基于空间分析和模型理论的大兴安岭地区林火分布与预测模型研究
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摘要
如何科学有效的对林火进行预测预报,最大限度的减少林火发生数量以及由火灾带来的损失一直是我国林业管理部门和科研部门十分关注的问题。由于我国林火研究起步较晚,与发达国家相比还有较大的差距,因此本文基于ArcGIS、S-Plus、SAS、R、GWR2等空间分析和数学统计手段,应用国际最前沿的模型拟合与检验理论,突破以往研究方法和手段,创新性的对符合我国黑龙江大兴安岭地区林分特征和气象条件的林火空间分布状况及林火预测模型进行了深入分析,并得出了十分有价值和意义的结论,为林火预测预报提供了科学有效的理论依据。
本文首先用传统手段对大兴安岭地区的林火数据进行了初步统计分析,得出了大兴安岭地区林火发生的时空分布情况及与气候因子的关系。
运用K-函数和L-函数,结合S-plus软件对大兴安岭地区雷击火和人为火空间分布类型进行计算判断,根据Lhat分布曲线显示结果说明,1973-1975雷击火分布主要是聚集分布,距离尺度为200 Km时出现聚集峰点。尺度大于350 Km时成随机分布。1976-1981雷击火分布趋势与1973-1975年间近似,聚集峰值也出现在200 Km附近。1982-1984雷击火主要成聚集分布,但是无明显聚集峰点。1985-1987年间雷击火分布成聚集分布,但聚集程度较平缓。1988-2005年间,除1991-1993年间成随机分布外,其余各年雷击火整体上是成聚集状分布的。1988-1990年在距离尺度为350 Km时出现聚集峰点。1994-1996年间距离尺度小于450 Km时雷击火成聚集分布,大于450 Km时成均匀分布。1997-1999年间雷击火在300 Km距离尺度下成聚集分布,大于300 Km时成均匀分布。1999-2005年间同样在300 Km距离尺度下成聚集分布,大于该尺度成均匀分布。整体看1973-2005年间的雷击火发生分布情况,显示雷击火主要成聚集分布,以大约260 Km为距离尺度出现聚集峰点。即在小尺度下,大兴安岭地区雷击火空间分布形式为聚集分布。1972-1976人为火分布主要是聚集分布,距离尺度为200 Km时出现聚集峰点。1977-1981大兴安岭地区人为火的空间分布在400Km尺度下为聚集分布。1982-1991年间,人为火也成聚集分布。1992-1996这4年间大兴安岭地区人为火在空间上呈现随即分布的趋势,并且贯穿各个不同计算尺度。1997-2001年间在距离尺度小于300 Km时人为火成聚集分布,大于300 Km时成均匀分布。2002-2005年间大兴安岭地区人为火在不同尺度下均呈聚集分布。
根据Kernel空间密度函数对大兴安岭雷击火和人为火发生的空间密度进行模拟推算,结果显示大兴安岭地区1973-1977年间雷击火空间分布密度存在几个“热点”和“次热点”地区,按行政区划分布在呼中北部和松岭西部,核心区经纬度坐标分别为123°23'E,52°12'N和123°58'E,51°11'N；1978-1982年间的雷击火分布存在两个明显的热点地区政区划分布在图强林业局西南大部和塔河林业局北部,核心区经纬度坐标分别为122°26'E,52°38'N和124°08'E,53°17'N。1983-1987年间的雷击火分布存在两个热点地区,坐标为124°06'E,52°04'N和124°04'E,52°23'N；行政区划位于呼中和新林北端交界处以及塔河南端与呼中东部交界处,1988-2005年间雷击火分布热点核心区地理坐标分别为123°06'E,52°20'N；123°41'E,51°34'N；124°08'E,50°48'N。大兴安岭地区1972-1976,1977-1981,1982-1986,1987-1991,4个时间间隔的人为火空间分布高密度地区即“热点区”基本相同,都集中在124°10'E,50°23'N；按照行政区划其位置为加格达奇林管局境内,1992-1996年间的人为火并无集中的分布区,而是零散分布于整个研究区域,并无“热点区”的存在。1997-2001年间人为火发生“热点地区”与前几个时间段的发生情况有所不同,该时期的“热点区”核心位置经纬度坐标为52°09'N,125°55'E,位于韩家园北部地区。2002-2005年此4年间人为火发生数量较大,且主要分布在大兴安岭地区南部,几个主要“热点区”的中心点经纬度坐标为51°35'N,125°25'E；51°13'N,125°27'E；51°07'N,124°38'E；50°34'N,125°26'E；51°16'N,123°41'E,其行政区划分别为,呼玛和松岭最北端交界处；松岭与加格达奇最东部交界处；松岭行政区中部；加格达奇东部和呼中与松岭交界带。
除传统模型OLS外,本文还选用Poisson和零膨胀Poisson (Zero-Inflated Poisson,ZIP),负二项(NB)和零膨胀负二项(ZINB)模型对大兴安岭雷击火和人为火与气象因子进行建模分析,其中NB和ZINB在国内首次应用于林业领域的相关研究中。将5种模型的建模结果进行对比分析,应用AIC和Voung方法确定最优模型。研究结果表明,大兴安岭地区雷击火与气象因子建模研究的最优模型为ZINB,得到的预测模型为：log(λ=E(Y))=-3.637929+0.124678MAT+0.010395MAE和log it(p)=-72.175944+0.962976MARH人为火与气象因子建模研究表明,ZINB也是5种模型中对数据拟合效果最好的,预测模型为：log(λ=E(Y))=-0.6534+0.0056MAE和log it(p)=-20.4509+0.0175MAP-0.2615MAT+0.3183MARH
应用ArcGIS, Arcmap技术和GWR2, Moran计算软件,对塔河地区的林火发生与空间因素的关系进行了详细的分析,文中主要分为不同尺度和空间模型进行计算和对比分析的,尺度主要为5Km×5Km和随机选取样点两种,模型选取Global Logistic模型和GWR模型。研究结果显示在5Km×5Km的栅格划分尺度下,只有距街区和铁路的距离对林火发生具有显著相关性。得到林火发生的Global Logistic模型概率表达式为：式中：x1为距街区的距离；x2为距铁路的距离。
