SPATIO-TEMPORAL ANOMALIES IN SURFACE BRIGHTNESS TEMPERATURE PRECEDING VOLCANO ERUPTIONS DETECTED BY THE LANDSAT-8 THERMAL INFRARED SENSOR (CASE STUDY: KARANGETANG VOLCANO)

Indonesia's geological as part of the “ring of fire” includes the consequence that community life could be affected by volcanic activity. The catastrophic incidence of volcanic eruptions in the last ten years has had a disastrous impact on human life. To overcome this problem, it is necessary to conduct research on the strengthening of the early warning system for volcanic eruptions utilising remote sensing technology.  This study analyses spatial and temporal anomalies of surface brightness temperature in the peak area of Karangetang volcano during the 2018-2019 eruption. Karangetang volcano is an active volcano located in North Sulawesi, with a magmatic eruption type that releases lava flow. We analyse the anomalies in the brightness temperature from channel-10 of the Landsat-8 TIRS (Thermal Infrared Scanner) time series during the period in question. The results of the research demonstrate that in the case of Karangetang Volcano the eruptions of 2018-2019 indicate increases in the surface brightness temperature of the crater region. As this volcano has many craters, the method is also very useful to establish in which crater the center of the eruption occurred

Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province

This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics.  The model was constructed using several predictor variables i.e.  Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%

Peranan Teknologi Penginderaan Jauh Dalam Mempercepat Perolehan Data Geografis Untuk Keperluan Pembangunan Nasional

Ketersediaan data geografis atau data spasial mempunyai peran penting dalam pembangunan nasional, mulai dari perencanaan tata ruang sampai pada penentuan tingkat kerawanan bencana. Ketersediaan dan kelengkapan data yang dimiliki akan berpengaruh terhadap efisiensi dan efektifitas pembangunan, mendorong pertumbuhan ekonomi, meningkatkan kualitas pengambilan keputusan serta tersedianya platform dalam membangun e-Goverment. Saat ini teknologi penginderaan jauh sangat besar perannya dalam pengumpulan data geografis suatu wilayah karena jumlah satelit/sensor yang beredar di orbit relatif banyak dan proses akuisisi data dapat dilakukan dengan cepat. Keuntungan lain teknologi penginderaan jauh ini adalah kemampuannya dalam menyajikan gambaran obyek atau fenomena di permukaan bumi dengan resolusi spasial sangat detail (misalnya 60 cm pada citra QuickBird) serta kemampuan dalam menyajikan liputan wilayah (area coverage) yang cukup luas (misalnya 2.000 km2 pada citra MODIS). Berbagai keuntungan ini sangat membantu proses pengumpulan dan revisi data geografis yang sangat diperlukan dalam pembangunan nasional Indonesia yang wilayahnya cukup luas

Paddy fields classification using a 2-dimensional scatterplot of growth phenological features from Sentinel-1 data

Rice plays an essential role in ensuring the food security of Indonesia. Hence, rice (paddy) field monitoring using synthetic aperture radar (SAR) satellite data is critical, particularly in tropical regions. This study presents a new algorithm to detect paddy fields in Subang, West Java, using Sentinel-1 SAR with a 12-day revisit acquisition. Three temporal phenological features of paddy growth were used, namely, the minimum and maximum backscatter, as well as their differences. Paddy fields were discriminated from other land covers using a simple thresholding algorithm based on their specific pattern of low minimum, high maximum, and high difference of vertical transmithorizontal receive polarization (VH) backscatter on a 2-dimensional (2D) scatter plot. The results showed that the proposed algorithm had an accuracy of 94.02%, comparable to that of the random forest algorithm and other studies using 3-dimensional (3D) parameters. The proposed algorithm reduces the dimensionality from 3D to 2D and is practical for mapping and monitoring paddy fields. In this context, the application of the algorithm to the surrounding regions of Karawang, Indramayu, and Bekasi achieved high accuracy rates of 93.37%, 92.87%, and 88.13%, respectively

ANALISIS METODE KOMPRESI BERDOMAIN WAVELET PADA CITRA SATELIT RESOLUSI SANGAT TINGGI

Masalah yang kerap terjadi pada citra satelit penginderaan jauh, terutama citra resolusi sangat tinggi, salah satunya adalah besarnya media penyimpanan dan bandwidth yang dibutuhkan saat data ditransmisi ke tempat lain. Pada pengolahan citra satelit, kompresi data perlu dilakukan pada data citra satelit yang ada demi memudahkan transmisi dan penyimpanan citra. Makalah ini melakukan komparasi pada metode-metode kompresi domain wavelet yaitu metode wavelet, bandelet, dan CCSDS agar ditemukan metode terbaik untuk mengompresi data citra satelit resolusi sangat tinggi Pleiades. Hasil percobaan menunjukkan bahwa metode wavelet dan bandelet lebih baik dalam hal mempertahankan kualitas citra dengan PSNR di kisaran 50 dB, sementara metode CCSDS lebih baik dalam hal mereduksi ukuran citra menjadi seperdelapan citra asli

Roof materials identification based on pleiades spectral responses using supervised classification

