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INDONESIA
Jurnal Penginderaan Jauh Indonesia
Published by Masyarakat Ahli Penginderaan Jauh Indonesia
ISSN : -     EISSN : 26570378     DOI : -
Core Subject : Science, Agriculture, Social, Engineering,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Engineering Environmental Science
Jurnal Penginderaan Jauh Indonesia (JPJI) adalah media komunikasi dan diseminasi hasil penelitian, kajian dan pemikiran terkait teori, sains, dan teknologi penginderaan jauh serta pemanfaatannya yang diterbitkan oleh Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN). Fokus jurnal mencakup penginderaan jauh untuk objek dipermukaan bumi, baik di darat, laut maupun atmosfer. JPJI terbit 2 kali setahun, pada bulan Februari dan Agustus.
Arjuna Subject : -
Articles 15 Documents
 1   2 
Estimasi Konsentrasi Klorofil-a menggunakan Refined Neural Network (Studi Kasus: Perairan Danau Kasumigaura) Aldila Syariz, Muhammad; Denaro, Lino Garda; Nabilah, Salwa; Heriza, Dewinta; Jaelani, Lalu Muhamad; Lin, Chao-Hung
Jurnal Penginderaan Jauh Indonesia Vol 1 No 1 (2019)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

Estimation of Chlorophyll-a Concentration using Refined Neural Network (Case Study: Lake Kasumigaura) Chlorophyll-a has been became one of clinical in-water constituents to represent water quality. Many researchers have used neural network method to estimate chlorophyll-a concentration in the water body. However, a few number of water samples limits the use of neural network, meaning that those number is insufficient to train the neural network model and makes the result is not reliable. One of famous interpolation method, that is Inverse Distance Weighting (IDW), is utilized in this study to enrich water samples dataset over non-station points. The data from those non-station points would further be used to train the neural network model. After the training, the neural network method was refined by using the water samples over stations such that the accuracy in chlorophyll-a estimation was increased. MERIS images are used in this study. Based on statistical analysis, RMSE value before and after the refinement is decreased from 6,7872 mg m-3 to 6,5606 mg m-3.
Aplikasi Citra WorldView-2 Untuk Pemetaan Batimetri Di Pulau Kemujan Taman Nasional Karimunjawa Rahman, Waskito; Wicaksono, Pramaditya
Jurnal Penginderaan Jauh Indonesia Vol 1 No 1 (2019): JPJI
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

The Application of WorldView-2 Image for Bathymetry Mapping in Kemujan Island Karimunjawa National Park The development of remote sensing technology gives an opportunity to extract bathymetry information on the optically shallow water area. This was done by utilizing the reflectance of spectral bands with the ability to penetrate water body. The aim of this research is to map bathymetry of Kemujan Island using remote sensing empirical modeling.  Quickbird image was used in this study. It has four spectral bands namely blue, green, red and near infrared band. These bands were rationed and acquired 12 band ratios. In total, 120 samples were used to produce bathymetry model and 379 samples were used for validation. The models were created for up to the depth of 7 m.  The result showed that the model from band ratio of green and blue band produced the highest accuracy with R² of 0.632 and SE of 1.2 m. The result proved that blue band is the most effective band to be combined with other bands for band ratio input for bathymetry modeling.
Analisis Spasial Distribusi SSS Menggunakan Data Citra Landsat 8-OLI Sebagai Pedoman Dalam Mitigasi Korosi Laut (Studi Kasus: Perairan Teluk Kendari) Nurgiantoro, Nurgiantoro; Hamdhana, Hamdhana
Jurnal Penginderaan Jauh Indonesia Vol 1 No 1 (2019): JPJI
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

Spatial Analysis of SSS Distribution Using 8-OLI Landsat Imagery As a Guide In Mitigating of Marine Corrosion (Case Study: Kendari Bay Waters) Sea surface salinity (SSS) is the amount of dissolved salt in sea water expressed in psu. Besides being able to be measured directly on the field, SSS could also be extracted from satellite image data. This study objective is to explain how SSS is extracted from Landsat 8 data to determine the distribution of SSS in Kendari Bay waters. Furthermore, distribution and patterns are used as guidelines for mitigating corrosion due to seawater on coastal building materials. In this study, SSS was extracted using channel-ratios with wavelengths of 450-510 nm and 530-590 nm, for 5 years (2014-2018). The results show that in 2014-2018 the estimated-SSS was 8,583 psu, 8,612 psu, 8,627 psu, 8,273 psu, and 8,372 psu; respectively. In the open sea, the salinity range is generally in the range of 33-37 psu with elative constant value. River runoff and high rainfall are the main factors in low SSS waters in Kendari Bay.
Normalisasi Radiometrik Relatif Multi Sensor dan Multi Temporal Berbasis Ekstraksi Fitur Pseudo-invariant Denaro, Lino Garda; Aldila Syariz, Muhammad; Nabilah, Salwa; Heriza, Dewinta; Lin, Chao-Hung
Jurnal Penginderaan Jauh Indonesia Vol 1 No 1 (2019)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