GWR模型拟合后的概率模型表达式是一个矩阵函数,截据的系数分为5个水平梯度,分别为IC≤-0.4；-0.4火发生概率方程为：式中：x1为距铁路的距离；x2为林型。GWR模型截据的系数分布为IC≤-0.4；-0.4火发生的预测结果,应用Kriging插值法对研究区域的林火发生概率进行的空间预测,并得到研究区域林火火险概率空间分布。
The biggest issues for our forestry management and research department are how to forecast the forest fire effectively and scientifically and limited the loss those caused by forest fire and decreased the number of forest fire greatly. There was a big disparity comparing with some developed country because of our study started relative late, so this paper made a deep research on the distribution of forest fire and modeling the relationship between forest fire and weather and some spatial factors in Daxing'an mountain in Heilongjiang Province of china basis on some statistic and spatial analysis software, for instance, ArcGIS, S-Plus, SAS, R, GWR2 and so on. The results of this paper are very meaningful and can provide a scientific thesis foundation for forest fire's forecast.
The first step for this paper is make a simple analysis on forest fire data of Daxing'an Mountain in Heilongjiang Province, the results show that the number of spring forest fire was decreased after 1987 year and it was increased for summer fire. The change trend of climate for the study area also be studied, which include temperature, precipitation, relative humid.
Computing and judging the types of distribution of lighting fire and human-caused fire using K-function and L-function meanwhile combine with S-plus software. The results show that lighting fire is cluster distribution and the peak in 200Km scale. It shows random distribution over that scale in the period of 1973-1975. The distribution type of lighting fire during 1976-1981 is similar with 1973-1975. The main distribution type of lighting fire is cluster and no obvious peak during 1982-1984. It is cluster distribution but the degree is not high in the period of 1985-1987. Except the random distribution of 1991-1993, the other periods are cluster during 1988-2005. When the distance scale is 350Km, it shows cluster peak during 1988-1990 and when the scale less than 450Km the lighting fire is cluster, greater than 450Km are uniform distribution in the period of 1994-1996. It is cluster distribution when the scale less than 300Km and it is uniform distribution when the scale greater than 300Km during 1997-1999 and the results are similar with the period of 1999-2005.the main distribution type of lighting fire is cluster in the period of 1973-2005, the cluster peak is in 260Km scale, namely the lighting fire are cluster distribution under small distance scale.
