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Spatial patterns of forest fires in Catalonia (NE of Spain) along the period 1975–1995
Forest Ecology and Management 147 (2001) 67±74
Spatial patterns of forest ®res in Catalonia (NE of Spain) along the period 1975±1995 Analysis of vegetation recovery after ®re Ricardo DõÂaz-Delgadoa,*, Xavier Ponsa,b a
CREAF (Center for Ecological Research and Forestry Applications), Fac. CieÁncies, Universitat AutoÁnoma of Barcelona, 08193 Bellaterra, Spain b Department of Geography, Universitat AutoÁnoma de Barcelona, 08193 Bellaterra, Spain
Abstract This paper revises the results of applying a semiautomatic methodology for ®re scars mapping from a time series of Landsat MSS images over the forest and shrubby surface of Catalonia (1975±1993). Perimeters of ®res which occurred in 1994 and 1995 were added enlarging the whole series to 21 years from TM imagery. Results are a map series of ®re history during 21 years as well as a map of the ®re recurrence level. Omission errors are 23% for burned areas greater than 2 km2 while commission errors are 8% for areas greater than 0.5 km2. Detected ®re scars were incorporated into a geographic information system in order to characterise the ®re regime of the study area. Fire size distribution and the number of spot ®res originated from each ®re as well as the maximum distance reached from the main ®re are analysed. A ®rst approach to monitor post-burn regeneration through normalised difference vegetation index is also shown. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Landsat MSS; NDVI; Lineal regression model; Fire regime; Regeneration; Time series
1. Introduction Since 1972 the MSS sensor, boarded on the Landsat satellite series, have been providing digital images of the terrestrial surface. Since 1975, images have been obtained periodically every 16±18 days. Such deeds, and its spatial resolution of about 6080 m2, make it one of the more interesting Earth observation satellites for monitoring and analysing plant cover dynamics (Hall et al., 1991). A good example can be shown
* Corresponding author. Tel.: 34-93-5811877; fax: 34-93-5811312. E-mail addresses: [email protected] (R. DõÂaz-Delgado), [email protected] (X. Pons).
when applied to characterise the ®re regime of natural areas (Minnich, 1983). Once ®re parameters are collected, several spatial patterns of ®re occurrence can be analysed such as ®re size distribution or the Lorenz curves, which allows us to evaluate the relevance of large ®res in the total burned area (Minnich and Chou, 1997). A complete list of ®re dates may aid to estimate temporal patterns like time since ®re distribution or ®re rotation periods (Johnson and Gutsell, 1994). There are several methods to discriminate radiometric changes produced by wild®res that commonly appear as a sudden decrease of plant recovery and, consequently, a change of its radiometric response. Some of the most employed methods are: principal components analysis (Fung and Le Drew, 1987); supervised classi®cation (Hall et al., 1991); change
0378-1127/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 1 1 2 7 ( 0 0 ) 0 0 4 3 4 - 5
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R. DõÂaz-Delgado, X. Pons / Forest Ecology and Management 147 (2001) 67±74
vector analysis (Lambin, 1996); and subtraction of images (Kasischke et al., 1993), the selected one in the present study. This work is part of the general project whose last objective is to characterise the ®re regime, i.e., intensity, frequency, seasonality, extent and type of ®re (Gill, 1975) of the Mediterranean plant communities of Catalonia (NE of Spain) and its in¯uence on the regeneration processes. The presented work mainly emphasises the results from the analysis of some spatial patterns of the detected burned areas. Some variables referred to the spotting maximum distance and the number of them were analysed. Also, the number of vegetation islands within the ®re (known as residual vegetation after Eberhart and Woodard, 1987) and their size, are currently being analysed in order to seek for relationships with the total burned area of every ®re. Simultaneously, radiometric response of vegetation recovery after ®re was monitored by means of the normalised difference vegetation index (NDVI). An example of such dynamics is also shown. 2. Methodology To broach this study, approximately 100 of whole images from the MSS sensor were acquired (4 bands; 2 in the visible [VIS] and 2 in the near infrared [NIR]). The MSS sensor was boarded on the Landsat 1±5 satellites. To include the whole surface of Catalonia, about 32 000 km2, two or three complete scenes are needed and, to improve results, at least two different seasons were retrieved. 2.1. Detection of burned areas Geometric and radiometric corrections were applied to the whole set of images by using the models of PalaÁ and Pons (1995) and Pons and SoleÂ-SugranÄes (1994), respectively. Fire scars detection was performed by means of the methodology described in Salvador et al. (in press). The detection method is based on the subtraction of NDVI images and uses variable thresholds. These thresholds are interpolated from two linear regression models ®tted to the empirical NDVI drop values of several control ®res also observed in the image series.