Relative Radiometric Normalization for Multi-sensors and Multi-temporal based on Pseudo-invariant Feature Extraction Technology developments promote the abundance availability of multi-temporal satellite imagery data. Otherwise, the utilization of such satellite data have not been implemented optimally due to several limitation requirements. The utilization of the multi-temporal satellite images for change detection is advantageous for modelling and predicting spatial changes in particular period of time. However, in the implementation, the radiometric correction on either multi-temporal or also multi-sensors is required. In this research, weighted regularized generalized canonical correlation analysis (WRGCCA) is proposed to select invariant features or pseudo-invariant features (PIFs) for multi-sensors and multi-temporal images. The method is the improvement of multivariate alteration detection (MAD) that adopts canonical correlation analysis (CCA) and its extension, generalized canonical correlation analysis (GCCA), to detect bitemporal and multi-temporal data respectively. However, each of the methods, CCA and GCCA, has the limitation on differentiating acceptable PIFs due to sensitive to spatial changes. Therefore, with the utilization of weighting and regularization functions to the algorithm, the proposed method WRGCCA with the aid of iterative reweighted multivariate alteration detection (IRMAD) can select reliable PIFs and yielding more accurate PIFs significantly to 10% - 50% determined from root mean square error (RMSE). The improved PIFs selection can be explained by qualitative and quantitative analysis based on the multi-temporal image normalization.
Pengaruh Perubahan Tutupan Lahan Terhadap Emisi GRK pada Wilayah Cepat Tumbuh di Kota Semarang Danar Dewa, Dimas; Sejati, Anang Wahyu
Jurnal Penginderaan Jauh Indonesia Vol 1 No 1 (2019): JPJI
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

Effects of Land Cover Changes on GHG Emissions in Fast-Growing Areas in Semarang City This article aims to convey the results of the calculation of greenhouse gas (GHG) emissions from land cover change factors. The method used is spatio-temporal with remote sensing supported by Support Vector Machine classification techniques from Deztsaka Tools. The results obtained were the most significant changes in land cover occurred in the forest land cover class which decreased by 1515.29 Ha (21%). Carbon reserves in fast-growing areas have decreased by 90,741.06 tons (68.58%) and 22% of the area has released GHG emissions in the amount of more than 1000 tons. This phenomenon requires serious attention because land cover change is very significant, so that control efforts through spatial planning policies are absolutely necessary.
Analisis Land Surface Emissivity menggunakan Data NDVI Landsat 8 dan Pengaruhnya terhadap Formasi Land Surface Temperature di Wilayah Kota Kendari Nurgiantoro, Nurgiantoro; Aris, Armayanti
Jurnal Penginderaan Jauh Indonesia Vol 2 No 1 (2019)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

Land surface emissivity (LSE) is an important part of urban environment studies, because these parameters are closely related to the material composition of an urban area, and also main role in analysis of land surface temperature (LST). The remote sensing approach using sensor OLI and TIRS onboard Landsat 8 will make it easier to find out the results. The goal of this study is to analyze the variation of LSE and its effect on LST formation in Kendari region within the last 5 years from 2014 and 2019 based on NDVI thresholds method (NDVITHM). The results show LSE values in the study area an average of ? > 0.96 in the emissivity unit. Whereas the NDVI has an average of 0.64 and 0.74 for 2014 and 2019, respectively. Formation of LST an average of 31.74 °C in 2014, and 23.47 °C in 2019 or approximately 7 °C difference from five years ago.
Analisis Pengaruh Tutupan Lahan terhadap Distribusi Suhu Permukaan: Kajian Urban Heat Island di Jakarta, Bandung dan Surabaya Muzaky, Handis; Jaelani, Lalu Muhamad
Jurnal Penginderaan Jauh Indonesia Vol 2 No 1 (2019)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

