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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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RADAR-BASED STOCHASTIC PRECIPITATION NOWCASTING USING THE SHORT-TERM ENSEMBLE PREDICTION SYSTEM (STEPS) (CASE STUDY: PANGKALAN BUN WEATHER RADAR) Ali, Abdullah; Supriatna, S.; Sa'adah, Umi
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 1 (2021)
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.2021.v18.a3527

Abstract

Nowcasting, or the short-term forecasting of precipitation, is urgently needed to support the mitigation circle in hydrometeorological disasters. Pangkalan Bun weather radar is single-polarization radar with a 200 km maximum range and which runs 10 elevation angles in 10 minutes with a 250 meters spatial resolution. There is no terrain blocking around the covered area. The Short-Term Ensemble Prediction System (STEPS) is one of many algorithms that is used to generate precipitation nowcasting, and is already in operational use. STEPS has the advantage of producing ensemble nowcasts, by which nowcast uncertainties can be statistically quantified. This research aims to apply STEPS to generate stochastic nowcasting in Pangkalan Bun weather radar and to analyze its advantages and weaknesses. Accuracy is measured by counting the possibility of detection and false alarms under the 5 dBZ threshold and plotting them in a relative operating characteristic (ROC) curve. The observed frequency and forecast probability is represented by a reliability diagram to evaluate nowcast reliability and sharpness. Qualitative analysis of the results showed that the STEPS ensemble produces smoothed reflectivity fields that cannot capture extreme values in an observed quasi-linear convective system (QLCS), but that the algorithm achieves good accuracy under the threshold used, up to 40 minutes lead time. The ROC shows a curved upper left-hand corner, and the reliability diagram is an almost perfect nowcast diagonal line.
MACHINE LEARNING APPLIED TO SENTINEL-2 AND LANDSAT-8 MULTISPECTRAL AND MEDIUM-RESOLUTION SATELLITE IMAGERY FOR THE DETECTION OF RICE PRODUCTION AREAS IN NGANJUK, EAST JAVA, INDONESIA Devara, Terry; Wijayanto, Arie Wahyu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 1 (2021)
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.2021.v18.a3538

Abstract

Statistics Indonesia (BPS) has been introducing the use of Area Sampling Frame (ASF) surveys from 2018 to estimate rice production areas, although the process continues to suffer from the high costs of human and other resources. To support this type of conventional field survey, a more scalable and inexpensive approach using publicly-available remote sensing data, for example from the Sentinel-2 and Landsat-8 satellites, has been explored. In this research, we compare the performance gain from Sentinel-2 and Landsat-8 images using a multiple composite-index enriched machine learning classifier to detect rice production areas located in Nganjuk, East Java, Indonesia as a case study area. We build a detection model from a set of machine learning classifiers, Decision Tree (CART), Support Vector Machine, Logistic Regression, Ensemble Bagging Methods (Random Forest and Extra Trees), and Ensemble Boosting Methods (AdaBoost and XGBoost). The composite indices consist of the NDVI and EVI for agricultural and forest areas, NDWI for water and cloud, and NDBI, NDTI, and BSI for built-up areas, fallows, and asphalt-based roads. Validated by k-fold cross-validation, Sentinel-2 and Landsat-8 achieved F1-scores of 0.930 and 0.919 respectively at the scale of 30 meters per pixel. Using a 10 meter resolution per pixel for the Sentinel-2 imagery showed an increased F1-score of up to 0.971. Our evaluation shows that the higher spatial resolution imagery of Sentinel-2 achieves a better prediction, not only performance-wise, but also as a better representation of actual conditions.
OIL PALM PLANTATION DETECTION IN INDONESIA USING SENTINEL-2 AND LANDSAT-8 OPTICAL SATELLITE IMAGERY (CASE STUDY: ROKAN HULU REGENCY, RIAU PROVINCE) Nurmasari, Yunita; Wijayanto, Arie Wahyu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 1 (2021)
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.2021.v18.a3537

Abstract

The objective of this work is to assess the capability of multispectral optical Landsat and Sentinel images to detect oil palm plantations in Rokan Hulu, Riau, one of the largest palm oil producers in Indonesia, by combining multispectral bands and composite indices. In addition to comparing two different sets of satellite images, we also ascertain which gives the best performance among the supervised machine learning classifiers CART Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. With the use of multispectral bands and derived composite indices, the best classifier achieved an overall accuracy of up to 92%. The findings and contributions of the study include: (1) insight into a set of feature combinations that provides the highest model accuracy, and (2) an extensive evaluation of machine learning-based classifiers on two different optical satellite imageries. Our study could further be beneficial for the government in providing more scalable plantation statistics.
HYDRO-METEOROLOGICAL ASPECTS OF THE 2021 SOUTH KALIMANTAN FLOOD: TOPOGRAPHY, TIDES, AND PRECIPITATION Pratama, Munawir Bintang; Multazima, Rafida; Azkiarizqi, Ismail Naufal
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 1 (2021)
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.2021.v18.a3539

