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Analyses on spatial and temporal characteristics of forest fires in Yunnan Province based on MODIS from 2001 to 2020
ZHANG Wenwen, WANG Jin, WANG Qiuhua, ZHANG Xiyan, CAO Hengmao, LONG Tengteng
JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (5) : 73-79.
PDF(2732 KB)
PDF(2732 KB)
Analyses on spatial and temporal characteristics of forest fires in Yunnan Province based on MODIS from 2001 to 2020
【Objective】 Studying the temporal and spatial characteristics of forest fires in Yunnan Province, help quantify the occurrence of forest fires, identify areas of high incidence and finally carry out risk zoning scientifically. 【Method】Based on data from Yunnan Province between 2001 and 2020 (the MODIS fire location/thermal anomaly product, MCD14DL), we studied the correlation and heterogeneity of fire locations to analyze spatial and temporal distribution by statistical analysis, center point, standard deviational ellipse, Ripley’s K function, kernel density and other methods. 【Result】(1)We observed large annual fluctuations of forest fires between 2001 and 2020, peaking in 2010. Forest fires in Yunnan Province concentrated in winter and particularly spring, with fire spots peaking in March. (2)The occurrence of forest fires varied greatly between the regions, with the highest incidence in the southwest of Yunnan Province, followed by the southeast and northwest. The northeast had the smallest number of forest fires. The median centre was Pu’er, while the northwest of Pu’er had the highest kernel density(0.43) followed by the central and western regions of Xishuang banna Prefecture, with a kernel density of 0.34, and the northwest of Dali Prefecture and northeast of Wenshan Prefecture, with a nuclear density of 0.26; these four regions were hotspots for forest fires. (3)In the past 20 years, the spatial distribution of forest fires in Yunnan Province has displayed obvious directionality; the center of gravity has gradually shifted from Pu’er to Dali-Chuxiong-Yuxi-Honghezhou. (4)The spatial distribution of forest fires in Yunnan Province fits an aggregation model (but not to a high degree) and is gradually dispersed.【Conclusion】Forest fires in Yunnan Province between 2001 and 2020 were not randomly distributed, but followed a spatial-temporal distribution law. This law can be applied to inform a regional fire management, strengthen the construction and publicity for the fire prevention in priority regions, scientifically organize fire prevention work, reduce the incidence of forest fires and realize the sustainable development of forests.
moderate-resolution imaging spectroradiometer(MODIS) fire products / fire point / forest fire occurrence / spatial-temporal pattern / Yunnan Province
| [1] |
|
| [2] |
Jolly, W. Matt; Freeborn, Patrick H. US Forest Serv, Rocky Mt Res Stn, Fire Sci Lab, Missoula, MT 59803 USA. Cochrane, Mark A.; Freeborn, Patrick H. S Dakota State Univ, GSCE, Brookings, SD 57007 USA. Holden, Zachary A. US Forest Serv Reg 1, Missoula, MT 59802 USA. Brown, Timothy J. Western Reg Climate Ctr, DRI, Reno, NV 89512 USA. Williamson, Grant J.; Bowman, David M. J. S. Univ Tasmania, Sch Biol Sci, Hobart, Tas 7001, Australia.
|
| [3] |
田晓瑞, 赵凤君, 舒立福, 等. 西南林区卫星监测热点及森林火险天气指数分析[J]. 林业科学研究, 2010, 23(4):523-529.
|
| [4] |
魏书精, 罗斯生, 罗碧珍, 等. 气候变化背景下森林火灾发生规律研究[J]. 林业与环境科学, 2020, 36(2):133-143.
|
| [5] |
|
| [6] |
The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
张运林, 郭妍, 胡海清. 2001-2017年西南地区森林火灾数据特征分析[J]. 西北林学院学报, 2021, 36(1):179-186.
|
| [11] |
张富精. 云南省普洱市林火发生规律及森林火险等级研究[D]. 北京: 北京林业大学, 2018.
|
| [12] |
白海峰, 刘晓东, 牛树奎, 等. 基于贝叶斯模型平均法的森林火灾预测模型构建研究——以云南省大理州为例[J]. 北京林业大学学报, 2021, 43(5):44-52.
|
| [13] |
陈波. 基于MODIS数据的云南省森林火灾时空分布及火险区划研究[D]. 北京: 北京林业大学, 2018.
|
| [14] |
田野, 牛树奎, 陈锋, 等. 丽江地区森林火灾的时空分布规律[J]. 西北林学院学报, 2018, 33(5):142-148.
|
| [15] |
何雨芩, 徐虹, 程晋昕. 云南省林火时空分布特征分析[J]. 中南林业科技大学学报, 2017, 37(5):36-41.
|
| [16] |
顾先丽, 吴志伟, 张宇婧, 等. 基于MODIS数据的2001—2015年江西省林火时空特征分析[J]. 广东农业科学, 2018, 45(6):129-134,173.
