Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital

PEMANFAATAN METODE SEMI-ANALITIK UNTUK PENENTUAN BATIMETRI MENGGUNAKAN CITRA SATELIT RESOLUSI TINGGI

2025-03-23T08:36:00+00:00

Semi-Analytical methods for detecting bathymetry using medium resolution satellite image data is the development of methods for determining satellite-based bathymetry. This method takes into account the principle of the propagation of light waves in water and the intensity of incident light which decreases according to the increase in depth traversed. The satellite image used is SPOT 7. The image is the latest generation of SPOT satellites which have 4 multispectral channels with a spatial resolution of 6 meters. Therefore, this high-resolution image is expected to produce bathymetry in shallow marine waters more accurately. Semi-analytical methods used to detect bathymetry are Benny and Dawson's methods. This method uses a comparison of the reflectance value between deep water and shallow water by taking into account the approach of the water column attenuation coefficient and the elevation angle of the satellite. The purpose of this study is to detect bathymetry in shallow sea waters. The study area is Karimunjawa Island coastal waters, Jepara, Central Java. The data used is the SPOT 7 acquisition image dated 18 May 2017 has been analysed, in situ depth data as well as tide data. The results showed that off the three SPOT 7 channels, the depth range of 0 - 11.45 meters for the blue channel band, 0 - 10.49 meters for the green channel and 0 - 9.72 meters for the channel red. The accuracy of the bathymetry detection results from the green channel shows quite good results to a depth of less than 5 meters. Green channel parameters of the Benny Dawson algorithm used are 0.3274 for Ld, 0.8932 for Lo, attenuation coefficient of 0.823 and Cosec E '0.6311272.

PEMANFAATAN DATA CITRA SENTINEL-3 SEA AND LAND SURFACE TEMPERATURE RADIOMETER (SLSTR) PAGI DAN MALAM HARI UNTUK ANALISIS INTENSITAS FENOMENA PULAU BAHANG PERMUKAAN (Studi Kasus: Kota Bandung)

2025-03-23T08:41:39+00:00

Suhu permukaan tanah perkotaan lebih tinggi dibanding pedesaan merupakan fenomena alam yang dikenal sebagai Surface Urban Heat Island (SUHI). SUHI memberikan dampak negatif yang besar seperti mempengaruhi kesehatan manusia, kualitas air, udara serta konsumsi energi makhluk hidup sehingga perlu ditemukan solusi yang tepat. Penelitian ini bertujuan untuk : (1) menganalisis pola spasial SUHI Intensity (SUHII) Kota Bandung pada pagi dan malam hari menggunakan data citra Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) dan (2) rancangan mitigasi iklim perkotaan bagi pemerintah dan masyarakat Kota Bandung. Penelitian SUHII ini menggunakan data multiwaktu Land Surface Temperature (LST) citra Sentinel-3 SLSTR pagi dan malam hari musim kemarau tahun 2019 (Agustus-Oktober) untuk menghitung selisih LST urban (Kota Bandung) dan area sub-urban. Berdasarkan pengolahan data tersebut, diperoleh SUHII maksimum pagi dan malam hari musim kemarau mencapai 5,6ºC dan 2,1ºC. Selain itu, diperoleh pula pola spasial SUHII di Kota Bandung menunjukkan dua area cenderung terjadi fenomena SUHI yaitu di pusat kota di sisi barat (Kecamatan Babakan Ciparai) dan di area permukiman padat (Kecamatan Antapani dan sekitarnya). Rancangan mitigasi pada area terindikasi SUHII tinggi bagi pemerintah dan masyarakat Kota Bandung yaitu berupa penambahan vegetasi.

IDENTIFIKASI AWAN PADA DATA TIME SERIES MULTITEMPORAL MENGGUNAKAN PERBANDINGAN DATA SEKUENSIAL

2025-03-23T08:47:42+00:00

Cloud identification is an important pre-processing step of remote sensing data.
Generally, cloud identifications could be classified into single-date and multi-date methods. Furthermore, the single-date method could be divided into physical-rules-based and machine-learning-based. Physical-rules-based method generally need data with sufficient spectral resolution while machine-learning-based method depend on training dataset. While the multi-date method usually using clear data as a reference. The clear data itself could be a whole scene or built from many scenes. Processing cloud-free data is a challenge in areas with high cloud coverage such as Indonesia. In this paper, a cloud identification method using multi-date time series scenes is proposed. This method only uses RGB channels which are common in remote sensing visual data. In addition, this method does not require or process cloud-free data mosaics in advance. A pixel value from an examined scene is compared to other pixel values from other scenes in the same position. The other scenes are the scenes that were acquired before and after the examined scene. The value differences between the examined pixel and it's before and after then evaluated using some thresholds to determine whether the pixel is a cloud or not. Assessment is done by using L8 Biome as a reference. The result shows that using some thresholds in our proposed method has a Kappa coefficient higher than 0.9.

