Estimate crop type distribution in South Africa using Google Earth Engine cloud computing

2021 ◽  
Author(s):  
Zhe Guo ◽  
Andrew Sheng
Keyword(s):  
South Africa ◽  
Cloud Computing ◽  
Google Earth ◽  
Type Distribution ◽  
Crop Type ◽  
Google Earth Engine

scholarly journals Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

2017 ◽  
Vol 126 ◽  
pp. 225-244 ◽  
Author(s):  
Jun Xiong ◽  
Prasad S. Thenkabail ◽  
Murali K. Gumma ◽  
Pardhasaradhi Teluguntla ◽  
Justin Poehnelt ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Google Earth ◽  
Google Earth Engine ◽  
Cropland Mapping

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals DETECTION OF FLOOD IMPACTED AREAS IN EAST NUSA TENGGARA USING SENTINEL-1 IMAGERY

CI-TECH ◽  
2021 ◽  
Vol 2 (01) ◽  
pp. 37-41
Author(s):  
Bagas Aryaseta
Keyword(s):  
Cloud Computing ◽  
Tropical Cyclone ◽  
Data Processing ◽  
High Intensity ◽  
Flash Floods ◽  
Google Earth ◽  
Classification Method ◽  
Small Signal ◽  
Pixel Value ◽  
Google Earth Engine

Flash Floods in East Nusa Tenggara occured on April 4th, 2021. These Flash Floods are scattered from East Flores Regency, Lembata Regency, Alor Regency, Malacca Regency, Sabu Raijua Regency, Kupang City, Kupang Regency, and Ende Regency. The cause of these Flash Floods is the high intensity of rain caused by the tropical cyclone Seroja. Mapping of flood locations plays an important role in prevention and mitigation efforts. In this study, InSAR data processing was carried out from the Sentinel 1A satellite to find flood-affected locations in East Nusa Tenggara. 32 images of Sentinel-1 were processed before and 31 images after the Flash Floods incident. The method used is the classification method using cloud computing, Google Earth Engine. The results show that the flood-affected areas can be detected based on a lower pixel value (indicating a very small signal backscatter value), then compared to the conditions before the flood. The four sample points identified, namely points A, B, C, and D each have pixel values ​​of -8.58, -9.99, -12.43, and -9.29 for the VV polarized image, respectively. For VH polarized image is -17.35, -17.96, -17.84, and -14.22, respectively.

ANALISIS SURFACE URBAN HEAT ISLAND MENGGUNAKAN TEKNIK PENGINDERAAN JAUH BERBASIS CLOUD COMPUTING PADA GOOGLE EARTH ENGINE DI KOTA SAMARINDA

2021 ◽  
pp. 75
Author(s):  
Bayu Elwantyo Bagus Dewantoro ◽  
Pavita Almira Natani ◽  
Zumrotul Islamiah
Keyword(s):  
Cloud Computing ◽  
Urban Heat Island ◽  
Google Earth ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Boundary Analysis ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Google Earth Engine

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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