easily computed inside the Code Editor. Image and Image Collection objects can be used for the calculation of all spectral indices in the catalogue if the specific dataset counts with the required bands. We anticipate that spectral will be used by most GEE users for Earth System research. Analyses conducted by the community will be sped up by avoiding hard-coding and RS investigations will be boosted.
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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.
Повний текст джерелаАнотація:
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 даних, який включає супутникові зображення, дані метеорологічних, геофізичних спостережень, тощо. У даній роботі виконано аналіз структури і функцій платформи, а також можливостей отримання відкритих даних дистанційного зондування, наданих каталогом GEE, для вирішення задач регіонального екологічного моніторингу. Здійснено систематичний огляд актуальних наукових публікацій, який підтвердив широкий спектр застосування даної платформи науковцями різних країн для аналізу навколишнього середовища як у регіональному, так і у глобальному масштабі. Одним із найбільш поширених типів завдань, які реалізовуються засобами GEE, є розрахунок нормалізованих диференційних індексів, які використовуються для картографування рослинності, врожаю, земельного покриву, біорізноманіття та моніторингу пожеж, засух та інших негативних природних і техногенних процесів. Для досліджуваної території Карпатського регіону виконано оцінку часового періоду наявних даних спостережень, покриття супутниковими знімками, їх роздільної здатності, дешифрувальних характеристик. За даними багатоканальних космознімків за допомогою редактора коду GEE і мови програмування JavaScript здійснено розрахунок нормалізованих диференційних індексів NDVI, MNDWI, NDBI і виконано візуалізацію отриманих результатів.
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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.
Повний текст джерелаАнотація:
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 разработать эффективную методику оценки послепожарных характеристик. Для исследований были выбраны данные с сенсора MODIS Collection 6 из-за их большей доступности и достаточного пространственного разрешения для нашей работы. Использованы данные за период с 2001 по 2019 год из пожарного архива FIRMS. В данной статье представлен метод определения некоторых характеристик пожаров с использованием "больших данных" и платформы Google Earth Engine. Алгоритмы, созданные для определения основных послепожарных характеристик, были применены на примере Верхоянского района Якутии. Результаты приведены на примере пожаров в период 2001-2019 годов. Для анализа использовались данные программы FIRMS из инструмента Modis и VIIRIS, а также данные Landsat.
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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 (December 20, 2019): 46. http://dx.doi.org/10.3390/rs12010046.
Повний текст джерелаАнотація:
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 (shipping, uranium mining, deforestation) and disaster assessment (flash floods). These highlight the advantages of the good temporal resolution resulting from cloud cover independence, short revisit times and near real time data availability.
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Papilaya, Patrich Phill Edrich. "Aplikasi Google Earth Engine Dalam Menyediakan Citra Satelit Sumberbedaya Alam Bebas Awan." MAKILA 16, no. 2 (November 12, 2022): 96–103. http://dx.doi.org/10.30598/makila.v16i2.6586.
Повний текст джерелаАнотація:
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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-pulau kecil di Provinsi Maluku.
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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 (May 6, 2020): 1471. http://dx.doi.org/10.3390/rs12091471.
Повний текст джерелаАнотація:
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 (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.
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Salinero-Delgado, Matías, José Estévez, Luca Pipia, Santiago Belda, Katja Berger, Vanessa Paredes Gómez, and Jochem Verrelst. "Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression." Remote Sensing 14, no. 1 (December 29, 2021): 146. http://dx.doi.org/10.3390/rs14010146.
Повний текст джерелаАнотація:
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) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.
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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 (January 24, 2021): 403. http://dx.doi.org/10.3390/rs13030403.
Повний текст джерелаАнотація:
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, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAIG) from Sentinel-2 imagery was imported. Although by running this GPR model into GEE any corner of the world can be mapped into LAIG at a resolution of 20 m, here we show some demonstration cases over western Europe with zoom-ins over Spain. Thanks to the computational power of GEE, the mapping takes place on-the-fly. Additionally, a GPR-based gap filling strategy based on pre-optimized kernel hyperparameters is also put forward for the generation of multi-orbit cloud-free LAIG maps with an unprecedented level of detail, and the extraction of regularly-sampled LAIG time series at a pixel level. The ability to plugin a locally-trained GPR model into the GEE framework and its instant processing opens up a new paradigm of remote sensing image processing.
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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 (February 23, 2021): 816. http://dx.doi.org/10.3390/rs13040816.
