Open Access
Issue
BIO Web Conf.
Volume 216, 2026
The 6th Sustainability and Resilience of Coastal Management (SRCM 2025)
Article Number 06005
Number of page(s) 11
Section Environmental Monitoring and Sustainability
DOI https://doi.org/10.1051/bioconf/202621606005
Published online 05 February 2026
  • P. Wang et al., “Determining the critical threshold of meteorological heat damage to tea plants based on MODIS LST products for tea planting areas in China,” Ecol. Inform., vol. 77, Nov. 2023, doi: 10.1016/j.ecoinf.2023.102235. [Google Scholar]
  • S.N. Salimah, A. Junaedi, and Sudradjat, “Pengelolaan Pemetikan Tanaman Teh (Camellia sinensis (L.). O. Kuntze) di Wonosobo, Jawa Tengah,” Buletin Agrohorti, vol. 11, no. 2, pp. 249-259, 2023, doi: 10.29244/agrob.v11i2.47161. [Google Scholar]
  • L.P. Caro, Wages and working conditions in the tea sector: the case of India, Indonesia and Viet Nam Background note. 2020. [Online]. Available: www.ilo.org/publns. [Google Scholar]
  • C. Wang, J. Han, Y. Pu, and X. Wang, “Tea (Camellia sinensis): A Review of Nutritional Composition, Potential Applications, and Omics Research,” Jun. 01, 2022, MDPI. doi: 10.3390/app12125874. [Google Scholar]
  • N. Sahu et al., “Analysis of Tea Plantation Suitability Using Geostatistical and Machine Learning Techniques: A Case of Darjeeling Himalaya, India,” Sustainability (Switzerland), vol. 15, no. 13, Jul. 2023, doi: 10.3390/su151310101. [Google Scholar]
  • D.D.P. Afner, A. Aprisal, and Y. Yulnafatmawita, “Indeks Stabilitas Agregat Tanah Pada Perkebunan Teh Berbasis Slope Dan Umur Tanaman Di Kecamatan Gunung Talang Kabupaten Solok,” Jurnal Tanah dan Sumberdaya Lahan, vol. 8, no. 1, pp. 75-81, 2020, doi: 10.21776/ub.jtsl.2021.008.1.10. [Google Scholar]
  • M.N. Cahyadi et al., “Analysis of the effect of the 2021 Semeru eruption on water vapor content and atmospheric particles using GNSS and remote sensing,” Geod. Geodyn., vol. 15, no. 1, pp. 33-41, Jan. 2024, doi: 10.1016/j.geog.2023.04.005. [Google Scholar]
  • M.N. Cahyadi, T. Asfihani, R. Mardiyanto, and R. Erfianti, “Loosely Coupled GNSS and IMU Integration for Accurate i-Boat Horizontal Navigation,” International Journal of Geoinformatics, vol. 18, no. 3, pp. 111-122, Jun. 2022, doi: 10.52939/ijg.v18i3.2233. [Google Scholar]
  • M.N. Cahyadi and I. Rwabudandi, “Integration of GNSS-IMU for increasing the observation accuracy in Condensed Areas (Infrastructure and Forest Canopies),” in E3S Web of Conferences, EDP Sciences, May 2019. doi: 10.1051/e3sconf/20199403015. [Google Scholar]
  • M.N. Cahyadi et al., “Comparative Analysis of Low-Cost GNSS OEM Board K706 and BX316 (Case Study: Bulusidokare Village Sidoarjo Regency),” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Apr. 2021. doi: 10.1088/1755-1315/731/1/012024. [Google Scholar]
  • M.A. Lazaridou and A.C. Karagianni, “Landsat 8 multispectral and pansharpened imagery processing on the study of civil engineering issues,” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing, 2016, pp. 941-945. doi: 10.5194/isprsarchives-XLI-B8-941-2016. [Google Scholar]
  • B. Mamaghani, M.G. Saunders, and C. Salvaggio, “Inherent reflectance variability of vegetation,” Agriculture (Switzerland), vol. 9, no. 11, Nov. 2019, doi: 10.3390/agriculture9110246. [Google Scholar]
  • M.B. Moisa, B.T. Gabissa, L.B. Hinkosa, I.N. Dejene, and D.O. Gemeda, “Analysis of land surface temperature using Geospatial technologies in Gida Kiremu, Limu, and Amuru District, Western Ethiopia,” Artificial Intelligence in Agriculture, vol. 6, pp. 90-99, Jan. 2022, doi: 10.1016/j.aiia.2022.06.002. [Google Scholar]
  • A. Sekertekin and S. Bonafoni, “Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation,” Remote Sens. (Basel)., vol. 12, no. 2, Jan. 2020, doi: 10.3390/rs12020294. [Google Scholar]
  • R. Madugundu et al., “Impact of Field Topography and Soil Characteristics on the Productivity of Alfalfa and Rhodes Grass: RTK-GPS Survey and GIS Approach,” Agronomy, vol. 12, no. 12, Dec. 2022, doi: 10.3390/agronomy12122918. [Google Scholar]

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