The current urban environment is very dynamic and always changes both physically and socio-economically very quickly. Monitoring urban areas is one of the most relevant issues related to evaluating human impacts on environmental change. Nowadays remote sensing technology is increasingly being used in a variety of applications including mapping and modeling of urban areas. The purpose of this paper is to classify the Pleiades data for the identification of roof materials. This classification is based on data from satellite image spectroscopy results with very high resolution. Spectroscopy is a technique for obtaining spectrum or wavelengths at each position from various spatial data so that images can be recognized based on their respective spectral wavelengths. The outcome of this study is that high-resolution remote sensing data can be used to identify roof material and can map further in the context of monitoring urban areas. The overall value of accuracy and Kappa Coefficient on the method that we use is equal to 92.92% and 0.9069

LAND USE AND LAND COVER (LULC) CLASSIFICATION WITH MACHINE LEARNING APPROACH USING ORTHOPHOTO DATA

Penggunaan teknologi penginderaan jauh semakin berkembang, salah satu aplikasinya adalah analisis perubahan penggunaan dan tutupan lahan (LULC). Informasi LULC dibutuhkan untuk berbagai analisis terkait permukaan bumi. Berbagai jenis data digunakan dalam analisis permukaan bumi dengan memanfaatkan data penginderaan jauh. Tujuan dari penelitian ini adalah untuk mengklasifikasikan LULC dengan pendekatan machine learning menggunakan data orthophoto. Lokasi penelitian adalah Desa Tanjung Karang, Mataram, Nusa Tenggara Barat. Metode yang digunakan untuk proses klasifikasi adalah algoritma machine learning yaitu Support Vector Machine (SVM). Dilakukan proses pemisahan band (band slicing) pada data orthophoto yaitu Red, Green, Blue, dan Near Infra Red (NIR). Band Normalized Difference Water Index (NDWI) digunakan untuk analisis badan air yang merupakan refleksi dari band Red dan NIR. Skema klasifikasi klasifikasi yang diterapkan dalam penelitian ini adalah membandingkan klasifikasi antara satu band dan kombinasi band untuk mendapatkan hasil klasifikasi terbaik. Hasil penelitian ini menunjukkan bahwa klasifikasi dengan kombinasi band memiliki akurasi yang lebih baik. Klasifikasi dengan satu band memiliki akurasi rata-rata di bawah 55%, sedangkan kombinasi band memiliki akurasi rata-rata di atas 60%. Hasil klasifikasi dengan nilai akurasi tertinggi adalah kombinasi band R-B-NDWI dengan nilai 71,81%

Comparison of different fusion algorithms in urban and agricultural areas using sar (palsar and radarsat) and optical (spot) images

Image fusion techniques of remote sensing data are formal frameworks for merging and using images originating from different sources. This research investigates the quality assessment of Synthetic Aperture Radar (SAR) data fusion with optical imagery. Two different SAR data from different sensors namely RADARSAT-1 and PALSAR were fused with SPOT-2 data. Both SAR data have the same resolutions and polarisations; however images were gathered in different frequencies as C band and L band respectively. This paper contributes to the comparative evaluation of fused data for understanding the performance of implemented image fusion algorithms such as Ehlers, IHS (Intensity-Hue-Saturation), HPF (High Pass Frequency), two dimensional DWT (Discrete Wavelet Transformation), and PCA (Principal Component Analysis) techniques. Quality assessments of fused images were performed both qualitatively and quantitatively. For the statistical analysis; bias, correlation coefficient (CC), difference in variance (DIV), standard deviation difference (SDD), universal image quality index (UIQI) methods were applied on the fused images. The evaluations were performed by categorizing the test area into two as "urban" and "agricultural". It has been observed that some of the methods have enhanced either the spatial quality or preserved spectral quality of the original SPOT XS image to various degrees while some approaches have introduced distortions. In general we noted that Ehlers' spectral quality is far better than those of the other methods. HPF performs almost best in agricultural areas for both SAR images

A Preliminary Study of the Physico-Chemical Parameters and Potential Pollutant Sources in Urban Lake Rawa Besar, Depok, Indonesia

Lake Rawa Besar is an urban lake surrounded by dense settlements and commercial areas that are currently experiencing physical and ecological pressures due to uncontrolled land-use change around the lake. Therefore, this preliminary study aimed to investigate the sustainable management of the lake in order to create a recreational destination area. It was carried out by ascertaining the lake water quality status through the analysis of the physical and chemical parameters and identifying the potential pollutant sources due to land use and human activities. The physical parameters include TDS, TSS, Turbidity, while the chemical parameters include Nitrate-N, Total Phosphate-P, and BOD. Furthermore, field surveys on 30 water samples were conducted once at noon and statistical analysis was used to ascertain the correlation between the physical and chemical parameters. Finally, Geographic Information System (GIS) tools were used to investigate the spatial distribution of the Physico-chemical parameters and the potential pollutant sources. The results showed that based on the six parameters of the water quality status, the lake was lightly polluted. It also showed that three parameters such as Turbidity, BOD, and TSS exceed the permissible limit with 93.3, 66.7, 43.7% of the total samples, respectively. Additionally, a strong correlation existed between BOD and Turbidity with r=0.95, while a medium correlation existed between Nitrate-N and Phosphate-P with r=0.40. The spatial distribution of the concentration of the physico-chemical parameters generally had a varied pattern,  however, Turbidity and BOD had a similar pattern, especially in the bank areas. Finally, domestic and organic wastes were indicated as pollutant sources, which increased eutrophication in the lake