Forest Fires in Peatlands Analyzed from Various Perspectives: Spatial, Temporal, and Spatial-Temporal

Nur'ainul Miftahul Huda, Nurfitri Imro'ah, Muhammad Yahya Ayyash, Hesty Pratiwi

Abstract


Peatland fires are characterized by the compaction of organic matter below the soil surface. If dry conditions occur, the organic matter can burn, making it difficult to extinguish the fire. This study aims to analyze peatland forest fires with three perspectives, namely temporal, spatial, and spatial-temporal. The data used is the confidence level data of hotspots in forest fires in Kubu Raya Regency, West Kalimantan from January 2014 to December 2023. The methodology used includes collecting fire data from satellite imagery and prepocessing the data. Furthermore, three different data analyzes were carried out, namely temporal, spatial, and spatial-temporal analysis. The results of the study obtained three perspectives, namely from the time period, handling of forest fire cases because they have an impact on the future as seen from the ARIMA model. Regarding spatiality, the distribution of hotspots spread to surrounding areas that were heavily affected by hotspots as seen from the contour map using Kriging interpolation. Finally, regarding spatiality and temporality, forest fire projections show that locations that are close together and have a history of being affected by forest fires have a strong potential for the distribution of forest fire cases as seen from the GSTAR space-time model.


Keywords


Spatial; Dependence; Peatland.

Full Text:

DOWNLOAD [PDF]

References


Alig, R. J. (2011). Land Use and Climate Change: A Global Perspective on Mitigation Options: Discussion. American Journal of Agricultural Economics, 93(2), 356–357. https://doi.org/10.1093/ajae/aaq085

Anselin, L. (2010). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

Bargali, H., Pandey, A., Bhatt, D., Sundriyal, R. C., & Uniyal, V. P. (2024). Forest fire management, funding dynamics, and research in the burning frontier: A comprehensive review. Trees, Forests and People, 16, 100526. https://doi.org/10.1016/j.tfp.2024.100526

Box, G. E. P., & Jankins, G. M. (1976). Time Series Analysis Forecasting and Control. Wiley.

Dupras, J., Marull, J., Parcerisas, L., Coll, F., Gonzalez, A., Girard, M., & Tello, E. (2016). The impacts of urban sprawl on ecological connectivity in the Montreal Metropolitan Region. Environmental Science & Policy, 58, 61–73. https://doi.org/10.1016/j.envsci.2016.01.005

Erdogan Erten, G., Yavuz, M., & Deutsch, C. V. (2022). Combination of Machine Learning and Kriging for Spatial Estimation of Geological Attributes. Natural Resources Research, 31(1), 191–213. https://doi.org/10.1007/s11053-021-10003-w

Eusemann, P., Petzold, A., Thevs, N., & Schnittler, M. (2013). Growth patterns and genetic structure of Populus euphratica Oliv. (Salicaceae) forests in NW China – Implications for conservation and management. Forest Ecology and Management, 297, 27–36. https://doi.org/10.1016/j.foreco.2013.02.009

Fischer, M., & Proppe, C. (2023). Enhanced universal kriging for transformed input parameter spaces. Probabilistic Engineering Mechanics, 74, 103486. https://doi.org/10.1016/j.probengmech.2023.103486

Fotheringham, A., & Rogerson, P. (2009). The SAGE Handbook of Spatial Analysis. SAGE Publications, Ltd. https://doi.org/10.4135/9780857020130

Gajendiran, K., Kandasamy, S., & Narayanan, M. (2024). Influences of wildfire on the forest ecosystem and climate change: A comprehensive study. Environmental Research, 240, 117537. https://doi.org/10.1016/j.envres.2023.117537

Glenk, K., Shrestha, S., Topp, C. F. E., Sánchez, B., Iglesias, A., Dibari, C., & Merante, P. (2017). A farm level approach to explore farm gross margin effects of soil organic carbon management. Agricultural Systems, 151, 33–46. https://doi.org/10.1016/j.agsy.2016.11.002

Gui, D., He, H., Liu, C., & Han, S. (2023). Spatio-temporal dynamic evolution of carbon emissions from land use change in Guangdong Province, China, 2000–2020. Ecological Indicators, 156, 111131. https://doi.org/10.1016/j.ecolind.2023.111131

Harenda, K. M., Lamentowicz, M., Samson, M., & Chojnicki, B. H. (2018). The Role of Peatlands and Their Carbon Storage Function in the Context of Climate Change (pp. 169–187). https://doi.org/10.1007/978-3-319-71788-3_12

Hein, L., Spadaro, J. V., Ostro, B., Hammer, M., Sumarga, E., Salmayenti, R., Boer, R., Tata, H., Atmoko, D., & Castañeda, J.-P. (2022). The health impacts of Indonesian peatland fires. Environmental Health, 21(1), 62. https://doi.org/10.1186/s12940-022-00872-w

Huda, N. M., & Imro’ah, N. (2023). Determination of the best weight matrix for the Generalized Space Time Autoregressive (GSTAR) model in the Covid-19 case on Java Island, Indonesia. Spatial Statistics, 54, 100734. https://doi.org/10.1016/j.spasta.2023.100734

Huda, N. M., Imro’ah, N., & Mailanda, R. (2023). Spatial autocorrelation using Moran’s Index to map the confirmed positive of Covid-19 cases in Java. 050006. https://doi.org/10.1063/5.0112014

Huda, N. M., Mukhaiyar, U., & Pasaribu, U. S. (2020). Forecasting dengue fever cases using autoregressive distributed lag model with outlier factor. 020005. https://doi.org/10.1063/5.0018450

