Mapping forest fire risk zones with spatial data and principal

140 Science in China: Series E Technological Sciences 2006 Vol.49 Supp. I 140—149

DOI: 10.1007/s11434-006-8115-1

Mapping forest fire risk zones with spatial data and principal component analysis

XU Dong1,2, Guofan Shao1,3, DAI Limin1, HAO Zhanqing1, TANG Lei4 & WANG Hui5

1. Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100039, China; 3. Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USA; 4. Department of Management, Shenyang Institute of Aeronautical Engineering, Shenyang 110034, China; 5. Department of City Development, Jinan University, Jinan 250002, China Correspondence should be addressed to Guofan Shao (email: [email protected]) Received October 10, 2005; accepted April 12, 2006

Abstract By integrating forest inventory data with remotely sensed data, new data layers for factors that affect forest fire potentials were generated for Baihe Forestry Bureau in Jilin Province of China. The principle component analysis was used to sort out the rela-tionships between forest fire potentials and environmental factors. The classifications of these factors were performed with GIS, generating three maps: a fuel-based fire risk map, a topography-based fire risk map, and an anthropogenic-factor fire risk map. These three maps were then synthesized to generate the final fire risk map. The linear regression method was used to analyze the relationship between an area-weighted value of forest fire risks and the frequency of historical forest fires at each forest farm. The results showed that the most important factor contributing to forest fire ignition was topography, followed by anthropogenic factors.

Keywords: wildfire risk, regression analysis, geographic information system, remote sensing, Baihe Forestry Bureau.

Forest fires threaten natural resources and even human lives in many areas of the world. Forest fires sometimes occur so frequently, so better understanding of forest fire risks is critically important for sustaining forest resources. Although it is impossible for human to control the Mother Nature, it is possible to forecast forest fire risks by analyz-ing the factors that influence forest fires and thereby reduce fire frequency and dam-ages[1,2]. The forecast of forest fire risks can be achieved with the use of fire risk zone maps. Forest fire risk zones are locations where a fire is likely to start, and from where it

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Mapping forest fire risk zones with spatial data and principal component analysis 141

can easily spread to other areas[2]. Researches on forest fire risks were developed well[2－5]. People studied forest fire risk

zones with a variety of methods. Most of them mapped forest fire risk zones by directly using remote sensing and geographic information systems (GIS) that contain topography, vegetation, land use, population, and settlement information[2,4,5]. A common practice was that forest fire risk zones were delineated by assigning subjective weights to the classes of all the layers according to their sensitivity to fire or their fire-inducing capabil-ity.

Studies on forest fire risks in China emerged as early as the 1950s[6], but quantitative analysis did not appear until the 1980s. Most of attention were paid to forest fire risk zones for entire country or a province[7－9]. They lacked detailed local variables so that the resolutions of their map products were too coarse to be meaningful to fire risk zones at a forestry-bureau level. Although there were few studies at a forestry-bureau level[6,10], they did not have detailed data to estimate variables such as stand ages, average diame-ters, geographical locations of farmlands and settlements, and spatial relations of forest stands with farmlands and settlements. Such a problem can be solved if digital forest in-ventory data and remotely sensed data are available and integrate within GIS.

Forest fire risk zoning requires integration of natural (fuel and topography) and an-thropogenic factors (such as roads, farmlands and settlements). Fuel represents the mate-rial available for fire ignition and combustion, and also represents a fire-related factor that humans can control or manage[11,12]. Fuel characteristics include vegetation structure, fuel type, and biomass[2,5,13]. Topography influences the occurrence and spread of forest fires through airflow and microclimate. Major topographical factors influencing forest fires are slope, aspect and altitude[5,13]. Anthropogenic factors can be described with the spatial distribution of certain human infrastructures such as roads, settlements, camping sites, and farmlands[14,15] and some socioeconomic fire-related variables, such as popula-tion density and activity sectors[16].

There are so many variables or factors that are needed for forest fire zoning and these variables are somehow related. This complicates forest fire zoning processes. The prin-cipal component analysis method[17,18] can be used to reduce the repetitiveness of the factors and, therefore, can make forest fire zoning relatively straightforward.

In this study, an attempt was made to map forest fire risk zones by spatially integrating inventory data, remote sensing images and other ancillary data such as roads, settlements and farmlands for a forestry bureau. The map products will be helpful to local forest manager to prevent or minimize fire risks within the forestry bureau and take proper re-sponses when fires break out[5].