The distribution of human-caused fire during 1972-1976 are cluster, the peak appears at 200Km scale. It is cluster distribution under 400Km scale during 1977-1981, and it also happens in the period of 1982-1991 but it is random distribution tendency of human-caused fire during 1992-1996. It is cluster distribution when the scale less than 300 Km and if scale is greater than 300 Km, it would be uniform distribution,1997-2001.The human-caused fire are cluster distribution in all different scales during 2002-2005.
Computing and modeling the spatial density of lighting and human-caused fire occurrence by the use of Kernel spatial function and the results show that there are several hot points and lower hot points in Daxing'an mountain area during 1973-1977, the coordinates of cores of hot and lower hot points are 123°23'E,52°12'N和123°58'E,51°11'N,separately, and located in north HuZhong forestry bureau and west of SongLing forestry bureau. In the period of 1978-1982, the lighting fire exist two obvious hot points and the coordinates of cores are located in 122°26'E,52°38'N and 124°08'E,53°17'N, located in much area of southwest of TuQiang forestry bureau and north of Tahe bureau. Two hot points exist in Daxing'an mountain area during 1983-1987. One is in the junction of HuZhong and north of XinLin forestry bureau and the other is in the junction of north of TaHe and east of HuZhong bureau, the coordinates of cores are located in 124°06'E,52°04'N and 124°04'E,52°23'N, respectively. duringl988-2005, the coordinates of cores are 123°06'E,52°20'N,123°41'E,51°34'N and 124°08'E, 50°48'N. The hot points of human-caused fire in Daxing'an mountain area are similar each other among the period of 1972-1976,1977-1981,1982-1986,1987-1991, all the hot points are located in 124°10'E,50°23'N, inside JIaGedaqi forestry bureau. There are no hot points of human-caused fire in Daxing'an mountain during 1992-1996. It is different comparing with previous periods, the coordinates of cores of human-caused fire during 1997-2001 are 52°09'N,125°55'E, located in north of Hanjiayuan bureau. The number of human-caused fire occurrence during 2002-2005 is bigger than other periods, the fire happens mainly focus on south of Daxing'an mountain, the coordinates of cores are 51°35'N,125°25'E; 51°13'N, 125°27'E; 51°07'N,124°38'E; 50°34'N,125°26'E; 51°16'N,123°41'E and located in the north junction of HuMa and SongLing bureau, east junction of SongLing and JiaGedaqi bureau, in the middle of SongLing area and the junction of east of JiaGedaqi, HuZhong and SongLing bureau.
Besides traditional OLS model, we choose Poisson, Zero-inflated Poisson (ZIP), negative binomial (NB) and Zero-inflated negative binomial (ZINB) models to model and analysis the relationship between lighting fire, human-caused fire and weather factors, furthermore, comparing those five models by the use of AIC and Voung methods and find out the best model. The results show that ZINB is the best model for fitting lighting fire data and weather factors data, the prediction model as following: log(λ= E(Y))=-3.637929+0.124678MAT+0.010395 MAE and log it(p)=-72.175944+0.962976 MARH
Meanwhile the ZINB model are also the best model for modeling and analyzing the relationship between human-caused fire and weather factors, the prediction models we get are log(λ=E(Y))=-0.6534+0.0056MAE and logit(p)=-20.4509+0.0175MAP-0.2615MAT+0.3183MARH.
