Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data
Abstract
:1. Introduction
2. Study Area and Data Resource
2.1. Study Area
2.2. Data Resource and Processing
3. Methods
3.1. Establishment of Forest-Fire Database
3.2. Setting Up Training and Validation Datasets
3.3. Liner Spectral Mixing Analysis
3.4. Hierarchical Analysis Process
3.5. Entropy Weight Method
3.6. Comprehensive Weight Method
3.7. Random Forest
4. Results
4.1. Forest-Fire Risk Mapping
4.2. Accuracy Verification
5. Discussion
5.1. The Importance of Conditioning Forest-Fire Factors
5.2. Impact of Climatic Factors on Forest Fires
5.3. Impact of Vegetation Factors on Forest Fires
5.4. Impact of Topographic Factors on Forest Fires
5.5. Limitations of the Study
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Giannakidou, S.; Radoglou-Grammatikis, P.; Lagkas, T.; Argyriou, V.; Goudos, S.; Markakis, E.K.; Sarigiannidis, P. Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey. Internet Things 2024, 26, 101171. [Google Scholar] [CrossRef]
- Dastour, H.; Ahmed, M.R.; Hassan, Q.K. Analysis of forest fire patterns and their relationship with climate variables in Alberta’s natural subregions. Ecol. Inform. 2024, 80, 102531. [Google Scholar] [CrossRef]
- Hu, M.; Liu, Y.; Sun, Z.; Zhang, K.; Liu, Y.; Miao, R.; Wan, S. Fire rather than nitrogen addition affects understory plant communities in the short term in a coniferous-broadleaf mixed forest. Ecol. Evol. 2018, 8, 8135–8148. [Google Scholar] [CrossRef] [PubMed]
- Mohajane, M.; Costache, R.; Karimi, F.; Bao Pham, Q.; Essahlaoui, A.; Nguyen, H.; Laneve, G.; Oudija, F. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecol. Indic. 2021, 129, 107869. [Google Scholar] [CrossRef]
- Siddique, A.A.; Alasbali, N.; Driss, M.; Boulila, W.; Alshehri, M.S.; Ahmad, J. Sustainable collaboration: Federated learning for environmentally conscious forest fire classification in Green Internet of Things (IoT). Internet Things 2024, 25, 101013. [Google Scholar] [CrossRef]
- Pfoch, K.A.; Pflugmacher, D.; Okujeni, A.; Hostert, P. Mapping forest fire severity using bi-temporal unmixing of Sentinel-2 data—Towards a quantitative understanding of fire impacts. Sci. Remote Sens. 2023, 8, 100097. [Google Scholar] [CrossRef]
- Sari, F. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS. For. Ecol. Manag. 2021, 480, 118644. [Google Scholar] [CrossRef]
- Sivrikaya, F.; Günlü, A.; Küçük, Ö.; Ürker, O. Forest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices. Ecol. Inform. 2024, 79, 102461. [Google Scholar] [CrossRef]
- Tien Bui, D.; Bui, Q.-T.; Nguyen, Q.-P.; Pradhan, B.; Nampak, H.; Trinh, P.T. A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric. For. Meteorol. 2017, 233, 32–44. [Google Scholar] [CrossRef]
- Das, J.; Mahato, S.; Joshi, P.K.; Liou, Y.-A. Forest Fire Susceptibility Zonation in Eastern India Using Statistical and Weighted Modelling Approaches. Remote Sens. 2023, 15, 1340. [Google Scholar] [CrossRef]
- Moayedi, H.; Mehrabi, M.; Bui, D.T.; Pradhan, B.; Foong, L.K. Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility. J. Environ. Manag. 2020, 260, 109867. [Google Scholar] [CrossRef] [PubMed]
- Arca, D.; Hacısalihoğlu, M.; Kutoğlu, Ş.H. Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Nat. Hazards 2020, 104, 73–89. [Google Scholar] [CrossRef]
- Makumbura, R.K.; Dissanayake, P.; Gunathilake, M.B.; Rathnayake, N.; Kantamaneni, K.; Rathnayake, U. Spatial mapping and analysis of forest fire risk areas in Sri Lanka—Understanding environmental significance. Case Stud. Chem. Environ. Eng. 2024, 9, 100680. [Google Scholar] [CrossRef]
- Parajuli, A.; Manzoor, S.A.; Lukac, M. Areas of the Terai Arc landscape in Nepal at risk of forest fire identified by fuzzy analytic hierarchy process. Environ. Dev. 2023, 45, 100810. [Google Scholar] [CrossRef]