The resulting burned areas were contrasted with the ®res inventoried by the Departament d'Agricultura, Ramaderia i Pesca (DARP) since 1983 as independent source. Such alphanumerical database has each ®re listed per municipality and also records the ®re date. When the detected burned area was located in the municipality where a ®re documented by DARP existed, and the date was between the dates of the pair of images, which also revealed a ®re, then both were matched. Statistical validation gives the omission error (®res counted by DARP but not detected) and the commission error (areas not really burned but detected as ®re scar) for all the ®res greater than 0.3 km2 (Chuvieco, 1996). Results are given as a map of ®re scars greater than 0.3 km2 for the period 1975±1993, as well as a map of ®re recurrence level. 2.2. Incorporation into a GIS and spatial patterns analysis At this point, all the ®re scars greater than 0.3 km2 detected since 1975±1993 plus burn perimeters from 1994 to 1995 were incorporated into a geographic information system (GIS). The main objective was to facilitate layer query by attributes of every burned area and statistical analysis of spatial and temporal patterns of ®re occurrence in the study region. The 1994 and 1995 burned areas were obtained by applying the nPDF process (n-dimensional probability density function, Cetin et al., 1993) to classify images from SPOT, TM and CASI (Baulies et al., 1995). Flies with CASI are planned for all the ®res greater than 0.5 km2 when already extinguished. No omission and commission errors are expected in terms of land cover change not burned, except for total size and perimeter detected per each ®re. The resulting binary images with a pixel size of 30 m were provided by DMA and ICC (Departament de Medi Ambient and Institut CartograÁ®c de Catalunya), re-sampled to 60 m and then added to the results from the MSS series 1975±1993. The most accurate results (low omission error) were selected as the basis to produce the GIS database, built in vectorial format, topologically structured, so capable of query and analysis (Salvador et al., 1998). This way, several layers of ancillary information may be crossed and overlaid to the ®re occurrence and
R. DõÂaz-Delgado, X. Pons / Forest Ecology and Management 147 (2001) 67±74
recurrence (frequency) database. Then, interesting variables as ®re size, number of ®res, number of spots or vegetation islands can be measured per ®re or on any spatial or temporal scale. Special attention should be paid on the method used to give the identity to every burned area detected by means of the remote sensing imagery. Thus, identity was attributed according to the distances among scars. When distance was equal to or less than 1 km among patches detected at the same interval of dates they were grouped. Such assumption is based on the maximum distance of ``spotting'' expected in Mediterranean forest (some 100 m, Trabaud, 1992). Also, it is supported by the observed maximum distance of spots for all the ®res occurred in 1994 and 1995 (concretely 1140 m, corresponding to a ®re occurred in the BerguedaÁ county, 4 July 1994, with a total burned area of 390 km2). 2.3. Fire spots or secondary fire sources analysis Moreover, we calculated the maximum distances reached by spots from the main ®re perimeter as well as the total number of spots by ®re. The former was obtained by a simple concentric calculation based on the pixel size, while the latter was achieved by contiguous category grouping including diagonal links among pixels and identifying each of the resulting groups. 2.4. Monitoring of plant regeneration through NDVI Several authors have tested the use of NDVI (Mather, 1999) to monitor plant regeneration after ®re (Viedma et al., 1997; Malingreau et al., 1985). Such sort of studies may be carried out because of the proportionality found between NDVI and the amount of green biomass, which do not depend on the plant species that constitutes the community (Gamon et al., 1995). To evaluate the variability of the post-®re response of the different plant communities, several burned areas were selected to monitor their regeneration processes. Three variables were monitored to study the recovery rate of vegetation after ®re: 1. the average of NDVI values for each burned area; 2. the standard deviation of NDVI values for each burned area;
69
3. the quotient between mean NDVI values of burned areas and mean NDVI values of control plots not burned (QNDVI), which shared similar phenological variation and dominant species composition before fire. The choice of ®res was made according to different parameters: precipitation, lithological substrate of the affected zone, type of vegetation prior to ®re and others like altitude, slope and aspect of every burned area selected (DõÂaz-Delgado et al., 1998). Empirically, several models of logarithmic regression were adjusted to the recovery rate. Correlation analyses were accomplished to compare the recovery rate of all the plots and to study their relationships with the mentioned environmental parameters. 3. Results 3.1. Statistical evaluation of the employed methodology The percentage of success reached by the method was given in relation to the minimum ®re size considered. This way, for all the areas burned equal to or greater than 0.3 km2, we reached a percentage of success of up to 53 from no omission (see Fig. 1). However, for ®res greater than 2 km2, the percentage of success from no omission increases up to 78. Concerning the commission error, the percentage of success, for all the areas burned equal to or greater than 0.3 km2, is about 90 (DõÂaz-Delgado et al., 1997). To some extent, such result is due to the big phenological effect that is produced in certain sparse shrubby communities during the summer period (drought). Table 1 displays the total area burned in relation to the recurrence level of burning, as well as the percentage of forest area burned since 1975±1993. 3.2. Fire size distribution Fire size was analysed through the frequency distribution of ®res and also by means of the percentage of area burned per each ®re and all the smaller in relation to its relative size (the total size of the largest ®re is 100%). Also, the Lorenz curve
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Fig. 2. Lorenz curves for Catalonia (1975±1995) and southern California (1956±1971). The straight line represents the line of equality.
Fig. 1. Percentages of success due to the lack of omission and commission errors of burned areas detected between 1983 and 1993. Percentages are given as a function of the fire size in square kilometres.
