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All Journal Emerging Science Journal
Jaroensutasinee, Krisanadej
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Environmental Science

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Government Policy Influence on Land Use and Land Cover Changes: A 30-Year Analysis Rattanarat, Jantira; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Sparrow, Elena B.
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-06

Abstract

This study investigated land use and land cover (LULC) patterns and changes in the Bandon Bay area of Thailand from 1991 to 2021 using satellite imagery, the first comprehensive effort to assess historical LULC trends over the past 30 years and forecast future LULC scenarios using the CA-Markov model for 2031, 2041, and 2051. Results showed the predominant LULC during 1991-2001 was the abandoned paddy fields, and during 2006-2021 was the oil palm plantations. During 1991-2001, the abandoned paddy fields changed significantly, with a net gain of 59.28 km2. From 2001-2011 and 2011-2021, the oil palm plantations experienced the most crucial change, with a net gain of 292.94 km2 and 70.06 km2. In 2031, 2041, and 2051, the LULC was predicted to be oil palms, shrimp farms, mangroves, and urban and built-up lands. The LULC changes were consistent with the government policies implemented and indicated government policy as a driving force in LULC dynamics on Bandon Bay area forestry, aquaculture, and agriculture, particularly on oil palm cultivation. Government management and regulation on land use is crucial for reducing the expansion of agricultural areas, especially oil palm plantations and aquaculture areas, to mitigate negative impacts on the Bandon Bay ecosystem. Doi: 10.28991/ESJ-2024-08-05-06 Full Text: PDF
PM2.5 IoT Sensor Calibration and Implementation Issues Including Machine Learning Srisang, Wacharapong; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Khongthong, Chonthicha; Piamonte, John Rex P.; Sparrow, Elena B.
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-08

Abstract

Affordable IoT PM2.5 sensors, enabled by the Internet of Things, offer new ways to monitor air quality. However, concerns exist about their data accuracy. This study aimed (1) to investigate the low-cost PM sensor's performance under various outdoor ambient circumstances and (2) to evaluate seven calibration methods, which include decision trees, gradient-boosted trees, linear regression, nearest neighbors, neural networks, random forests, and the Gaussian Process. The Davis AirLink was used as a reference to compare the Plantower PMS3003 sensor's performance. The data from the Plantower PMS3003 sensor were then compared to the Davis AirLink values using calibration curves created by machine learning algorithms. Calibration curves were generated using machine learning algorithms trained on sensor measurements collected in two Thai cities (Nakhon Si Thammarat and Phuket). Our results show that all machine learning methods outperformed traditional linear regression, with decision trees and neural networks demonstrating the most significant improvement. This research highlights the need for sensor calibration and the limitations of current calibration methods and paves the way for advancements in cloud-based calibration and machine learning for improved data accuracy in IoT PM2.5sensor technology. Doi: 10.28991/ESJ-2024-08-06-08 Full Text: PDF
Driving Mangrove Recovery: Community Engagement and Socio-Economic Shifts in Aquaculture Areas Rattanarat, Jantira; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Sparrow, Elena B.
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-09

Abstract

Land-use change and recovery patterns of mangroves in the Tha Sak subdistrict, Nakhon Si Thammarat, Thailand, were examined utilizing multi-temporal Landsat images and socio-economic data from 1988 to 2023. Land use was classified through visual interpretation, and potential changes were predicted using a Markov chain model. The results showed a significant expansion of mangrove forests (1.11 km² to 9.10 km²), indicating a clear recovery. At the same time, the aquaculture area decreased drastically (from 25.69 km² to 8.79 km²), indicating a significant change in land use. The recovery of mangroves is primarily attributed to the cessation of aquaculture and the active involvement of the Tha Sak subdistrict's Small-Scale Fishermen Group, highlighting the success of community-based restoration. This study provides evidence of the critical role local communities play in bringing about positive environmental change and enabling Sustainable Development Goals (SDGs) 15: Life on Land from ecosystem restoration, SDG 14: Life Below Water for conservation of coastal areas, and SDG 11: Sustainable Cities and Communities for increasing community resilience. Involving local communities in mangrove restoration and preservation is key to long-term sustainability.
Co-Authors Jaroensutasinee, Mullica Khongthong, Chonthicha Piamonte, John Rex P. Rattanarat, Jantira Sparrow, Elena B. Srisang, Wacharapong