Abstract

The 2021 South Kalimantan flood was recorded as the most serious ever to have taken place in the province. It occurred due to high-intensity rain during the period 10-19 January, accompanied by a spring tide. This study provides an overview of the disaster, with reference to the hydro-meteorological conditions (topography, tides, and precipitation). The method used was the analysis of the precipitation and its monthly rainfall pattern anomalies using remote sensing data. A Digital Elevation Model (DEM) was also analyzed to indicate the most noticeably flood-affected area. In certain areas, total precipitation during the ten days reached 672.8 mm, with daily precipitation peaking at 255 mm on January 14, greater than the 25-year return period value. The flood coincided with a spring tide, which peaked at 1.21 m on the evening of January 15. Using 20- year GPM data, it was found that ENSO and IOD coexisted with both the highest and lowest anomalies. With a La Niña event at the end of 2020,  a positive precipitation anomaly in 2021 was expected. The extreme precipitation is suspected to be the main driver of the  2021 South Kalimantan flood, whose impact was worsened by the spring tides. This  study conducts further research on the correlation between land-use change, rainfall, spring tide and flooding in South Kalimantan. In addition, it is recommended that the government plan flood risk  management by prioritizing areas based on vulnerability to climate hazards.
Front Pages IJReSES Vol. 18, No. 1 (2021) Editor, Journal
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 1 (2021)
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.2021.v18.a3668

Abstract

Front Pages IJReSES Vol. 18, No. 1 (2021)
Backpages Vol.18 No.1 (2021) Editor, Journal
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 1 (2021)
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.2021.v18.a3669

Abstract

Backpages Vol.18 No.1 (2021)
ESTIMATION OF ABOVEGROUND CARBON STOCK USING SAR SENTINEL-1 IMAGERY IN SAMARINDA CITY Dewanto, Bayu Elwanto Bagus; Jatmiko, Retnadi Heru
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 1 (2021)
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.2021.v18.a3609

Abstract

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples. The results show that Samarinda Utara subdistrict had the highest carbon stock of 3,765,255.9 tons in the VH exponential model. Total carbon stocks in the exponential VH models were 6,489,478.1 tons, with the highest maximum accuracy of 87.6 %, and an estimated error of 0.57 tons/pixel.
A NEW INTERPRETATION OF THE EXISTENCE OF THE PANJANG REGIONAL FAULT BASED ON DEM AND FIELD OBSERVATIONS IN LAMPUNG, SUMATRA, INDONESIAD LAMPUNG, SUMATRA, INDONESIAOBSERVATION AT LAMPUNG, SUMATRA, INDONESIA Siringoringo, Luhut
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 2 (2021)
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.2021.v18.a3405

Abstract

Referring to the regional geological map sheet of Tanjung Karang, the existence of the Panjang regional fault in the Sukarame area (the research area) is still debated. This can be seen from the dashed line on the map, which indicates that the existence of the fault is still unclear. The objective of this research is to ascertain the existence of the Panjang Fault, together with information on its type and direction. The method used was to integrate the tectonic geomorphological sections through Digital Elevation Model (DEM) interpretations and field observations result. Field observations were made to confirm the existence of these structures. We found that the Panjang regional fault in the research area does exist. From the south of research area, the fault apparently continues into the research area. It is a normal fault in a northwest-southeast direction. The existence of the fault is also supported by the discovery of water springs during the field observations. The fault has cut aquifers so that the groundwater appears on the surface as water springs.
SPECTRAL CHARACTERISTICS OF FLASH FLOOD AREAS FROM MEDIUM SPATIAL OPTICAL IMAGERY Priyatna, Muhammad; Khomarudin, Muhammad Rokhis; Chulafak, Galdita Aruba; Wijaya, Sastra Kusuma
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 2 (2021)
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.2021.v18.a3666

Abstract

This study aims to investigate surface reflectance changes over flash flood areas in Nusa Tenggara Timur, Indonesia. Fifteen sample points from Sentinel-2 satellite imagery were used to analyse the differences in reflectance of areas before and after flash flood events. The method used involved analysis of the significant differences in the dreflectance values of each Sentinel-2 channel. The analysis results show that channels 6, 7, and 8A displayed significant differences compared to the others with regard to reflectance before and after flooding, for both settlements and shrubs. The results could be used for further research in building a reflectance index for the rapid detection of affected areas, with a focus on these channels.
GROUNDWATER LEVEL ESTIMATION MODEL ON PEATLANDS USING SAR SENTINEL-1 DATA IN PART OF RIAU, INDONESIA Yananto, Ardila; Sartohadi, Junun; Marhaento, Hero; Awaluddin, .
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 18, No 2 (2021)
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.2021.v18.a3618

Abstract

The character of peatlands has the ability to store large amounts of water, but the surface of the peatlands dries quickly and easy to burn during the dry season. Research aims to build a model to estimate groundwater level of peatland. Statistical analysis of Karl Pearson Product Moment correlation test was used to determine the relationship between the back scatter values and the Surface Soil Moisture (SSM) values from the Sentinel-1 SAR data processing with the groundwater level values measured using the Sipalaga instrument. Regression analysis was used to determine the model that could be used to estimate the groundwater level of peatlands in the study area based on the results of Sentinel-1 SAR data processing. The results showed that the Sentinel-1 SAR data with the Sigma_0 format in decibel (db) units with VV polarization had the highest correlation value with the groundwater level data of peatlands measured using the Sipalaga instrument, with a value of r -0.648 (moderate correlation). Model to estimate water level of peatlands was Y = -101.629 + (-7.414 x), where 'Y' was the groundwater level of peatlands in the study area and 'x' was the Sentinel-1 SAR data with Sigma_0 format in decibel (db) units with VV polarization. The spatial and temporal patterns of peatlands groundwater level in the study area from Sentinel-1 SAR data showed peatlands that to survive at a water level <40 cm was in the area around of the Rokan River and also in plantation areas, especially Acacia plantations, where canals were made to irrigate and land management.

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