|
| [17] |
\n\nFor Chinese fire cases, it was established that the active fire data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km2 spatial resolution and their subsequent analysis are more accurate and spatially precise than those obtained from the statistical fire data collected by the State Forestry Administration (SFA) of P. R. China. Most (37.5%) of the biomass burning detections from 2000 to 2011 were found in croplands, followed by broadleaf forests (21.2%). Three high-density fire regions were found during the 12-year study period: (1) Heilongjiang Province, where many large forest fires occurred in April–May and September–October; (2) Yunnan Province, where many small forest fires occurred in December–May and (3) Guangdong Province and Guangxi Autonomous Regions, where most fires occurred in croplands in November–March. The largest percentage (10.72%) of the total active fire points was in Heilongjiang Province during 2000–2011, followed by Yunnan Province (10.14%), with several fires taking place in February, April and June.\n
|
| [18] |
|
| [19] |
曾爱聪, 郭新彬, 郑文霞, 等. 基于MODIS卫星火点数据的浙江省林火时空动态变化特征[J]. 北京林业大学学报, 2020, 42(11):39-46.
|
| [20] |
马文苑, 冯仲科, 成竺欣, 等. 山西省林火驱动因子及分布格局研究[J]. 中南林业科技大学学报, 2020, 40(9):57-69.
|
| [21] |
姜汉侨. 云南植被分布的特点及其地带规律性[J]. 云南植物研究, 1980(1):22-32.
|
| [22] |
王秋华, 李晓娜, 叶彪, 等. 华山松纯林地表可燃物的潜在燃烧火行为研究[J]. 林业工程学报, 2019, 4(6):151-157.
|
| [23] |
龙腾腾, 殷继艳, 欧朝蓉, 等. 云南省森林火灾风险综合评价及空间格局研究[J]. 中国安全科学学报, 2021, 31(9):167-173.
为有效揭示森林火灾风险的空间异质性和关联性,给林火防控资源配置和区域联动提供科学参考,综合考虑自然因素和人为因素,利用熵权法和综合指数法评估云南省森林火灾风险,继而利用空间自相关模型,分析森林火灾风险的空间格局。研究结果表明:云南省西部森林火灾风险高于东部地区,绝大部分地区处于较高风险和高风险等级,尤其是滇西北、滇西和滇中地区;火灾风险趋势线有2条,1条从云南省的西北部沿着中部延伸到东北部,呈现出两端高中间低的态势,另1条呈现南北低,中间高的态势;火灾风险在空间上呈现出空间集聚特征,但显著程度一般,森林火灾风险的局部相关性较弱。
|
| [24] |
周瑞伍, 彭明春, 张一平. 云南主要森林植被碳储量及固碳潜力模拟研究[J]. 云南大学学报(自然科学版), 2017, 39(6):1089-1103.
|
| [25] |
|
| [26] |
Identifying road accident hotspots is a key role in determining effective strategies for the reduction of high density areas of accidents. This paper presents (1) a methodology using Geographical Information Systems (GIS) and Kernel Density Estimation to study the spatial patterns of injury related road accidents in London, UK and (2) a clustering methodology using environmental data and results from the first section in order to create a classification of road accident hotspots. The use of this methodology will be illustrated using the London area in the UK. Road accident data collected by the Metropolitan Police from 1999 to 2003 was used. A kernel density estimation map was created and subsequently disaggregated by cell density to create a basic spatial unit of an accident hotspot. Appended environmental data was then added to the hotspot cells and using K-means clustering, an outcome of similar hotspots was deciphered. Five groups and 15 clusters were created based on collision and attribute data. These clusters are discussed and evaluated according to their robustness and potential uses in road safety campaigning.
|
| [27] |
\n\nThe Scripps Experimental Climate Prediction Center has been making experimental, near-real-time, weekly to seasonal fire danger forecasts for the past 5 years. US fire danger forecasts and validations are based on standard indices from the National Fire Danger Rating System (NFDRS), which include the ignition component (IC), energy release component (ER), burning index (BI), spread component (SC), and the Keetch–Byram drought index (KB). The Fosberg fire weather index, which is a simplified form of the BI, has been previously used not only for the USA but also for other global regions and is thus included for comparison. As will be shown, all of these indices can be predicted well at weekly times scales and there is even skill out to seasonal time scales over many US West locations. The most persistent indices (BI and ER) tend to have the greatest seasonal forecast skill. The NFDRS indices also have a weak relation to observed fire characteristics such as fire counts and acres burned, especially when the validation fire danger indices are used.\n
|
| [28] |
赵凤君, 王明玉, 舒立福, 等. 气候变化对林火动态的影响研究进展[J]. 气候变化研究进展, 2009, 5(1):50-55.
|
| [29] |
|
| [30] |
任金鑫, 肖慧娟, 张超, 等. 持续干旱对森林火灾的影响研究综述[J]. 内蒙古林业调查设计, 2013, 36(5):135-138.
|
| [31] |
何诚, 舒立福, 刘超, 等. 南方人工林地阴燃火温度变化特征研究[J]. 林业工程学报, 2020, 5(2):151-157.
|
| [32] |
王秋华, 李伟, 刘世远, 等. 滇中昆明地区森林火灾的火环境研究[J]. 江西农业大学学报, 2015, 37(1):108-113.
|
| [33] |
田晓瑞, 舒立福, 赵凤君, 等. 未来情景下西南地区森林火险变化[J]. 林业科学, 2012, 48(1):121-125+192-193.
|
| [34] |
王金亮, 高雁. 云南省近20年植被动态变化遥感时序分析[J]. 云南地理环境研究, 2010, 22(6):1-7.
|
| [35] |
刘晓娜, 封志明, 姜鲁光, 等. 西双版纳土地利用/土地覆被变化时空格局分析[J]. 资源科学, 2014, 36(2):233-244.
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