Comparison Normalized Dryness Built-Up Index (NDBI) with Enhanced Built-Up and Bareness Index (EBBI) for Identification Urban in Buleleng Sub-District

Nuraini, Lilis; Nugraha, A Sediyo Adi; Yanti, Restu Ade; Janah, Lutfiatul

doi:10.23887/mkfis.v21i1.43007

Cited by 3 publications

(2 citation statements)

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“…Setiap objek di permukaan bumi memiliki respon pantulan spektral yang dapat oleh sensor satelit penginderaan jauh (Lillesand et al, 2008). Secara umum, penelitian mengenai pantulan spektral objek dalam studi penginderaan jauh terkait tanah, air dan vegetasi (Aziz & Nugraha, 2022;Hasan et al, 2019;Nuraini et al, 2021). Hal ini dikarenakan ketiga objek tersebut dapat dikenali secara langsung melalui citra penginderaan jauh (Swain & Davis, 1978).…”

Section: Pendahuluanunclassified

Karakteristik Spektral Vegetasi di Gunung Api Galunggung Berdasarkan Hasil Pengolahan Citra Multispektral dan Hiperspektral

Himayah

Ridwana

Mariyono

et al. 2023

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Galunggung is an active volcano with a height of 2168 meters above sea level. Galunggung Vulcano forest area is a very important ecosystem in supporting the environment in Tasikmalaya. This forest area also functions as a water storage area for agriculture, plantations, and tourism. The function of the Galunggung Vulcano forest in the fields of ecology, hydrology, biodiversity conservation is largely determined by the current condition of the forest, especially the condition of the vegetation. The spectral characteristics of vegetation can be observed using remote sensing technology with infrared band. Therefore, this study aims to analyse the spectral characteristics of the vegetation on Galunggung Vulcano based on the altitude zone. This study uses Landsat 8 imagery, Aster GDEM imagery, and Hyperion imagery. NDVI transformation using Landsat 8 and Hyperion is difference. The Landsat NDVI is -0.2 to 0.62, while on Hyperion is -0.3 to 0.66. The NDVI results of Hyperion images in the lower rainforest zone have the highest value (0.63) which represents the greenness of high-density vegetation. While the lowest value is found in the NDVI results of Hyperion images in the central rainforest zone with a value of 0.1 which is generally categorized as a non-vegetation object. It is recommended to conduct further research to analyse how the use of different images and the physical conditions of the research area can affect the research results.

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Section: Pendahuluanunclassified

Karakteristik Spektral Vegetasi di Gunung Api Galunggung Berdasarkan Hasil Pengolahan Citra Multispektral dan Hiperspektral

Himayah

Ridwana

Mariyono

et al. 2023

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“…Remote sensing is an appropriate method for observing sedimentation using remote sensing imagery. In addition, remote sensing can also be used for measurements related to vegetation, physical geography, or urban areas [3][4][5][6][7][8][9]. SST and TSS is part of method in remote sensing and is mostly used in coastal areas to determine the magnitude of changes in the coastal line or sedimentation carried by rivers to the sea and also to determine sea surface temperature.…”

Section: Introductionmentioning

confidence: 99%

Identification of Surface Temperature Relation to Total Suspended Solid in Beratan Lake Using Multi-Temporal Remote Sensing Imagery

Atmaja,

Jayantara

2023

Proceedings of the 5th International Conference on Law, Social Sciences and Education, ICLSSE 2023, 1st June 2023, Singaraja, B

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This study aims to find out the correlation between lake surface temperature and the distribution of sedimentation from Landsat 8 OLI/TIRS imagery. As it is known that the Lake Beratan area is a tourism area that continues to grow and experiences changes in land use. As a result, the Beratan Lake area experiences changes in surface temperature and pollution both directly and indirectly and is supported by the potential for silting due to lake sedimentation. Therefore, a quick study is needed to carry out monitoring through an approach with remote sensing imagery using the SST and TSS methods in the identification carried out. The results show that the low (r2= 0.47) relationship or not relationship between the surface temperature of the lake and the sedimentation that occurs, even so, there is a decrease in surface temperature that occurs as a result of changes in the microclimate that exists in the tourism area of Lake Beratan.

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Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm

Mabdeh,

Ajin,

Razavi-Termeh

et al. 2024

Remote Sensing

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Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) > XGBoost-GWO (AUC: 0.961) > SVR-GWO (AUC: 0.960) > RNN (AUC: 0.956) > XGBoost (AUC: 0.953) > SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors.

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Comparison Normalized Dryness Built-Up Index (NDBI) with Enhanced Built-Up and Bareness Index (EBBI) for Identification Urban in Buleleng Sub-District

Cited by 3 publications

References 0 publications

Karakteristik Spektral Vegetasi di Gunung Api Galunggung Berdasarkan Hasil Pengolahan Citra Multispektral dan Hiperspektral

Karakteristik Spektral Vegetasi di Gunung Api Galunggung Berdasarkan Hasil Pengolahan Citra Multispektral dan Hiperspektral

Identification of Surface Temperature Relation to Total Suspended Solid in Beratan Lake Using Multi-Temporal Remote Sensing Imagery

Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm

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