1 Study area

The study area, Baihe Forestry Bureau, is located in the Changbai Mountains of Jilin Province, China (Fig. 1). With a total area of 1900 km2, its geographical range is between 127°53′ and 128°34′ E, and between 42°01′ and 42°48′N. Baihe Forestry Bureau in-cludes 10 Forest Farms (Fig. 1).

142 Science in China: Series E Technological Sciences

Fig. 1. Geographic location (a) and administrative division (b) of Baihe Forestry Bureau.

The Baihe Forestry Bureau has a forest cover of 89.9% in area. There are over 1,700

higher plant species, 210 lichen species, and 350 epiphyte species. The dominant forest type is broadleaved-Korean pine mixed forest[19].

The climate of the study area is temperate continental, where winter is cold and sum-mer is cool and moist. The average annual temperature is 2.2 ; frost℃ -free period lasts 110 days; average annual precipitation is about 800 mm. The average elevation is 800 m and the average gradient is about 5 degrees within the forestry bureau.

Extensive forest harvesting did not start until the 1970s in the study area. Most of naturally regenerated and undisturbed forests were damaged at a variety of degrees within the forestry bureau. Some harvested forests were replaced with plantations[20,21]. Therefore, it is hard to find a clear relationship between forest characteristics and topog-raphic conditions. Forest fires often occurred in the forestry bureau but their damages were not extensive partly due to overwhelming suppressing efforts from the local forestry administrations.

2 Material and methods

2.1 Material

It is assumed that forest fire risks are related with fuel, topography, and human or an-thropogenic factors. The fuel factor includes 4 variables: tree species, canopy density, stand age, and average diameter; topographical factor includes 3 variables: slope, aspect, and altitude; anthropogenic factor includes 4 variables: population density, road density, distance to roads or residential areas, and distance to farmlands. The Landsat Thematic Mapper (TM) data acquired in 1987 and Enhanced Landsat Thematic Mapper (ETM+) data acquired in 2000, forestry inventory data of 1987 and 2000 and Digital elevation models (DEM) with 25 m of cell size of the forestry bureau were collected for the study.


2.2 Methods

Remote sensing has opened up opportunities for qualitative analyses of forest and other ecosystems at all geographic and spatial scales. And GIS has also developed func-tions such as analyzing available information and using them as a decision and a support system as well as it compiles the information as a whole and stores it, and GIS technol-ogy can serve as a vital technological core for forest fire crisis management. Here, GIS provides a means of overlaying and combining data for analysis.

The Landsat TM and ETM+ data were used to extract information for roads, settle-ments and farmlands by using ERDAS IMAGINE software. The satellite images were corrected for the influence of atmosphere and topographic relief. And the atmospheri-cally, radiometrically and geometrically corrected/geo-coded images were interpreted and classified. The selection of the bands used for classification was made in considera-tion of the spectral profile analysis. According to the analysis, Landsat TM bands 5, 4 and 3 were selected to make supervised classification. The classified raster layers were converted to vector layers for further analysis in GIS. Forest inventory data in 1987 and 2000 were used to obtain stand information, such as species composition, canopy density, stand age, and average diameter. DEM were used to generate topographic information with ArcView (www.esri.com) using spatial analyst function. GIS analysis was used to integrate the existing data to produce the data layers of population density, road density, and buffers around roads, residential areas or farmlands (Fig.2).

According to the potential contributions to forest fire risks, each variable was assigned a numerical value (Table 1).

Table 1 Numerical values of tree species, stand age and aspect

Variable Numerical value (Fire rating classes )

Pinus koraiensis, Pinus sylvestris, mongolica, Larix olgensis, Abies nephrolpis 1 (high)

Picea jezoensis, Tilia amurensis or Tilia mandshurica, Quercus mongo-lica, Acer mono, Betula costata, Betula platypylla, Populus davidiana and miscellaneous trees

2 (medium) Dominant tree species

Fraxinus mandshurica, Juglans mandshurica, Phellodendron amurense, Ulmus japonica and hardwood forest

