Spatial Patterns of Cumulative Hotspots and Their Relationships with Topographical Factors and Land Use in Kanchanaburi Province, Thailand

Authors

DOI:

https://doi.org/10.24259/fs.v8i2.32194

Keywords:

Spatial pattern, Fire, Hotspots, Land use, Spatial statistics, Thailand

Abstract

The clustering of hotspots represents fires occurring at specific locations across various time intervals, and is an increasingly important interdisciplinary research phenomenon. This article investigates the spatial distribution of cumulative hotspots and their relationships with topographical factors and land use in Kanchanaburi province. Data from the Suomi NPP VIIRS system spanning from 2012 to 2021 were utilized for the analysis of Getis-Ord (Gi*) spatial autocorrelation using Fire Radiative Power values. The analysis included the correlation with topographic data such as elevation, slope, aspect, and overlay with land use data. The results reveal that significant hotspots are concentrated in the districts of Si Sawat, Thong Pha Phum, Sai Yok, Sangkhla Buri, and Mueang Kanchanaburi. The majority of hotspots were statistically insignificant (85%), with hotspots (10%) and cold spots (5%) predominantly occurring in forested and agricultural areas. Hotspots were particularly prevalent in the northern and northeastern regions. Therefore, the utilization of Suomi NPP VIIRS data in conjunction with spatial statistics can identify the occurrence of hotspots and cold spots, aiding in planning and policy-making efforts to mitigate hotspot occurrences.

References

Adámek, M., Jankovská, Z., Hadincová, V., Kula, E., & Wild, J. (2018). Drivers of forest fire occurrence in the cultural landscape of Central Europe. Landscape Ecology, 33(11), 2031-2045. https://doi.org/10.1007/s10980-018-0712-2

Akyürek, Ö. (2023). Spatial and temporal analysis of vegetation fires in Europe. Natural Hazards, 117(1), 1105-1124. https://doi.org/10.1007/s11069-023-05896-0

Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

Benesty, J., Chen, J., Huang, Y., Cohen, I. (2009). Pearson Correlation Coefficient. In Benesty, J., Chen, J., Huang, Y., & Cohen, I. (Eds.), Noise reduction in speech processing (Vol. 2) (pp. 1-4). Springer Science & Business Media. https://doi.org/10.1007/978-3-642-00296-0_5

Brotons, L., Aquilué, N., De Cáceres, M., Fortin, M.-J., & Fall, A. (2013). How fire history, fire suppression practices and climate change affect wildfire regimes in Mediterranean landscapes. PloS one, 8(5), e62392. https://doi.org/10.1371/ journal.pone.0062392

Cizungu, N. C., Tshibasu, E., Lutete, E., Mushagalusa, C. A., Mugumaarhahama, Y., Ganza, D., ... & Bogaert, J. (2021). Fire risk assessment, spatiotemporal clustering and hotspot analysis in the Luki biosphere reserve region, western DR Congo. Trees, Forests and People, 5, 100104. https://doi.org/10.1016/j.tfp. 2021.100104

de México, C. E. (2017). Spatial modeling of forest fires in Mexico: an integration of two data sources. Bosque, 38(3), 563-574.

Department of Pollution Control. (2020). The 5-Year Strategic Plan (B.E. 2566 - 2570) of the Department of Pollution Control. Retrieved 09/10/2023 from https://www.pcd.go.th/strategy/แผนปฏิบัติราชการระยะ-5-ปี-พ-ศ-2566-2570-ของกรมควบคุมมลพิษ

FIRMS. (2020). Country Yearly Summary. FIRM. Retrieved 25/03/2022 from https://firms.modaps.eosdis.nasa.gov/country/

Fu, Y., Gao, H., Liao, H., & Tian, X. (2021). Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation. Remote Sensing, 13(19), 3880. https://www.mdpi.com/2072-4292/13/19/3880

Fu, Y., Gao, H., Liao, H., & Tian, X. (2021). Spatiotemporal variations and uncertainty in crop residue burning emissions over North China plain: Implication for atmospheric co2 simulation. Remote Sensing, 13(19), 3880. https://doi.org/ 10.3390/rs13193880

Geo-Informatics and Space Technology Development Agency (Public Organization). (2020). Report on the situation of forest fires and haze from satellite data for the year 2020. Geo-Informatics and Space Technology Development Agency Retrieved 25/03/2022 from https://fire.gistda.or.th/fire_report/Fire_2563.pdf

