Comparative Analysis of SAVI and NDVI Correlations with Land Surface Temperature in Mandalika Special Economic Zone Using Landsat 8 Imagery

The rapid infrastructure development within the Mandalika Special Economic Zone (SEZ) has significantly altered land cover and potentially influenced land surface temperature (LST). This study aims to compare the correlation strength of two remote sensing-based vegetation indices, Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) with LST to determine which index better represents surface temperature variability in areas undergoing rapid development. Landsat 8 imagery from 2014 to 2023 was used to derive NDVI, SAVI, and LST values. Spearman’s Rho correlation and simple linear regression were employed to evaluate the strength and consistency of the relationships between vegetation indices and LST. The Shapiro – Wilk test confirmed that all variables were not normally distributed, leading to the use of Spearman's rho correlation. Both indices showed significant negative correlations with LST, with NDVI slightly stronger (r = -0.555) than SAVI (r = -0.536). Simple linear regression revealed NDVI had a higher R² (0.392) and lower residual error than SAVI, indicating a more robust model fit. Although SAVI is more suitable in mixed land cover conditions due to its soil background correction, NDVI provides stronger statistical performance in modeling LST in Mandalika SEZ. These findings support the strategic use of NDVI as a primary indicator in environmental planning and sustainable development monitoring or for Urban Heat Island mitigation policy in developing regions

Spatial Temporal Analysis of Mesoscale Convective System to Asia-Australia Monsoon in East Java

Indonesia maritime continent has the formation of clouds that can develop and evolution into MCSs (Mesoscale Convective System). Asian-Australian monsoon has an important influence in determining activities of MCSs. Research gap is analysis of relation between monsoon and MCSs in East Java where is greatly influenced by the monsoon. The data are weather satellite of Himawari, zonal wind and meridional wind ERA-Interim Model 850 mb. Determination of the MCSs follows the physical characteristics in the Maddox algorithm and the AUSMI index follows the Kajikawa algorithm. The method used is quantitative analysis of coefficient of correlation and determination, and qualitative in the form of descriptive analytic. It can be known that the Asian-Australian monsoon has weak influence on the MCSs in the East Java. AUSMI index has the same pattern and phase with frequency of MCSs on seasonal.

Effectiveness of Normalized Difference Built-Up Index in Mapping Built-Up Features across Arid Rural Regions

Normalized Difference Built-up Index (NDBI) is a widely used remote sensing method for detecting built-up areas. However, its effectiveness in distinguishing built-up land from open land in dry rural regions remains underexplored. This study aims to evaluate the performance of NDBI in identifying built-up areas in Bayat Sub-district, Klaten Regency, Central Java, a predominantly rural area with semi-arid land characteristics during October 2023. The analysis employed Landsat 8 OLI imagery acquired in 2023, which was processed to generate NDBI values. These values were classified into four built-up intensity levels using the natural breaks (Jenks) method: Very Low, Low, Medium, and High. Validation was conducted using 36 ground truth points representing land cover types such as vegetation, built-up land, open land, and water bodies. Classification accuracy was assessed through a confusion matrix. The results revealed a significant degree of misclassification. NDBI is computed from the difference in reflectance between the Shortwave Infrared (SWIR) and Near Infrared (NIR) bands, where built-up areas typically exhibit high SWIR and low NIR values. However, dry open land (e.g., bare soil or unvegetated areas) displays a similar spectral pattern, high SWIR reflectance due to dry surfaces, and low NIR reflectance from the absence of biomass. This similarity causes elevated NDBI values for dry open areas, making them difficult to distinguish from actual built-up regions. The confusion matrix yielded an overall accuracy of 75.00% and a Kappa coefficient of 0.628, indicating moderate agreement between the classification results and ground data. These findings highlight the limitations of NDBI in differentiating built-up land from non-vegetated open land in semi-arid rural settings

Analysis of SO2 Emissions and Thermal Anomalies from the Eruption of Mount Lewotobi Laki-laki in November 2024 Using Google Earth Engine

Mount Lewotobi is one of the active volcanoes located in Wulanggitang District, East Flores Regency, East Nusa Tenggara. Mount Lewotobi Laki-Laki in November 2024 has been detected showing significant volcanic activity. This volcanic activity has been detected emitting volcanic gas emissions and significant lava flows that could affect air quality, structures, and the surrounding ecosystem. SO2 emissions and hotspot areas were analyzed using remote sensing data from Sentinel-5P (TROPOMI), Sentinel-2 (MSI), and Landsat-8 (OLI). Data processing was conducted using the Google Earth Engine platform to obtain spatial and temporal analyses of SO2 concentrations in the air and heat sources generated by volcanic activity. The Normalized Hotspot Indices (NHI) method was applied to identify and map hotspots generated by volcanic activity. The results of SO2 levels showed a maximum value of 300,831 µg/m³ and an average of 71,928 µg/m³ occurring on November 9, 2024. The classification of hotspot distribution indicated a range from high to moderate to low. The total number of hotspots measured was 51 on Landsat-8 and 278 on Sentinel-2. The statistical test results for Landsat-8 data showed no significant correlation between SO2 measurements and hotspot measurements, whereas the results for Sentinel-2 showed an inverse correlation

Analysis of Mangrove Species Detection Performance on Multiresolution Satellite Imagery Using Linear Spectral Unmixing

The Pamurbaya mangrove conservation area in East Surabaya is crucial for coastal protection, but it is vulnerable to degradation due to human activities and land-use changes. Species distribution maps are essential for understanding ecological functions, such as carbon sequestration, salinity tolerance, and ecosystem stability. This study utilizes multiresolution remote sensing data from WorldView-2 satellite imagery to map mangrove and detailed species-level. Random Forest is utilized to differentiate mangrove and non-mangrove, while Linear Spectral Unmixing allows for detailed mangrove species distribution. Further analysis was carried out to determine at what resolution the LSU works optimally. The imagery was served in 0.5 meter resolution and down-sampled to 5 meter, 10, 20, 30, and 50 meter resolutions. This study obtained that LSU were able to differentiate mangroves according to its endmember and working optimally at medium resolution (10–30 m), with overall accuracy increasing from 70% (10 m) to 75% (30 m) and Kappa value increasing from 53.7 to 60.41. High resolution (0.5–10 m) provides more detailed mapping but is optimal for species with small and scattered distributions. Meanwhile, low resolution (20–50 m) tends to cause overestimation or aggregation of species

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

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. &nbsp

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)

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

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)

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

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

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