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All issues Volume 566 (2024) E3S Web Conf., 566 (2024) 03007 References
Open Access
Issue
E3S Web Conf.
Volume 566, 2024
2024 6th International Conference on Environmental Sciences and Renewable Energy (ESRE 2024)
Article Number 03007
Number of page(s) 13
Section Environmental Pollution Monitoring and Ecological Environment Management
DOI https://doi.org/10.1051/e3sconf/202456603007
Published online 06 September 2024
  1. S.T. Jarnagin, Regional and global patterns of population, land use, and land cover change: An overview of stressors and impacts. GIScience Remote Sens. 41, 207–227 (2004). [CrossRef] [Google Scholar]
  2. E. Nelson, H. Sander, P. Hawthorne, M. Conte, D. Ennaanay, S. Wolny, S. Manson, S. Polasky, Projecting global land-use change and its effect on ecosystem service provision and biodiversity with simple models. PLoS OnE 5, e14327 (2010). [CrossRef] [PubMed] [Google Scholar]
  3. T. Desalegn, F. Cruz, M. Kindu, M.B. Turrión, Gonzalo, Land-use/land-cover (LULC) change and socioeconomic conditions of local community in the central highlands of Ethiopia. Int. J. Sustain. Dev. World Ecol. 21, 406–413 (2014). [CrossRef] [Google Scholar]
  4. B. Asenso Barnieh, L. Jia, M. Menenti, J. Zhou, Y. Zeng, Mapping land use land cover transitions at different spatiotemporal scales in West Africa. Sustainability 12, 8565 (2020). [CrossRef] [Google Scholar]
  5. M. Dede, S. Sunardi, K.C. Lam, S. Withaningsih, Relationship between landscape and river ecosystem services. Glob. J. Environ. Sci. Manag. 9, 637–652 (2023). [Google Scholar]
  6. M.A. Widiawaty, A. Ismail, M. Dede, N. Nurhanifah, Modeling land use and land cover dynamic using geographic information system and Markov-CA. Geos. Ind. 5, 210 (2020). [CrossRef] [Google Scholar]
  7. A.H. Strahler, A. Strahler, Introducing Physical Geography (Wiley, ed. 6, 2013). [Google Scholar]
  8. T. Day, Core themes in textbook definitions of physical geography: Core themes in physical geography. Can. Geogr. 61, 28–40 (2017). [CrossRef] [Google Scholar]
  9. M. Dede, C. Asdak, I. Setiawan, Spatial-ecological approach in Cirebon’s peri-urban regionalization. IOP Conf. Ser. Earth Environ. Sci. 1089, 012080 (2022). [CrossRef] [Google Scholar]
  10. T.A. Carleton, S.M. Hsiang, Social and economic impacts of climate. Science 353, aad9837 (2016). [CrossRef] [PubMed] [Google Scholar]
  11. F.N. Muhammad, K.C. Lam, Forest mapping in Peninsular Malaysia using Random Forest and Support Vector Machine Classifiers on Google Earth Engine. Geografia Malays. J. Soc. Space 19, 1–16 (2023). [Google Scholar]
  12. M.A. Widiawaty, N. Nurhanifah, A. Ismail, M. Dede, The impact of Cirebon coal-fired power plants on water quality in Mundu Bay, Cirebon Regency. Sustinere J. Environ. Sustain. 4 (2020). [Google Scholar]
  13. A. Hafiudzan, A. Sulistyarini, Z. Hikmah, R.S. Pratiwi, L. Ardyagarini, N. Zalianti Safitri, A. Azhiimi, M. Ibrohim, R. Jatmiko, Analysis of the relationships between land surface temperature and spectral indices pre- and during pandemic using Landsat-8 images (case study: Gerbangkertosusila). SPIE Conf. Proc. 12082, 231–237 (2021). [Google Scholar]
  14. M. Dede, G.P. Pramulatsih, M.A. Widiawaty, Y.R. Ramadhan, A. Ati, Dinamika suhu permukaan dan kerapatan vegetasi di Kota Cirebon. J. Meteorol. Klimatol. dan Geofis. 6, 23–31 (2019). [CrossRef] [Google Scholar]
