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All Journal IJOG : Indonesian Journal on Geoscience Indonesian Journal of Electrical Engineering and Computer Science
Sukristiyanti Sukristiyanti, Sukristiyanti
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Earth & Planetary Sciences

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Assessment of cloud-free normalized difference vegetation index data for land monitoring in Indonesia Hadiyanto, Ahmad Luthfi; Sukristiyanti, Sukristiyanti; Hidayat, Arif; Pratiwi, Indri
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp845-853

Abstract

Continuous land monitoring in Indonesia using optical remote sensing satellites is difficult due to frequent clouds. Therefore, we studied the feasibility of monthly land monitoring during the second half of 2019, using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from Terra and Aqua satellites. We divide the Indonesian area into seven regions (Sumatra, Java, Kalimantan, Sulawesi, Nusa Tenggara, Maluku, and Papua) and examine NDVI data for each of the regions. We also calculated the cloud occurrence percentage every hour using Himawari-8 data to compare cloud conditions at different acquisition times. This research shows that Terra satellite provides more cloud-free pixels than Aqua while combining data from both significantly increase the cloud-free NDVI pixels. Monthly monitoring is feasible in most regions because the cloudy areas are less than 10%. However, in Sumatra, the cloudy area was more than 10% in October 2019. We need to include further data processing to improve the feasibility of continuous monitoring in Sumatra. This research concludes that monthly monitoring is still feasible in Indonesia, although some data require further processing. The use of additional data from other satellites in the monitoring can be an option for further research.
Hyperparameter Tuning on Machine Learning-Based Landslide Susceptibility Mapping (Case study: Palu City and Its Surrounding areas) Sukristiyanti, Sukristiyanti; Pamela, Pamela; Putra, Moch Hilmi Zaenal; Arifianti, Yukni; Rozie, Andri Fachrur; Lestiana, Hilda; Susantoro, Tri Muji; Sumaryono, Sumaryono; Kristiawan, Yohandi; Putra, Iqbal Eras
Indonesian Journal on Geoscience Vol. 12 No. 1 (2025)
Publisher : Geological Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17014/ijog.12.1.43-53

Abstract

Landslide susceptibility mapping (LSM) produces a zonation map of landslide susceptibility levels, representing the future probability of landslides. It is necessary to give a guideline regarding spatial planning. A machine learning method was used, namely a random forest (RF) algorithm to map landslide susceptibility in Python. The case study is Palu City and its surrounding areas, which were attacked by a big earthquake on September 28th, 2018. Some earlier LSM studies did not discuss hyperparameter tuning, and several others did not mention the training accuracy. Therefore, this study is to find out whether the fast model without hyperparameter tuning and frequently overfitting, can well produce landslide susceptibility maps. The research questions were addressed by comparing two landslide susceptibility maps built with and without hyperparameter tuning using receiver operating characteristics (ROC) and landslide density (LD) analyses. This study shows that the area under the curve (AUC) of the landslide susceptibility map from the fast RF model without hyperparameter tuning is as high as the AUC from the tuned model map. It also happened in both landslide density (LD) maps, and there is no anomaly in the fast model map. Nevertheless, there are strange appearances in the fast model map. Therefore, hyperparameter tuning to obtain the optimal model with no overfitting is mandatory to predict landslide susceptibility spatially.
Co-Authors Arif Hidayat Hadiyanto, Ahmad Luthfi Hilda Lestiana Indri Pratiwi, Indri Kristiawan, Yohandi Pamela, Pamela Putra, Iqbal Eras Putra, Moch Hilmi Zaenal Rozie, Andri Fachrur Sumaryono Sumaryono Tri Muji Susantoro, Tri Muji Yukni Arifianti, Yukni