Повний текст джерелаАнотація:
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 previously implemented Burned Area Mapping Software (BAMS) because of GEE parallel processing capabilities and preloaded geospatial datasets. BAMT also allows temporal image composites to be exploited in order to obtain BA maps over a larger extent and longer temporal periods. The tools consist of four scripts executable from the GEE Code Editor. The tools’ performance was discussed in two case studies: in the 2019/2020 fire season in Southeast Australia, where the BA cartography detected more than 50,000 km2, using Landsat data with commission and omission errors below 12% when compared to Sentinel-2 imagery; and in the 2018 summer wildfires in Canada, where it was found that around 16,000 km2 had burned.
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Hamunyela, Eliakim, Sabina Rosca, Andrei Mirt, Eric Engle, Martin Herold, Fabian Gieseke, and Jan Verbesselt. "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 (September 11, 2020): 2953. http://dx.doi.org/10.3390/rs12182953.
Повний текст джерелаАнотація:
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 satellite data. Change detection methods with the capability to detect abnormal changes in time series data while accounting for normal seasonal variations have also been developed but are computationally intensive. Here, we report an implementation of BFASTmonitor (Breaks For Additive Season and Trend monitor) on GEE to support large-area and sub-annual change monitoring using satellite data available in GEE. BFASTmonitor is a data-driven unsupervised change monitoring approach that detects abnormal changes in time series data, with near real-time monitoring capabilities. Although BFASTmonitor has been widely used in forest cover loss monitoring, it is a generic change monitoring approach that can be used to monitor changes in a various time series data. Using Landsat time series for normalised difference moisture index (NDMI), we evaluated the performance of our GEE BFASTmonitor implementation (GEE BFASTmonitor) by detecting forest disturbance at three forest areas (humid tropical forest, dry tropical forest, and miombo woodland) while comparing it to the original R-based BFASTmonitor implementation (original BFASTmonitor). A map-to-map comparison showed that the spatial and temporal agreements on forest disturbance between the original and our GEE BFASTmonitor implementations were high. At each site, the spatial agreement was more than 97%, whereas the temporal agreement was over 94%. The high spatial and temporal agreement show that we have properly translated and implemented the BFASTmonitor algorithm on GEE. Naturally, due to different numerical solvers being used for regression model fitting in R and GEE, small differences could be observed in the outputs. These differences were most noticeable at the dry tropical forest and miombo woodland sites, where the forest exhibits strong seasonality. To make GEE BFASTmonitor accessible to non-technical users, we developed a web application with simplified user interface. We also created a JavaScript-based GEE BFASTmonitor package that can be imported as a module. Overall, our GEE BFASTmonitor implementation fills an important gap in large-area environmental change monitoring using earth observation data.
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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.
Повний текст джерелаАнотація:
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 agricultural area in Tuscany (Italy). Four different scenarios were implemented in GEE combining different information such as optical and Synthetic Aperture Radar (SAR) data, indices and time series. Among the five classifiers used the best performers were RF and SVM. Integrating Sentinel-1 (S1) and Sentinel-2 (S2) slightly improves the classification in comparison to the only S2 image classifications. The use of time series substantially improves supervised classifications. The analysis carried out so far lays the foundation for the integration of time series of SAR and optical data.
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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 (September 30, 2022): 4906. http://dx.doi.org/10.3390/rs14194906.
Повний текст джерелаАнотація:
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 recently at higher (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE at their native resolution. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making OC data at native resolution readily available in GEE.
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Aghlmand, Majid, and Gordana Kaplan. "Monitoring Urban Expansion Using Remote-Sensing Data Aided by Google Earth Engine." European Journal of Geosciences 3, no. 1 (January 25, 2021): 1–8. http://dx.doi.org/10.34154/2021-ejgs-0012/euraass.
Повний текст джерелаАнотація:
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 years. Thus, in this paper we use Landsat satellite images from 1986 and 2019, integrated into GEE, implementing Support vector machine (SVM) classification method. The accuracy assessment for the classified images showed high accuracy (95-96%), while the results showed a significant increase in the urban area of the city of Isfahan, occupying more than 70% of the study area. For future studies, we recommend a more detailed investigation about the city expansion and the negative impacts that may occur due to urban expansion.
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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 (August 11, 2018): 1265. http://dx.doi.org/10.3390/rs10081265.
Повний текст джерелаАнотація:
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 (SMOS) and Soil Moisture Active Passive (SMAP) missions’ satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation- and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them.