3 (low) (Hu, 2003)

young forest and overmature forest 1 (high) mid-age forest 2 (medium) Stand age near-mature forest and mature forest 3 (low) (Hu, 2003) south (136－225°) 1 (very high) west (226－315°) 2 (high) east (46－135°) 3 (medium)

Aspect

north (316－45°) 4 (low)

The mapping unit is sub-compartment. There were 8,475 sub-compartments for Baihe

Forestry Bureau in 1987. The numerical value according to Table 1 and other data were put into SPSS software to produce forest fire risk score for each sub-compartment by using the principle component analysis method. Components with Cumulative Initial Eigenvalues >90% were selected. And the forest fire risk scores were grouped with natu-ral breaks in five fire-risk levels (i.e. Ikij in eq. (1)) under each factor (Fig. 2).


Fig. 2. A flow chart of the study methods. The past forest fires were recorded by individual forest farms. To relate the forest fire

risk levels with the frequency of forest fires (F), an area-weighted value of forest fire risks at each forest farm was expressed as

(1) (1

,n

k j ij k iji

R A I=

= ∗∑ )where Rk j is area-weighted risk value of forest fires at the jth forest farm corresponding to the kth fire-related factor (k=1 representing fuel factor, 2 representing topographical factor, 3 representing anthropogenic factor, and 4 representing synthetic factor); Aij is area of the ith sub-compartment in the jth forest farm; and Ikij is fire-risk level for the kth fire-related factor at the ith sub-compartment in the jth forest farm (Ikij = 1, 2, 3, 4, or 5).

The implication of eq. (1) is that the total fire-risk values within a forest farm are related to both forest area and fire-risk index values or fire-risk levels. When a sub-compartment has low fire-risk level, its contribution to ignition may be ignored. To increase


predictability of fire frequency with Rkj, low-value fire-risk indices may be screened out of Rk j calculations. To figure out the sensitivities of Ikij, the values of Rk j were calculated by selectively using different Ikij values:

Rk j5 was calculated when Ikij = 5; Rk j54 was calculated when Ikij = 5 and 4; Rk j543 was calculated when Ikij = 5, 4, and 3; Rk j5432 was calculated when Ikij = 5, 4, 3, and 2; Rk j54321 was calculated when Ikij = 5, 4, 3, 2, and 1.

The linear regression method was used to analyze the relationship between each of five Rk j values and the historical forest fire frequency (Fj) (Fig. 2).

3 Results

All the scores of the kth fire-related factor at the ith sub-compartment in the jth forest farm that calculated with principal component analysis, were grouped with natural breaks into 5 classes, the smaller the score is, the higher the fire-risk level is (Table 2): the 5th level (extremely high risk, Ikij=5), the 4th level (high risk, Ikij=4), the 3rd level (medium risk, Ikij=3), the 2nd level (low risk, Ikij=2) and the 1st level (no risk, Ikij=1) (Fig. 3 and Table 2). Rk j was calculated. And Regression Coefficient Squares (R2) between Rk j and Fj were compared according to different Ikij values (Fig. 4).

Table 2 Ranges of fire-risk levels for the kth fire-related factor (Ikij) Fire-risk level

k 5 4 3 2 1

1 −2.04－−1.38 −1.379－−0.56 −0.559－−0.01 −0.009－0.41 0.411－1.12 2 −2.46－−0.72 −0.719－−0.28 −0.279－0.12 0.121－0.66 0.661－1.68 3 −0.63－−0.23 −0.229－0.28 0.281－0.9 0.901－1.8 1.801－4.05 4 −1.36－−0.58 −0.579－−0.22 −0.219－0.09 0.091－0.44 0.441－1.95

The results of R2 between Rk j543 and Fj (especially between R2j543, R4j543 and Fj) were

better than that between R4 j5, R4 j54, R4 j5432, R4 j54321 and Fj. So Rk j543 was selected out for further analysis.

R2 between R1j543 and Fj was 0.096 (Fig. 4), indicating that R1j543 and Fj were basically independent, and this shows that fuel factor contributes little to forest fire’s ignition. R2 between R2 j543 and Fj was 0.91 (Fig. 4), indicating that R2 j543 and Fj were closely related, and this shows that topographical factor contributes greatly to forest fire’s ignition. R2 between R3 j543 and Fj was 0.479, indicating that R3 j and F j were related in some degree since most people mainly live below 900m above sea level in Baihe Forestry Bureau. R2

between R3 j543 and Fj was analyzed again for the forestlands where altitude is below 900m. R2 between them was 0.878 (Fig. 4), indicating that R3j543 and Fj were also related when altitude is below 900 m. R2 between R4 j543 and Fj was 0.925 (Fig. 4), indicating that R4 j543 is consistently related with Fj, and this shows that anthropogenic factor is relevant with forest fire’s ignition.