Getis, A., & Ord, J. K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189-206. https://doi.org/10.1111/j. 1538-4632.1992.tb00261.x

Hazaymeh, K., Almagbile, A., & Alomari, A. H. (2022). Spatiotemporal Analysis of Traffic Accidents Hotspots Based on Geospatial Techniques. ISPRS International Journal of Geo-Information, 11(4), 260. https://doi.org/10.3390/ijgi11040260

Hysa, A., Spalevic, V., Dudic, B., Roșca, S., Kuriqi, A., Bilașco, Ș., & Sestras, P. (2021). Utilizing the available open-source remotely sensed data in assessing the wildfire ignition and spread capacities of vegetated surfaces in Romania. Remote Sensing, 13(14), 2737. https://doi.org/10.3390/rs13142737

Kanchanaburi Provincial Statistical Office. (2022). Kanchanaburi Province Statistical Report 2022. Kanchanaburi Provincial Statistical Office http://kanchanaburi. nso.go.th/index.php?option=com_content&view=article&id=957:2565&catid=120:2021-01-15-22-48-53&Itemid=579

Kganyago, M., & Shikwambana, L. (2019). Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis. Sustainability, 11(23), 6811. https://doi.org/10.3390/su11236811

Kumharn, W., Sudhibrabha, S., Hanprasert, K., Janjai, S., Masiri, I., Buntoung, S., ... & Jankondee, Y. (2024). Estimating hourly full-coverage PM2. 5 concentrations model based on MODIS data over the northeast of Thailand. Modeling Earth Systems and Environment, 10(1), 1273-1280. https://doi.org/10.1007/s40808-023-01839-7

Land Development Department. (2023). Land Use. Land Development Department. Retrieved 20/01/2023 from: http://webapp.ldd.go.th/Soilservice/

Lanorte, A., Danese, M., Lasaponara, R., & Murgante, B. (2013). Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis. International Journal of Applied Earth Observation and Geoinformation, 20, 42-51. https://doi.org/10.1016/j.jag.2011.09.005

Li, R., He, X., Wang, H., Wang, Y., Zhang, M., Mei, X., ... & Chen, L. (2022). Estimating emissions from crop residue open burning in Central China from 2012 to 2020 using statistical models combined with satellite observations. Remote Sensing, 14(15), 3682. https://doi.org/10.3390/rs14153682

Marsha, A. L., & Larkin, N. K. (2022). Evaluating satellite fire detection products and an ensemble approach for estimating burned area in the United States. Fire, 5(5), 147. https://doi.org/10.3390/fire5050147

Melo, K. D. S., Delgado, R. C., Pereira, M. G., & Ortega, G. P. (2024). The Consequences of Climate Change in the Brazilian Western Amazon: A New Proposal for a Fire Risk Model in Rio Branco, Acre. Forests, 15(1), 211. https://doi.org/10.3390/ f15010211

Mesquitela, J., Elvas, L. B., Ferreira, J. C., & Nunes, L. (2022). Data Analytics Process over Road Accidents Data—A Case Study of Lisbon City. ISPRS International Journal of Geo-Information, 11(2), 143. https://doi.org/10.3390/ijgi11020143

Mpakairi, K. S., Tagwireyi, P., Ndaimani, H., & Madiri, H. T. (2019). Distribution of wildland fires and possible hotspots for the Zimbabwean component of Kavango-Zambezi Transfrontier Conservation Area. South African Geographical Journal, 101(1), 110-120. https://doi.org/10.1080/03736245.2018.1541023

Mupfiga, U. N., Mutanga, O., Dube, T., & Kowe, P. (2022). Spatial Clustering of Vegetation Fire Intensity Using MODIS Satellite Data. Atmosphere, 13(12), 1972. https://doi.org/10.3390/atmos13121972

NASA JPL (2020). NASADEM Merged DEM Global 1 arc second V001 [Data set]. NASA EOSDIS Land Processes DAAC. NASA JPL Accessed 2020-12-30 from https://10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001

Phonphan, W. (2020). Analysis Forest Fire Cause and Different Land Use Within Buffer Zones in Kanchanaburi Province, Thailand. In Monprapussorn, S., Lin, Z., Sitthi, A., & Wetchayont, P. (Eds.), Geoinformatics for Sustainable Development in Asian Cities (pp. 118-127). Springer International Publishing.

