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International Journal of Remote Sensing and Earth Sciences (IJReSES)

ijreses
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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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DETECTING DEFORMATION DUE TO THE 2018 MERAPI VOLCANO ERUPTION USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) FROM SENTINEL-1 TOPS Suwarsono Suwarsono; Indah Prasasti; Jalu Tejo Nugroho; Jansen Sitorus; Rahmat Arief; Khalifah Insan Nur Rahmi; Djoko Triyono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

This paper describes the application of Sentinel-1 TOPS (Terrain Observation with Progressive Scans), the latest generation of SAR satellite imagery, to detect displacement of the Merapi volcano due to the May–June 2018 eruption. Deformation was detected by measuring the vertical displacement of the surface topography around the eruption centre. The Interferometric Synthetic Aperture Radar (InSAR) technique was used to measure the vertical displacement. Furthermore, several Landsat-8 Thermal Infra Red Sensor (TIRS) imageries were used to confirm that the displacement was generated by the volcanic eruption. The increasing temperature of the crater was the main parameter derived using the Landsat-8 TIRS, in order to determine the increase in volcanic activity. To understand this phenomenon, we used Landsat-8 TIRS acquisition dates before, during and after the eruption. The results show that the eruption in the May–June 2018 period led to a small negative vertical displacement. This vertical displacement occurred in the peak of volcano range from -0.260 to -0.063 m. The crater, centre of eruption and upper slope of the volcano experienced negative vertical displacement. The results of the analysis from Landsat-8 TIRS in the form of an increase in temperature during the 2018 eruption confirmed that the displacement detected by Sentinel-1 TOPS SAR was due to the impact of volcanic activity. Based on the results of this analysis, it can be seen that the integration of SAR and thermal optical data can be very useful in understanding whether deformation is certain to have been caused by volcanic activity.

DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE Yudhi Prabowo; Kenlo Nishida Nasahara
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

This paper describes the detection of coconut trees using very-high-resolution optical satellite imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients (HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this detection. The main objective of this study is to find out the parameter combination for the HOG algorithm that could provide the best performance for coconut-tree detection. The study shows that the best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins, and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of approximately 80%, 73% and 87%, respectively.

VARIABILITY OF SEA SURFACE TEMPERATURE (SST) AND CHLOROPHYLL-A (CHL-A) CONCENTRATIONS IN THE EASTERN INDIAN OCEAN DURING THE PERIOD 2002–2017 Michelia Mashita; Jonson Lumban-Gaol
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

We analysed the variability of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) in the eastern Indian Ocean (EIO). We used monthly mean Chl-a and SST data with a 4-km spatial resolution derived from Level-3 Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) distributed by the Asia-Pacific Data-Research Center (APDRC) for the period 2002–2017. Wavelet analysis shows the annual and interannual variability of SST and Chl-a concentration in the EIO. The annual variability of SST and Chl-a is influenced by monsoon systems. During a southeast monsoon, SST falls while Chl-a increases due to upwelling. The annual variability of SST and Chl-a is also influenced by the Indian Ocean Dipole (IOD). During positive phases of the IOD (2006, 2012 and 2015), there was more intense upwelling in the EIO caused by the negative anomaly of SST and the positive anomaly of Chl-a concentration.

INTEGRATION OF GIS AND REMOTE SENSING FOR HOTSPOT DISTRIBUTION ANALYSIS IN BERBAK SEMBILANG NATIONAL PARK Andita Minda Mora; Bambang Hero Saharjo; Lilik Budi Prasetyo
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Abstract. Remote sensing is composed of many interrelated processes to be able to consider physical objects such as buildings, land, and plants which are objects that can be discussed by applications discussed in various disciplines that discuss geology, forestry, soil science, and geography. The use of GIS and remote sensing for fire monitoring has been widely used. However, this study is the first study conducted in the TNBS area after the Berbak National Park (TNB) in Jambi to join the Sembilang National Park (TNS) in South Sumatra. Hotspot distribution in this study was obtained using Getis-Ord-Gi * statistics, hotspot data collected from 2000-2018 in the TNBS area. The results of the hotspot distribution during the 2000-2018 recorded by MODIS satellites with time acquisition and statistical analysis using Gi* show the results that the hotspots gathered (80% confidence level) outside the TNBS area, which is a mixed fields area. Further studies on causes of fire in terms of socio-economic and cultural needs to be done to get the right advice in reducing the risk of loss of forest cover and diversity in TNBS. Keywords: mitigation, hydrology, DAS

