Detecting the brightness temperature from Landsat-8 thermal infra red scanner preceding the Rinjani strombolian eruption 2015

2017 ◽  
Author(s):  
Suwarsono ◽  
Hidayat ◽  
Totok Suprapto ◽  
Indah Prasasti ◽  
Parwati ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Landsat 8 ◽  
Strombolian Eruption ◽  
Infra Red

scholarly journals ANALISIS KARAKTERISTIK TEMPERATUR AREA TERBAKAR (BURNED AREA) MENGGUNAKAN DATA LANDSAT-8 TIRS DI KALIMANTAN

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
. Suwarsono ◽  
Any Zubaidah ◽  
. Parwati ◽  
M. Rokhis Khomarudin
Keyword(s):  
Brightness Temperature ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Burned Area ◽  
Landsat 8 ◽  
Burnt Area ◽  
Burned Areas ◽  
Good Ability ◽  
Infra Red ◽  
D Value

Biomass burning in an area will leave traces of fire such as charcoal, ash, and outcrop of land in the area known as the burned area. The burnt area is thought to have a relatively higher temperature than the surrounding area were not burned. This study aims to determine the characteristics of the temperature of the burned area using remote sensing data of Landsat-8 TIRS (Thermal Infra Red Sensor). The selected research locations are parts of Central Kalimantan and South Kalimantan incoming Landsat scene-8 path / row 118/062. The research method is a data processing Landsat-8 TIRS (channels 10 and 11) to produce an image of the brightness temperature as well as data analysis includes a statistical analysis of central tendency of the values of the brightness temperature of the sample (calculation of mean and standard deviation) as well as distance calculation (D-value). The results showed that the brightness temperature data either channel 10 or channel 11 Landsat-8 TIRS has good ability in separating the burned area and bare soil, but has a low ability to separate the burned areas and settlements. Thus, the brightness temperature parameter cannot be used as a single variable for the extraction of burned areas in a scene image of a single acquisition. Abstrak Peristiwa kebakaran biomassa pada suatu daerah akan menyisakan bekas-bekas kebakaran seperti arang, abu, serta singkapan tanah pada daerah tersebut yang dikenal dengan burned area. Daerah bekas kebakaran tersebut diduga memiliki temperatur yang relatif lebih tinggi dibandingkan dengan daerah sekitarnya yang tidak terbakar. Penelitian ini bertujuan untuk mengetahui karakteristik temperatur burned area menggunakan data penginderaan jauh Landsat-8 Thermal Infra Red Sensor (TIRS). Lokasi penelitian yang dipilih adalah sebagian wilayah Kalimantan Tengah dan Kalimantan Selatan yang masuk scene Landsat-8 path/row 118/062. Metode penelitian yang dilakukan adalah pengolahan data Landsat-8 TIRS (kanal 10 dan 11) untuk menghasilkan citra suhu kecerahan serta analisis data yang meliputi analisis statistik tendensi sentral dari nilai-nilai suhu kecerahan dari sampel (perhitungan rerata dan standar deviasi) serta perhitungan jarak (D-value). Hasil penelitian menunjukkan bahwa data suhu kecerahan baik kanal 10 maupun kanal 11 Landsat-8 TIRS memiliki kemampuan yang baik dalam memisahkan burned area dan lahan terbuka, namun memiliki kemampuan yang rendah untuk memisahkan burned area dan permukiman. Dengan demikian, parameter suhu kecerahan belum bisa dipergunakan sebagai variabel tunggal untuk ekstraksi burned area pada suatu scene citra perekaman tunggal.

scholarly journals Komparasi Kemampuan Citra Satelit Landsat Dalam Mengidentifikasi Suhu Permukaan Daratan Di Kota Pekalongan

2020 ◽  
Author(s):  
Trida Ridho Fariz ◽  
Tjaturahono Budi Sanjoto ◽  
Dewi Liesnoor Setyowati
Keyword(s):  
Brightness Temperature ◽  
Landsat 8 ◽  
Landsat 7