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Mehmood, Hamid, Crystal Conway, and Duminda Perera. "Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform." Atmosphere 12, no. 7 (July 3, 2021): 866. http://dx.doi.org/10.3390/atmos12070866.
Повний текст джерелаАнотація:
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 using the Modified Normalized Difference Water Index (MNDWI) and site-specific elevation and land use data. The results were assessed by calculating a confusion matrix for nine flood events spread over the globe. The FMA had a high true positive accuracy ranging from 71–90% and overall accuracy in the range of 74–89%. In short, observations from FMA in GEE can be used as a rapid and robust hindsight tool for mapping flood inundation areas, training AI models, and enhancing existing efforts towards flood mitigation, monitoring, and management.
29
Estrabis, N. V., L. Osco, A. P. Ramos, W. N. Gonçalves, V. Liesenberg, H. Pistori, and J. Marcato Junior. "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.
Повний текст джерелаАнотація:
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; and V- scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91.
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Elnashar, Abdelrazek, Hongwei Zeng, Bingfang Wu, Ning Zhang, Fuyou Tian, Miao Zhang, Weiwei Zhu, et al. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing." Remote Sensing 12, no. 23 (November 25, 2020): 3860. http://dx.doi.org/10.3390/rs12233860.
Повний текст джерелаАнотація:
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 Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.
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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 (December 15, 2019): 3023. http://dx.doi.org/10.3390/rs11243023.
Повний текст джерелаАнотація:
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 spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.
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Safanelli, José, Raul Poppiel, Luis Ruiz, Benito Bonfatti, Fellipe Mello, Rodnei Rizzo, and José Demattê. "Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis." ISPRS International Journal of Geo-Information 9, no. 6 (June 17, 2020): 400. http://dx.doi.org/10.3390/ijgi9060400.
Повний текст джерелаАнотація:
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 surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE.
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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 (March 13, 2021): 1098. http://dx.doi.org/10.3390/rs13061098.
Повний текст джерелаАнотація:
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 using geo-coordinates for pixel extraction from the original data. When the Sentinel Application Platform (SNAP) Pixel Extraction Tool is used, an additional distance check has to be conducted to exclude pixels that lay further than 212 m from the point of interest. Even geo-coordinates-based pixel extraction requires the homogeneity of the target area at a 700 m diameter (49 ha) footprint (double of the pixel resolution). The GEE OLCI dataset can be safely used if the homogeneity assumption holds at 2700 m diameter (9-by-9 OLCI pixels) or if the uncertainty in the radiance of 10% is not critical for the application. Further analysis showed that the scaling factors reported in the GEE dataset description must not be used. Finally, observation geometry and meteorological data are not present in the GEE OLCI dataset, but they are crucial for most applications. Therefore, we propose to calculate angles and extraterrestrial solar fluxes and to use an alternative data source—the Copernicus Atmosphere Monitoring Service (CAMS) dataset—for meteodata.
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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 (November 17, 2020): 3776. http://dx.doi.org/10.3390/rs12223776.
Повний текст джерелаАнотація:
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, this work is aimed at developing and testing an OO classification approach combining the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices, and two ML algorithms (Random Forest (RF) or Support Vector Machine (SVM)) to perform the final classification. A Principal Components Analysis (PCA) is applied to the main seven GLCM indices to synthesize in one band the textural information used for the OO classification. The proposed approach is implemented in a user-friendly, freely available GEE code useful to perform the OO classification, tuning various parameters (e.g., choose the input bands, select the classification algorithm, test various segmentation scales) and compare it with a PB approach. The accuracy of OO and PB classifications can be assessed both visually and through two confusion matrices that can be used to calculate the relevant statistics (producer’s, user’s, overall accuracy (OA)). The proposed methodology was broadly tested in a 154 km2 study area, located in the Lake Trasimeno area (central Italy), using Landsat 8 (L8), Sentinel 2 (S2), and PlanetScope (PS) data. The area was selected considering its complex LULC mosaic mainly composed of artificial surfaces, annual and permanent crops, small lakes, and wooded areas. In the study area, the various tests produced interesting results on the different datasets (OA: PB RF (L8 = 72.7%, S2 = 82%, PS = 74.2), PB SVM (L8 = 79.1%, S2 = 80.2%, PS = 74.8%), OO RF (L8 = 64%, S2 = 89.3%, PS = 77.9), OO SVM (L8 = 70.4, S2 = 86.9%, PS = 73.9)). The broad code application demonstrated very good reliability of the whole process, even though the OO classification process resulted, sometimes, too demanding on higher resolution data, considering the available computational GEE resources.
