An improved spatio-temporal clustering method for extracting fire footprints based on MCD64A1 in the Daxing’anling Area of north-eastern China
Huiyi Su
A , Xiu Ma B C and Mingshi Li A B *
+ Author Affiliations
- Author Affiliations
A College of Forestry, Nanjing Forestry University, Nanjing 210037, China.
B Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China.
C East China Inventory and Planning Institute of the State Administration of Forestry and Grassland, Hangzhou 310019, China.
* Correspondence to: nfulms@njfu.edu.cn
International Journal of Wildland Fire 32(5) 679-693 https://doi.org/10.1071/WF22198
Submitted: 28 September 2022 Accepted: 29 March 2023 Published: 26 April 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Background: Understanding the spatio-temporal dynamics associated with a wildfire event is essential for projecting a clear profile of its potential ecological influences.
Aims: To develop a reliable framework to extract fire footprints from MODIS-based burn products to facilitate the understanding of fire event evolution.
Methods: This study integrated the Jenks natural breaks classification method and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the fire footprints in Daxing’anling region of China between 2001 and 2006 from MCD64A1 burned area data.
Key results: The results showed that the fire footprints extracted by the model gained an overall accuracy of 80% in spatial and temporal domains after an intensive validation by using the historical fire records provided by the local agency. The agreement of burned area between the extracted fire patches and the historical fire records for those matched fire points was characterised by an overall determination coefficient R2 at 0.91.
Conclusions: The proposed framework serves as an efficient and convenient wildfire management tool for areas requiring large-scale and long-term wildfire monitoring.
Implications: The current framework can be used to create a reliable large-scale fire event database by providing an important alternative for the improvement of field investigation.
Keywords: clustering, Daxing’anling, DBSCAN, fire footprint, Jenks natural breaks, MCD64A1, remote sensing, wildfire.
References
Anchang JY, Ananga EO, Pu R (2016) An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery. International Journal of Applied Earth Observation and Geoinformation 50, 211–220.| An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery.Crossref | GoogleScholarGoogle Scholar |
Archibald S, Lehmann CER, Gómez-Dans JL, Bradstock RA (2013) Defining pyromes and global syndromes of fire regimes. Proceedings of the National Academy of Sciences 110, 6442–6447.
| Defining pyromes and global syndromes of fire regimes.Crossref | GoogleScholarGoogle Scholar |
Artés T, Oom D, de Rigo D, Durrant TH, Maianti P, Libertà G, San-Miguel-Ayanz J (2019) A global wildfire dataset for the analysis of fire regimes and fire behaviour. Sci Data 6, 296
| A global wildfire dataset for the analysis of fire regimes and fire behaviour.Crossref | GoogleScholarGoogle Scholar |
Bird MI, Cali JA (1998) A million-year record of fire in sub-Saharan Africa. Nature 394, 767–769.
| A million-year record of fire in sub-Saharan Africa.Crossref | GoogleScholarGoogle Scholar |
Boschetti L, Roy D, Barbosa P, Boca R, Justice C (2008) A MODIS assessment of the summer 2007 extent burned in Greece. International Journal of Remote Sensing 29, 2433–2436.
| A MODIS assessment of the summer 2007 extent burned in Greece.Crossref | GoogleScholarGoogle Scholar |
Brockway DG, Gatewood RG, Paris RB (2002) Restoring fire as an ecological process in shortgrass prairie ecosystems: initial effects of prescribed burning during the dormant and growing seasons. Journal of Environmental Management 65, 135–152.
| Restoring fire as an ecological process in shortgrass prairie ecosystems: initial effects of prescribed burning during the dormant and growing seasons.Crossref | GoogleScholarGoogle Scholar |
Cary GJ, Keane RE, Gardner RH, Lavorel S, Flannigan MD, Davies ID, Li C, Lenihan JM, Rupp TS, Mouillot F (2006) Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather. Landscape Ecology 21, 121–137.
| Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather.Crossref | GoogleScholarGoogle Scholar |
Chen J, Yang ST, Li HW, Zhang B, Lv JR (2013) Research on geographical environment unit division based on the method of natural breaks (Jenks). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W3, 47–50.
| Research on geographical environment unit division based on the method of natural breaks (Jenks).Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Congalton RG (1989) Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment 29, 147–159.
