Mapping global flying aircraft activities using Landsat 8 and cloud computing

Peningkatan intensitas pembangunan fisik dan sosial di kawasan perkotaan Samarinda sebagai indikator kemajuan suatu kawasan perkotaan secara tidak langsung berdampak terhadap stabilitas kondisi atmosfer. Fenomena urban heat island sebagai turunan dari dinamika iklim mikro perkotaan sebagai dampak dari pembangunan fisik dan sosial tersebut semakin meluas, sehingga kebutuhan akan teknik monitoring yang efektif dan efisien menjadi sangat penting. Penginderaan jauh mampu melakukan pemantauan dan deteksi titik panas dalam rangka mitigasi dan pengendalian efek urban heat island dalam cakupan wilayah yang luas dengan waktu singkat. Penelitian ini berfokus pada kajian surface urban heat island (SUHI) yang bertujuan untuk mengetahui distribusi spasial intensitas SUHI di Kota Samarinda. Metode yang digunakan pada penelitian ini berupa integrasi teknik penginderaan jauh dan cloud computing pada Google Earth Engine menggunakan band termal citra Landsat 8 OLI/TIRS serta analisis statistik citra menggunakan Buffer Boundary Analysis untuk identifikasi potensi terjadinya SUHI di Kota Samarinda. Ekstraksi suhu permukaan diperoleh dari persamaan Planck yang diintegrasikan dengan koreksi atmosfer untuk koreksi emisivitas permukaan menggunakan syntax dengan bahasa Javacript pada Google Earth Engine. Hasil pengolahan menunjukkan adanya potensi SUHI dengan intensitas tinggi dengan nilai 3,001-6,000°C pada radius 5 km dari pusat kota dan intensitas semakin turun seiring radius yang semakin jauh dari pusat kota. Secara administratif, intensitas SUHI tertinggi relatif berada pada kecamatan Samarinda Kota, Samarinda Ilir, dan Samarinda Seberang dengan rentang intensitas SUHI sebesar 1,5001-6,000°C, sementara intensitas SUHI terendah relatif berada pada kecamatan Sungai Kunjang dan Palaran dengan rentang intensitas SUHI sebesar -10,000-1,500°C.

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CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE

Irriga ◽

10.15809/irriga.2020v25n1p160-169 ◽

2020 ◽

Vol 25 (1) ◽

pp. 160-169

Author(s):

Cesar De Oliveira Ferreira Silva

Keyword(s):

Remote Sensing ◽

Cloud Computing ◽

Supervised Classification ◽

Source Code ◽

Regression Trees ◽

Google Earth ◽

Landsat 8 ◽

Classification And Regression ◽

Google Earth Engine

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: [email protected]. 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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Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

Remote Sensing ◽

10.3390/rs12081279 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1279 ◽

Cited By ~ 1

Author(s):

Sosdito Mananze ◽

Isabel Pôças ◽

Mário Cunha

Keyword(s):

Cloud Computing ◽

Land Cover ◽

Google Earth ◽

Agricultural Landscapes ◽

Landsat 8 ◽

Textural Features ◽

Observation Data ◽

Ground Truth Data ◽

Spectral Bands ◽

Google Earth Engine

Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.

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Anomaly Detection of Outlier Features from Spatio-temporal Databases of Landsat-8 Sensor, using Cloud Computing Platform

Current Topics on Mathematics and Computer Science Vol. 2 ◽

10.9734/bpi/ctmcs/v2/3616d ◽

2021 ◽

pp. 134-140

Author(s):

C. Rajabhushanam

Keyword(s):

Cloud Computing ◽

Anomaly Detection ◽

Temporal Databases ◽

Landsat 8 ◽

Computing Platform ◽

Cloud Computing Platform ◽

Spatio Temporal

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Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data

Remote Sensing ◽

10.3390/rs11232881 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2881 ◽

Cited By ~ 3

Author(s):

Leandro Parente ◽

Evandro Taquary ◽

Ana Silva ◽

Carlos Souza ◽

Laerte Ferreira

Keyword(s):

