scholarly journals Monitoring Seawater Quality in the Kali Porong Estuary as an Area for Lapindo Mud Disposal leveraging Google Earth Engine

2021 ◽  
Vol 936 (1) ◽  
pp. 012011
Author(s):  
Filsa Bioresita ◽  
Muhammad Hidayatul Ummah ◽  
Mega Wulansari ◽  
Nabilla Aprillia Putri
Keyword(s):  
Water Quality ◽  
Cloud Computing ◽  
Chlorophyll A ◽  
Trophic Status ◽  
Image Data ◽  
Google Earth ◽  
Correlation Test ◽  
Google Earth Engine ◽  
Test Result ◽  
Sentinel 2

Abstract The hot mudflow released by Lapindo Mud Volcano periodically requires a large storage space. It is resulting the change in the main function of Kali Porong which is the channel for mud to the river mouth. This causes changes in water quality at Kali Porong estuary. The purpose of this study was to monitor water quality at Kali Porong estuary using Sentinel 2 image data with Total Suspended Solid (TSS) and chlorophyll-a analysis. Cloud computing technology can process image data into useful information. One of the open source cloud computing platforms is Google Earth Engine (GEE). In this platform, there is a database for storing satellite image data, including Sentinel-2. In addition to storing remote sensing data, GEE can process images quickly using the Java scripting language. In this study, monitoring was carried out in February-June 2021. The results show the average value of chlorophyll-a each month from February to June was 2.78 μg/m3, 2.76 μg/m3, 2.74 μg/m3, 2.98 μg/m3, and 3.2 mg/l. The average monthly TSS values from February to June were 16.11 μg/m3, 15.91 μg/m3, 15.76 μg/m3, 17.45 μg/m3, and 19.86 μg/m3, respectively. The correlation test result for chlorophyll-a estimation is 0.654. In the other hand, the correlation test result for the estimated TSS is 0.652. The trophic status of the waters at Kali Porong estuary is in the eutrophic class or has been polluted. The results show a tendency for the area with polluted trophic status to increase from February to June.

scholarly journals WATER QUALITY MONITORING OVER FINGER LAKES REGION USING SENTINEL-2 IMAGERY ON GOOGLE EARTH ENGINE CLOUD COMPUTING PLATFORM

2021 ◽  
Vol V-3-2021 ◽  
pp. 279-283
Author(s):  
R. M. Khan ◽  
B. Salehi ◽  
M. Mahdianpari ◽  
F. Mohammadimanesh
Keyword(s):  
Water Quality ◽  
Cloud Computing ◽  
Agricultural Runoff ◽  
Google Earth ◽  
Computing Platform ◽  
Finger Lakes ◽  
Cloud Computing Platform ◽  
Google Earth Engine ◽  
Sentinel 2 ◽  
Finger Lakes Region

Abstract. Surface water quality is degrading continuously both due to natural and anthropogenic causes. There are several indicators of water quality, among which sediment loading is mainly determined by turbidity. Normalized Difference Water Index (NDWI) is one indirect measure of sediments present in water. This study focuses on detecting and monitoring sediments through NDWI over the Finger Lakes region, New York. Time series analysis is performed using Sentinel 2 imagery on the Google Earth Engine (GEE) platform. Finger Lakes region holds high socio-economic value because of tourism, water-based recreation, industry, and agriculture sector. The deteriorating water quality within the Finger Lake region has been reported based on ground sampling techniques. This study takes advantage of a cloud computing platform and medium resolution atmospherically corrected satellite imagery to detect and analyse water quality through sediment detection. In addition, precipitation data is used to understand the underlying cause of sediment increase. The results demonstrate the amount of sediments is greater in the early spring and summer months compared to other seasons. This can be due to the agricultural runoff from the nearing areas as a result of high precipitation. The results confirm the necessity for monitoring the quality of these lakes and understanding the underlying causes, which are beneficial for all the stakeholders to devise appropriate policies and strategies for timely preservation of the water quality.

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

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.