Kajian pemetaan suhu permukaan daratan (LST) berbasis citra Landsat sudah sering dilakukan di Indonesia. Tetapi kajian yang membandingkan kemampuan citra satelit Landsat-7 dan Landsat-8 masih jarang dilakukan. Padahal kedua saluran termal pada citra satelit Landsat-7 dan Landsat-8 memiliki kelebihan dan kekurangan masing-masing, sehingga perlu dilakukan kajian untuk membandingkan kemampuan kedua citra satelit tersebut. Penelitian ini bertujuan untuk membandingkan kemampuan band termal antara citra satelit Landsat 7 dengan citra satelit Landsat 8 hanya untuk identifikasi LST, selain itu juga mengetahui perubahannya secara temporal.Data yang digunakan dalam penelitian ini adalah citra satelit Landsat 7 dan Landsat 8. Tahapan analisis data dimulai dengan pengolahan citra satelit untuk suhu perukaan daratan yang terdiri dari kalibrasi radian, koreksi atmosferik, konversi brightness temperature lalu diakhiri dengan konversi suhu permukaan daratan. Setiap peta suhu permukaan daratan dianalisis statistik berupa regresi linier dengan data suhu permukaan daratan hasil pengukuran dilapangan.Hasil penelitian ini menunjukkan bahwa citra satelit Landsat 8 cenderung lebih baik dalam memetakan LST di Kota Pekalongan. Citra satelit Landsat 8 juga digunakan untuk mengidentifikasi perubahan LST di Kota Pekalongan. Kota Pekalongan dalam kurun tahun 2015 sampai 2019 telah terjadi peningkatan suhu sekitar 0,60C. Wilayah yang menngalami perubahan suhu terbsar adalah Kecamatan Pekalongan Selatan.

scholarly journals RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

Estimation of Sea Surface Temperature (SST) Using Split Window Methods for Monitoring Industrial Activity in Coastal Area

2017 ◽  
Vol 862 ◽  
pp. 90-95 ◽  
Author(s):  
Agung Budi Cahyono ◽  
Dian Saptarini ◽  
Cherie Bhekti Pribadi ◽  
Haryo Dwito Armono
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Coastal Development ◽  
Sea Surface ◽  
Distribution Area ◽  
Landsat 8 ◽  
Remote Sensing Technology ◽  
Sensing Technology

The three drivers of environmental change: climate change, population growth and economic growth, result in a range of pressures on our coastal environment. Coastal development for industry and farming are a major pressure on terrestrial and environmental quality. In their process most of industry using sea water as cooling water. When water used as a coolant is returned to the natural environment at a higher temperature, the change in temperature decreases oxygen supply and affects marine ecosystem. This research is presents results from ongoing study on application of Landsat 8 for monitoring the intensity and distribution area of sea surface temperature changed by the heated effluent discharge from the power plant on Paiton coast, Probolinggo, East Java province. Remote sensing technology using a thermal band in Operational Land Imager (OLI) sensor of Landsat 8 sattelite imagery (band 10 and band 11) are used to determine the intensity and distribution of temperature changes. Estimation of sea surface temperature (SST) using remote sensing technology is applied to provide ease of marine temperature monitoring with a large area coverage. The method used in this research using the Split Window Algorithm (SWA) methods which is an algorithm with ability to perform extraction of sea surface temperature (SST) with brigthness temperature (BT) value calculation on the band 10 and band 11 of Landsat 8. Formula which was used in this area is Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (Ts is the surface temperature value (°C), BT10 is the brightness temperature value (°C) Band 10, BT11 is the brightness temperature value (°C) Band 11. The result of this algorithm shows the good performance with Root Mean Square Error (RMSE) amount 0.406.

scholarly journals SPATIOTEMPORAL FUSION OF HIGH RESOLUTION LAND SURFACE TEMPERATURE USING THERMAL SHARPENED IMAGES FROM REGRESSION-BASED URBAN INDICES

2020 ◽  
Vol V-3-2020 ◽  
pp. 247-254
Author(s):  
M. Kim ◽  
K. Cho ◽  
H. Kim ◽  
Y. Kim
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Thermal Images ◽  
Urban Regions

Abstract. Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied to enhance spatial and temporal resolutions simultaneously, but most STF approaches for the generation of Land Surface Temperature (LST) have not focused specifically on urban regions. This study therefore proposes a two-phase approach using Landsat 8 and MODIS images acquired on a study area in Beijing to first, investigate the sharpening of the fine resolution image input with urban-related spectral indices and second, to explore the potential of implementing the sharpened results into the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature Mapping (SADFAT) to generate high spatiotemporal resolution LST images in urban areas. For this test, five urban indices were selected based on their correlation with brightness temperature. In the thermal sharpening phase, the Fractional Urban Cover (FUC) index was able to delineate spatial details in urban regions whilst maintaining its correlation with the original brightness temperature image. In the STF phase however, FUC sharpened results returned relatively high levels of correlation coefficient values up to 0.689, but suffered from the highest Root Mean Squared Error (RMSE) and Average Absolute Difference (AAD) values of 4.260 K and 2.928 K, respectively. In contrast, Normalized Difference Building Index (NDBI) sharpened results recorded the lowest RMSE and AAD values of 3.126 K and 2.325 K, but also the lowest CC values. However, STF results were effective in delineating fine spatial details, ultimately demonstrating the potential of using sharpened urban or built-up indices as a means to generate sharpened thermal images for urban areas, as well as for input images in the SADFAT algorithm. The results from this study can be used to further improve STF approaches for daily and spatially continuous mapping of LST in urban areas.