  15. W. Nurdian, M. Dede, M.A. Widiawaty, Y. Ramadhan, Y. Purnama, Pemanfaatan sensor mikro DHT11-Arduino untuk monitoring suhu dan kelembaban udara, in Prosiding Pertemuan Ilmiah Tahunan dan Seminar Nasional Ilmu Lingkungan (Unpad Press, Bandung, 2020). [Google Scholar]
  16. N.I. Fawzi, Mengukur urban heat island menggunakan penginderaan jauh, kasus di Kota Yogyakarta. Maj. Ilm. Globe 19, 195–206 (2017). [CrossRef] [Google Scholar]
  17. R.D. Pratiwi, I. S. Fatimah, A. Munandar, A spatial planning for green infrastructure in Yogyakarta City based on land surface temperature. IOP Conf. Ser. Earth Environ. Sci. 179, 012004 (2018). [CrossRef] [Google Scholar]
  18. L. Atianta, Suhu permukaan lahan dan intensitas pemanfaatan ruang di perkotaan Yogyakarta. J. Pengemb. Kota 8, 151–162 (2020). [CrossRef] [Google Scholar]
  19. L.M. Jaelani, C.A. Handayani, Spatio-temporal analysis of land surface temperature changes in Java Island from Aqua and Terra MODIS satellite imageries using Google Earth Engine. Int. J. Geoinformatics 18, 1–12 (2022). [Google Scholar]
  20. N. Nandi, M. Dede, Urban heat island assessment using remote sensing data in West Java, Indonesia: From literature review to experiments and analyses. Indones. J. Sci. Technol. 7, 105–116 (2022). [Google Scholar]
  21. E. Tasser, G. Leitinger, U. Tappeiner, Climate change versus land-use change—What affects the mountain landscapes more?. Land Use Policy 60, 60–72 (2017). [CrossRef] [Google Scholar]
  22. S. Sunardi, I. Nursamsi, M. Dede, A. Paramitha, M.C.W. Arief, M. Ariyani, P. Santoso, Assessing the influence of land-use changes on water quality using remote sensing and GIS: A study in Cirata Reservoir, Indonesia. Sci. Technol. Indones. 7, 106–114 (2022). [CrossRef] [Google Scholar]
  23. Q. Weng, M.K. Firozjaei, M. Kiavarz, S.K. Alavipanah, S. Hamzeh, Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Sci Total Environ. 650, 515–529 (2019). [CrossRef] [PubMed] [Google Scholar]
  24. M. Dede, S. Sunardi, K.C. Lam, S. Withaningsih, Morphometric analysis based on multi-sources data and geographic information system in the Cirasea Watershed, Indonesia, in The 1st International Conference on Environmental Science, Development, and Management (ICESDM) (EUDL, 2023). [Google Scholar]
  25. I. Prawiranegara, River as Project: Commodification of Citarum River Stream (Agrarian Resources Center, 2020). [Google Scholar]
  26. I. Faizal, N.P. Purba, M.K. Martasuganda, A. Abimanyu, M.R. Akbar, E. Sugianto, Physical control on marine debris spreading around Muara Gembong, Jakarta Bay. J. Ecol. Eng. 23, 12–20 (2022). [CrossRef] [Google Scholar]
  27. M.A. Widiawaty, Mari Mengenal Sains Informasi Geografis (Aria Mandiri Group, Bandung, 2019). [Google Scholar]
  28. C.F. Li, J.Y. Yin, J.J. Zhao, Study on the relationships between ground bright temperature and land-use types of city based on Landsat image. Int. J. Environ. Sci. Dev., 268–272 (2010). [Google Scholar]
  29. W. Prasomsup, P. Piyatadsananon, W. Aunphoklang, A. Boonrang, Extraction technic for built-up area classification in Landsat 8 imagery. Int. J. Environ. Sci. Dev. 11, 15–20 (2020). [CrossRef] [Google Scholar]
  30. M. Dede, S. Sunardi, K.C. Lam, S. Withaningsih, H. Hendarmawan, T. Husodo, Landscape dynamics and its related factors in the Citarum River Basin. in BPDP Unpublished Report Universitas Padjadjaran (2023). [Google Scholar]
  31. S.L. Ermida, P. Soares, V. Mantas, F.M. Göttsche, I.F. Trigo, Google Earth Engine open-source code for land surface temperature estimation from the Landsat series. Remote Sens. 12, 1471 (2020). [CrossRef] [Google Scholar]