Forest fire risk zones were studied again with inventory data and Landsat ETM+ data in 2000 (Fig. 5). And R4j543s were calculated and compared to analyze forest fire risk zone changes between 1987 and 2000 (Fig. 6).


Fig. 3. Synthetic factors-based forest fire risk map in 1987.

Fig. 4. R2 between Rk j5, Rk j54, Rk j543, Rk j5432, Rk j54321 and Fj. F, Fuel factor; T, topographical factor; A, anthropogenic factor; S, synthetic factor.

Liangjiang, Baoma, Erdao and Chunlei forest farms are still the main parts that should be controlled. And Dongfanghong forest farm has a rise in forest fires risks because of more and more human activities.

4 Discussion and conclusions

Forest fire risk is mainly influenced by fuel and environment factors including natural environment factors (topography) and social environment factors (anthropogenic factors). The three category factors were analyzed separately so that high-risk area influenced by each factor can be sorted out and the most important factor contributing to forest fires can be determined. The relationships between forest fire ignition and fuel, topography or an-thropogenic factors were analyzed by using linear regression method in this paper.


Fig. 5. Synthetic factors-based forest fire risk map in 2000.

Fig. 6. R4j543 of 1987 and 2000.

Fuel has a marginal role in fire ignition according to R2=0.096 in the bureau. This shows that forest fires’ ignition is influenced slightly by fuel factor. Perhaps there will rarely be forest fires if human activities are restricted, although there is high risk of fuel.

Forest fires ignition is influenced greatly by topography according to R2=0.910. There are two reasons leading to the results. One is that topography can influence forest fire ignition environment such as relative humidity, temperature and evapotranspiration and fuel moisture content that is a critical parameter in fire ignition[22]. The other is that to-pography reflects the locations of human activities so that it will influence the spatial distribution trends of forest fires ignition. For example, farmlands are mainly distributed in sunny slope and lower elevation but forest fires often occur in these areas[23].

Anthropogenic factors are closely related with forest fires ignition when altitude of


forestlands is below 900 m (R2=0.878), but the relationship between anthropogenic fac-tors and forest fires ignition was inconspicuous when altitude of forestlands is above 900 m. There are two reasons leading to this result. One is that chances of forest fires ignition will be fewer at higher altitude due to lower temperature and higher rainfall. And the other reason is that most of people in the Baihe Forestry Bureau live in the areas where altitude is below 900 m. Therefore, the chances of forest fires ignition caused by human being are obvious in these areas. For the same reason, forest fires ignition is mainly in-fluenced by anthropogenic factors and topography when altitude is below 900 m. How-ever, the main factor influencing forest fires ignition will be topography when altitude is above 900 m. And the statistical results of the Baihe Forestry Bureau forest fires records have shown that most fires were ignited due to anthropogenic factors. While the topog-raphical conditions stay the same over time, anthropogenic factors can change. More at-tention should be given to anthropogenic factors to control or minimize forest fires.

The method has some advantages, which integrates the principal component analysis, GIS and remote sensing for Baihe Forestry Bureau forest fire risk zone mapping in this study: (1) detailed data can be gotten by integrating inventory data with remotely sensed data; (2) forest fire managers can access to high fire risk locations easily and manage effectively because that sub-compartments are the basic units of analysis; and (3) each factor influencing forest fires risk was analyzed, so forest fire managers can find out why forest fires risks of some sub-compartments are high and then can manage these areas effectively.

Acknowledgements This work was jointly supported by the National Natural Science Founda-tion of China (Grant Nos. 70373044 and 30470302), Jilin Yanbian Forestry Group, China’s Minis-try of Science and Technology (Grant No. 04EFN216600328), Liaoning Key Technologies R&D Program (Grant Nos. 2004207002 and 2004201003), and the Northeast Rejuvenation Program of the Chinese Academy of Sciences.

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Mapping forest fire risk zones with spatial data and principal