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International Journal of Remote Sensing and Earth Sciences (IJReSES)
Published by Lembaga Penerbangan dan Antariksa Nasional
ISSN : 02166739     EISSN : 2549516X     DOI : -
Core Subject : Science,
Earth & Planetary Sciences
International Journal of Remote Sensing and Earth Sciences (IJReSES) is expected to enrich the serial publications on earth sciences, in general, and remote sensing in particular, not only in Indonesia and Asian countries, but also worldwide. This journal is intended, among others, to complement information on Remote Sensing and Earth Sciences, and also encourage young scientists in Indonesia and Asian countries to contribute their research results. This journal published by LAPAN.
Arjuna Subject : -
Articles 320 Documents
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TENDENCY FOR CLIMATE-VARIABILITY-DRIVEN RISE IN SEA LEVEL DETECTED IN THE ALTIMETER ERA IN THE MARINE WATERS OF ACEH, INDONESIA Rahmawan, Guntur adhi; wisha, ulung jantama
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (778.66 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3091

Abstract

Long-term sea level rise (SLR) leads to increasing frequency in overtopping events resulting from polar ice liquefaction triggered by rising global temperatures. Aceh province is directly bordered by the Indian Ocean, and is subject to the influence of ocean–atmosphere interactions which have a role in triggering temperature and sea level anomalies. Elevated sea level is possibly caused by temperature-induced water mass redistributions. This study aimed to prove that the Indian Ocean Dipole (IOD) and El-Nino–Southern Oscillation (ENSO) had an influence on sea level change in Aceh waters over the six years 2009–2015. Sea level anomaly (SLA) was identified using Jason-2 satellite data for the 2009–2015 period, to enable the mathematical prediction of SLR rate for further years. We found that SLR was approximately 0.0095 mm/year with an upward trend during the six years of observation. Overall, negative mode of IOD and positive phase of ENSO tend to trigger anomalies of sea level at certain times, and have a stronger influence on increasing SLA and sea surface temperature anomaly (SSTA) which takes place in a ‘see-saw’ fashion. Over the period of observation, the strongest evidence of IOD-correlated SLA, ENSO-correlated SLA and SSTA-correlated SLA were identified in second transitional seasons, with more than 50% of R2 value. The upward trend in SLA is influenced by climatic factors that successively control ocean–atmosphere interactions in Aceh’s marine waters. 
VERTICAL LAND MOTION AND INUNDATION PROCESSES BASED ON THE INTEGRATION OF REMOTELY SENSED DATA AND IPCC AR5 SCENARIOS IN COASTAL SEMARANG, INDONESIA Nandika, Muhammad Rizki; Susilo, Setyo Budi; Siregar, Vincentius
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (989.882 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3272

Abstract

Vertical land motion (VLM) is an important indicator in obtaining information about relative sea-level rise (SLR) in the coastal environment, but this remains an area of study poorly investigated in Indonesia. The purpose of this study is to investigate the significance of the influence of VLM and SLR on inundation. We address this issue for Semarang, Central Java, by estimating VLM using the small baseline subset time series interferometry SAR method for 24 Sentinel-1 satellite data for the period March 2017 to May 2019. The interferometric synthetic aperture radar (InSAR) method was used to reveal the phase difference between two SAR images with two repetitions of satellite track at different times. The results of this study indicate that the average land subsidence that occurred in Semarang between March 2017 and May 2019 was from (-121) mm/year to + 24 mm/year. Through a combination of VLM and SLR scenario data obtained from the Intergovernmental Panel on Climate Change (IPCC), it was found that the Semarang coastal zone will continue to shrink due to inundation (forecast at 7% in 2065 and 10% in 2100).
APPLICATION OF LAND SURFACE TEMPERATURE DERIVED FROM ASTER TIR TO IDENTIFY VOLCANIC GAS EMISSION AROUND BANDUNG BASIN Hilman, Zaki; Saepuloh, Asep; Susanto, Very
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (657.454 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3254

Abstract

Gas emission in volcanic areas is one of the features that can be used for geothermal exploration and to monitor volcanic activity. Volcanic gases are usually emitted in permeable zones in geothermal fields. The use of thermal infrared radiometers (TIR) onboard of advanced spaceborne thermal emission and reflection radiometers (ASTER) aims to detect thermal anomalies at the ground surface related to gas emissions from permeable zones. The study area is located around Bandung Basin, West Java (Indonesia), particularly the Papandayan and Domas craters. This area was chosen because of the easily detected land surface temperature (LST) following emissivity and vegetation corrections (Tcveg). The ASTER TIR images used in this study were acquired by direct night and day observation, including observations made using visible to near-infrared radiometers (VNIR). Field measurements of volcanic gases composed of SO2 and CO2 were performed at three different zones for each of the craters. The measured SO2 concentration was found to be constant over time, but CO2 concentration showed some variation in the craters. We obtained results suggesting that SO2 gas measurements and Tcveg are highly correlated. At Papandayan crater, the SO2 gas concentration was 334.34 ppm and the Tcveg temperature was 35.67 °C,  results that are considered highly anomalous. The same correlation was also found at Domas crater, which showed an increased SO2 gas concentration of 35.39 ppm located at a high-anomaly Tcveg of 30.65 °C. Therefore, the ASTER TIR images have potential to identify volcanic gases as related to high Tcveg.
FISHING-VESSEL DETECTION USING SYNTHETIC APERTURE RADAR (SAR) SENTINEL-1 (CASE STUDY: JAVA SEA) Fitriani, Sarah Putri; Gaol, Jonson Lumban; Kushardono, Dony
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (809.168 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3235