- Pham, V.T.; Do, T.A.T.; Tran, H.D.; Do, A.N.T. Classifying forest cover and mapping forest fire susceptibility in Dak Nong province, Vietnam utilizing remote sensing and machine learning. Ecol. Inform. 2024, 79, 102392. [Google Scholar] [CrossRef]
- Xu, H.; Zhang, G.; Chu, R.; Zhang, J.; Yang, Z.; Wu, X.; Xiao, H. Detecting forest fire omission error based on data fusion at subpixel scale. Int. J. Appl. Earth Obs. Geoinf. 2024, 128, 103737. [Google Scholar] [CrossRef]
- Kayet, N.; Chakrabarty, A.; Pathak, K.; Sahoo, S.; Dutta, T.; Hatai, B.K. Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk (FFR) mapping in Melghat Tiger Reserve (MTR) forest. J. For. Res. 2020, 31, 565–579. [Google Scholar] [CrossRef]
- Nuthammachot, N.; Stratoulias, D. Multi-criteria decision analysis for forest fire risk assessment by coupling AHP and GIS: Method and case study. Environ. Dev. Sustain. 2021, 23, 17443–17458. [Google Scholar] [CrossRef]
- Joshi, K.P.; Adhikari, G.; Bhattarai, D.; Adhikari, A.; Lamichanne, S. Forest fire vulnerability in Nepal’s chure region: Investigating the influencing factors using generalized linear model. Heliyon 2024, 10, e28525. [Google Scholar] [CrossRef]
- Barrera-Animas, A.Y.; Oyedele, L.O.; Bilal, M.; Akinosho, T.D.; Delgado, J.M.D.; Akanbi, L.A. Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Mach. Learn. Appl. 2022, 7, 100204. [Google Scholar] [CrossRef]
- Conciatori, M.; Valletta, A.; Segalini, A. Improving the quality evaluation process of machine learning algorithms applied to landslide time series analysis. Comput. Geosci. 2024, 184, 105531. [Google Scholar] [CrossRef]
- Islam, R.; Chowdhury, P. Local-scale flash flood susceptibility assessment in northeastern Bangladesh using machine learning algorithms. Environ. Chall. 2024, 14, 100833. [Google Scholar] [CrossRef]
- Surbhi Singh, S.; Jeganathan, C. Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh, India. Adv. Space Res. 2024, 73, 2969–2987. [Google Scholar] [CrossRef]
- Chen, W.; Yan, X.; Zhao, Z.; Hong, H.; Bui, D.T.; Pradhan, B. Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China). Bull. Eng. Geol. Environ. 2018, 78, 247–266. [Google Scholar] [CrossRef]
- Su, H.; Wang, X.; Chen, W.; Ding, N.; Cui, X.; Bai, M.; Chen, Z.; Li, M. A novel framework for identifying causes of forest fire events using environmental and temporal characteristics of the ignition point in fire footprint. Ecol. Indic. 2024, 160, 111899. [Google Scholar] [CrossRef]
- Mehmood, K.; Anees, S.A.; Luo, M.; Akram, M.; Zubair, M.; Khan, K.A.; Khan, W.R. Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies. Trees For. People 2024, 16, 100521. [Google Scholar] [CrossRef]
- Reis, H.C.; Turk, V. Detection of forest fire using deep convolutional neural networks with transfer learning approach. Appl. Soft Comput. 2023, 143, 110362. [Google Scholar] [CrossRef]
- Saleh, A.; Zulkifley, M.A.; Harun, H.H.; Gaudreault, F.; Davison, I.; Spraggon, M. Forest fire surveillance systems: A review of deep learning methods. Heliyon 2024, 10, e23127. [Google Scholar] [CrossRef]
- Rihan, M.; Ali Bindajam, A.; Talukdar, S.; Shahfahad; Waseem Naikoo, M.; Mallick, J.; Rahman, A. Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms. Adv. Space Res. 2023, 72, 426–443. [Google Scholar] [CrossRef]
- Zheng, S.; Zou, X.; Gao, P.; Zhang, Q.; Hu, F.; Zhou, Y.; Wu, Z.; Wang, W.; Chen, S. A Forest Fire Recognition Method Based on Modified Deep CNN Model. Forests 2024, 15, 111. [Google Scholar] [CrossRef]
- Jaafari, A.; Panahi, M.; Pham, B.T.; Shahabi, H.; Bui, D.T.; Rezaie, F.; Lee, S. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. CATENA 2019, 175, 430–445. [Google Scholar] [CrossRef]