  32. M. Wang, Z. Zhang, T. Hu, G. Wang, G. He, Z. Zhang, H. Li, Z. Wu, An efficient framework for producing landsat-based land surface temperature data using Google Earth Engine. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 4689–4701 (2020). [CrossRef] [Google Scholar]
  33. M. Ridho, Analyzing land surface temperature (LST) with Landsat 8 data in Google Earth Engine, Medium (2023). https://medium.com/@ridhomuh002/analyzing-land-surface-temperature-lst-with-landsat-8-data-in-google-earth-engine-f4dd7ca28e70.. [Google Scholar]
  34. D.C. Phan, T.H. Trung, V.T. Truong, T. Sasagawa, T.P.T. Vu, D. Tien-Bui, M. Hayashi, T. Tadono, K.N. Nasahara, First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam. Sci. Rep. 11, 9979 (2021). [CrossRef] [Google Scholar]
  35. E.J. Krieg, Statistics and Data Analysis for Social Science (SAGE Publications, 2019). [Google Scholar]
  36. W. Widhiarso, Catatan dalam Penggunaan Eta-Squared dalam Analisis Varians, (Fakultas Psikologi UGM, 2010). [Google Scholar]
  37. J.T. Richardson, Eta squared and partial eta squared as measures of effect size in educational research. Educ. Res. Rev. 6, 135–147 (2011). [CrossRef] [Google Scholar]
  38. N. Danapriatna, I. Ismarani, M. Dede, Application of biochar and biological fertilizer to improve soil quality and Oryza sativa L. productivity. Cogent Food Agric. 9, 2207416 (2023). [CrossRef] [Google Scholar]
  39. Danapriatna, I. Ismarani, R. Lutfiadi, M. Dede, Effect of straw compost (Oryza sativa L.) on crop production. Pertanika J. Trop. Agric. Sci. 46 (2023). [Google Scholar]
  40. H. Susiati, M. Dede, M.A. Widiawaty, R. Risko, P.M. Udiyani, TSS in West Kalimantan based on remote sensing data: A preliminary study for siting nuclear power plant. AIP Conf. Proc. 2501, 020005 (2022). [CrossRef] [Google Scholar]
  41. M. Dede, H. Susiati, M.A. Widiawaty, K.C. Lam, K. Aiyub, N.H. Asnawi, Multivariate analysis and modeling of shoreline changes using geospatial data. Geocarto Int. 38, 2159070 (2023). [CrossRef] [Google Scholar]
  42. M. Dede, S.B. Wibowo, Y. Prasetyo, I.W. Nurani, P.B. Setyowati, S. Sunardi, Water resources carrying capacity before and after volcanic eruption. Glob. J. Environ. Sci. Manag. 8, 473–484 (2022). [Google Scholar]
  43. B. Pfaff, J. Darrington, M. H. Satman, A. Mead, F. Beckmann, J. Williams, M. Kiefte, P. Kobly, R. van Son, B. Díaz, H. Alejandro, PSPP users’ Guide: GNU PSPP statistical analysis software release 1.6.2-g78a33a (Free Software Foundation, 2020).. [Google Scholar]
  44. N.T. Sugito, I. Gumilar, A. Hernandi, A.P. Handayani, M. Dede, Utilizing semi-variograms and geostatistical approach for land value model in urban region. Int. J. Eng. 36, 2222–2231 (2023). [CrossRef] [Google Scholar]
  45. A.N. Chaidar, I. Soekarno, A. Wiyono, J. Nugroho, Spatial analysis of erosion and land criticality of the upstream Citarum watershed. Int. J. Geomate 13, 133–140 (2022). [Google Scholar]
  46. J. Jupri, A. Mulyadi, W. Eridiana, Y. Malik, Land critical study and farmers responses in upland area in Indonesia. IOP Conf. Ser. Earth Environ. Sci. 145, 012101 (2018). [CrossRef] [Google Scholar]
  47. S. Setiadi, A. Sumaryana, H. Bekti, D. Sukarno, The flood management policy in Bandung city: Challenges and potential strategies. Cogent Soc. Sci 9, 2282434 (2023). [Google Scholar]
  48. A. Leblois, O. Damette, J. Wolfersberger, What has driven deforestation in developing countries since the 2000s? Evidence from new remote-sensing data. World Dev. 92, 82–102 (2017). [CrossRef] [Google Scholar]