In 2015, more than half of Indonesia's population lived in urban areas that caused the growth of impervious surface area. The impervious surface is high heat-absorbance. Thus, the air temperature in this area is higher compared to the surrounding area. This phenomenon is known as Urban Heat Island (UHI). To examine the impact of UHI, continuous temperature monitoring is needed. Temperature monitoring using weather stations has limitations in terms of coverage area, then remote sensing methods could be useful to obtain data with sufficient spatial distribution. This study examines UHI phenomena in three densest cities in Indonesia (Jakarta, Bandung, and Surabaya) using Landsat 8 OLI/TIRS imagery. Estimation of Land Surface Temperature (LST) using the Single Channel (SC) algorithm and land cover identification using three spectral indices: Vegetation Difference Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Visible Red Near Infrared Build-Up Index (VrNIR-BI). The average surface temperature in Jakarta, Bandung, and Surabaya were 35.21°C, 28.52°C, and 31.69°C respectively. The relationship between LST and land cover was analyzed using Pearson product-moment correlation. The correlation value between LST and NDVI in Jakarta, Bandung, and Surabaya were -0.49; -0,51; and -0.49 while between LST and VrNIR-BI were 0.49; 0.51; and 0.48.
IDENTIFIKASI FASE PERTUMBUHAN TANAMAN JAGUNG MENGGUNAKAN CITRA SAR SENTINEL-1A (STUDI KASUS: KECAMATAN GERUNG, LOMBOK BARAT, NTB) Wulandari, Baiq Arasya; Jaelani, Lalu Muhamad
Jurnal Penginderaan Jauh Indonesia Vol 2 No 1 (2019)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

Accurate land resource information is needed to support agricultural management planning in Indonesia. Mapping regarding corn land needs to be performed because corn is one of the commodities that are highly required both for food and as a primary material for industry. Cornland mapping and identification is performed by monitoring its growth phase. By considering the area and spatial distribution of agricultural land, remote sensing becomes an efficient and accurate monitoring solution. In the tropical region, optical remote sensing is often obstructed by clouds; thus, a Synthetic Aperture Radar (SAR) sensor that has the ability to penetrate clouds and can operate in rainy weather conditions is required. For this reason,  time-series SAR Sentinel-1A radar image data recorded over Gerung District, West Lombok, in April 2018 to April 2019 were used for corn farms monitoring. The growth phase classification is carried out by the supervised classification method using the RGB: VV, VH, and cross-ratio of VV/VH composites. By analyzing the backscattering value (?°; backscatter), identification of corn stands, and the growth phase pattern of corn plants can be performed. The coefficient value of ?° of corn plant was known to be in the range of -14 to -23 dB for VH polarization and -8 to -14 dB for VV polarization with kappa accuracy of 67%.
Pemetaan Presentase Kepadatan Bangunan Menggunakan Model Regresi Berdasarkan Citra Landsat 8 Rosyadi, Amri; Azahra, Maulidini Fatimah
Jurnal Penginderaan Jauh Indonesia Vol 2 No 1 (2020)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

The development of cities and the population causes an increasing number of buildings in an area. Building density identification can use relatively faster remote sensing imagery. This study aims to map the density of buildings in the city of Bandung based on the regression model. The method used to identify building density in this study is regression using the Normalized Difference Built-up Index (NDBI) index. The results of this study are regression models Y = 0.071 + 0.001X which can be used to map building densities with r square values at 0.383. This method is considered to be very fast and accurate compared to other methods for building density.
Pemanfaatan Data Sentinel-2 untuk Analisis Indeks Area Terbakar (Burned Area) Rahmi, Khalifah Insan Nur; Febrianti, Nur
Jurnal Penginderaan Jauh Indonesia Vol 2 No 1 (2020)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

The Use of Sentinel-2 Image to Analysis Burned Area Index Burned area mapping can be extracted from remote sensing imagery using burned area index. Various indices have been developed to identify burned areas including NBR, NBR2, MIRBI, and BAIS2. This study aims to determine the index that best distinguishes burning and non-burning areas in the detailed scale of small fires. Burned areas were identified from the delta index before and after the fire. Date of Sentinel-2 image before fires on May 1, 2019, after fires on September 8, 2019. The NBR index uses the comparison of SWIR and NIR band, the NBR2 and MIRBI indexes use the comparison of SWIRL and SWIRS band, while the BAIS2 index plays the red-edge spectral range, NIR, and SWIR. The result of the separability index analysis shows that the MIRBI index is good for distinguishing burned areas from bare land. The NBR index is good at distinguishing burned areas from vegetation and built-up land while the NBR2 index is good at distinguishing smoked burned areas from vegetation and built-up land.

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