scholarly journals PERABIAKAN ALGORITMA SUHU PERMUKAAN LAUT LANDSAT 8 UNTUK PERAIRAN PONELO

2018 ◽  
Vol 5 (2) ◽  
pp. 84
Author(s):  
Romansah Wumu ◽  
Iqrimha Staddal ◽  
Evi Sunarti Antu ◽  
Burhan Liputo ◽  
Farid Darise
Keyword(s):  
Root Mean Square Error ◽  
Brightness Temperature ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Landsat 8 ◽  
Mean Square

Suhu permukaan laut (SPL) merupakan salah satu parameter yang mempengaruhi kehidupan biota laut. Landsat menyediakan data suhu permukaan bumi sejak tahun 1978. Penelitian ini memfokuskan pada pebraikan algoritma Brightness Temperature (BT) pada band 10 dan 11 data Landsat 8 (L8) menjadi Suhu Permukaan Laut (SPL) untuk perairan Ponelo dengan metode regresi. Regresi yang diperoleh diuji menggunakan metode R-squared (R 2 ) dan Root Mean Square Error (RMSE). Berdasarkan hasil pengambilan data dan analisis data L8 pada tanggal 21 Mei 2017 diperoleh algoritma polinomial 2 merupakan algoritma terbaik dengan dengan koefisien a 0 , a 1 , dan a 2 secara berturut turut 3568.14, 329.64, dan -7.55 (R 2 = 0.86 dan RMSE = 0.73).

scholarly journals Mapping Soil Salinity in Western United Arab Emirates (UAE) Using Satellite Data

2018 ◽  
Author(s):  
Abdelgadir Abuelgasim ◽  
Rubab Ammad
Keyword(s):  
United Arab Emirates ◽  
Soil Salinity ◽  
Crop Yields ◽  
Agricultural Soils ◽  
Image Data ◽  
Saline Soils ◽  
Landsat 8 ◽  
Agricultural Lands ◽  
Infra Red ◽  
Semi Arid

Soil salinity, whether natural or human induced, is a major geo-hazard in arid and semi-arid landscapes. In agricultural lands, it negatively affects plant growth, crop yields, whereas in semi-arid and arid non-agricultural areas it affects urban structures due to subsidence, corrosion and ground water quality, leading to further soil erosion and land degradation Accurately mapping soil salinity through remote sensing techniques has been an active area of research in the past few decades particularly for agricultural lands. Most of this research has focused on the utilization and development of salinity indices for properly mapping and identifying saline agricultural soils. This research study develops a soil salinity index and model using Landsat 8 OLI image data from the near infra-red and shortwave infra-red spectral information with emphasis on soil salinity mapping and assessment in non-agricultural desert arid and semi-arid surfaces. The developed index when integrated into a semi-empirical model outperformed in its soil salinity mapping overall accuracy (60%) in comparison to other salinity indices (~50%). The newly developed index further outperformed other indices in its accuracy in mapping and identifying high saline soils (67%) and excessively high saline soils (90%).

PENGGUNAAN CITRA SATELIT LANDSAT UNTUK ANALISIS URBAN HEAT ISLAND (Studi Kasus: Kota Bandung)

2019 ◽  
Vol 3 ◽  
pp. 1137
Author(s):  
Irland Fardani ◽  
Ibrahim Aziz Adisurya ◽  
Saraswati Saraswati
Keyword(s):  
Brightness Temperature ◽  
Urban Heat Island ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat

Daerah perkotaan memiliki suhu yang lebih tinggi dibandingkan wilayah lainnya. Hal ini dipengaruhi oleh kegiatan manusia dan tingkat pembangunan yang tinggi. Fenomena ini terjadi karena perubahan penggunaan lahan yang terjadi di suatu kota. Ada beberapa faktor yang menyebabkan terjadinya fenomena Pulau Panas Perkotaan atau Urban Heat Island (UHI) di kawasan perkotaan. Salah satunya adalah material pembangun kota yang memiliki albedo berbeda-beda. Dalam pengamatan fenomena UHI dapat digunakan teknologi penginderaan jarak jauh, salah satunya dengan memanfaatkan band thermal yang terdapat pada Citra Satelit Landsat (untuk Landsat 8 ada pada band 10 dan 11). Dari band thermal tersebut dilakukan proses seperti koreksi radiometrik, perhitungan brightness temperature dan akhirnya dinalisis mengenai fenomena Urban Heat Island. Dari profil suhu permukaan di Kota Bandung dapat telihat bahwa di pusat perkotaan / Urban (Bandung Tengah) memiliki suhu permukaan yang lebih tinggi dibandingkan pada bagian pinggiran kota / Sub Urban (Bandung Utara dan Bandung selatan). Selain itu juga dari tahun 1990 – 2017 terlihat terjadi trend kenaikan suhu permukaan, terlihat bahwa pada tahun 1990 suhu maksimal adalah 19o C, sementara pada tahun 2017 suhu maksimal adalah 24o C. Dari hasil kenaikan suhu permukaan di daerah perkotaan / Urban dan terjadinya trend kenaikan suhu di setiap tahun, maka dapat disimpulkan bahwa di Kota Bandung telah terjadi sebuah fenomena Urban Heat Island.