Use of Sentinel-2A/B satellites and Google Earth Engine for monitoring estuarine systems: a study case in the Western Mediterranean

2021 ◽  
Author(s):  
Mar Roca Mora ◽  
Gabriel Navarro Almendros ◽  
Javier García Sanabria ◽  
Isabel Caballero de Frutos
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Western Mediterranean ◽  
Coastal Areas ◽  
Google Earth ◽  
Local Data ◽  
Sensing Applications ◽  
Google Earth Engine ◽  
Sentinel 2A ◽  
Sentinel 2

<p>Coastal areas are being rapidly transformed in the last 50 years due to anthropogenic causes. New infrastructures and intensive activities have changed the natural behaviour of coastal ecosystems, promoting problems related to water quality, eutrophication and coastal erosion. This situation increases the vulnerability to climate change, requiring important efforts in monitoring and defining protocols for optimizing operational decision-making and strategic management. Remote sensing techniques are becoming a key tool for coastal mapping in terms of resolution, effectiveness and cost reduction. In the last decade, the European Commission launched the Copernicus programme for Earth Observation as a way of improving coastal monitoring with higher resolution. Sentinel-2A/B twin satellites are part of this free and open policy programme available since 2015, but atmospheric corrections or cloud cover are still challenges to face. In order to process this data, cloud computing platforms such as Google Earth Engine (GEE) have revolutionized the way satellite images are processed, without the need to download and store local data. The present study aimed at developing a GEE-based technique for selecting cloud-free Sentinel-2 Level-2A images in the Guadiaro estuary in the Western Mediterranean (Spain) during the last four years (2017-2020).  It has been used to analyse the evolution of the sand bar and to identify hotspots in its sedimentary variation along the coast, at 10 m and 5 days spatial and temporal resolution respectively. NDWI index was evaluated using 0.05 to 0.15 threshold, revealing 0.1 as the best threshold to be used for land/water mapping, easily incorporated in the GEE platform. In addition to Sentinel-2 potential, this study also demonstrates the power of GEE, computing more than 400 images for statistical analysis in terms of seconds, which enabled the automatic filtering method developed for cloud-free images selection with a 95% of effectiveness. Moreover, ACOLITE processor has been used on Sentinel-2 L1A images for atmospheric and sunglint correction to generate Level-2 data and for analysing turbidity and water quality patterns during extreme rainfall events, providing key information as early-warning indicators development. This improvement will be useful for near future implementation of remote sensing applications for coastal managers, ensuring a continuous and detailed monitoring and helping to support an ecosystem-based approach for coastal areas.</p>

scholarly journals Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

2021 ◽  
Vol 11 (9) ◽  
pp. 4258
Author(s):  
Jordan R. Cissell ◽  
Steven W. J. Canty ◽  
Michael K. Steinberg ◽  
Loraé T. Simpson
Keyword(s):  
Google Earth ◽  
Mangrove Forests ◽  
Random Forest Classification ◽  
High Resolution Mapping ◽  
Mangrove Ecosystems ◽  
Forest Classification ◽  
Resolution Mapping ◽  
Google Earth Engine ◽  
Terrestrial Biodiversity ◽  
Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

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

ANALISIS TEMPORAL PERUBAHAN HUTAN MANGROVE MENGGUNAKAN CITRA SATELIT SENTINEL-2 (Studi Kasus di Pulau Tanakeke, Kabupaten Takalar)

2021 ◽  
pp. 777
Author(s):  
Andi Tenri Waru ◽  
Athar Abdurrahman Bayanuddin ◽  
Ferman Setia Nugroho ◽  
Nita Rukminasari
Keyword(s):  
Random Forest ◽  
Google Earth ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Sentinel 2

Pulau Tanakeke merupakan salah satu pulau dengan hutan mangrove yang luas di pesisir Sulawesi Selatan. Hutan mangrove ini menjadi ekosistem penting bagi masyarakat sekitar karena nilai ekologi maupun ekonominya. Namun, dalam kurun waktu sekitar tahun 1980-2000, keberadaan mangrove tersebut terancam oleh perubahan penggunaan lahan dan juga pemanfaatan yang berlebihan. Penelitian ini bertujuan untuk menganalisis perubahan temporal luas dan tingkat kerapatan hutan mangrove di Pulau Tanakeke antara tahun 2016 dan 2019. Metode analisis perubahan luasan hutan mangrove menggunakan data citra satelit Sentinel-2 multi temporal berdasarkan hasil klasifikasi hutan mangrove dengan menggunakan random forest pada platform Google Earth Engine. Akurasi keseluruhan hasil klasifikasi hutan mangrove tahun 2016 dan 2019 sebesar 91% dan 98%. Berdasarkan hasil analisis spasial diperoleh perubahan penurunan luasan mangrove yang signifikan dari 800,21 ha menjadi 640,15 ha. Kerapatan mangrove di Pulau Tanakeke sebagian besar tergolong kategori dalam kerapatan tinggi.

Wetland Mapping of Northern Provinces of Iran Using Sentinel-1 and Sentinel-2 in Google Earth Engine

2021 ◽  
Author(s):  
Mohammad Ali Hemati ◽  
Mahdi Hasanlau ◽  
Masaud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Google Earth ◽  
Wetland Mapping ◽  
Google Earth Engine ◽  
Sentinel 2
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