(see Fig. 2), was depicted in order to compare with the one obtained by Minnich and Chou (1997). A Lorenz curve is a cumulative frequency curve that compares two sets of ordinal, interval or ratio data. Lorenz curves demonstrate the degree of concentration of a variable, or the inequality of a variable, in this case the ®re size. Both curves are far from the equality line, which indicates that ®re sizes are unevenly spread. Table 1 Total area burned according to its recurrence level of burning in Catalonia. The percentage of the total burned area for each recurrence level and of forest area burned is also shown (fire size 0.3 km2, period 1975±1993) Recurrence level
Burned area (km2)
Total burned area (%)
Total forest area (%)
Once Twice Three times Four times Five times
1110.09 145.06 15.32 3.96 0.67
87.06 11.38 1.20 0.31 0.05
5.7 0.74 0.078 0.02 0.0034
The inequality of Lorenz curves, i.e., the variable ®re size, can be measured via the GINI coef®cient varying between 0 (maximum equality) and 1 (maximum inequality) widely used as a measure of earnings equality in econometrics (Lee and Seyoung, 1998). Such coef®cient is calculated from the area between the curve and the diagonal line divided by the area of the triangle below the diagonal. The former can be obtained by means of ®nite integration of the curves. For these cases southern California GINI coef®cient, 0.91, was clearly greater than Catalonia GINI coef®cient, 0.76. Therefore, southern California ®re size distribution is more unequal, due to the abundance of large ®res, more frequent than in Catalonia. In Fig. 3, curve A shows the number of ®res occurred in Catalonia between 1975 and 1995 ordered by size. It can be noticed that large ®res are not frequent. In curve B, where the 100% of X-axis represents the largest ®re occurred in the series (BerguedaÁ ®re, 400 km2), around the 75% of total burned area is due to ®res smaller than 80 km2 (20%) and the 60% to sizes under 40 km2 (10%). Average size of ®res was 6.451 km2 and the median value 1.332 km2. 3.3. Spots of 1994 and 1995 fires: number and maximum distance At ®rst sight, Fig. 4 shows an obvious and tight relation between ®re size and the total number of secondary ®re sources generated from the burn. At the same time, ®re size is also proportional to the maximum distances reached by the spots produced by every main ®re. Current work is studying the same variables but taking into account all the burned areas detected between 1975 and 1995.
R. DõÂaz-Delgado, X. Pons / Forest Ecology and Management 147 (2001) 67±74
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Fig. 5. Example of QNDVI monitoring (averaged NDVI burned area/averaged NDVI control plot) before and after the fire. The logarithmic model fitted enables to calculate the distance to the previous seasonal mean at time t2544, which is a common point for all the analysed series (corresponding to the time value of the shorter series) and allows to compare among series (see DõÂazDelgado et al., 1998). From graphics like this one, the recovery time can be estimated when fire severity, fire history and post-fire vegetation dynamics make it possible.
Fig. 3. Size distribution of fires which occurred in Catalonia (1975±1995). Curve A shows the frequency distribution for fire size. Curve B displays the per cent of burned area of each fire and all the smaller (Y-axis) in relation to their relative sizes (the largest fire area is 100%).
3.4. Regeneration monitoring through NDVI Fig. 5 represents the QNDVI variations before and after the ®re from one of the 10 monitored areas (DõÂazDelgado et al., 1998). This graphic belongs to a ®re of
0.7 km2 occurred on 10 February 1984 in the municipality of Tordera (Barcelona). It burned mostly a zone covered by cork-oak (Quercus suber). The calculated time for the model to reach the seasonal mean prior to ®re is quite smaller than other affected areas that were covered by dominant vegetation not able to resprout. Certainly, such rate of recovery indicates the relevant role played by the previous history of the studied area (Christensen, 1993). However, the recovery level achieved by QNDVI at a constant time for all the series (t 2544 days after the ®re, the shorter series), was not correlated with any environmental parameter considered in such study (slope, altitude, and ®re extent). 4. Discussion
Fig. 4. Total number of spots and averaged maximum distance reached (in metres) in relation to the size class of fires occurred in 1994 and 1995 (both variables are depicted in the Y-axis). Cursive figures indicate the total area burned by each size class.
Despite the fact that the measured omission error for detected areas greater than 0.3 km2 was about 50%, they were all considered in Fig. 3 (curve A) due to the lack of geographic inventories of ®res before 1983. So, the lack of an independent source did not allow the statistical contrast with patches detected from 1975 to 1983. Fig. 6 is a map which shows the obtained results from the method of maximum and minimum thresholds (the most accurate) and ®re frequencies for each burned area in the Cape of Creus (NE of Catalonia).
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Fig. 6. Map of forest areas burned and frequency of fires in the Cape of Creus (NE of Catalonia) between 1975 and 1995. Only burned areas greater than 0.3 km2 are represented. This area has undergone the maximum frequency of fires in Catalonia in 21 years (five times).
According to the observed spatial pattern of ®re size distribution, it seems really relevant the role played by large ®res on the total burned area in Catalonia. Thus, the 7% of ®res occurred from 1975 to 1995 were large ®res (size greater than 20 km2), which burned practically the 60% of the total burned area during the analysed period. With any doubt, taking into account the proportion of burned area ascribed to the different ®re size classes may help to assign prevention and suppression forces, awareness of regional variability observed in the spatial size distribution (Strauss et al., 1989). Fig. 2 depicts the Lorenz curve for ®res occurred in Catalonia (1975±1995) and the one corresponding to the southern California series from 1956 to 1971. The former is closer to the diagonal line than the latter, so there is a difference in ®re size distributions (in terms of GINI coef®cient, 0.15), mainly due to the abundance of large events occurred in southern