Imro’ah, N., Huda, N. M., Utami, D. S., Umairah, T., & Arini, N. F. (2024). Control Chart for Correcting the ARIMA Time Series Model of GDP Growth Cases. JTAM (Jurnal Teori Dan Aplikasi Matematika), 8(1), 312. https://doi.org/10.31764/jtam.v8i1.19612

Kaur, J., Parmar, K. S., & Singh, S. (2023). Autoregressive models in environmental forecasting time series: a theoretical and application review. Environmental Science and Pollution Research, 30(8), 19617–19641. https://doi.org/10.1007/s11356-023-25148-9

Khan, M., Almazah, M. M. A., EIlahi, A., Niaz, R., Al-Rezami, A. Y., & Zaman, B. (2023). Spatial interpolation of water quality index based on Ordinary kriging and Universal kriging. Geomatics, Natural Hazards and Risk, 14(1). https://doi.org/10.1080/19475705.2023.2190853

Kumar, P., Rao, B., Burman, A., Kumar, S., & Samui, P. (2023). Spatial variation of permeability and consolidation behaviors of soil using ordinary kriging method. Groundwater for Sustainable Development, 20, 100856. https://doi.org/10.1016/j.gsd.2022.100856

Leknoi, U., & Likitlersuang, S. (2020). Good practice and lesson learned in promoting vetiver as solution for slope stabilisation and erosion control in Thailand. Land Use Policy, 99, 105008. https://doi.org/10.1016/j.landusepol.2020.105008

Li, L., Sali, A., Noordin, N. K., Ismail, A., & Hashim, F. (2023). Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning. Forests, 14(7), 1472. https://doi.org/10.3390/f14071472

Li, M., Shen, S., Barzegar, V., Sadoughi, M., Hu, C., & Laflamme, S. (2021). Kriging-based reliability analysis considering predictive uncertainty reduction. Structural and Multidisciplinary Optimization, 63(6), 2721–2737. https://doi.org/10.1007/s00158-020-02831-w

Ligas, M. (2022). Comparison of kriging and least-squares collocation – Revisited. Journal of Applied Geodesy, 16(3), 217–227. https://doi.org/10.1515/jag-2021-0032

Nobre, C. A., Sampaio, G., Borma, L. S., Castilla-Rubio, J. C., Silva, J. S., & Cardoso, M. (2016). Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences, 113(39), 10759–10768. https://doi.org/10.1073/pnas.1605516113

Nunes, L. J. R. (2023). The Rising Threat of Atmospheric CO2: A Review on the Causes, Impacts, and Mitigation Strategies. Environments, 10(4), 66. https://doi.org/10.3390/environments10040066

Page, S. E., Rieley, J. O., & Banks, C. J. (2011). Global and regional importance of the tropical peatland carbon pool. Global Change Biology, 17(2), 798–818. https://doi.org/10.1111/j.1365-2486.2010.02279.x

Parajuli, A., Gautam, A. P., Sharma, S. P., Bhujel, K. B., Sharma, G., Thapa, P. B., Bist, B. S., & Poudel, S. (2020). Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomatics, Natural Hazards and Risk, 11(1), 2569–2586. https://doi.org/10.1080/19475705.2020.1853251

Sharma, A. K., Punj, P., Kumar, N., Das, A. K., & Kumar, A. (2024). Lifetime Prediction of a Hydraulic Pump Using ARIMA Model. Arabian Journal for Science and Engineering, 49(2), 1713–1725. https://doi.org/10.1007/s13369-023-07976-6

Soltani, A., & Askari, S. (2017). Exploring spatial autocorrelation of traffic crashes based on severity. Injury, 48(3), 637–647. https://doi.org/10.1016/j.injury.2017.01.032

Suhardono, S., Fitria, L., Suryawan, I. W. K., Septiariva, I. Y., Mulyana, R., Sari, M. M., Ulhasanah, N., & Prayogo, W. (2024). Human activities and forest fires in Indonesia: An analysis of the Bromo incident and implications for conservation tourism. Trees, Forests and People, 15, 100509. https://doi.org/10.1016/j.tfp.2024.100509

Sukkuea, A., & Heednacram, A. (2022). Prediction on spatial elevation using improved kriging algorithms: An application in environmental management. Expert Systems with Applications, 207, 117971. https://doi.org/10.1016/j.eswa.2022.117971

Wang, X., Huang, K., Yu, Y., Hu, T., & Xu, Y. (2016). An input–output structural decomposition analysis of changes in sectoral water footprint in China. Ecological Indicators, 69, 26–34. https://doi.org/10.1016/j.ecolind.2016.03.029

Yundari, Y., Huda, N. M., Pasaribu, U. S., Mukhaiyar, U., & Sari, K. N. (2020). Stationary Process in GSTAR(1;1) through Kernel Function Approach. 020010. https://doi.org/10.1063/5.0016808




DOI: https://doi.org/10.31764/jtam.v9i2.28884

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Nur’ainul Miftahul Huda, Nurfitri Imro’ah, Muhammad Yahya Ayyash, Hesty Pratiwi

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

_______________________________________________

JTAM already indexing:

                     


_______________________________________________

 

Creative Commons License

JTAM (Jurnal Teori dan Aplikasi Matematika) 
is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

______________________________________________

_______________________________________________

_______________________________________________ 

JTAM (Jurnal Teori dan Aplikasi Matematika) Editorial Office: