DETEKSI SEBARAN MUATAN PADATAN TERSUSPENSI DENGAN MODEL  EMPIRIS DAN MODEL SEMI-ANALITIK DI PERAIRAN BEKASI

Salah satu parameter dalam kualitas air adalah muatan padatan tersuspensi (MPT). Muatan padatan tersuspensi terdiri dari lumpur, pasir dan jasad renik yang disebabkan pengikisan tanah yang terbawa ke badan air. Penelitian ini bertujuan untuk mendeteksi sedimen tersuspensi di perairan Bekasi. Landsat 8 digunakan untuk analisis padatan tersuspensi dengan platform Google Earth Engine dengan membandingkan antara model empiris dan semi-analitik. Alur studi ini meliputi deliniasi wilayah non air menggunakan data citra surface reflectance, analisis MPT, dan visualisasi. Selanjutnya dilakukan validasi dengan data in situ, pemilihan model dan implementasi time series. Hasil deteksi MPT tertampil dengan tampilan warna yang berbeda sesuai dengan konsentrasinya. Hasil uji validasi dengan data in situ menunjukkan nilai Normalized Mean Absolute Error (NMAE) model semi-analitik lebih mendekati syarat minimum yaitu sebesar 66,8%, berbeda jauh dengan model empiris sebesar 43768%. Nilai Root Mean Square Error (RMSE) pun terlihat bahwa model semi-analitik menghasilkan nilai yang jauh lebih kecil sebesar 51,4 dan model empiris sebesar 58577,2. Hal ini menunjukkan bahwa model semi-analitik memiliki nilai yang lebih baik dalam mendeteksi sebaran MPT. Analisis time series menunjukkan bahwa persebaran MPT tahun 2015 – 2019 di perairan pesisir memiliki sebaran MPT yang sangat tinggi, karena banyaknya tambak dan muara sungai. Oleh karena itu, model semi-analitik lebih direkomendasikan untuk mengestimasi konsentrasi MPT dibandingkan dengan model empiris.

Download Full-text

Monitoring of water table dynamics in peatlands with OPTRAM: Towards globally applicable algorithms in Google Earth Engine using Landsat and Sentinel-2

10.5194/egusphere-egu21-4698 ◽

2021 ◽

Author(s):

Iuliia Burdun ◽

Michel Bechtold ◽

Viacheslav Komisarenko ◽

Annalea Lohila ◽

Elyn Humphreys ◽

...

Keyword(s):

Time Series ◽

Water Table ◽

Input Data ◽

Short Wave ◽

Google Earth ◽

Google Earth Engine ◽

Short Wave Infrared ◽

Northern Peatlands ◽

Sentinel 2

Fluctuations of water table depth (WTD) affect many processes in peatlands, such as vegetation development and emissions of greenhouse gases. Here, we present the OPtical TRApezoid Model (OPTRAM) as a new method for satellite-based monitoring of the temporal variation of WTD in peatlands. OPTRAM is based on the response of short-wave infrared reflectance to the vegetation water status. For five northern peatlands with long-term in-situ WTD records, and with diverse vegetation cover and hydrological regimes, we generate a suite of OPTRAM index time series using (a) different procedures to parametrise OPTRAM (peatland-specific manual vs. globally applicable automatic parametrisation in Google Earth Engine), and (b) different satellite input data (Landsat vs. Sentinel-2). The results based on the manual parametrisation of OPTRAM indicate a high correlation with in-situ WTD time-series for pixels with most suitable vegetation for OPTRAM application (mean Pearson correlation of 0.7 across sites), and we will present the performance differences when moving from a manual to an automatic procedure. Furthermore, for the overlap period of Landsat and Sentinel-2, which have different ranges and widths of short-wave infrared bands used for OPTRAM calculation, the impact of the satellite input data to OPTRAM will be analysed. Eventually, the challenge of merging different satellite missions in the derivation of OPTRAM time series will be explored as an important step towards a global application of OPTRAM for the monitoring of WTD dynamics in northern peatlands.

Download Full-text

BUILDING OF TEMPERATURE MAPS OF NOVOSIBIRSK CITY BASED ON LANDSAT 8 DATA

Interexpo GEO-Siberia ◽

10.33764/2618-981x-2021-4-1-52-59 ◽

2021 ◽

Vol 4 (1) ◽

pp. 52-59

Author(s):

Elena A. Mamash ◽

Igor A. Pestunov ◽

Dmitrii L. Chubarov

Keyword(s):

Time Series ◽

Satellite Data ◽

Google Earth ◽

Underlying Surface ◽

Landsat 8 ◽

Google Earth Engine ◽

Engine System ◽

Temperature Maps

An algorithm for constructing temperature maps of the underlying surface based on a multi-time series of atmospheric corrected satellite data from Landsat 8, implemented in the Google Earth Engine system, is presented. The results of the construction of temperature maps of Novosibirsk using this algorithm are discussed.