California, contributing considerably to the total burned area. On the other hand, proportionality between size, number of spots and maximum distance reached by them, even though obvious, enables to corroborate the existent relationship between size and ®re intensity, since the greater the emitted energy from a ®re front, the bigger the achieved temperatures. Also, the higher the convection column able to transport pieces of hot coal (bark pieces or pine cone scales) incandescent, and so the greater will be the distance to be reached by spots and subsequently generate a secondary ®re source. Similarly, the number of produced spots as a consequence of an intense forest ®re with such characteristics will increase. In most cases, intense ®res correspond to forest tree ®res and strongly wind driven through the canopies. It looks then, feasible, to use such information as ancillary data to assign a level of intensity to each ®re. Such data are being combined
R. DõÂaz-Delgado, X. Pons / Forest Ecology and Management 147 (2001) 67±74
with other variables of interest, as the maximum length value of each ®re, which is proportionally related to the dominant winds at the moment of burning, and with the fuel spatial distribution (Pons and Vayreda, 1996). This relevant data will allow to complete the characterisation of the Catalan ®re regime in the analysed period, as an example of a Mediterranean region. Concerning the monitoring of post-®re regeneration of selected burned areas, it must be stressed that considering the previous seasonal interval of phenological variation prior to ®re in the study area is an interesting approach to be incorporated in similar works. Such range of variation may be used as a reference to establish the highest plant recovery level expected (by means of the quotient among NDVIs) which will be achieved during the post-®re regeneration process (Malingreau et al., 1985). That aspect may also be useful in studies based on chronosequences after ®re (Gracia and SabateÂ, 1996). However, more pilot areas are needed in order to ®nd relationships between the recovery level and environmental parameters of each burned area. Finally, the continuous updating of the ®re database, helps to increase the time series rede®ning the different recurrence levels, intensities and types of ®res as well as the ®re sizes and season of occurrence. 5. Conclusions Satellite imagery has a high interest to map burned areas at regional scale. Consequently, they may be used to characterise spatial and temporal patterns that compose the fire regime of a region, for considerable time periods (21 years in this case). The applied lineal models are based on NDVI subtraction and use of thresholds. Results seem to be acceptable when comparing with administration inventories data. Even though, the differences between estimates of total burned area could be considered as discrepancies because of the difficulty in determining the actual burned area with field methods. Fire size distribution in Catalonia (1975±1995) shows a great contribution (60%) of large fires (>20 km2), but they are only the 7% of the total number of fires occurred. However, comparison
73
among Lorenz curves from Catalonia and southern California shows that the latter has a greater influence from large events than the former. Number of spots and maximum distance reached by them are proportionally related to fire size and, most likely, to the intensity and type of fire. Plant response to fire may be monitored and modelled by means of the NDVI variations, in order to quantify the different recovery rates. Also, such regeneration processes may be compared among different areas to search for the parameters involved in the vegetation dynamics after fire. Acknowledgements The authors wish to thank the Institut CartograÁ®c de Catalunya for its help when processing the images to correct them geometrically, to the Departament de Medi Ambient (DMA, Generalitat de Catalunya) for the provided information about the ®res of 1994 and 1995, to the Departament d'Agricultura, Ramaderia i Pesca (DARP, Generalitat de Catalunya) and the old Instituto para la ConservacioÂn de la Naturaleza (ICONA) for the ®re statistics supplied. This project has been funded by the ComisioÂn Interdepartamental de Ciencia y TecnologõÂa (CICYT AMB94-0881), the LUCIFER EC project (ENV-CT96-0320), and a grant of F.P.I. by the Ministerio de EducacioÂn y Cultura to Ricardo DõÂaz-Delgado. References Baulies, X., Joaniquet, M., TardaÁ, A., 1995. Evaluation of forest fires effects using CASI data. In: Chuvieco, E. (Ed.), Remote Sensing and GIS Applications to Forest Fire Management. Proceedings of the EARSeL International Workshop. Universidad de Alcala de Henares, pp. 82±84. Cetin, H., Warner, T.A., Levandowsky, D.W., 1993. Data classification, visualization, and enhancement using n-dimensional probability functions (nPDF): AVIRIS, TIMS, TM, and geophysical applications. Photgramm. Eng. Rem. S. 59, 1755±1764. Christensen, N.L., 1993. Fire regimes and ecosystem dynamics. In: Crutzen, P.J., Goldammer, J.G. (Eds.), Fire in the Environment: The Ecological, Atmospheric, and Climate Importance of Vegetation Fires. Wiley, New York, pp. 233±244. Chuvieco, E., 1996. Fundamentos de TeledeteccioÂn Espacial. Rialp, Madrid. DõÂaz-Delgado, R., Salvador, R., Valeriano, J., Pons, X., 1997. Resultados y evaluacioÂn estadõÂstica de un meÂtodo automaÂtico
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de deteccioÂn de superficies forestales quemadas en CatalunÄa (perõÂodo 1975±1993). In: HernaÂndez, C., Arias, J. (Eds.), VII Congreso Nacional de TeledeteccioÂn. A.E.T., Santiago de Compostela, pp. 215±218. DõÂaz-Delgado, R., Salvador, R., Pons, X., 1998. Monitoring of plant regeneration after fire by remote sensing. In: Trabaud, L. (Ed.), Fire Management and Landscape Ecology. International Association of Wildland Fire, Fairfield, pp. 315±326. Eberhart, K.E., Woodard, P.M., 1987. Distribution of residual vegetation associated with large fires in Alberta. Can. J. For. Res. 17, 1207±1212. Fung, T., Le Drew, E., 1987. Application of principal components analysis to change detection. Photgramm. Eng. Rem. S. 12, 1649±1658. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., PenÄuelas, J., Valentini, R., 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl. 5, 28±41. Gill, A.M., 1975. Fire and the Australian flora: a review. Aust. For. 38, 4±25. Gracia, C., SabateÂ, S., 1996. Canvis en l'estructura de la vegetacioÂ. In: Terradas, J. (Ed.), Ecologia del Foc. Proa, Barcelona, pp. 162±172. Hall, F.G., Botkin, D.B., Strebel, D.E., Woods, K.D., Goetz, S.J., 1991. Large-scale patterns of forest succession as determined by remote sensing. Ecology 72, 628±640. Johnson, E.A., Gutsell, S.L., 1994. Fire frequency models, methods and interpretations. Adv. Ecol. Res. 25, 239±283. Kasischke, E.S., French, H.F., Harrell, P., Christiensen, N.L., Ustin, S.L., Barry, D., 1993. Monitoring of wildfires in Boreal forests using large area AVHRR NDVI composite image data. Remote Sensing Environ. 45, 61±71. Lambin, E.F., 1996. Change detection at multiple temporal scales: seasonal and annual variations in landscape variables. Photgramm. Eng. Rem. S. 62, 931±938.
