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All Journal Bulletin of Electrical Engineering and Informatics Forest and Society
Sitthi, Asamaporn
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Agriculture, Biological Sciences & Forestry Electrical & Electronics Engineering Environmental Science Social Sciences

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Spatial Patterns of Cumulative Hotspots and Their Relationships with Topographical Factors and Land Use in Kanchanaburi Province, Thailand Sae-ngow, Pornperm; Losiri, Chudech; Sitthi, Asamaporn
Forest and Society Vol. 8 No. 2 (2024): DECEMBER
Publisher : Forestry Faculty, Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24259/fs.v8i2.32194

Abstract

The clustering of hotspots represents fires occurring at specific locations across various time intervals, and is an increasingly important interdisciplinary research phenomenon. This article investigates the spatial distribution of cumulative hotspots and their relationships with topographical factors and land use in Kanchanaburi province. Data from the Suomi NPP VIIRS system spanning from 2012 to 2021 were utilized for the analysis of Getis-Ord (Gi*) spatial autocorrelation using Fire Radiative Power values. The analysis included the correlation with topographic data such as elevation, slope, aspect, and overlay with land use data. The results reveal that significant hotspots are concentrated in the districts of Si Sawat, Thong Pha Phum, Sai Yok, Sangkhla Buri, and Mueang Kanchanaburi. The majority of hotspots were statistically insignificant (85%), with hotspots (10%) and cold spots (5%) predominantly occurring in forested and agricultural areas. Hotspots were particularly prevalent in the northern and northeastern regions. Therefore, the utilization of Suomi NPP VIIRS data in conjunction with spatial statistics can identify the occurrence of hotspots and cold spots, aiding in planning and policy-making efforts to mitigate hotspot occurrences.
Machine learning-based and synthetic aperture radar time-series data for rice classification over Sentinel-1 imagery Nardkulpat, Attawut; Boonpook, Wuttichai; Sitthi, Asamaporn; Tan, Yumin
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.7833

Abstract

Rice extraction is critical in remote sensing, especially in Suphan Buri province, Thailand, using Sentinel-1 synthetic aperture radar (SAR) time-series data and advanced machine learning algorithms. Given the challenges of varied terrains and diverse crop types, the research employs different polarization modes (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV+VH) to enhance classification accuracy. The study evaluates the performance of three machine learning algorithms: random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The results demonstrate that combined VV+VH polarization outperforms VV and VH alone, providing better accuracy due to its ability to capture more detailed object features. LightGBM emerged as the most effective among the algorithms, particularly when dealing with large datasets. After hyperparameter tuning (n_estimators: 820, max_depth: 10, and learning_rate: 0.01), LightGBM achieved the highest accuracy. The rice class showed exceptional precision, recall, and F1-score, surpassing other land-use classes (agriculture/forest and urban areas). However, these classes still pose challenges, highlighting the need for future studies to integrate multi-sensor data and explore more sophisticated machine-learning models. This research offers a promising approach to enhancing rice monitoring and management in diverse agricultural landscapes, contributing to more accurate and efficient farming practices.
Co-Authors Boonpook, Wuttichai Losiri, Chudech Nardkulpat, Attawut Sae-ngow, Pornperm Tan, Yumin