By the use of ArcGIS, Arcmap, GWR2 and Moran software to analyze the relationship between forest fire occurrence and spatial relative factors. This thesis to analyze compute and compare the models basis on different spatial scale which included 5KmX5Km grid and random choosing samples two scales. The models we choice were global logistic and GWR model. The result shows that the spatial factors, distance to street and distance to railway, have a significant correlation with fire occurrence, and the Global Logistic model we get is where x1is the distance to street; x2 is the distance to railway. The probability function we get from the model result of GWR is a matrix function, the intercept coefficient (IC) have five levels, they are IC≤-0.4,-0.4 We get fire occurrence probability function as following according to global logistic under the condition of choosing sample randomly. Distribution of intercept coefficient are IC≤-0.4,-0.4 To test the two models above by the use of Z test, the results show that GWR model are better than Global logistic model in fitting results no matter in 5Km×5Km or random samples scale, besides, using Kriging method to make a spatial prediction of fire occurrence probability according to the predict values from those models above and the distribution of fire risk in the study region.
本文首先用传统手段对大兴安岭地区的林火数据进行了初步统计分析,得出了大兴安岭地区林火发生的时空分布情况及与气候因子的关系。
运用K-函数和L-函数,结合S-plus软件对大兴安岭地区雷击火和人为火空间分布类型进行计算判断,根据Lhat分布曲线显示结果说明,1973-1975雷击火分布主要是聚集分布,距离尺度为200 Km时出现聚集峰点。尺度大于350 Km时成随机分布。1976-1981雷击火分布趋势与1973-1975年间近似,聚集峰值也出现在200 Km附近。1982-1984雷击火主要成聚集分布,但是无明显聚集峰点。1985-1987年间雷击火分布成聚集分布,但聚集程度较平缓。1988-2005年间,除1991-1993年间成随机分布外,其余各年雷击火整体上是成聚集状分布的。1988-1990年在距离尺度为350 Km时出现聚集峰点。1994-1996年间距离尺度小于450 Km时雷击火成聚集分布,大于450 Km时成均匀分布。1997-1999年间雷击火在300 Km距离尺度下成聚集分布,大于300 Km时成均匀分布。1999-2005年间同样在300 Km距离尺度下成聚集分布,大于该尺度成均匀分布。整体看1973-2005年间的雷击火发生分布情况,显示雷击火主要成聚集分布,以大约260 Km为距离尺度出现聚集峰点。即在小尺度下,大兴安岭地区雷击火空间分布形式为聚集分布。1972-1976人为火分布主要是聚集分布,距离尺度为200 Km时出现聚集峰点。1977-1981大兴安岭地区人为火的空间分布在400Km尺度下为聚集分布。1982-1991年间,人为火也成聚集分布。1992-1996这4年间大兴安岭地区人为火在空间上呈现随即分布的趋势,并且贯穿各个不同计算尺度。1997-2001年间在距离尺度小于300 Km时人为火成聚集分布,大于300 Km时成均匀分布。2002-2005年间大兴安岭地区人为火在不同尺度下均呈聚集分布。
根据Kernel空间密度函数对大兴安岭雷击火和人为火发生的空间密度进行模拟推算,结果显示大兴安岭地区1973-1977年间雷击火空间分布密度存在几个“热点”和“次热点”地区,按行政区划分布在呼中北部和松岭西部,核心区经纬度坐标分别为123°23'E,52°12'N和123°58'E,51°11'N；1978-1982年间的雷击火分布存在两个明显的热点地区政区划分布在图强林业局西南大部和塔河林业局北部,核心区经纬度坐标分别为122°26'E,52°38'N和124°08'E,53°17'N。1983-1987年间的雷击火分布存在两个热点地区,坐标为124°06'E,52°04'N和124°04'E,52°23'N；行政区划位于呼中和新林北端交界处以及塔河南端与呼中东部交界处,1988-2005年间雷击火分布热点核心区地理坐标分别为123°06'E,52°20'N；123°41'E,51°34'N；124°08'E,50°48'N。大兴安岭地区1972-1976,1977-1981,1982-1986,1987-1991,4个时间间隔的人为火空间分布高密度地区即“热点区”基本相同,都集中在124°10'E,50°23'N；按照行政区划其位置为加格达奇林管局境内,1992-1996年间的人为火并无集中的分布区,而是零散分布于整个研究区域,并无“热点区”的存在。1997-2001年间人为火发生“热点地区”与前几个时间段的发生情况有所不同,该时期的“热点区”核心位置经纬度坐标为52°09'N,125°55'E,位于韩家园北部地区。2002-2005年此4年间人为火发生数量较大,且主要分布在大兴安岭地区南部,几个主要“热点区”的中心点经纬度坐标为51°35'N,125°25'E；51°13'N,125°27'E；51°07'N,124°38'E；50°34'N,125°26'E；51°16'N,123°41'E,其行政区划分别为,呼玛和松岭最北端交界处；松岭与加格达奇最东部交界处；松岭行政区中部；加格达奇东部和呼中与松岭交界带。