Lee, Ch., Seyoung, K., 1998. Measuring earnings inequality and median earnings in the tourism industry. Tourism Mgmt. 19, 341±348. Malingreau, J.P., Stephens, G., Fellows, L., 1985. Remote sensing of forest fires: Kalimantan and North Borneo in 1982±83. Ambio 14, 314±321. Mather, P.M., 1999. Computer Processing of Remotely-sensed Images. Wiley, New York. Minnich, R.A., 1983. Fire mosaics in southern California and northern Baja California. Science 219, 1287±1294. Minnich, R.A., Chou, Y.H., 1997. Wildland fire patch dynamics in the chaparral of southern California and northern Baja California. Int. J. Wildland Fire 7, 221±248. PalaÁ, V., Pons, X., 1995. Incorporation of relief into a geometric correction based on polynomials. Photgramm. Eng. Rem. S. 7, 935±944. Pons, X., SoleÂ-SugranÄes, L., 1994. A simple radiometric correction model to improve automatic mapping of vegetation from multispectral satellite data. Remote Sensing Environ. 48, 191±204. Pons, X., Vayreda, J., 1996. La prediccio del risc: poder calorõÂfic, inflamabilitat i combustibilitat. In: Terradas, J. (Ed.), Ecologia del Foc. Proa, Barcelona, pp. 162±172. Salvador, R., DõÂaz-Delgado, R., Valeriano, J., Pons, X., 1998. Remote sensing of forest fires. In: Proceedings of the GisPlaNet Congress (CD-ROM Edition), Lisbon, p. 99. Salvador, R., Pons, X., Valeriano, J., DõÂaz-Delgado, R., in press. A semiautomatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series. Int. J. Remote Sensing. 21, 655±673. Strauss, D., Bednar, L., Mees, R., 1989. Do one percent of forest fires cause ninety-nine percent of the damage? For. Sci. 35, 319±328. Trabaud, L., 1992. Les feux de foreÃts. MeÂcanismes, comportement et environnement. France-Selection, Aubervilliers. Viedma, O., MeliaÂ, J., Segarra, D., GarcõÂa-Haro, J., 1997. Modeling rates of ecosystem recovery after fires by using Landsat TM data. Remote Sensing Environ. 61 (3), 383±398.
Spatial patterns of forest ®res in Catalonia (NE of Spain) along the period 1975±1995 Analysis of vegetation recovery after ®re Ricardo DõÂaz-Delgadoa,*, Xavier Ponsa,b a
CREAF (Center for Ecological Research and Forestry Applications), Fac. CieÁncies, Universitat AutoÁnoma of Barcelona, 08193 Bellaterra, Spain b Department of Geography, Universitat AutoÁnoma de Barcelona, 08193 Bellaterra, Spain
Abstract This paper revises the results of applying a semiautomatic methodology for ®re scars mapping from a time series of Landsat MSS images over the forest and shrubby surface of Catalonia (1975±1993). Perimeters of ®res which occurred in 1994 and 1995 were added enlarging the whole series to 21 years from TM imagery. Results are a map series of ®re history during 21 years as well as a map of the ®re recurrence level. Omission errors are 23% for burned areas greater than 2 km2 while commission errors are 8% for areas greater than 0.5 km2. Detected ®re scars were incorporated into a geographic information system in order to characterise the ®re regime of the study area. Fire size distribution and the number of spot ®res originated from each ®re as well as the maximum distance reached from the main ®re are analysed. A ®rst approach to monitor post-burn regeneration through normalised difference vegetation index is also shown. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Landsat MSS; NDVI; Lineal regression model; Fire regime; Regeneration; Time series
1. Introduction Since 1972 the MSS sensor, boarded on the Landsat satellite series, have been providing digital images of the terrestrial surface. Since 1975, images have been obtained periodically every 16±18 days. Such deeds, and its spatial resolution of about 6080 m2, make it one of the more interesting Earth observation satellites for monitoring and analysing plant cover dynamics (Hall et al., 1991). A good example can be shown
* Corresponding author. Tel.: 34-93-5811877; fax: 34-93-5811312. E-mail addresses: [email protected] (R. DõÂaz-Delgado), [email protected] (X. Pons).
when applied to characterise the ®re regime of natural areas (Minnich, 1983). Once ®re parameters are collected, several spatial patterns of ®re occurrence can be analysed such as ®re size distribution or the Lorenz curves, which allows us to evaluate the relevance of large ®res in the total burned area (Minnich and Chou, 1997). A complete list of ®re dates may aid to estimate temporal patterns like time since ®re distribution or ®re rotation periods (Johnson and Gutsell, 1994). There are several methods to discriminate radiometric changes produced by wild®res that commonly appear as a sudden decrease of plant recovery and, consequently, a change of its radiometric response. Some of the most employed methods are: principal components analysis (Fung and Le Drew, 1987); supervised classi®cation (Hall et al., 1991); change
0378-1127/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 1 1 2 7 ( 0 0 ) 0 0 4 3 4 - 5
68
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vector analysis (Lambin, 1996); and subtraction of images (Kasischke et al., 1993), the selected one in the present study. This work is part of the general project whose last objective is to characterise the ®re regime, i.e., intensity, frequency, seasonality, extent and type of ®re (Gill, 1975) of the Mediterranean plant communities of Catalonia (NE of Spain) and its in¯uence on the regeneration processes. The presented work mainly emphasises the results from the analysis of some spatial patterns of the detected burned areas. Some variables referred to the spotting maximum distance and the number of them were analysed. Also, the number of vegetation islands within the ®re (known as residual vegetation after Eberhart and Woodard, 1987) and their size, are currently being analysed in order to seek for relationships with the total burned area of every ®re. Simultaneously, radiometric response of vegetation recovery after ®re was monitored by means of the normalised difference vegetation index (NDVI). An example of such dynamics is also shown. 2. Methodology To broach this study, approximately 100 of whole images from the MSS sensor were acquired (4 bands; 2 in the visible [VIS] and 2 in the near infrared [NIR]). The MSS sensor was boarded on the Landsat 1±5 satellites. To include the whole surface of Catalonia, about 32 000 km2, two or three complete scenes are needed and, to improve results, at least two different seasons were retrieved. 2.1. Detection of burned areas Geometric and radiometric corrections were applied to the whole set of images by using the models of PalaÁ and Pons (1995) and Pons and SoleÂ-SugranÄes (1994), respectively. Fire scars detection was performed by means of the methodology described in Salvador et al. (in press). The detection method is based on the subtraction of NDVI images and uses variable thresholds. These thresholds are interpolated from two linear regression models ®tted to the empirical NDVI drop values of several control ®res also observed in the image series.