| Application of remote sensing and geographic information systems to forest fire hazard mapping.Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Roteta E, Sali M, Stroppiana D, Boettcher M, Kirches G, Storm T, Khairoun A, Pettinari ML, Franquesa M, Albergel C (2022) Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images. Science of the Total Environment 845, 157139
| Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images.Crossref | GoogleScholarGoogle Scholar |
Demange M, Di Fonso A, Di Stefano G, Vittorini P (2022) A graph theoretical approach to the firebreak locating problem. Theoretical Computer Science 914, 47–72.
| A graph theoretical approach to the firebreak locating problem.Crossref | GoogleScholarGoogle Scholar |
Ester M (2009) Density-based clustering. In ‘Encyclopedia of database systems’. (Eds L Liu, MT ÖZsu) pp. 795–799. (Springer Publishing: Boston, MA, USA)
Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In ‘Proceedings of the second international conference on knowledge discovery and data mining’. pp. 226–231. (AAAI Publishing: Portland, OR, USA)
Fang L, Yang J, Zu J, Li G, Zhang J (2015) Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape. Forest Ecology and Management 356, 2–12.
| Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape.Crossref | GoogleScholarGoogle Scholar |
French BJ, Prior LD, Williamson GJ, Bowman DMJS (2016) Cause and effects of a megafire in sedge-heathland in the Tasmanian temperate wilderness. Australian Journal of Botany 64, 513–525.
| Cause and effects of a megafire in sedge-heathland in the Tasmanian temperate wilderness.Crossref | GoogleScholarGoogle Scholar |
Giglio L, Loboda T, Roy DP, Quayle B, Justice CO (2009) An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment 113, 408–420.
| An active-fire based burned area mapping algorithm for the MODIS sensor.Crossref | GoogleScholarGoogle Scholar |
Giglio L, Schroeder W, Justice CO (2016) The collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment 178, 31–41.
| The collection 6 MODIS active fire detection algorithm and fire products.Crossref | GoogleScholarGoogle Scholar |
Girardin MP, Mudelsee M (2008) Past and future changes in Canadian boreal wildfire activity. Ecological Applications 18, 391–406.
| Past and future changes in Canadian boreal wildfire activity.Crossref | GoogleScholarGoogle Scholar |
Goldammer JG, Furyaev VV (1996) Fire in ecosystems of boreal Eurasia: ecological impacts and links to the global system. In ‘Fire in ecosystems of Boreal Eurasia’. (Eds JG Goldammer, VV Furyaev) pp. 1–20. (Springer Publishing: Dordrecht, Netherlands)
Grogan P, Burns TD, Chapin Iii FS (2000) Fire effects on ecosystem nitrogen cycling in a Californian bishop pine forest. Oecologia 122, 537–544.
| Fire effects on ecosystem nitrogen cycling in a Californian bishop pine forest.Crossref | GoogleScholarGoogle Scholar |
Gruber N, Friedlingstein P, Field CB, Valentini R, Heimann M, Richey JE, Lankao PR, Schulze E-D, Chen C-TA (2004) The vulnerability of the carbon cycle in the 21st century: an assessment of carbon-climate-human interactions. In ‘Scope-Scientific committee on problems of the environment international council of scientific unions. Vol. 62’. (Eds CB Field, MR Raupach) pp. 45–76. (Island Press)
Hantson S, Pueyo S, Chuvieco E (2015) Global fire size distribution is driven by human impact and climate. Global Ecology and Biogeography 24, 77–86.
| Global fire size distribution is driven by human impact and climate.Crossref | GoogleScholarGoogle Scholar |
Hope A, Albers N, Bart R (2012) Characterizing post-fire recovery of fynbos vegetation in the Western Cape Region of South Africa using MODIS data. International Journal of Remote Sensing 33, 979–999.
| Characterizing post-fire recovery of fynbos vegetation in the Western Cape Region of South Africa using MODIS data.Crossref | GoogleScholarGoogle Scholar |
Humber ML, Boschetti L, Giglio L, Justice CO (2019) Spatial and temporal intercomparison of four global burned area products. International Journal of Digital Earth 12, 460–484.
| Spatial and temporal intercomparison of four global burned area products.Crossref | GoogleScholarGoogle Scholar |
Jaafari A, Zenner EK, Panahi M, Shahabi H (2019) Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology 266–267, 198–207.
| Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability.Crossref | GoogleScholarGoogle Scholar |
Jin Y, Randerson JT, Goetz SJ, Beck PSA, Loranty MM, Goulden ML (2012) The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests. Journal of Geophysical Research: Biogeosciences 117, G01036
| The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests.Crossref | GoogleScholarGoogle Scholar |
Kristianto A, Sediyono E, Hartomo KD (2020) Implementation DBSCAN algorithm to clustering satellite surface temperature data in Indonesia. Register: Jurnal Ilmiah Teknologi Sistem Informasi 6, 109–118.
| Implementation DBSCAN algorithm to clustering satellite surface temperature data in Indonesia.Crossref | GoogleScholarGoogle Scholar |
Laurent P, Mouillot F, Yue C, Ciais P, Moreno MV, Nogueira JMP (2018) FRY, a global database of fire patch functional traits derived from space-borne burned area products. Scientific Data 5, 180132
| FRY, a global database of fire patch functional traits derived from space-borne burned area products.Crossref | GoogleScholarGoogle Scholar |
Lentini JJ (2006) ‘Scientific protocols for fire investigation.’ (CRC Publishing: Melbourne, VIC, Australia)
Liu P, Zhou D, Wu N (2007) VDBSCAN: varied density based spatial clustering of applications with noise, In ‘2007 International conference on service systems and service management’, 9–11 June 2007. pp. 1–4. (IEEE Publishing: Chengdu, China)
Loboda T, O’Neal KJ, Csiszar I (2007) Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data. Remote Sensing of Environment 109, 429–442.
| Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data.Crossref | GoogleScholarGoogle Scholar |
Loepfe L, Rodrigo A, Lloret F (2014) Two thresholds determine climatic control of forest fire size in Europe and northern Africa. Regional Environmental Change 14, 1395–1404.
| Two thresholds determine climatic control of forest fire size in Europe and northern Africa.Crossref | GoogleScholarGoogle Scholar |
Maier SW, Russell-Smith J, Edwards AC, Yates C (2013) Sensitivity of the MODIS fire detection algorithm (MOD14) in the savanna region of the Northern Territory, Australia. ISPRS Journal of Photogrammetry and Remote Sensing 76, 11–16.
| Sensitivity of the MODIS fire detection algorithm (MOD14) in the savanna region of the Northern Territory, Australia.Crossref | GoogleScholarGoogle Scholar |
Mallinis G, Mitsopoulos I, Chrysafi I (2018) Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GIScience & Remote Sensing 55, 1–18.
| Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece.Crossref | GoogleScholarGoogle Scholar |
McMaster R (1997) In memoriam: George F. Jenks (1916-1996). Cartography and Geographic Information Systems 24, 56–59.
| In memoriam: George F. Jenks (1916-1996).Crossref | GoogleScholarGoogle Scholar |
Mitri GH, Gitas IZ (2013) Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery. International Journal of Applied Earth Observation and Geoinformation 20, 60–66.
| Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery.Crossref | GoogleScholarGoogle Scholar |
Nisa KK, Andrianto HA, Mardhiyyah R (2014) Hotspot clustering using DBSCAN algorithm and shiny web framework. In ‘2014 international conference on advanced computer science and information system’, 18–19 October 2014. pp. 129–132. (IEEE Publishing: Jakarta, Indonesia)
Patil YS, Vaidya M (2012) A technical survey on cluster analysis in data mining. Journal of Emerging Technology and Advance Engineering 2, 503–513.
| A technical survey on cluster analysis in data mining.Crossref | GoogleScholarGoogle Scholar |
Peng G, Li J, Chen Y, Norizan AP (2007) A forest fire risk assessment using ASTER images in Peninsular Malaysia. Journal of China University of Mining and Technology 17, 232–237.
| A forest fire risk assessment using ASTER images in Peninsular Malaysia.Crossref | GoogleScholarGoogle Scholar |
Qi X, Wei M, Wang Z, Jiang T, Wang P, Teng M, Yan Z (2022) Effects of the COVID-19 lockdown in Hubei, China: cessation of incense burning reduces regional landscape fire. Ecology and Society 27, 5
| Effects of the COVID-19 lockdown in Hubei, China: cessation of incense burning reduces regional landscape fire.Crossref | GoogleScholarGoogle Scholar |
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65.
| Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.Crossref | GoogleScholarGoogle Scholar |
Roy DP, Boschetti L (2009) Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned-area products. IEEE Transactions on Geoscience and Remote Sensing 47, 1032–1044.
| Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned-area products.Crossref | GoogleScholarGoogle Scholar |
Roy DP, Boschetti L, Justice CO, Ju J (2008) The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. Remote Sensing of Environment 112, 3690–3707.
| The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product.Crossref | GoogleScholarGoogle Scholar |
Sander J, Ester M, Kriegel H-P, Xu X (1998) Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery 2, 169–194.
| Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications.Crossref | GoogleScholarGoogle Scholar |
Schroeder W, Csiszar I, Morisette J (2008a) Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon. Remote Sensing of Environment 112, 456–470.
| Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon.Crossref | GoogleScholarGoogle Scholar |
Schroeder W, Prins E, Giglio L, Csiszar I, Schmidt C, Morisette J, Morton D (2008b) Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data. Remote Sensing of Environment 112, 2711–2726.
| Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data.Crossref | GoogleScholarGoogle Scholar |
Sheng D, Deng J, Xiang J (2021) Automatic smoke detection based on SLIC-DBSCAN enhanced convolutional neural network. IEEE Access 9, 63933–63942.
| Automatic smoke detection based on SLIC-DBSCAN enhanced convolutional neural network.Crossref | GoogleScholarGoogle Scholar |
Shu L, Wang M, Li Z, Xiao R, Tian X (2004) Dwarf Siberian pine forest fire environment in Daxingan Mountains. Journal of Mountain Science 22, 36–39.
Sulla-Menashe D, Friedl MA (2018) ‘User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product. Vol. 1’. 18 pp. (USGS: Reston, VA, USA) Available at https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf [Accessed 10 April 2022]
Thompson MP, Calkin DE (2011) Uncertainty and risk in wildland fire management: a review. Journal of Environmental Management 92, 1895–1909.
| Uncertainty and risk in wildland fire management: a review.Crossref | GoogleScholarGoogle Scholar |
Tian X, McRae DJ, Jin J, Shu L, Zhao F, Wang M (2011) Wildfires and the Canadian Forest Fire Weather Index system for the Daxing’anling region of China. International Journal of Wildland Fire 20, 963–973.
| Wildfires and the Canadian Forest Fire Weather Index system for the Daxing’anling region of China.Crossref | GoogleScholarGoogle Scholar |
Tian X, Shu L, Wang M, Zhao F, Chen L (2013) The fire danger and fire regime for the Daxing’anling region for 1987-2010. Procedia Engineering 62, 1023–1031.
| The fire danger and fire regime for the Daxing’anling region for 1987-2010.Crossref | GoogleScholarGoogle Scholar |
Tobler W (2004) On the first law of geography: a reply. Annals of the Association of American Geographers 94, 304–310.
| On the first law of geography: a reply.Crossref | GoogleScholarGoogle Scholar |
Usman M, Sitanggang IS, Syaufina L (2015) Hotspot distribution analyses based on peat characteristics using density-based spatial clustering. Procedia Environmental Sciences 24, 132–140.
| Hotspot distribution analyses based on peat characteristics using density-based spatial clustering.Crossref | GoogleScholarGoogle Scholar |
Van Leeuwen WJD (2008) Monitoring the effects of forest restoration treatments on post-fire vegetation recovery with MODIS multitemporal data. Sensors 8, 2017–2042.
| Monitoring the effects of forest restoration treatments on post-fire vegetation recovery with MODIS multitemporal data.Crossref | GoogleScholarGoogle Scholar |
Van Mantgem PJ, Stephenson NL, Knapp E, Battles J, Keeley JE (2011) Long-term effects of prescribed fire on mixed conifer forest structure in the Sierra Nevada, California. Forest Ecology and Management 261, 989–994.
