Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital

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

Elisabet Oknisia — 2025-06-26

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.

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

David Sampelan — 2025-06-27

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

Prasetyo Firdianto — 2025-06-28

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.

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

Febryanto Pratama — 2025-07-12

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

Indah Fultriasantri — 2025-07-15

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.