THE USE OF C-BAND SYNTHETIC APERTURE RADAR SATELLITE DATA FOR RICE PLANT GROWTH PHASE IDENTIFICATION Anugrah Indah Lestari; Dony Kushardono
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Identification of the rice plant growth phase is an important step in estimating the harvest season and predicting rice production. It is undertaken to support the provision of information on national food availability. Indonesia’s high cloud coverage throughout the year means it is not possible to make optimal use of optical remote sensing satellite systems. However, the Synthetic Aperture Radar (SAR) remote sensing satellite system is a promising alternative technology for identifying the rice plant growth phase since it is not influenced by cloud cover and the weather. This study uses multi-temporal C-Band SAR satellite data for the period May–September 2016. VH and VV polarisation were observed to identify the rice plant growth phase of the Ciherang variety, which is commonly planted by farmers in West Java. Development of the rice plant growth phase model was optimized by obtaining samples spatially from a rice paddy block in PT Sang Hyang Seri, Subang, in order to acquire representative radar backscatter values from the SAR data on the age of certain rice plants. The Normalised Difference Polarisation Index (NDPI) and texture features, namely entropy, homogeneity and the Grey-Level Co-occurrence Matrix (GLCM) mean, were included as the samples. The results show that the radar backscatter value (σ0) of VH polarisation without the texture feature, with the entropy texture feature and GLCM mean texture feature respectively exhibit similar trends and demonstrate potential for use in identifying and monitoring the rice plant growth phase. The rice plant growth phase model without texture feature on VH polarisation is revealed as the most suitable model since it has the smallest average error.

Front Pages IJReSES Vol. 16, No. 1 (2019) Editor Journal
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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Front Pages IJReSES Vol. 16, No. 1 (2019)

RETRIEVING COASTAL SEA SURFACE TEMPERATURE FROM LANDSAT-8 TIRS FOR WANGI-WANGI ISLAND, WAKATOBI, SOUTHEAST SULAWESI, INDONESIA Eko Susilo; Rizki Hanintyo; Adi Wijaya
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 1 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

The new Landsat generation, Landsat-8, is equipped with two bands of thermal infrared sensors (TIRS). The presence of two bands provides for improved determination of sea surface temperature (SST) compared to existing products. Due to its high spatial resolution, it is suitable for coastal zone monitoring. However, there are still significant challenges in converting radiance measurements to SST, resulting from the limitations of in-situ measurements. Several studies into developing SST algorithms in Indonesia waters have provided good performance. Unfortunately, however, they have used a single-band windows approach, and a split-windows approach has yet to be reported. In this study, we investigate both single-band and split-window algorithms for retrieving SST maps in the coastal zone of Wangi-Wangi Island, Wakatobi, Southeast Sulawesi, Indonesia. Landsat-8 imagery was acquired on February 26, 2016 (01: 51: 44.14UTC) at position path 111 and and row 64. On the same day, in-situ SST was measured by using Portable Multiparameter Water Quality Checker – 24. We used the coefficient of correlation (r) and root mean square error (RMSE) to determine the best algorithm performance by incorporating in-situ data and the estimated SST map. The results showed that there were differences in brightness temperature retrieved from TIRS band10 and band 11. The single-band algorithm based on band 10 for Poteran Island clearly showed superior performance (r = 69.28% and RMSE = 0.7690°C). This study shows that the split-window algorithm has not yet produced a accurate result for the study area.

