This work is licensed under a Creative Commons Attribution 4.0 International License.
Spatial Patterns of Cumulative Hotspots and Their Relationships with Topographical Factors and Land Use in Kanchanaburi Province, Thailand
Corresponding Author(s) : Chudech Losiri
Forest and Society,
Vol. 8 No. 2 (2024): DECEMBER
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- 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
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