- Tien Bui, D.; Hoang, N.-D.; Samui, P. Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam). J. Environ. Manag. 2019, 237, 476–487. [Google Scholar] [CrossRef] [PubMed]
- Wang, X. Morphological Characteristics and Influencing Factors of Fine Grooves on the Road Surface of Forest Fire Prevention Passages in the Anning River Valley Area; China West Normal University: Nanchong, China, 2023. [Google Scholar] [CrossRef]
- Wang, S.; Niu, S. Fuel Classes in Conifer Forests of Southwest Sichuan, China, and Their Implications for Fire Susceptibility. Forests 2016, 7, 52. [Google Scholar] [CrossRef]
- Peng, Y.; Su, H.; Sun, M.; Li, M. Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province, southwestern China. For. Ecosyst. 2024, 11, 100170. [Google Scholar] [CrossRef]
- Li, H.; Long, Z.; Yang, Z.; Xu, Z.; Li, Y. Analysis of forest fire risk in Sichuan Liangshan based on Logistic model. J. Saf. Environ. 2021, 21, 498–505. [Google Scholar] [CrossRef]
- Zhu, P. A Study on the Prediction of Forest Fires in Liangshan Prefecture under the Role of Multiple Factors; Southwest University of Science and Technology: Mianyang, China, 2024. [Google Scholar] [CrossRef]
- Tsangaratos, P.; Ilia, I.; Hong, H.; Chen, W.; Xu, C. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides 2016, 14, 1091–1111. [Google Scholar] [CrossRef]
- Ruan, Y.; Mohtadi, M.; Dupont, L.M.; Hebbeln, D.; van der Kaars, S.; Hopmans, E.C.; Schouten, S.; Hyer, E.J.; Schefuß, E. Interaction of Fire, Vegetation, and Climate in Tropical Ecosystems: A Multiproxy Study Over the Past 22,000 Years. Glob. Biogeochem. Cycles 2020, 34, e2020GB006677. [Google Scholar] [CrossRef]
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ. 1974, 351, 309. [Google Scholar]
- Mina, U.; Dimri, A.P.; Farswan, S. Forest fires and climate attributes interact in central Himalayas: An overview and assessment. Fire Ecol. 2023, 19, 14. [Google Scholar] [CrossRef]
- Wasserman, T.N.; Mueller, S.E. Climate influences on future fire severity: A synthesis of climate-fire interactions and impacts on fire regimes, high-severity fire, and forests in the western United States. Fire Ecol. 2023, 19, 43. [Google Scholar] [CrossRef]
- Nami, M.H.; Jaafari, A.; Fallah, M.; Nabiuni, S. Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. Int. J. Environ. Sci. Technol. 2018, 15, 373–384. [Google Scholar] [CrossRef]
- Tehrany, M.S.; Jones, S.; Shabani, F.; Martínez-Álvarez, F.; Tien Bui, D. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data. Theor. Appl. Climatol. 2019, 137, 637–653. [Google Scholar] [CrossRef]
- Alshqaq, S.S.; Abuzaid, A.H. An Efficient Method for Variable Selection Based on Diagnostic-Lasso Regression. Symmetry 2023, 15, 2155. [Google Scholar] [CrossRef]
- Ji, C.; Li, X.; Wei, H.; Li, S. Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval. Remote Sens. 2020, 12, 115. [Google Scholar] [CrossRef]
- Chein, I.C.; Heinz, D.C. Constrained subpixel target detection for remotely sensed imagery. IEEE Trans. Geosci. Remote Sens. 2000, 38, 1144–1159. [Google Scholar] [CrossRef]
- Heinz, D.C.; Chein, I.C. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 2001, 39, 529–545. [Google Scholar] [CrossRef]
- Adams, J.; Smith, M.; Gillespie, A. Imaging spectroscopy: Interpretation based on spectral mixture analysis. In Remote Geochemical Analysis: Elemental and Mineralogical Composition; Cambridge University Press: Cambridge, UK, 1993. [Google Scholar]
- Dolui, S. Chapter 15—Forest fire susceptibility mapping and risk assessment using integrated AHP and DEMATEL method for Purulia District, West Bengal (India). In Water, Land, and Forest Susceptibility and Sustainability; Elsevier: Amsterdam, The Netherlands, 2023; Volume 1, pp. 401–452. [Google Scholar] [CrossRef]