The resulting burned areas were contrasted with the ®res inventoried by the Departament d'Agricultura, Ramaderia i Pesca (DARP) since 1983 as independent source. Such alphanumerical database has each ®re listed per municipality and also records the ®re date. When the detected burned area was located in the municipality where a ®re documented by DARP existed, and the date was between the dates of the pair of images, which also revealed a ®re, then both were matched. Statistical validation gives the omission error (®res counted by DARP but not detected) and the commission error (areas not really burned but detected as ®re scar) for all the ®res greater than 0.3 km2 (Chuvieco, 1996). Results are given as a map of ®re scars greater than 0.3 km2 for the period 1975±1993, as well as a map of ®re recurrence level. 2.2. Incorporation into a GIS and spatial patterns analysis At this point, all the ®re scars greater than 0.3 km2 detected since 1975±1993 plus burn perimeters from 1994 to 1995 were incorporated into a geographic information system (GIS). The main objective was to facilitate layer query by attributes of every burned area and statistical analysis of spatial and temporal patterns of ®re occurrence in the study region. The 1994 and 1995 burned areas were obtained by applying the nPDF process (n-dimensional probability density function, Cetin et al., 1993) to classify images from SPOT, TM and CASI (Baulies et al., 1995). Flies with CASI are planned for all the ®res greater than 0.5 km2 when already extinguished. No omission and commission errors are expected in terms of land cover change not burned, except for total size and perimeter detected per each ®re. The resulting binary images with a pixel size of 30 m were provided by DMA and ICC (Departament de Medi Ambient and Institut CartograÁ®c de Catalunya), re-sampled to 60 m and then added to the results from the MSS series 1975±1993. The most accurate results (low omission error) were selected as the basis to produce the GIS database, built in vectorial format, topologically structured, so capable of query and analysis (Salvador et al., 1998). This way, several layers of ancillary information may be crossed and overlaid to the ®re occurrence and
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recurrence (frequency) database. Then, interesting variables as ®re size, number of ®res, number of spots or vegetation islands can be measured per ®re or on any spatial or temporal scale. Special attention should be paid on the method used to give the identity to every burned area detected by means of the remote sensing imagery. Thus, identity was attributed according to the distances among scars. When distance was equal to or less than 1 km among patches detected at the same interval of dates they were grouped. Such assumption is based on the maximum distance of ``spotting'' expected in Mediterranean forest (some 100 m, Trabaud, 1992). Also, it is supported by the observed maximum distance of spots for all the ®res occurred in 1994 and 1995 (concretely 1140 m, corresponding to a ®re occurred in the BerguedaÁ county, 4 July 1994, with a total burned area of 390 km2). 2.3. Fire spots or secondary fire sources analysis Moreover, we calculated the maximum distances reached by spots from the main ®re perimeter as well as the total number of spots by ®re. The former was obtained by a simple concentric calculation based on the pixel size, while the latter was achieved by contiguous category grouping including diagonal links among pixels and identifying each of the resulting groups. 2.4. Monitoring of plant regeneration through NDVI Several authors have tested the use of NDVI (Mather, 1999) to monitor plant regeneration after ®re (Viedma et al., 1997; Malingreau et al., 1985). Such sort of studies may be carried out because of the proportionality found between NDVI and the amount of green biomass, which do not depend on the plant species that constitutes the community (Gamon et al., 1995). To evaluate the variability of the post-®re response of the different plant communities, several burned areas were selected to monitor their regeneration processes. Three variables were monitored to study the recovery rate of vegetation after ®re: 1. the average of NDVI values for each burned area; 2. the standard deviation of NDVI values for each burned area;
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3. the quotient between mean NDVI values of burned areas and mean NDVI values of control plots not burned (QNDVI), which shared similar phenological variation and dominant species composition before fire. The choice of ®res was made according to different parameters: precipitation, lithological substrate of the affected zone, type of vegetation prior to ®re and others like altitude, slope and aspect of every burned area selected (DõÂaz-Delgado et al., 1998). Empirically, several models of logarithmic regression were adjusted to the recovery rate. Correlation analyses were accomplished to compare the recovery rate of all the plots and to study their relationships with the mentioned environmental parameters. 3. Results 3.1. Statistical evaluation of the employed methodology The percentage of success reached by the method was given in relation to the minimum ®re size considered. This way, for all the areas burned equal to or greater than 0.3 km2, we reached a percentage of success of up to 53 from no omission (see Fig. 1). However, for ®res greater than 2 km2, the percentage of success from no omission increases up to 78. Concerning the commission error, the percentage of success, for all the areas burned equal to or greater than 0.3 km2, is about 90 (DõÂaz-Delgado et al., 1997). To some extent, such result is due to the big phenological effect that is produced in certain sparse shrubby communities during the summer period (drought). Table 1 displays the total area burned in relation to the recurrence level of burning, as well as the percentage of forest area burned since 1975±1993. 3.2. Fire size distribution Fire size was analysed through the frequency distribution of ®res and also by means of the percentage of area burned per each ®re and all the smaller in relation to its relative size (the total size of the largest ®re is 100%). Also, the Lorenz curve
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Fig. 2. Lorenz curves for Catalonia (1975±1995) and southern California (1956±1971). The straight line represents the line of equality.