除传统模型OLS外,本文还选用Poisson和零膨胀Poisson (Zero-Inflated Poisson,ZIP),负二项(NB)和零膨胀负二项(ZINB)模型对大兴安岭雷击火和人为火与气象因子进行建模分析,其中NB和ZINB在国内首次应用于林业领域的相关研究中。将5种模型的建模结果进行对比分析,应用AIC和Voung方法确定最优模型。研究结果表明,大兴安岭地区雷击火与气象因子建模研究的最优模型为ZINB,得到的预测模型为：log(λ=E(Y))=-3.637929+0.124678MAT+0.010395MAE和log it(p)=-72.175944+0.962976MARH人为火与气象因子建模研究表明,ZINB也是5种模型中对数据拟合效果最好的,预测模型为：log(λ=E(Y))=-0.6534+0.0056MAE和log it(p)=-20.4509+0.0175MAP-0.2615MAT+0.3183MARH
应用ArcGIS, Arcmap技术和GWR2, Moran计算软件,对塔河地区的林火发生与空间因素的关系进行了详细的分析,文中主要分为不同尺度和空间模型进行计算和对比分析的,尺度主要为5Km×5Km和随机选取样点两种,模型选取Global Logistic模型和GWR模型。研究结果显示在5Km×5Km的栅格划分尺度下,只有距街区和铁路的距离对林火发生具有显著相关性。得到林火发生的Global Logistic模型概率表达式为：式中：x1为距街区的距离；x2为距铁路的距离。
GWR模型拟合后的概率模型表达式是一个矩阵函数,截据的系数分为5个水平梯度,分别为IC≤-0.4；-0.4
The biggest issues for our forestry management and research department are how to forecast the forest fire effectively and scientifically and limited the loss those caused by forest fire and decreased the number of forest fire greatly. There was a big disparity comparing with some developed country because of our study started relative late, so this paper made a deep research on the distribution of forest fire and modeling the relationship between forest fire and weather and some spatial factors in Daxing'an mountain in Heilongjiang Province of china basis on some statistic and spatial analysis software, for instance, ArcGIS, S-Plus, SAS, R, GWR2 and so on. The results of this paper are very meaningful and can provide a scientific thesis foundation for forest fire's forecast.
The first step for this paper is make a simple analysis on forest fire data of Daxing'an Mountain in Heilongjiang Province, the results show that the number of spring forest fire was decreased after 1987 year and it was increased for summer fire. The change trend of climate for the study area also be studied, which include temperature, precipitation, relative humid.
Computing and judging the types of distribution of lighting fire and human-caused fire using K-function and L-function meanwhile combine with S-plus software. The results show that lighting fire is cluster distribution and the peak in 200Km scale. It shows random distribution over that scale in the period of 1973-1975. The distribution type of lighting fire during 1976-1981 is similar with 1973-1975. The main distribution type of lighting fire is cluster and no obvious peak during 1982-1984. It is cluster distribution but the degree is not high in the period of 1985-1987. Except the random distribution of 1991-1993, the other periods are cluster during 1988-2005. When the distance scale is 350Km, it shows cluster peak during 1988-1990 and when the scale less than 450Km the lighting fire is cluster, greater than 450Km are uniform distribution in the period of 1994-1996. It is cluster distribution when the scale less than 300Km and it is uniform distribution when the scale greater than 300Km during 1997-1999 and the results are similar with the period of 1999-2005.the main distribution type of lighting fire is cluster in the period of 1973-2005, the cluster peak is in 260Km scale, namely the lighting fire are cluster distribution under small distance scale.
