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All Journal International Journal of Public Health Science (IJPHS) Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Konversi Tropical Wetland Journal Journal of Applied Data Sciences Aceh International Journal of Science and Technology
Isna Syauqiah
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Health Professions Medicine & Pharmacology Other

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Journal : Journal of Applied Data Sciences

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Assessing Drought Risk in Forest Zones Near Coal Mines with TVDI Taati, La; Sunardi, Sunardi; Syauqiah, Isna; Jauhari, A
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.220

Abstract

This study is quantitative research employing survey techniques and spatial modeling. In the research area, particularly in forested areas, the NDVI values range from 0.25 to 0.55 with LST values of 29°C to 37°C. Cooler temperatures below 34°C were observed in the southwest (outside the mining area). The LST values indicate high temperatures above 37°C in the coal mining area, with effects extending up to 6 km. The linear regression equation between NDVI and LST in the coal mining area, with a regression equation of y = -20.888x + 40.458; R^2 = 0.83; r = -0.91, shows an inverse relationship between NDVI increase and ground surface temperature, indicating a good model fit with the data and a strong negative linear relationship between the two variables. The Urban Heat Island (UHI) effect in the mining area, especially at the mining center, shows a UHI with a temperature difference of more than 0.6 degrees Celsius compared to the cooler surrounding area. At the center of the coal mining area, the TVDI value is 0.6-0.8 (high-very high), but in the eastern part of the mine in forested areas with a certain soil type, the TVDI value is 0.2-0.6 (moderately dry - dry), while in other parts of the forested area with a different soil type, the NDVI value is 0.2 (moist). There is a difference in response to different soil types. Drought increases in the forested areas around the mining site, affecting ecosystem productivity and soil moisture.
Biophysical Model of Mount Babaris for Predicting Carbon Potential using Remote Sensing Jauhari, Ahmad; Syauqiah, Isna; Taati, La; Munsyi, Munsyi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.321

Abstract

The biophysical model of Mount Babaris aims to predict carbon potential using remote sensing technology to address high levels of greenhouse gases, particularly CO2. This study combines satellite data with field measurements to create a validated model analyzing Forest Canopy Height (FCH), Normalized Difference Vegetation Index (NDVI), Vegetation Density (VD), and Land Surface Temperature (LST). A multiple regression analysis shows a strong correlation between these parameters and VD, with an R² value of 0.8673, indicating that 86.73% of the variation in vegetation density can be explained by these variables. Field validation, including drone photographs, crown and stem base density measurements, and plant size, ensures the accuracy of the satellite-derived data. The model uses the equation VD = 123.295486 x NDVI - 0.413961 x LST - 0.410253 x FCH - 3.173195, validated through field data. For processing field measurements, the equation LBDstemCor = 0.007645 x LBDcrown + 0.034348 x VD - 1.575439, with an R² value of 0.9564, further demonstrates its accuracy. To estimate carbon potential in kilograms per pixel (CPP), the equation CPP = LBDstemCor x FCHcor x 0.7 x 680 x 1.34 x 0.47 was used. The predicted carbon potential for Mount Babaris (1,576 ha) ranges from 607,767.55 to 607,829.54 tons, reflecting the model's precision in estimating carbon storage. This model plays a crucial role in monitoring and predicting carbon potential, supporting environmental management and climate change mitigation efforts. By integrating GIS and remote sensing, the model offers a scalable, replicable methodology adaptable to other regions with similar characteristics. It enhances the accuracy of carbon stock estimations and provides essential data for developing strategies to increase carbon sequestration, contributing to global climate change mitigation. The combination of satellite data, field measurements, and statistical analysis makes this model an invaluable tool for effective ecosystem conservation and restoration.
Co-Authors Ahmad Jauhari BAYU ANDHIKA Deni Fakhrizal Eko Suhartono Erlena, Erlena Faturrahman, Arief Hafifah, Ifa Heldawati, Heliyanur Iskandar Iskandar Jamiyaturrasyidah, Jamiyaturrasyidah Jauhari, A Meina Wulansari Yusniar Munsyi, Munsyi Noor, Meitria Syahdatina Nopi Stiyati Prihatini Nurandini*, Desi Prihartini, Nopi Stiyati Putera, Gusti Muhammad Perdana Rahimah, Zikri Sekartaji, Hapsari Lintang Sunardi Sunardi Taati, La Trang, Ha Thi Thu Umi Baroroh Lili Utami Victor M., Stevano Wiyono, Noerhadi