Fig. 1. Percentages of success due to the lack of omission and commission errors of burned areas detected between 1983 and 1993. Percentages are given as a function of the fire size in square kilometres.
(see Fig. 2), was depicted in order to compare with the one obtained by Minnich and Chou (1997). A Lorenz curve is a cumulative frequency curve that compares two sets of ordinal, interval or ratio data. Lorenz curves demonstrate the degree of concentration of a variable, or the inequality of a variable, in this case the ®re size. Both curves are far from the equality line, which indicates that ®re sizes are unevenly spread. Table 1 Total area burned according to its recurrence level of burning in Catalonia. The percentage of the total burned area for each recurrence level and of forest area burned is also shown (fire size 0.3 km2, period 1975±1993) Recurrence level
Burned area (km2)
Total burned area (%)
Total forest area (%)
Once Twice Three times Four times Five times
1110.09 145.06 15.32 3.96 0.67
87.06 11.38 1.20 0.31 0.05
5.7 0.74 0.078 0.02 0.0034
The inequality of Lorenz curves, i.e., the variable ®re size, can be measured via the GINI coef®cient varying between 0 (maximum equality) and 1 (maximum inequality) widely used as a measure of earnings equality in econometrics (Lee and Seyoung, 1998). Such coef®cient is calculated from the area between the curve and the diagonal line divided by the area of the triangle below the diagonal. The former can be obtained by means of ®nite integration of the curves. For these cases southern California GINI coef®cient, 0.91, was clearly greater than Catalonia GINI coef®cient, 0.76. Therefore, southern California ®re size distribution is more unequal, due to the abundance of large ®res, more frequent than in Catalonia. In Fig. 3, curve A shows the number of ®res occurred in Catalonia between 1975 and 1995 ordered by size. It can be noticed that large ®res are not frequent. In curve B, where the 100% of X-axis represents the largest ®re occurred in the series (BerguedaÁ ®re, 400 km2), around the 75% of total burned area is due to ®res smaller than 80 km2 (20%) and the 60% to sizes under 40 km2 (10%). Average size of ®res was 6.451 km2 and the median value 1.332 km2. 3.3. Spots of 1994 and 1995 fires: number and maximum distance At ®rst sight, Fig. 4 shows an obvious and tight relation between ®re size and the total number of secondary ®re sources generated from the burn. At the same time, ®re size is also proportional to the maximum distances reached by the spots produced by every main ®re. Current work is studying the same variables but taking into account all the burned areas detected between 1975 and 1995.
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Fig. 5. Example of QNDVI monitoring (averaged NDVI burned area/averaged NDVI control plot) before and after the fire. The logarithmic model fitted enables to calculate the distance to the previous seasonal mean at time t2544, which is a common point for all the analysed series (corresponding to the time value of the shorter series) and allows to compare among series (see DõÂazDelgado et al., 1998). From graphics like this one, the recovery time can be estimated when fire severity, fire history and post-fire vegetation dynamics make it possible.
Fig. 3. Size distribution of fires which occurred in Catalonia (1975±1995). Curve A shows the frequency distribution for fire size. Curve B displays the per cent of burned area of each fire and all the smaller (Y-axis) in relation to their relative sizes (the largest fire area is 100%).
3.4. Regeneration monitoring through NDVI Fig. 5 represents the QNDVI variations before and after the ®re from one of the 10 monitored areas (DõÂazDelgado et al., 1998). This graphic belongs to a ®re of
0.7 km2 occurred on 10 February 1984 in the municipality of Tordera (Barcelona). It burned mostly a zone covered by cork-oak (Quercus suber). The calculated time for the model to reach the seasonal mean prior to ®re is quite smaller than other affected areas that were covered by dominant vegetation not able to resprout. Certainly, such rate of recovery indicates the relevant role played by the previous history of the studied area (Christensen, 1993). However, the recovery level achieved by QNDVI at a constant time for all the series (t 2544 days after the ®re, the shorter series), was not correlated with any environmental parameter considered in such study (slope, altitude, and ®re extent). 4. Discussion
Fig. 4. Total number of spots and averaged maximum distance reached (in metres) in relation to the size class of fires occurred in 1994 and 1995 (both variables are depicted in the Y-axis). Cursive figures indicate the total area burned by each size class.
Despite the fact that the measured omission error for detected areas greater than 0.3 km2 was about 50%, they were all considered in Fig. 3 (curve A) due to the lack of geographic inventories of ®res before 1983. So, the lack of an independent source did not allow the statistical contrast with patches detected from 1975 to 1983. Fig. 6 is a map which shows the obtained results from the method of maximum and minimum thresholds (the most accurate) and ®re frequencies for each burned area in the Cape of Creus (NE of Catalonia).
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Fig. 6. Map of forest areas burned and frequency of fires in the Cape of Creus (NE of Catalonia) between 1975 and 1995. Only burned areas greater than 0.3 km2 are represented. This area has undergone the maximum frequency of fires in Catalonia in 21 years (five times).