The distribution of human-caused fire during 1972-1976 are cluster, the peak appears at 200Km scale. It is cluster distribution under 400Km scale during 1977-1981, and it also happens in the period of 1982-1991 but it is random distribution tendency of human-caused fire during 1992-1996. It is cluster distribution when the scale less than 300 Km and if scale is greater than 300 Km, it would be uniform distribution,1997-2001.The human-caused fire are cluster distribution in all different scales during 2002-2005.
Computing and modeling the spatial density of lighting and human-caused fire occurrence by the use of Kernel spatial function and the results show that there are several hot points and lower hot points in Daxing'an mountain area during 1973-1977, the coordinates of cores of hot and lower hot points are 123°23'E,52°12'N和123°58'E,51°11'N,separately, and located in north HuZhong forestry bureau and west of SongLing forestry bureau. In the period of 1978-1982, the lighting fire exist two obvious hot points and the coordinates of cores are located in 122°26'E,52°38'N and 124°08'E,53°17'N, located in much area of southwest of TuQiang forestry bureau and north of Tahe bureau. Two hot points exist in Daxing'an mountain area during 1983-1987. One is in the junction of HuZhong and north of XinLin forestry bureau and the other is in the junction of north of TaHe and east of HuZhong bureau, the coordinates of cores are located in 124°06'E,52°04'N and 124°04'E,52°23'N, respectively. duringl988-2005, the coordinates of cores are 123°06'E,52°20'N,123°41'E,51°34'N and 124°08'E, 50°48'N. The hot points of human-caused fire in Daxing'an mountain area are similar each other among the period of 1972-1976,1977-1981,1982-1986,1987-1991, all the hot points are located in 124°10'E,50°23'N, inside JIaGedaqi forestry bureau. There are no hot points of human-caused fire in Daxing'an mountain during 1992-1996. It is different comparing with previous periods, the coordinates of cores of human-caused fire during 1997-2001 are 52°09'N,125°55'E, located in north of Hanjiayuan bureau. The number of human-caused fire occurrence during 2002-2005 is bigger than other periods, the fire happens mainly focus on south of Daxing'an mountain, the coordinates of cores are 51°35'N,125°25'E; 51°13'N, 125°27'E; 51°07'N,124°38'E; 50°34'N,125°26'E; 51°16'N,123°41'E and located in the north junction of HuMa and SongLing bureau, east junction of SongLing and JiaGedaqi bureau, in the middle of SongLing area and the junction of east of JiaGedaqi, HuZhong and SongLing bureau.
Besides traditional OLS model, we choose Poisson, Zero-inflated Poisson (ZIP), negative binomial (NB) and Zero-inflated negative binomial (ZINB) models to model and analysis the relationship between lighting fire, human-caused fire and weather factors, furthermore, comparing those five models by the use of AIC and Voung methods and find out the best model. The results show that ZINB is the best model for fitting lighting fire data and weather factors data, the prediction model as following: log(λ= E(Y))=-3.637929+0.124678MAT+0.010395 MAE and log it(p)=-72.175944+0.962976 MARH
Meanwhile the ZINB model are also the best model for modeling and analyzing the relationship between human-caused fire and weather factors, the prediction models we get are log(λ=E(Y))=-0.6534+0.0056MAE and logit(p)=-20.4509+0.0175MAP-0.2615MAT+0.3183MARH.
By the use of ArcGIS, Arcmap, GWR2 and Moran software to analyze the relationship between forest fire occurrence and spatial relative factors. This thesis to analyze compute and compare the models basis on different spatial scale which included 5KmX5Km grid and random choosing samples two scales. The models we choice were global logistic and GWR model. The result shows that the spatial factors, distance to street and distance to railway, have a significant correlation with fire occurrence, and the Global Logistic model we get is where x1is the distance to street; x2 is the distance to railway. The probability function we get from the model result of GWR is a matrix function, the intercept coefficient (IC) have five levels, they are IC≤-0.4,-0.4
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