Remote Sensing ◽

Cloud Computing ◽

Deep Learning ◽

Random Forest ◽

Short Term Memory ◽

Learning Algorithms ◽

Google Earth ◽

Landsat 8 ◽

Learning Approaches ◽

Validation Data

The rapid growth of satellites orbiting the planet is generating massive amounts of data for Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region. The deep learning algorithms were implemented using TensorFlow, while the random forest utilized the Google Earth Engine platform. The accuracy assessment presented F1 scores for U-Net, LSTM, and random forest of, respectively, 96.94%, 98.83%, and 95.53% in the validation data, and 94.06%, 87.97%, and 82.57% in the test data, indicating a better classification efficiency using the deep learning approaches. Although the use of deep learning algorithms depends on a high investment in calibration samples and the generalization of these methods requires further investigations, our results suggest that the neural network architectures developed in this study can be used to map large geographic regions that consider a wide variety of satellite data (e.g., PlanetScope, Sentinel-2, Landsat-8).

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Pemetaan Potensi Lahan Jagung Menggunakan Citra Satelit Dan Random Forest Pada Cloud computing Google Earth Engine

Seminar Nasional Official Statistics ◽

10.34123/semnasoffstat.v2021i1.889 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 1001-1011

Author(s):

Dwi Wahyu Triscowati ◽

Widyo Pura Buana ◽

Arif Handoyo Marsuhandi

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Random Forest ◽

Input Data ◽

Google Earth ◽

Landsat 8 ◽

Google Earth Engine ◽

Sentinel 2

Ketersediaan informasi potensi lahan jagung yang cepat terbaharui penting untuk mendukung pemulihan ekonomi pasca covid 19. Pemetaan jagung menjadi suatu tantangan tersendiri di bidang pertanian karena areal penanaman jagung tidak memiliki ciri khusus seperti sawah, jagung belum memiliki peta luas baku, serta penanamannya dapat dilakukan di sawah maupun lahan-lahan kering hutan. Permasalahan lainnya, perlu sumberdaya komputasi yang tinggi jika pemetaan jagung dilakukan secara langsung ataupun identifikasi secara manual. Dalam penelitian ini dilakukan pemetaan potensi jagung di Jawa Timur pada Kabupaten terpilih secara otomatis menggunakan Machine learning pada cloud computing google earth engine. Dengan penggunaan cloud computing GEE, pemetaan jagung dapat dilakukan pada area luas tanpa terkendala kemampuan komputer. Penelitian ini menggunakan algoritma pembelajaran mesin Random Forest(RF) berbasis piksel dengan input data dari satelit Landsat-8, Sentinel-1 dan Sentinel-2. Data referensi untuk melatih model klasifikasi menggunakan hasil KSA jagung. Akurasi hasil Machine learning paling baik berasal dari kombinasi Landsat-8 dan Sentinel-2 dengan rataan akurasi sebesar 0.79. Model klasifikasi kemudian diaplikasikan pada 10 Kabupaten dimana hasil terbaik adalah pada Kabupaten Banyuwangi dengan akurasi 0.89. Dilihat dari luas potensi jagung pada daerah Banyuwangi luasan berkisar dari 22.256,82 – 58.992,3 Ha berdasarkan pixel yang terprediksi sebagai jagung minimal 3 kali/bulan. Dari hasil kajian ini terbukti bahwa penggunaan cloud computing dapat melakukan penghitungan pada 10 Kabupaten secara cepat baik dari sisi pembangunan model maupun dari prediksinya. Selain itu karena menggunakan cloud computing pemanfaatan citra satelit dapat dimanfaatkan secepat mungking setelah citra satelit terbit/rilis sehingga prediksi dari potensi jagung dapat secara cepat dan tepat dihasilkan. Kajian ini juga menyoroti kekurangan yang terjadi yaitu dari sisi jumlah sampel untuk data latih dan keterbatasan algoritma yang digunakan sehingga kedepannya dapat dikembangkan lebih baik lagi.

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