  49. P.M. Venodha, Landscape degradation and restoration — A planning approach. Int. J. Environ. Sci. Dev. 7, 229–233 (2016). [CrossRef] [Google Scholar]
  50. A.A. Lacis, J.E. Hansen, G.L. Russell, V. Oinas, J. Jonas, The role of long-lived greenhouse gases as principal LW control knob that governs the global surface temperature for past and future climate change. Tellus B Chem. Phys. Meteorol. 65, 19734 (2013). [CrossRef] [Google Scholar]
  51. M. Hertzberg, A. Siddons, H. Schreuder, Role of greenhouse gases in climate change. Energy Environ. 28, 530–539 (2017). [CrossRef] [Google Scholar]
  52. M. Dede, M.A. Widiawaty, Utilization EOS Platform as cloud-based GIS to analyze vegetation greenness in Cirebon Regency, Indonesia. J. Inf. Technol. Util. 3, 1–4 (2020). [Google Scholar]
  53. M. Winterdahl, M. Futter, S. Köhler, H. Laudon, J. Seibert, K. Bishop, Riparian soil temperature modification of the relationship between flow and dissolved organic carbon concentration in a boreal stream. Water Resour. Res. 47 (2011). [CrossRef] [PubMed] [Google Scholar]
  54. S.J. Dugdale, L.A. Malcolm, K. Kantola, D.M. Hannah, Stream temperature under contrasting riparian forest cover: Understanding thermal dynamics and heat exchange processes. Sci. Total Environ. 610, 1375–1389 (2018). [CrossRef] [Google Scholar]
  55. C.A. Navas, J.M. Carvajalino-Fernández, L.P. Saboyá-Acosta, L.A. Rueda-Solano, M.A. Carvajalino-Fernández, The body temperature of active amphibians along a tropical elevation gradient: Patterns of mean and variance and inference from environmental data. Funct. Ecol. 27, 1145–1154 (2013). [CrossRef] [Google Scholar]
  56. A.A. Bindajam, J. Mallick, S. AlQadhi, C.K. Singh, H.T. Hang, Impacts of vegetation and topography on land surface temperature variability over the semi-arid mountain cities of Saudi Arabia. Atmosphere 11, 762 (2020). [CrossRef] [Google Scholar]
  57. S. Jiang, X. Chen, K. Smettem, T. Wang, Climate and land use influences on changing spatiotemporal patterns of mountain vegetation cover in southwest China. Ecol. Indic. 121, 107193 (2021). [CrossRef] [Google Scholar]
  58. J. Qiu, X. Li, W. Qian, Optimizing the spatial pattern of the cold island to mitigate the urban heat island effect. Ecol. Indic. 154, 110550 (2023). [CrossRef] [Google Scholar]
  59. Y. Song, C. Wu, Examining human heat stress with remote sensing technology. GIScience Remote Sens. 55, 19–37 (2018). [Google Scholar]
  60. J. Hobbie, G.W. Kling, H.E. Adams, N.D. Bettez, W.B. Bowden, B.C. Crump, A.E. Giblin, M. Stieglitz, Alaska’s changing Arctic: Ecological consequences for tundra, streams, and lakes (Oxford University Press, 2014). [CrossRef] [Google Scholar]
  61. W. Qian, X. Li, A cold island connectivity and network perspective to mitigate the urban heat island effect. Sustain. Cities Soc. 94, 104525 (2023). [CrossRef] [Google Scholar]
  62. S. Ira, Modeling of land surface temperature (LST) and normalized difference vegetation index (NDVI) in Nepal: 2000-2015 (Prince of Songkla University at Pattani Campus, 2018). [Google Scholar]
  63. V. Fitriani, L.I. Gandri, S. Bana, Analisis hubungan land surface temperature (LST) dan indeks kerapatan vegetasi (NDVI) DAS Wanggu, Sulawesi Tenggara. J. Ilmu-Ilmu Kehutan. 7, 1-8 (2023). [Google Scholar]
  64. M. Dede, M.A. Widiawaty, M.A., Y.R. Ramadhan, A. Ismail, W. Nurdian, Prediksi Suhu permukaan menggunakan artificial neural network-cellular automata di wilayah Cirebon dan sekitarnya. Semin. Nas. Geomatika 4, 153-160 (2020). [Google Scholar]

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