MONITORING OF MANGROVE GROWTH AND COASTAL CHANGES ON THE NORTH COAST OF BREBES, CENTRAL JAVA, USING LANDSAT DATA Susantoro, Tri Muji; Wikantika, Ketut; Yayusman, Lissa Fajri; Tan, Alex; Ghozali, M. Firman
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Severe abrasion occurred in the coastal area of Brebes Regency, Central Java between 1985 and 1995. Since 1997, mangroves have been planted around the location as a measure intended to prevent further abrasion. Between 1996 and 2018, monitoring has been carried out to assess coastal change in the area and the growth and development of the mangroves. This study aims to monitor mangrove growth and its impact on coastal area changes on the north coast of Brebes, Central Java Province using Landsat series data, which has previously proven suitable for wetland studies including mangrove growth and change. Monitoring of mangrove growth was analysed using the normalised difference vegetation index (NDVI) and the green normalised difference vegetation index (GNDVI) of the Landsat data, while the coastal change was analysed based on the overlaying of shoreline maps. Visual field observations of WorldView 2 images were conducted to validate the NDVI and GNDVI results. It was identified from these data that the mangroves had developed well during the monitoring period. The NDVI results showed that the total mangrove area increased between 1996 and 2018 about 9.82 km2, while the GNDVI showed an increase of 3.20 km2. Analysis of coastal changes showed that the accretion area about 9.17 km2 from 1996 to 2018, while the abrasion being dominant to the west of the Pemali River delta about 4.81 km2. It is expected that the results of this study could be used by government and local communities in taking further preventative actions and for sustainable development planning for coastal areas.

CLOUD IDENTIFICATION FROM MULTITEMPORAL LANDSAT-8 USING K-MEANS CLUSTERING Sunarmodo, Wismu; Hayati, Anis Kamilah
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

In the processing and analysis of remote-sensing data, cloud that interferes with earth-surface data is still a challenge. Many methods have already been developed to identify cloud, and these can be classified into two categories: single-date and multi-date identification. Most of these methods also utilize the thresholding method which itself can be divided into two categories: local thresholding and global thresholding. Local thresholding works locally and is different for each pixel, while global thresholding works similarly for every pixel. To determine the global threshold, two approaches are commonly used: fixed value as threshold and adapted threshold. In this paper, we propose a cloud-identification method with an adapted threshold using K-means clustering. Each related multitemporal pixel is processed using K-means clustering to find the threshold. The threshold is then used to distinguish clouds from non-clouds. By using the L8 Biome cloud-cover assessment as a reference, the proposed method results in Kappa coefficient of above 0.9. Furthermore, the proposed method has lower levels of false negatives and omission errors than the FMask method.

ROLLING MOSAIC METHOD TO SUPPORT THE DEVELOPMENT OF POTENTIAL FISHING ZONE FORECASTING FOR COASTAL AREAS Suniada, Komang Iwan; Susilo, Eko; Siwi, Wingking Era Rintaka; Widagti, Nuryani
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

The production of the Indonesian Institute for Marine Research and Observationâ€™s mapping of forecast fishing areas (peta prakiraan daerah penangkapan ikan or PPDPI) based on passive satellite imagery is often constrained by high-cloud-cover issues, which lead to sub-optimal results. This study examines the use of the rolling mosaic method for providing geophysical variables, in particular, seasurface temperature (STT) together with minimum cloud cover, to enable clearer identification of oceanographic conditions. The analysis was carried out in contrasting seasons: dry season in July 2018 and rainy season in December 2018. In general, the rolling mosaic method is able to reduce cloud cover for sea-surface temperature (SST) data. A longer time range will increase the coverage percentage (CP) of SST data. In July, the CP of SST data increased significantly, from 15.3 % to 30.29% for the reference 1D mosaic and up to 84.19 % to 89.07% for the 14D mosaic. In contrast, the CP of SST data in December tended to be lower, from 4.93 % to 13.03% in the 1D mosaic to 41.48 % to 51.60% in the14D mosaic. However, the longer time range decreases the relationship between the reference SST data and rolling mosaic method data. A strong relationship lies between the 1D mosaic and 3D mosaics, with correlation coefficients of 0.984 for July and 0.945 for December. Furthermore, a longer time range will decrease root mean square error (RMSE) values. In July, RMSE decreased from 0.288Â°C (3D mosaic) to 0.471Â°C (14D mosaic). The RMSE value in December decreased from 0.387Â°C (3D mosaic) to 0.477Â°C (14D mosaic). Based on scoring analysis of CP, correlation coefficient and RMSE value, results indicate that the 7D mosaic method is useful for providing low-cloud-coverage SST data for PPDPI production in the dry season, while the 14D mosaic method is suitable for the rainy season.

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