Download Full-text

Fractional differentiation and its use in machine learning

International Journal of Advances in Engineering Sciences and Applied Mathematics ◽

10.1007/s12572-021-00299-5 ◽

2021 ◽

Author(s):

Rafał Walasek ◽

Janusz Gajda

Keyword(s):

Machine Learning ◽

Time Series ◽

Stock Prices ◽

Predictive Power ◽

Mean Absolute Error ◽

Real Data ◽

Absolute Error ◽

Mean Square ◽

Fractional Differentiation ◽

Stock Indexes

AbstractThis article covers the implementation of fractional (non-integer order) differentiation on real data of four datasets based on stock prices of main international stock indexes: WIG 20, S&P 500, DAX and Nikkei 225. This concept has been proposed by Lopez de Prado [5] to find the most appropriate balance between zero differentiation and fully differentiated time series. The aim is making time series stationary while keeping its memory and predictive power. In addition, this paper compares fractional and classical differentiation in terms of the effectiveness of artificial neural networks. Root mean square error (RMSE) and mean absolute error (MAE) are employed in this comparison. Our investigations have determined the conclusion that fractional differentiation plays an important role and leads to more accurate predictions in case of ANN.

Download Full-text

Trends of CO and NO2 Pollutants Change in Iran during Covid-19 Pandemic using Time-Series Sentinel-5 Images in Google Earth Engine

10.21203/rs.3.rs-773367/v1 ◽

2021 ◽

Author(s):

Siavash Shami ◽

Babak Ranjgar ◽

Mahdi Khoshlahjeh Azar ◽

Armin Moghimi ◽

Samaneh Sabetghadam ◽

...

Keyword(s):

Time Series ◽

Environmental Pollutants ◽

Google Earth ◽

First Year ◽

Industrial Activities ◽

Global Pandemic ◽

Google Earth Engine ◽

Second Year ◽

The Government

Abstract The first cases of Covid-19 in Iran were reported shortly after the disease outbreak in Wuhan, China. The end of the Persian year and the beginning of the Nowruz holidays in the following year (March 2020) coincided with its global pandemic, which led to quarantine and lockdown in the country. Many studies have shown that with the spread of this disease and the decline of industrial activities, environmental pollutants were drastically reduced. Among these pollutants, Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes of these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three time periods from March 11 to April 8 of 2019, 2020, and 2021 were investigated. To this end, time-series of the Sentinel-5P TROPOMI and in-situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results obtained from the satellite data were in agreement with the in-situ data (average correlation coefficient = 0.7). Moreover, the results showed that the concentration of NO2 and CO pollutants in 2020 (the first year of the Covid-19 pandemic) was 5% lower than in 2019, indicating the observance of quarantine rules as well as people’s initial fear of the Coronavirus. Contrarily, these pollutants in 2021 (the second year of the Covid-19 pandemic) were higher than those in 2020 by 5%, which could be due to high vehicle traffic and the lack of serious policy and law-making by the government to ban urban and interurban traffic. Furthermore, the increase of the NO2 and CO in 2021 was followed by an increase in the deaths caused by Covid-19 and triggering the fourth peak in the Covid-19 cases, signifying a link between exposure to air pollution and Covid-19 mortality in Iran.

Download Full-text

Drought Monitoring in Gresik Regency East Java using Satellite Image Time Series and Google Earth Engine

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/936/1/012003 ◽

2021 ◽

Vol 936 (1) ◽

pp. 012003

Author(s):

Rosmalisa Dwiyaniek ◽

Bangun Muljo Sukojo ◽

Filsa Bioresita

Keyword(s):