- Ji, C.; Yang, H.; Pei, X.; Zhang, X.; Chen, L.; Liang, D.; Cao, Y.; Pan, J.; Chen, M. Dynamic Integrated Ecological Assessment along the Corridor of the Sichuan–Tibet Railway. Land 2024, 13, 857. [Google Scholar] [CrossRef]
- Sivrikaya, F.; Küçük, Ö. Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecol. Inform. 2022, 68, 101537. [Google Scholar] [CrossRef]
- Roszkowska, E.; Wachowicz, T. Impact of Normalization on Entropy-Based Weights in Hellwig’s Method: A Case Study on Evaluating Sustainable Development in the Education Area. Entropy 2024, 26, 365. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Thi Hang, H.; Mallick, J.; Alqadhi, S.; Bindajam, A.A.; Abdo, H.G. Exploring Forest Fire Susceptibility and Management Strategies in Western Himalaya: Integrating Ensemble Machine Learning and explainable AI for Accurate Prediction and Comprehensive Analysis. Environ. Technol. Innov. 2024, 35, 103655. [Google Scholar] [CrossRef]
- Sun, X. Research on Forest Fire Risk Prediction Method Based on Remote Sensing Technology in Liangshan Prefecture; National Institute of Natural Hazards: Beijing, China, 2023. [Google Scholar] [CrossRef]
- Zumbrunnen, T.; Pezzatti, G.B.; Menéndez, P.; Bugmann, H.; Bürgi, M.; Conedera, M. Weather and human impacts on forest fires: 100 years of fire history in two climatic regions of Switzerland. For. Ecol. Manag. 2011, 261, 2188–2199. [Google Scholar] [CrossRef]
- Chang, C.; Chang, Y.; Xiong, Z.; Ping, X.; Zhang, H.; Guo, M.; Hu, Y. Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China. Remote Sens. 2023, 15, 2999. [Google Scholar] [CrossRef]
- Zhang, F.; Zhang, B.; Luo, J.; Liu, H.; Deng, Q.; Wang, L.; Zuo, Z. Forest Fire Driving Factors and Fire Risk Zoning Based on an Optimal Parameter Logistic Regression Model: A Case Study of the Liangshan Yi Autonomous Prefecture, China. Fire 2023, 6, 336. [Google Scholar] [CrossRef]
- Ma, W.; Feng, Z.; Cheng, Z.; Chen, S.; Wang, F. Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm. Forests 2020, 11, 507. [Google Scholar] [CrossRef]
- Butler, B.; Quarles, S.; Standohar-Alfano, C.; Morrison, M.; Jimenez, D.; Sopko, P.; Wold, C.; Bradshaw, L.; Atwood, L.; Landon, J.; et al. Exploring fire response to high wind speeds: Fire rate of spread, energy release and flame residence time from fires burned in pine needle beds under winds up to 27 m s−1. Int. J. Wildland Fire 2020, 29, 81–92. [Google Scholar] [CrossRef]
- Guerschman, J.P.; Hill, M.J.; Renzullo, L.J.; Barrett, D.J.; Marks, A.S.; Botha, E.J. Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sens. Environ. 2009, 113, 928–945. [Google Scholar] [CrossRef]
- Newnham, G.J.; Verbesselt, J.; Grant, I.F.; Anderson, S.A.J. Relative Greenness Index for assessing curing of grassland fuel. Remote Sens. Environ. 2011, 115, 1456–1463. [Google Scholar] [CrossRef]
- Akıncı, H.A.; Akıncı, H. Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey. Earth Sci. Inform. 2023, 16, 397–414. [Google Scholar] [CrossRef]
- Margolis, E.Q.; Swetnam, T.W. Historical fire–climate relationships of upper elevation fire regimes in the south-western United States. Int. J. Wildland Fire 2013, 22, 588–598. [Google Scholar] [CrossRef]
- Spittlehouse, D.; Dymond, C. Interaction of elevation and climate change on fire weather risk. Can. J. For. Res. 2022, 52, 237–249. [Google Scholar] [CrossRef]
- Adab, H.; Kanniah, K.D.; Solaimani, K. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat. Hazards 2013, 65, 1723–1743. [Google Scholar] [CrossRef]
- Maingi, J.; Henry, M. Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. Int. J. Wildland Fire 2007, 16, 23–33. [Google Scholar] [CrossRef]
- Abid, F. A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems. Fire Technol. 2021, 57, 559–590. [Google Scholar] [CrossRef]