KESESUAIAN WILAYAH BUDI DAYA IKAN KERAPU BERDASARKAN CITRA SATELIT LANDSAT-8 OPERATIONAL LAND IMAGER (OLI)/THERMAL INFRARED SENSOR (TIRS) (STUDI KASUS PERAIRAN KECAMATAN GEROKGAK, KABUPATEN BULELENG, PROVINSI BALI)

2025-03-23T08:54:52+00:00

he waters in Gerokgak District are one of the aquatic region in Indonesia that have potential as regional land for the development of aquaculture, one of which is grouper cultivation. To increase the potential of grouper cultivation, it is necessary to know the right location of grouper cultivation. This study applies a method using an overlay between oceanographic parameters, namely sea surface temperature (SST), salinity, chlorophyll, and Total Suspended Solid (TSS). In addition, this study also uses a remote sensing approach by utilizing Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) satellite imagery data. The results of this study indicate that the waters in the Teluk Penerusan, Gerokgak District, Bali have waters that are suitable for grouper cultivation. Based analysis result between the values of sea surface temperature and chlorophyll with in situ values, it shows good accuracy with values of R2 = 0,661; 0,686 for chlorophyll in situ, and 0,658 for TSS with in situ.

KLASIFIKASI PENUTUP LAHAN MENGGUNAKAN DATA LIDAR DENGAN PENDEKATAN MACHINE LEARNING

2025-03-23T09:01:39+00:00

Lidar is a remote sensing technology. Lidar data is widely used and has been developed for mapping, detailed spatial planning, and natural disaster analysis. In its development for Lidar data management, software applications are widely used as well as by using built algorithms such as machine learning. The research aims to utilize Lidar data for land cover classification using machine learning, namely Support Vector Machine (SVM). The research location is Tanjung Karang village, Mataram City, Lombok. The classification applied is a supervised classification in which the training data is needed to perform the classification. The predicted land cover class in this study is limited to buildings, vegetation, roads, open land. The data used for classification is derived from Lidar, namely DTM, DSM, nDSM, and Intensity. The classification scheme used is one data input and a combination of data. The reference data used is a topographic map (Topographic map of Indonesia). The results showed that the classification with a data combination scheme had a better accuracy value than the one data classification scheme, which increased accuracy by about 15-20%. This shows that there are complementary factors between the data to be able to identify objects in the classification process.

PERANCANGAN SISTEM MONITORING CLOUD COVER UNTUK PEMANTAUAN DAN PREDIKSI CLOUD COVER MENGGUNAKAN METODE DATABASE MANAGEMENT SYSTEM DAN LONG SHORT-TERM MEMORY

2025-03-23T09:07:05+00:00

The quality of optical satellite image data obtained by the Center for Remote Sensing Data and Technology is affected by weather conditions and cloud cover. Based on these conditions, the satellite image data obtained are divided into three categories including very cloudy, cloudy, and cloud-free. Based on annual data information, it is found that the amount of cloudy satellite image data is three times greater than the amount of cloud-free satellite imagery data. So we need a system that can monitor the percentage of the extent of cloud cover from the acquisition of satellite image data. In addition, it is hoped that the creation of a system that can predict cloud cover, where the results of this cloud cover prediction can be used as a reference at the time of the next satellite image acquisition. . Through research and development of this cloud cover monitoring system, both the user and the acquisition officer can monitor the cloud cover of the acquisition result and also determine the location of cloud-free image data acquisition with predictive data. The method used for the development of the monitoring system uses a DBMS (Database Management System), while predictive research on cloud cover in an area wear the LSTM (Long short-term memory) method for Time Series Forecasting. The results of this research and development are in the form of a monitoring system that can monitor the results of acquisitions with data management principles and predict cloud cover conditions from cloud cover monitoring data.