Reddy, C. S., Unnikrishnan, A., Bird, N. G., Faseela, V. S., Asra, M., Manikandan, T. M., & Rao, P. V. N. (2020). Characterizing vegetation fire dynamics in Myanmar and South Asian Countries. Journal of the Indian Society of Remote Sensing, 48, 1829-1843. https://doi.org/10.1007/s12524-020-01205-5

Royal Thai Survey Department. (2023). Thailand - Subnational Administrative Boundaries. Royal Thai Survey Department. Retrieved 20/01/2023 from https://data.humdata.org/dataset/cod-ab-tha

Schroeder, W., Oliva, P., Giglio, L., & Csiszar, I. A. (2014). The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment. Remote Sensing of Environment, 143, 85-96. https://doi.org/10.1016/j.rse. 2013.12.008

Shekede, M. D., Gwitira, I., & Mamvura, C. (2021, 2021/05/09). Spatial modelling of wildfire hotspots and their key drivers across districts of Zimbabwe, Southern Africa. Geocarto International, 36(8), 874-887. https://doi.org/10.1080/1010 6049.2019.1629642

Tzoumas, G., Pitonakova, L., Salinas, L., Scales, C., Richardson, T., & Hauert, S. (2023). Wildfire detection in large-scale environments using force-based control for swarms of UAVs. Swarm Intelligence, 17(1-2), 89-115. https://doi.org/10.1007/ s11721-022-00218-9

Unnikrishnan, A., & Reddy, C. S. (2020). Characterizing distribution of forest fires in Myanmar using earth observations and spatial statistics tool. Journal of the Indian Society of Remote Sensing, 48, 227-234. https://doi.org/10.1007/ s12524-019-01072-9

Vadrevu, K. P., Lasko, K., Giglio, L., Schroeder, W., Biswas, S., & Justice, C. (2019). Trends in vegetation fires in south and southeast Asian countries. Scientific reports, 9(1), 1-13. https://doi.org/10.1038/s41598-019-43940-x

Vadrevu, K., Eaturu, A., Casadaban, E., Lasko, K., Schroeder, W., Biswas, S., ... & Justice, C. (2022). Spatial variations in vegetation fires and emissions in South and Southeast Asia during COVID-19 and pre-pandemic. Scientific Reports, 12(1), 18233. https://doi.org/10.1038/s41598-022-22834-5

Wongnakae, P., Chitchum, P., Sripramong, R., & Phosri, A. (2023). Application of satellite remote sensing data and random forest approach to estimate ground-level PM2. 5 concentration in Northern region of Thailand. Environmental Science and Pollution Research, 30(38), 88905-88917. https://doi.org/10.1007/s11356-023-28698-0

Ye, J., Wu, M., Deng, Z., Xu, S., Zhou, R., & Clarke, K. C. (2017). Modeling the spatial patterns of human wildfire ignition in Yunnan province, China. Applied Geography, 89, 150-162. https://doi.org/10.1016/j.apgeog.2017.09.012

Yu, J., Jiang, X., Zeng, Z. C., & Yung, Y. L. (2024). Fire Monitoring and Detection Using Brightness-Temperature Difference and Water Vapor Emission from the Atmospheric InfraRed Sounder. Journal of Quantitative Spectroscopy and Radiative Transfer, 108930. https://doi.org/10.1016/j.jqsrt.2024.108930

Yue, W., Ren, C., Liang, Y., Lin, X., Yin, A., & Liang, J. (2023). Wildfire Risk Assessment Considering Seasonal Differences: A Case Study of Nanning, China. Forests, 14(8), 1616. https://doi.org/10.3390/f14081616

Zhang, T., de Jong, M. C., Wooster, M. J., Xu, W., & Wang, L. (2020). Trends in eastern China agricultural fire emissions derived from a combination of geostationary (Himawari) and polar (VIIRS) orbiter fire radiative power products. Atmos. Chem. Phys., 20(17), 10687-10705. https://doi.org/10.5194/acp-20-10687-2020

Zúñiga-Vásquez, J. M., & Pompa-García, M. (2019). The occurrence of forest fires in Mexico presents an altitudinal tendency: a geospatial analysis. Natural Hazards, 96(1), 213-224. https://doi.org/10.1007/s11069-018-3537-z

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Published

2024-08-20

How to Cite

Spatial Patterns of Cumulative Hotspots and Their Relationships with Topographical Factors and Land Use in Kanchanaburi Province, Thailand. (2024). Forest and Society, 8(2), 314-330. https://doi.org/10.24259/fs.v8i2.32194

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