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Kaplan, Gordana. "Broad-Leaved and Coniferous Forest Classification in Google Earth Engine Using Sentinel Imagery." Environmental Sciences Proceedings 3, no. 1 (November 11, 2020): 64. http://dx.doi.org/10.3390/iecf2020-07888.
Повний текст джерелаАнотація:
Forest structures knowledge is fundamental to understanding, managing, and preserving the biodiversity of forests. With the well-established need within the remote sensing community for better understanding of canopy structure, in this paper, the effectiveness of Sentinel-2 imagery for broad-leaved and coniferous forest classification within the Google Earth Engine (GEE) platform has been assessed. Here, we used Sentinel-2 image collection from the summer period over North Macedonia, when the canopy is fully developed. For the sample collection of the coniferous areas and the accuracy assessment of the classification, we used imagery from the spring period, when the broad-leaved forests are in the early green stage. A Support Vector Machine (SVM) classifier has been used for discriminating forest cover groups, namely, broadleaved and coniferous forests. According to the results, more than 90% of the canopy in North Macedonia is broad-leaved, while less than 10% is conifers. The results in this study show that, with the use of GEE, Sentinel-2 data alone can be effectively used to obtain rapid and accurate mapping of main forest types (conifers-broadleaved) with a fine resolution.
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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 (December 15, 2022): 2309. http://dx.doi.org/10.3390/land11122309.
Повний текст джерелаАнотація:
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 GEE to observe the changes in soil erosion in the Ordos area from 2013 to 2021. The Theil–Sen procedure and Mann–Kendall significance test methods were used to evaluate the trend of land erosion in the Ordos area from 2013 to 2021. Based on GEE, the RUSLE is applied to evaluate soil erosion and analyze the changing trend. As a result, (1) we found that the annual change of soil and water loss in the Ordos Plateau showed three stages: 2013–2015, 2016–2018, and 2018–2021. After 2018, soil loss decreased from 14 × 1017 Mg in 2018 to 4 × 1017 Mg in 2021, which indicates that the environmental restoration project started in 2018 has achieved encouraging results. (2) The results showed that 40.9% of the regional soil erosion trend showed a significant decline, and 45.7% of the regional soil erosion trend showed a slight decline. Only 13.3% of the regional soil erosion is on the rise. (3) The test results of different land use types show that 87.3% of soil erosion occurs in bare and cultivated land. Because the terrain of Ordos is relatively flat, 95.39–96.17% of soil erosion occurs in areas with a slope of 0 to 5. (4) The reliability of the RUSLE model based on the GEE platform is proved by regression model verification of observation data and model prediction results. (5) GEE’s cloud-based features can provide data and scripts to users in developing countries which lack sufficient observation data or the necessary computing resources to develop these data. The results show that GEE has robust analysis and processing ability, can analyze a large amount of data, and can provide efficient digital products for soil erosion research.
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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 (March 19, 2020): 160–69. http://dx.doi.org/10.15809/irriga.2020v25n1p160-169.
Повний текст джерелаАнотація:
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 nuvem um grande potencial, como na ferramenta Google Earth Engine (GEE), que facilita o processo de busca, filtragem e manipulação de grandes volumes de dados de sensoriamento remoto sem a necessidade de softwares pagos ou de download de imagens. O presente trabalho apresenta uma implementação de classificação supervisionada de áreas irrigadas e não-irrigadas na região de Sorriso e Lucas do Rio Verde/MT com o algoritmo Classification and Regression Trees (CART) em ambiente GEE utilizando as bandas 2-7 do satélite Landsat-8 e os índices NDVI, NDWI e SAVI. A acurácia da classificação supervisionada foi de 99,4% ao utilizar os índices NDWI, NDVI e SAVI e de 98,7% sem utilizar esses índices, todas consideradas excelentes. O tempo de processamento médio, refeito 10 vezes, foi de 52 segundos, considerando todo o código-fonte desenvolvido desde a filtragem das imagens até a conclusão da classificação. O código-fonte desenvolvido é apresentado em anexo de modo a difundir e incentivar o uso do GEE para estudos de inteligência espacial em irrigação e drenagem por sua usabilidade e fácil manipulação.
Keywords: computação em nuvem, sensoriamento remoto, hidrologia, modelagem.
SILVA, C. O .F
SUPERVISED CLASSIFICATION OF IRRIGATED AREA USING SPECTRAL INDEXES FROM LANDSAT-8 IMAGES WITH GOOGLE EARTH ENGINE
2 ABSTRACT
Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat- 8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation.
Keywords: cloud computing, remote sensing, hydrology, modeling.
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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.
Повний текст джерелаАнотація:
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 trends in erosion and progradation areas along the mouths and sides of the delta. Our algorithm performs well at delineating the shorelines, although special care must be taken to clean the images from clouds and shadows that may alter the definition of the shoreline. Results show that the Atrato delta has lost around 10 km2 due to erosion and has gained around 18 km2 in progradation during the period of assessment. Overall, progradation is the dominant process at the delta’s mouths, while erosion is dominant only in areas far from the mouths, which agrees with a river-dominated environment of high sediment loads and is coherent with other studies made in the region. The algorithm in GEE is a versatile tool, appropriate to assess short and long-term changes of coastal areas that do not count with land-based information.
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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 (April 7, 2021): 1424. http://dx.doi.org/10.3390/rs13081424.
Повний текст джерелаАнотація:
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 process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses the Bruun rule formula implemented in GEE and can determine the coastline retreat of a profile by creatting a simple vector line from topo-bathymetric data. The model shows a very high correlation (0.97) with a classical Bruun rule study performed in the Aveiro coast (NW Portugal). Therefore, the achieved results disclose that the GEE platform is suitable to perform these analysis. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific community.
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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 (April 30, 2022): 369–78. http://dx.doi.org/10.18006/2022.10(2).369.378.
Повний текст джерелаАнотація:
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 google earth engine platform is used primarily to download and prepare the images for the dates 1986, 2000, and 2019, then a supervised classification with the Random Forest (RF) algorithm to produce land use maps of selected dates with an overall accuracy exceeding 80%. This was followed by the production of maps and change matrices for the periods 1986-2000 and 2000-2019. The results obtained have shown a decline in grassland, forest land, and water body in parallel with an increase in the following classes: buildings, farmland, and arboriculture during the last 30 years. In addition, elevation was the most important characteristic variable for land-use classification in the study area. Obtained results provide theoretical support for adjusting and optimizing land use in the High Oum Er-Rbia watershed.
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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.
Повний текст джерелаАнотація:
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 storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers.
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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 (August 7, 2020): 390–95. http://dx.doi.org/10.30871/jagi.v4i2.2256.
Повний текст джерелаАнотація:
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 satellite imagery. The selected year is 2019, with mapped land cover classes consisting of bodies of water, vegetation cover, open land, and built-up area. Machine learning that tested included CART, Random Forest, GMO Max Entropy, SVM Voting, and SVM Margin.
The results of this study indicate that the best machine learning in mapping land cover in the Kapuas watershed is GMO Max Entropy, then CART. This research still has many limitations, especially mapped land cover classes. So that research needs to be developed with more detailed land cover classes, more diverse and multi-time input data.
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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.
Повний текст джерелаАнотація:
<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 downloading and processing of remote sensing images is very time-consuming, especially the processing of remote sensing images with large-scale and long time series often involves hundreds of gigabytes of data, which requires a high level of personal computers and is inefficient. As a cloud platform dedicated to data processing and analysis of geoscience, Google Earth Engine(GEE) integrates many excellent remote sensing image processing algorithms. It does not need to download images and supports online remote sensing image processing, which greatly improves the output efficiency. Based on GEE, the monthly data of Yarlung Zangbo River at Nuxia Hydrological Station and the annual data of typical lakes were extracted and vectorized from the pre-processed Landsat series images. It was found that the area of Yarlung Zangbo River at Nuxia Hydrological Station varies periodically. The changing trend of typical lakes is also revealed.</p>
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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.
Повний текст джерелаАнотація:
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 order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.
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Amani, Meisam, Sahel Mahdavi, Majid Afshar, Brian Brisco, Weimin Huang, Sayyed Mohammad Javad Mirzadeh, Lori White, Sarah Banks, Joshua Montgomery, and Christopher Hopkinson. "Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results." Remote Sensing 11, no. 7 (April 8, 2019): 842. http://dx.doi.org/10.3390/rs11070842.
Повний текст джерелаАнотація:
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 innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km2). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands.
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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 (August 1, 2021): 85–92. http://dx.doi.org/10.21776/ub.jsal.2021.008.02.4.
Повний текст джерелаАнотація:
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 digunakan antara lain band 1 sampai 7 pada citra Landsat 8, transformasi NDVI dan NDBI serta nilai elevasi dari DEM SRTM. Adapun tahun yang dipilih adalah tahun 2015 dan 2020 dengan machine learning yang diujikan meliputi CART, Random forest dan Voting SVM. Hasil penelitian ini menunjukkan bahwa machine learning yang terbaik dalam memetakan penutup lahan di DAS Kreo adalah Random forest. Penelitian ini masih terdapat banyak keterbatasan terutama kelas penutup lahan yang dipetakan.
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Supe, Hitesh, Ram Avtar, Deepak Singh, Ankita Gupta, Ali P. Yunus, Jie Dou, Ankit A. Ravankar, et al. "Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments." Remote Sensing 12, no. 9 (May 5, 2020): 1466. http://dx.doi.org/10.3390/rs12091466.
Повний текст джерелаАнотація:
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 platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.
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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 (September 4, 2022): 4395. http://dx.doi.org/10.3390/rs14174395.
Повний текст джерелаАнотація:
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 cosine transform (DCT-PLS) method. This method iteratively identifies cloud-contaminated NDVI over NDVI time series from the Sentinel-2 surface reflectance data by adjusting the weights. The NDVI and surface reflectance time series are then reconstructed from cloud-free NDVI and surface reflectance using the adjusted weights as constraints. We have made some improvements to the DCT-PLS method. First, the traditional discrete cosine transformation (DCT) in the DCT-PLS method is matrix generated from discrete and equally spaced data, we reconfigured the DCT formulas to adapt for irregular interval time series, and optimized the control parameters N and s according to the typical vegetation samples in China. Second, the DCT-PLS method was deployed in the Google Earth Engine (GEE) platform for the efficiency and convenience of data users. We used the DCT-PLS method to reconstruct the Sentinel-2 NDVI time series and surface reflectance time series in the blue, green, red, and near infrared (NIR) bands in typical vegetation samples and the Zhangjiakou and Hangzhou study area. We found that this method performed better than the SG filter method in reconstructing the NDVI time series, and can identify and reconstruct the contaminated NDVI as well as surface reflectance with low root mean square error (RMSE) and high coefficient of determination (R2). However, in cases of a long range of cloud contamination, or above water surface, it may be necessary to increase the control parameter s for a more stable performance. The GEE code is freely available online and the link is in the conclusions of this article, researchers are welcome to use this method to generate cloudless Sentinel-2 NDVI and surface reflectance time series with 10 m spatial resolution, which is convenient for landcover classification and many other types of research.
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Tew, Yi Lin, Mou Leong Tan, Narimah Samat, Ngai Weng Chan, Mohd Amirul Mahamud, Muhammad Azizan Sabjan, Lai Kuan Lee, Kok Fong See, and Seow Ta Wee. "Comparison of Three Water Indices for Tropical Aquaculture Ponds Extraction using Google Earth Engine." Sains Malaysiana 51, no. 2 (February 28, 2022): 369–78. http://dx.doi.org/10.17576/jsm-2022-5102-04.
Повний текст джерелаАнотація:
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 Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automated Water Extraction Index (AWEI), in mapping of the aquaculture ponds in Sungai Udang, Pulau Pinang, Malaysia. The results show that MNDWI is the best index for aquaculture ponds extraction in Sungai Udang, with an accuracy of 81.87% and Kappa coefficient of 0.61. Meanwhile, the accuracy of NDWI and AWEI as compared to the digitized aquaculture ponds are 58.21 and 61.60%, and Kappa coefficient of 0.33 and 0.36, respectively. Then, MNDWI was applied to calculate the spatial changes of aquaculture ponds from 2014 to 2020. The result indicates that the area of aquaculture ponds has expanded by 26.16% since the past seven years.
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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 (December 21, 2022): 909–16. http://dx.doi.org/10.32985/ijeces.13.10.6.
Повний текст джерелаАнотація:
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 the GEE platform. Sentinel-1 provides Synthetic Aperture Radar (SAR) datasets that have a temporally dense and high spatial resolution, which is renowned for its cloud penetration characteristics and round-the-year observations irrespective of the weather. VV and VH polarization sentinel-1 time series data were automatically classified using a support vector machine (SVM) and Random Forest (RF) machine learning algorithms. Overall accuracies (OA), ranging from 82.3% to 90%, were obtained depending on polarization and methodology used. RF algorithm with VV polarization dataset stands better in comparison to SVM achieving OA of 90% and Kappa coefficient of 0.86. The highest user accuracy was obtained for the water class with both classifiers.