Time Series ◽

Satellite Image ◽

Data Retrieval ◽

Google Earth ◽

Landsat 8 ◽

Severe Drought ◽

Dry Land ◽

Aerial Photos ◽

Two Factors ◽

Google Earth Engine

Abstract Gresik is one of the areas with severe drought levels in East Java. This drought disaster caused by low rainfall and the high average surface temperature in an area. These two factors are currently difficult to predict due to uncertain climate change, this is also related to the global warming that is happening. This disaster cannot be completely avoided but can be minimized. This research was conducted to periodically check or time series of droughts by utilizing the Google Earth Engine platform. Drought identification obtained from multitemporal Landsat 8 satellite imagery with the TVDI (Temperature Vegetation Dryness Index) algorithm and field data retrieval in the form of aerial photos using a thermal camera from the DJI Mavic Enterprise Dual Thermal. From this study, it can be monitoring the distribution of drought in the 2015-2020 period in Gresik Regency occurred in 9 sub-districts, there are Wringinanom, Driyorejo, Kedamean, Balongpanggang, Benjeng, Menganti sub-districts as well as several areas in Duduksampeyan, Cerme and Panceng sub-districts. The identification of dry land also correlates well with the rainfall that occurs, namely?100 mm/month, which has low rainfall during drought events.

Download Full-text

Implemetasi Model Autoregressive (AR) Dan Autoregressive Conditional Heteroskedasticity (ARCH) Untuk Memprediksi Harga Emas

Indonesian Journal on Computing (Indo-JC) ◽

10.21108/indojc.2018.3.2.225 ◽

2018 ◽

Vol 3 (2) ◽

pp. 29

Author(s):

Ni Luh Ketut Dwi Murniati ◽

Indwiarti Indwiarti ◽

Aniq Atiqi Rohmawati

Keyword(s):

Time Series ◽

Maximum Likelihood ◽

Mean Absolute Error ◽

Likelihood Estimation ◽

Absolute Error ◽

Gold Price ◽

Conditional Heteroskedasticity ◽

Mean Square ◽

Basic Information ◽

Estimation Of Parameters

Gold is a one of high selling value items in the market, and it can be used as an investment item. The price of gold in the market tends to be stable and not undergoing too significant changes which makes gold be a very valuable item. The aim of this research is to predict gold price using AR (1) and ARCH (1) model which are the part of time series methods. The data of gold price is obtained from ANTAM's daily historical website from 2007 - 2017. Here, the basic information about data is given by using descriptive statistic and the estimation of parameters in each model is condacted by using Maximum Likelihood Estimation (MLE). To evaluate the model, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used. In this research, the estimated model of AR (1) and ARCH (1) given as X_t = -0.012X_{t-1}+epsion_t and X_t = epsilon_t sqrt{0.000053+0.011958X^2_{t-1}} respectively. Moreover, the result of MAE and RMSE using AR (1) model are 0.0261 and 0.0342 respectively, meanwhile for ARCH (1) model are 0.0170 and 0.0251 respectively.

Download Full-text

30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

Remote Sensing ◽

10.3390/rs11050489 ◽

2019 ◽

Vol 11 (5) ◽

pp. 489 ◽

Cited By ~ 25

Author(s):

Tengfei Long ◽

Zhaoming Zhang ◽

Guojin He ◽

Weili Jiao ◽

Chao Tang ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Google Earth ◽

Burned Area ◽

Landsat 8 ◽

Landsat Images ◽

Burned Areas ◽

Random Decision Forests ◽

Burned Area Mapping ◽

Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

Download Full-text

Landsat 8 OLI Broadband Albedo Validation in Antarctica and Greenland

Remote Sensing ◽

10.3390/rs13040799 ◽

2021 ◽

Vol 13 (4) ◽

pp. 799

Author(s):

Giacomo Traversa ◽

Davide Fugazza ◽

Antonella Senese ◽

Massimo Frezzotti

Keyword(s):

Narrow Band ◽

Mean Absolute Error ◽

Absolute Error ◽

Landsat 8 ◽

Polar Regions ◽

Landsat 8 Oli ◽

Anomalous Data ◽

The Antarctic ◽

Good Agreement

The albedo is a fundamental component of the processes that govern the energy budget, and particularly important in the context of climate change. However, a satellite-based high-resolution (30 m) albedo product which can be used in the polar regions up to 82.5° latitude during the summer seasons is lacking. To cover this gap, in this study we calculate satellite-based broadband albedo from Landsat 8 OLI and validate it against broadband albedo measurements from in situ stations located on the Antarctic and Greenland icesheets. The model to derive the albedo from raw satellite data includes an atmospheric and topographic correction and conversion from narrow-band to broadband albedo, and at each step different options were taken into account, in order to provide the best combination of corrections. Results, after being cleaned from anomalous data, show a good agreement with in situ albedo measurements, with a mean absolute error between in situ and satellite albedo of 0.021, a root mean square error of 0.026, a standard deviation of 0.015, a correlation coefficient of 0.995 (p < 0.01) and a bias estimate of −0.005. Considering the structure of the model, it could be applied to data from previous sensors of the Landsat family and help construct a record to analyze albedo variations in the polar regions.