Abstract

The synthetic aperture radar (SAR) instrument of Sentinel-1 is a remote sensing technology being developed to enable the detection of vessel distribution. The purpose of this research is to study fishing-vessel detection using SAR Sentinel-1 data. In this study, the constant false alarm rate method (CFAR) for Sentinel-1 data is used for the detection of fishing vessels in Indramayu sea waters. The data used to detect ships includes SAR Sentinel-1A images and vessel monitoring system (VMS) data acquired on 8 March and 20 March 2018. SAR Sentinel-1 imagery data is obtained through pre-processing and object identification using Sentinel Application Platform (SNAP) software. Overlay analysis is then used to enable discrimination of immovable and movable objects and validation of ships detected from SAR Sentinel-1 imagery is performed using VMS data. From overlay analysis, 46 ships were detected on 8 March 2018 and 39 ships on 20 March 2018. Of all the ship points detected using SAR Sentinel-1, 7.06% could be detected by VMS data while 92.94% could not. The number of ships detected by SAR Sentinel-1 is greater than those detected by VMS because not all ships use VMS devices. 
CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA Triscowati, Dwi Wahyu; Sartono, Bagus; Kurnia, Anang; Dirgahayu, Dede; Wijayanto, Arie Wahyu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3217

Abstract

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.
HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN Rahmi, Khalifah Insan Nur; Sulma, Sayidah; Prasasti, Indah
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1112.862 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3293

Abstract

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%.
Front Pages IJReSES Vol. 16, No. 2 (2019) Editor, Journal
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (608.4 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3343

Abstract

Front Pages IJReSES Vol. 16, No. 2 (2019)
Back Pages IJReSES Vol. 16, No. 2 (2019) Editor, Journal
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (315.044 KB) | DOI: 10.30536/j.ijreses.2019.v16.a3344

Abstract

Back Pages IJReSES Vol. 16, No. 2 (2019)
SPATIAL AND TEMPORAL ANALYSIS OF LAND SURFACE TEMPERATURE CHANGE ON NEW BRITAIN ISLAND Devi, Rafika Minati; Prasetya, Tofan Agung Eka; Indriani, Diah
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3342

Abstract

Land Surface Temperature (LST) is a parameter to estimate the temperature of the Earth’s surface and to detect climate change. Papua New Guinea is a tropical country with rainforests, the greatest proportion of which are located on the island of New Britain. Hectares of rainforests have been logged and deforested because of infrastructure construction. This study aims to investigate the change in land surface temperatures on the island from 2000 to 2019. The temperature data were taken from National Aeronautics and Space Administration (NASA) Terra satellites and were analysed using two statistical models: spatial and temporal. The spatial model used multivariate regression, while the temporal one used autoregression (AR). In this study, a cubic spline fitted curve was employed because this has the advantage of being smoother and providing good visuals. The results show that almost all the sub-regions of New Britain have experienced a significant increase in land surface temperature, with a Z value of 7.97 and a confidence interval (CI) of 0.264 – 0.437. The study only investigated land surface temperature change on New Britain Island using spatial and temporal analysis, so further analysis is needed which takes into account other variables such as vegetation and land cover, or which establishes correlations with other variables such as human health.
MONITORING CHANGES IN CORAL REEF HABITAT COVER ON BERALAS PASIR ISLAND USING SPOT 4 AND SPOT 7 IMAGERY FROM 2011 AND 2018 Kuncoro Teguh Setiawan; Viv Djanat Prasita; Damai, Rosaria Ria
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 2 (2020)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3428

Abstract

Beralas Pasir is part of the Regional Marine Conservation Area (KKLD), which was established by the Bintan Regency Government with Bintan Regent Decree No. 261 / VIII / 2007. Water tourism activities undertaken by tourists on the island have had an impact on the condition of the coral reefs, as have other factors, such as bauxite, granite and land sand mining activities around the island. This research aims to determine changes in the coral reef habitat cover and the condition of the coral reefs around Beralas Pasir Island with a remote sensing function, using SPOT 4 imagery acquired on June 1, 2011 and SPOT 7 imagery from April 5, 2020. Data collection of environmental parameters related to the coral reefs was also made. The image processing used the Lyzenga algorithm to simplify the image classification process. The percentage of coral live cover around the island ranges from 26% -53%; this has experienced a significant change, from 67,560 hectares in 2011 to 38,338 hectares in 2018, a total decrease in the area of 29,222 hectares. Some of the natural factors found in the research which have caused damage to the reefs were Drupella snails, the abundance of Caulerpa racemosaalgae, and sea urchins. The majority of the coral reef types consist of Non-Acropora: Coral Massive, Coral, Coral Foliose, Coral Encrusting, Acropora: Acropora Tabulate, Acropora Encrusting, and Acropora Digitate

Page 23 of 32 | Total Record : 320

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