Mr. A Sediyo Adi Nugraha Profile

A Sediyo Adi Nugraha

at Univ Pendidikan Ganesha

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Proceedings Article | 29 January 2024 Paper

Thermal index effect in forest canopy density (FCD) methods based on remote sensing imagery

A Sediyo Adi Nugraha, Wayan Damar Windu Kurniawan

Proceedings Volume 12977, 129771Y (2024) https://doi.org/10.1117/12.3009322

KEYWORDS: Vegetation, Landsat, Data processing, Data modeling, Thermal effects

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The objective of this study is to identify variations in the level of accuracy of the Forest Canopy Density (FCD) method's utilization of thermal index (TI). This is significant because the FCD approach can be used to determine vegetation cover without relying on the TI indicator. The four primary indicators used in the initial development of the FCD approach by Rikimaru et al. were the vegetation index, shadow index, soil index, and thermal index. The Split Windows Algorithm (SWA), which is the most effective for Landsat 8 OLI/TIRS imagery with a combination of bands 10 and 11, is utilized as the thermal index calculation method. SWA is obtained by concentrating on variations in the vegetation index value used to calculate surface emissivity. Hence, two types of FCD—SWA FCD and non-SWA FCD—are developed. The results showed that accuracy is obtained using the error matrix: the non-SWA FCD is 42% and the SWA FCD is 53%. In addition, the 1 × 1 test plot further show that SWA FCD tends to overestimate, while non-SWA FCD tends to underestimate. The overall accuracy of the analysis conditions may be impacted by the availability of additional samples and the occurrence of the COVID-19 incident. Based on this, that FCD with four indicators may be more accurate than FCD without TI. Despite the need to deep attention to the high-FCD class of analysis, which has a propensity to overestimate.

Proceedings Article | 21 November 2019 Open Access

Downscaling land surface temperature on multi-scale image for drought monitoring

A. Sediyo Adi Nugraha, Totok Gunawan, Muhammad Kamal

Proceedings Volume 11311, 113110A (2019) https://doi.org/10.1117/12.2544550

KEYWORDS: Earth observing sensors, Landsat, MODIS, Principal component analysis, Remote sensing, Temperature metrology, Clouds, Algorithm development, Image processing, Spatial resolution

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Land Surface Temperature (LST) is an important indicator of environment changes, especially related drought monitoring. It is necessary to accurately detect drought events using advanced technology proved information regarding the drought areas. Remote sensing images have proven to be efficient in detecting drought events. MODIS Terra and Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8 OLI/TIRS (The Operational Land Imager and the Thermal Infrared Scanner) represent remote imaging images with different spatial resolutions that enable us proved drought information. However, proper methods are needed to optimize these images for monitoring drought events. The purpose of this study is to find out the ability of multi-scale images proved information about drought monitoring using LST methods. The method used in LST is Temperature Condition Index (TCI), Crop Water Stress Index (CWSI), and Principal Component Analysis (PCA). All three equations are selected because they represent a modification of the method for LST input. The results suggest that the three equations used in multi-level imagery have a critical alignment of information regarding drought. The results show that drought pattern identified by MODIS Terra image was similar to the one detected by Landsat ETM+ and OLI/TIRS images. However, we found a temperature difference in dry season (especially in October) between Landsat ETM+ and OLI/TIRS. The degree of LST estimation accuracy between MODIS Terra and Landsat (ETM+ and OLI/TIRS) is indicated by the average difference between the results of those images, which was 1 degree Celsius (1°C). The use of these three equations for drought monitoring with multi-level imagery suggests that there is a positive relationship. This relationship manifests the same pattern, shape, and association that are produced, thus using a common equation for drought monitoring is more focused.

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