scholarly journals Dynamics of Post-Fire Effects in Larch Forests of Central Siberia Based on Satellite Data

2020 ◽  
Vol 149 ◽  
pp. 03008 ◽  
Author(s):  
Nikita Yakimov ◽  
Evgenii Ponomarev
Keyword(s):  
Surface Temperature ◽  
Brightness Temperature ◽  
Satellite Data ◽  
Vegetation Cover ◽  
Maximum Temperature ◽  
Landsat 8 ◽  
Central Siberia ◽  
Temperature Anomalies ◽  
Larch Forests ◽  
Fire Impact

The article represents the results of Terra, Aqua / MODIS, Landsat-8/OLI satellite data analysis for fire damaged plots in larch forests of Central Siberia. The analysis of averaged surface temperature (brightness temperature) and vegetation index (NDVI) was performed for post-fire circumstances. Estimates of the state and dynamics of fire-damaged vegetation cover were obtained on the basis of inter-seasonal variation of the NDVI index. It was found that post-fire dynamics of vegetation cover determines the surface temperature anomalies within the fire scar plots during at least five years after wildfire impact. It was instrumentally registered that the maximum excess of brightness temperature on post-fire areas can reach up to 11°C comparing to that of background areas under the same conditions. Such anomalies are determined by higher level of insolation due to partial or total tree mortality, as well as by decreasing of on-ground cover thickness after fire impact on grass and moss-lichen covers. During the first year after a fire in larch forests of Siberia, the maximum temperature anomalies of the underlying surface was recorded in the third decade of June. In the course of 2—5 years after burning, the maximum temperature anomalies shift to the second or even third decade of July within the phenological season. The suggested approach allows to assess the degree of fire impact on vegetation, as well as to predict changes in the active layer of permafrost soils, which may be a consequence of extra thermal flow at the surface in the circumstances of disturbed larch forests of Siberia.

scholarly journals The Contribution of Multi-Sensor Infrared Satellite Observations to Monitor Mt. Etna (Italy) Activity during May to August 2016

2018 ◽  
Vol 10 (12) ◽  
pp. 1948 ◽  
Author(s):  
Francesco Marchese ◽  
Marco Neri ◽  
Alfredo Falconieri ◽  
Teodosio Lacava ◽  
Giuseppe Mazzeo ◽  
...  
Keyword(s):  
Summit Crater ◽  
Satellite Observations ◽  
Fumarolic Activity ◽  
Landsat 8 ◽  
Surveillance Systems ◽  
Average Value ◽  
North East ◽  
The North ◽  
Strombolian Eruption ◽  
Mt Etna

In May 2016, three powerful paroxysmal events, mild Strombolian activity, and lava emissions took place at the summit crater area of Mt. Etna (Sicily, Italy). During, and immediately after the eruption, part of the North-East crater (NEC) collapsed, while extensive subsidence affected the Voragine crater (VOR). Since the end of the May eruptions, a diffuse fumarolic activity occurred from a fracture system that cuts the entire summit area. Starting from 7 August, a small vent (of ~20–30 m in diameter) opened up within the VOR crater, emitting high-temperature gases and producing volcanic glow which was visible at night. We investigated those volcanic phenomena from space, exploiting the information provided by the satellite-based system developed at the Institute of Methodologies for Environmental Analysis (IMAA), which monitors Italian volcanoes in near-real time by means of the RSTVOLC (Robust Satellite Techniques–volcanoes) algorithm. Results, achieved integrating Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) observations, showed that, despite some issues (e.g., in some cases, clouds masking the underlying hot surfaces), RSTVOLC provided additional information regarding Mt. Etna activity. In particular, results indicated that the Strombolian eruption of 21 May lasted longer than reported by field observations or that a short-lived event occurred in the late afternoon of the same day. Moreover, the outcomes of this study showed that the intensity of fumarolic emissions changed before 7 August, as a possible preparatory phase of the hot degassing activity occurring at VOR. In particular, the radiant flux retrieved from MODIS data decreased from 30 MW on 4 July to an average value of about 7.5 MW in the following weeks, increasing up to 18 MW a few days before the opening of a new degassing vent. These outcomes, in accordance with information provided by Sentinel-2 MSI (Multispectral Instrument) and Landsat 8-OLI (Operational Land Imager) data, confirm that satellite observations may also contribute greatly to the monitoring of active volcanoes in areas where efficient traditional surveillance systems exist.

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