Download Full-text

Trends of CO and NO2 Pollutants Change in Iran during Covid-19 Pandemic using Time-Series Sentinel-5 Images in Google Earth Engine

10.21203/rs.3.rs-773367/v2 ◽

2021 ◽

Author(s):

Siavash Shami ◽

Babak Ranjgar ◽

Mahdi Khoshlahjeh Azar ◽

Armin Moghimi ◽

Samaneh Sabetghadam ◽

...

Keyword(s):

Time Series ◽

Environmental Pollutants ◽

Google Earth ◽

Average Correlation ◽

Industrial Activities ◽

In Situ Data ◽

Global Pandemic ◽

Google Earth Engine ◽

The Government

Abstract The end of the Persian year (March 2020) coincided with its global pandemic, which led to quarantine and lockdown in Iran. Many studies have shown that with the spread of this disease and the decline of industrial activities, environmental pollutants were drastically reduced. Among these pollutants, Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes of these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three time periods from March 11 to April 8 of 2019, 2020, and 2021 were investigated. To this end, time-series of the Sentinel-5P TROPOMI and in-situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results obtained from the satellite data were in agreement with the in-situ data (average correlation coefficient =0.7). Moreover, the concentration of NO 2 and CO pollutants in 2020 was 5% lower than in 2019, indicating the observance of quarantine rules as well as people’s initial fear of the Coronavirus. Contrarily, these pollutants in 2021 were higher than those in 2020 by 5%, which could be due to high vehicle traffic and the lack of serious policy by the government to ban urban and interurban traffic. Furthermore, the increase of these pollutants in 2021 was followed by an increase in the deaths caused by Covid-19 and triggering the fourth peak in the Covid-19 cases, signifying a link between exposure to air pollution and Covid-19 mortality in Iran.

Download Full-text

UJI AKURASI PRODUK ESTIMASI CURAH HUJAN SATELIT GPM IMERG DI SURABAYA, INDONESIA

Jurnal Sains & Teknologi Modifikasi Cuaca ◽

10.29122/jstmc.v19i2.3153 ◽

2018 ◽

Vol 19 (2) ◽

pp. 83

Author(s):

Mukhamad Adib Azka ◽

Prabu Aditya Sugianto ◽

Andreas Kurniawan Silitonga ◽

Imma Redha Nugraheni

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Absolute Error ◽

Absolute Error ◽

Mean Square

Curah hujan merupakan parameter meteorologi yang sangat berpengaruh dalam kehidupan. Saat ini, pengamatan secara in situ sangat kurang representatif untuk digunakan sebagai analisis karena jangkauannya yang sangat sempit sehingga memerlukan instrumen pendukung seperti satelit agar dapat memberikan gambaran yang lebih baik terkait distribusi hujan. Namun, data satelit juga belum tentu sepenuhnya benar karena resolusi dan kondisi dari setiap wilayah berbeda. Penelitian ini bertujuan untuk mendapatkan nilai akurasi, bias, korelasi, root mean square error (RMSE), dan mean absolute error (MAE) data estimasi curah hujan GPM IMERG dengan data curah hujan pengamatan langsung. Penelitian ini dilakukkan di Surabaya dengan menggunakan data estimasi curah hujan GPM IMERG dan data curah hujan pengamatan langsung dari Stasiun Meteorologi Kelas I Juanda Surabaya selama tahun 2017 mewakili musim hujan, musim kemarau, dan periode transisi. Hasil penelitian menunjukkan bahwa data curah hujan produk GPM IMERG memiliki korelasi yang sangat baik untuk memperkirakan akumulasi curah hujan bulanan. Sedangkan, untuk akumulasi harian, memiliki korelasi yang sangat rendah. Sementara itu untuk akumulasi sepuluh harian, data curah hujan produk satelit GPM IMERG memiliki korelasi yang baik terutama di periode musim hujan dan musim kemarau, akan tetapi memiliki korelasi yang rendah selama periode transisi dari musim hujan ke musim kemarau atau sebaliknya. Pada umumnya, produk ini sangat bagus dalam menentukan ada atau tidaknya hujan, tetapi performanya sangat rendah dalam menentukan besarnya intensitas curah hujan.

Download Full-text