- Davis, M.; Shekaramiz, M. Desert/Forest Fire Detection Using Machine/Deep Learning Techniques. Fire 2023, 6, 418. [Google Scholar] [CrossRef]
- Milanović, S.; Marković, N.; Pamučar, D.; Gigović, L.; Kostić, P.; Milanović, S.D. Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method. Forests 2021, 12, 5. [Google Scholar] [CrossRef]
Name | Source | Identifier |
---|---|---|
Landsat 7 ETM+ Landsat 8 OLI | United States Geological Survey (USGS) | https://earthengine.google.com/ (accessed on 8 April 2024) |
NDVI, humidity | LANDSAT/LE07/C01/T1_SR LANDSAT/LC08/C01/T1_SR | https://earthengine.google.com/ (accessed on 12 April 2024) |
Plant species | China Plant Theme Database | http://www.plant.csdb.cn/ (accessed on 29 April 2024) |
Elevation | USGS/SRTMGL1_003 | https://earthengine.google.com/ (accessed on 5 April 2024) |
Rainfall, wind speed | OpenLandMap/CLM/CLM_PRECIPITATION_SM2RAIN_M/v01 | https://earthengine.google.com/ (accessed on 5 April 2024) |
Temperature | LANDSAT/LC08/C01/T1_SR | https://earthengine.google.com/ |
Soli type | China Soil Science Database | http://vdb3.soil.csdb.cn/ (accessed on 8 April 2024) |
Land use | Earth Big Data Science Engineering | https://data.casearth.cn/ (accessed on 9 April 2024) |
Distance to road, distance to residential | National Earth System Science Data Sharing Platform | http://www.geodata.cn/Portal/index.jsp (accessed on 8 April 2024) |
Historical Fire Point Data | Official local government statistics |
Score | Meaning |
---|---|
1 | Equally important |
3 | Moderately important |
5 | Strongly important |
7 | Very strongly important |
9 | Extremely important |
2, 4, 6, 8 | Intermediate values between the preference |
AHP | High | Medium | Low | Very low | |
Common forest fire | 50 | 79 | 8 | 3 | |
Larger forest fire | 15 | 21 | 4 | 0 | |
Severe forest fire | 2 | 2 | 0 | 0 | |
Percentage (%) | 36.41 | 55.43 | 6.52 | 1.63 | |
CWM | High | Medium | Low | Very low | |
Common forest fire | 49 | 80 | 8 | 3 | |
Larger forest fire | 15 | 21 | 4 | 0 | |
Severe forest fire | 2 | 2 | 0 | 0 | |
Percentage (%) | 35.87 | 55.98 | 6.52 | 1.63 | |
RF | High | Medium | Low | Very low | |
Common forest fire | 33 | 100 | 4 | 3 | |
Larger forest fire | 10 | 23 | 6 | 1 | |
Severe forest fire | 1 | 3 | 0 | 0 | |
Percentage (%) | 23.91 | 68.48 | 5.43 | 2.17 |
Year | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Type | |||||||||||||
Mean PV (%) | 84.7 | 87.4 | 82 | 82.4 | 81.3 | 83.7 | 85.3 | 88.1 | 88.1 | 87.4 | 84.5 | 81.5 | |
Mean NPV (%) | 14.1 | 11.6 | 17.1 | 11.6 | 16.5 | 13.4 | 10.4 | 8.9 | 8.4 | 9.1 | 9.7 | 10.4 | |
Historical fire frequency | 17 | 1 | 41 | 13 | 28 | 13 | 9 | 7 | 7 | 37 | 8 | 4 |
Year | −4 | −3 | −2 | −1 | YFF | 1 | 2 | 3 | 4 | |
---|---|---|---|---|---|---|---|---|---|---|
NDVI | ||||||||||
Maximum | 0.923 | 0.984 | 1 | 0.973 | 1 | 0.982 | 0.979 | 0.979 | 1 | |
Mean | 0.741 | 0.763 | 0.77 | 0.781 | 0.773 | 0.793 | 0.797 | 0.812 | 0.816 | |
Minimum | 0.388 | 0.474 | 0.462 | 0.513 | 0.49 | 0.548 | 0.544 | 0.565 | 0.655 |
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Share and Cite
Ji, C.; Yang, H.; Li, X.; Pei, X.; Li, M.; Yuan, H.; Cao, Y.; Chen, B.; Qu, S.; Zhang, N.; et al. Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data. Forests 2024, 15, 1523. https://doi.org/10.3390/f15091523
Ji C, Yang H, Li X, Pei X, Li M, Yuan H, Cao Y, Chen B, Qu S, Zhang N, et al. Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data. Forests. 2024; 15(9):1523. https://doi.org/10.3390/f15091523
Chicago/Turabian StyleJi, Cuicui, Hengcong Yang, Xiaosong Li, Xiangjun Pei, Min Li, Hao Yuan, Yiming Cao, Boyu Chen, Shiqian Qu, Na Zhang, and et al. 2024. "Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data" Forests 15, no. 9: 1523. https://doi.org/10.3390/f15091523