| Long-term effects of prescribed fire on mixed conifer forest structure in the Sierra Nevada, California.Crossref | GoogleScholarGoogle Scholar |
Vatresia A, Rais RR, Miliana RS (2020) Spatio-Temporal Clustering for Fire Hot Spots Occurrence Over Sulawesi Island. In ‘2020 FORTEI-international conference on electrical engineering (FORTEI-ICEE)’, 23–24 September 2020. pp. 156–161. (IEEE Publishing: Bandung, Indonesia)
Wang S, Wang G, Zhang J (2019) Data analysis method of terrorist attacks based on AHP-DBSCAN method. Journal of Physics: Conference Series 1168, 032029
| Data analysis method of terrorist attacks based on AHP-DBSCAN method.Crossref | GoogleScholarGoogle Scholar |
Wiedinmyer C, Neff JC (2007) Estimates of CO2 from fires in the United States: implications for carbon management. Carbon Balance and Management 2, 10
| Estimates of CO2 from fires in the United States: implications for carbon management.Crossref | GoogleScholarGoogle Scholar |
Wiedinmyer C, Akagi SK, Yokelson RJ, Emmons LK, Al-Saadi JA, Orlando JJ, Soja AJ (2011) The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geoscientific Model Development 4, 625–641.
| The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning.Crossref | GoogleScholarGoogle Scholar |
Winoto-Lewin S, Sanger JC, Kirkpatrick JB (2020) Propensities of old growth, mature and regrowth wet eucalypt forest, and Eucalyptus nitens plantation, to burn during wildfire and suffer fire-induced crown death. Fire 3, 13
| Propensities of old growth, mature and regrowth wet eucalypt forest, and Eucalyptus nitens plantation, to burn during wildfire and suffer fire-induced crown death.Crossref | GoogleScholarGoogle Scholar |
Xing W, Bao K, Gallego-Sala AV, Charman DJ, Zhang Z, Gao C, Lu X, Wang G (2015) Climate controls on carbon accumulation in peatlands of Northeast China. Quaternary Science Reviews 115, 78–88.
| Climate controls on carbon accumulation in peatlands of Northeast China.Crossref | GoogleScholarGoogle Scholar |
Xu Y (1987) Daxinganling recovered after the great fire. In ‘China forestry yearbook 1987’. (Eds M Gao, Y Qian) pp. 524–526. (China Forestry Press Publishing: Beijing, China) [In Chinese]
Yalcin M, Kilic Gul F (2017) A GIS-based multi criteria decision analysis approach for exploring geothermal resources: Akarcay basin (Afyonkarahisar). Geothermics 67, 18–28.
| A GIS-based multi criteria decision analysis approach for exploring geothermal resources: Akarcay basin (Afyonkarahisar).Crossref | GoogleScholarGoogle Scholar |
Yanuarsyah I, Suwarno Y, Hudjimartsu S (2016) The use of hotspot spatial clustering and multitemporal satellite imagery to facilitate peat land degradation in West Kalimantan, Indonesia (Case Study in Mensiku Miniwatershed of Kapuas River). In ‘IOP Conference Series: Earth and Environmental Science. Vol. 47’. p. 012046. (IOP Publishing: Bristol, UK)
Zhang Y, Hu H (2008) Climatic change and its impact on forest fire in Daxing’anling Mountain. Journal of Northeast Forestry University 36, 29–31. [In Chinese]
Zhang Z, Craft CB, Xue Z, Tong S, Lu X (2016) Regulating effects of climate, net primary productivity, and nitrogen on carbon sequestration rates in temperate wetlands, Northeast China. Ecological Indicators 70, 114–124.
| Regulating effects of climate, net primary productivity, and nitrogen on carbon sequestration rates in temperate wetlands, Northeast China.Crossref | GoogleScholarGoogle Scholar |
Zhao F, Liu Y, Shu L (2020) Change in the fire season pattern from bimodal to unimodal under climate change: the case of Daxing’anling in Northeast China. Agricultural and Forest Meteorology 291, 108075
| Change in the fire season pattern from bimodal to unimodal under climate change: the case of Daxing’anling in Northeast China.Crossref | GoogleScholarGoogle Scholar |
Zhou H, Qu S, Yang X, Yuan Q (2020) Regional credit, technological innovation, and economic growth in China: a spatial panel analysis. Discrete Dynamics in Nature and Society 2020, 1738279
| Regional credit, technological innovation, and economic growth in China: a spatial panel analysis.Crossref | GoogleScholarGoogle Scholar |
Zong X, Tian X, Yao Q, Brown PM (2022) An analysis of fatalities from forest fires in China, 1951–2018. International Journal of Wildland Fire 31, 507–517.
| An analysis of fatalities from forest fires in China, 1951–2018.Crossref | GoogleScholarGoogle Scholar |