According to the observed spatial pattern of ®re size distribution, it seems really relevant the role played by large ®res on the total burned area in Catalonia. Thus, the 7% of ®res occurred from 1975 to 1995 were large ®res (size greater than 20 km2), which burned practically the 60% of the total burned area during the analysed period. With any doubt, taking into account the proportion of burned area ascribed to the different ®re size classes may help to assign prevention and suppression forces, awareness of regional variability observed in the spatial size distribution (Strauss et al., 1989). Fig. 2 depicts the Lorenz curve for ®res occurred in Catalonia (1975±1995) and the one corresponding to the southern California series from 1956 to 1971. The former is closer to the diagonal line than the latter, so there is a difference in ®re size distributions (in terms of GINI coef®cient, 0.15), mainly due to the abundance of large events occurred in southern
California, contributing considerably to the total burned area. On the other hand, proportionality between size, number of spots and maximum distance reached by them, even though obvious, enables to corroborate the existent relationship between size and ®re intensity, since the greater the emitted energy from a ®re front, the bigger the achieved temperatures. Also, the higher the convection column able to transport pieces of hot coal (bark pieces or pine cone scales) incandescent, and so the greater will be the distance to be reached by spots and subsequently generate a secondary ®re source. Similarly, the number of produced spots as a consequence of an intense forest ®re with such characteristics will increase. In most cases, intense ®res correspond to forest tree ®res and strongly wind driven through the canopies. It looks then, feasible, to use such information as ancillary data to assign a level of intensity to each ®re. Such data are being combined
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with other variables of interest, as the maximum length value of each ®re, which is proportionally related to the dominant winds at the moment of burning, and with the fuel spatial distribution (Pons and Vayreda, 1996). This relevant data will allow to complete the characterisation of the Catalan ®re regime in the analysed period, as an example of a Mediterranean region. Concerning the monitoring of post-®re regeneration of selected burned areas, it must be stressed that considering the previous seasonal interval of phenological variation prior to ®re in the study area is an interesting approach to be incorporated in similar works. Such range of variation may be used as a reference to establish the highest plant recovery level expected (by means of the quotient among NDVIs) which will be achieved during the post-®re regeneration process (Malingreau et al., 1985). That aspect may also be useful in studies based on chronosequences after ®re (Gracia and SabateÂ, 1996). However, more pilot areas are needed in order to ®nd relationships between the recovery level and environmental parameters of each burned area. Finally, the continuous updating of the ®re database, helps to increase the time series rede®ning the different recurrence levels, intensities and types of ®res as well as the ®re sizes and season of occurrence. 5. Conclusions Satellite imagery has a high interest to map burned areas at regional scale. Consequently, they may be used to characterise spatial and temporal patterns that compose the fire regime of a region, for considerable time periods (21 years in this case). The applied lineal models are based on NDVI subtraction and use of thresholds. Results seem to be acceptable when comparing with administration inventories data. Even though, the differences between estimates of total burned area could be considered as discrepancies because of the difficulty in determining the actual burned area with field methods. Fire size distribution in Catalonia (1975±1995) shows a great contribution (60%) of large fires (>20 km2), but they are only the 7% of the total number of fires occurred. However, comparison
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among Lorenz curves from Catalonia and southern California shows that the latter has a greater influence from large events than the former. Number of spots and maximum distance reached by them are proportionally related to fire size and, most likely, to the intensity and type of fire. Plant response to fire may be monitored and modelled by means of the NDVI variations, in order to quantify the different recovery rates. Also, such regeneration processes may be compared among different areas to search for the parameters involved in the vegetation dynamics after fire. Acknowledgements The authors wish to thank the Institut CartograÁ®c de Catalunya for its help when processing the images to correct them geometrically, to the Departament de Medi Ambient (DMA, Generalitat de Catalunya) for the provided information about the ®res of 1994 and 1995, to the Departament d'Agricultura, Ramaderia i Pesca (DARP, Generalitat de Catalunya) and the old Instituto para la ConservacioÂn de la Naturaleza (ICONA) for the ®re statistics supplied. This project has been funded by the ComisioÂn Interdepartamental de Ciencia y TecnologõÂa (CICYT AMB94-0881), the LUCIFER EC project (ENV-CT96-0320), and a grant of F.P.I. by the Ministerio de EducacioÂn y Cultura to Ricardo DõÂaz-Delgado. References Baulies, X., Joaniquet, M., TardaÁ, A., 1995. Evaluation of forest fires effects using CASI data. In: Chuvieco, E. (Ed.), Remote Sensing and GIS Applications to Forest Fire Management. Proceedings of the EARSeL International Workshop. Universidad de Alcala de Henares, pp. 82±84. Cetin, H., Warner, T.A., Levandowsky, D.W., 1993. Data classification, visualization, and enhancement using n-dimensional probability functions (nPDF): AVIRIS, TIMS, TM, and geophysical applications. Photgramm. Eng. Rem. S. 59, 1755±1764. Christensen, N.L., 1993. Fire regimes and ecosystem dynamics. In: Crutzen, P.J., Goldammer, J.G. (Eds.), Fire in the Environment: The Ecological, Atmospheric, and Climate Importance of Vegetation Fires. Wiley, New York, pp. 233±244. Chuvieco, E., 1996. Fundamentos de TeledeteccioÂn Espacial. Rialp, Madrid. DõÂaz-Delgado, R., Salvador, R., Valeriano, J., Pons, X., 1997. Resultados y evaluacioÂn estadõÂstica de un meÂtodo automaÂtico
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