Fire Scar Detection in the Canadian Boreal Forest. Plummer, S.E., Gerard, F.F., Iliffe, L. and Wyatt, B.K.

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1 Fire Scar Detection in the Canadian Boreal Forest Plummer, S.E., Gerard, F.F., Iliffe, L. and Wyatt, B.K. Centre for Ecology and Hydrology, Monks Wood, Abbots Ripton, Cambs, PE17 2LS, UK Tel: , Fax: , Abstract The boreal ecosystem forms a discontinuous belt of forest stretching across the Northern Hemisphere, covering approximately 10% of the Earth s land surface. Because it contains approximately 40% of the terrestrial carbon it plays an extremely important role in the global carbon budget. It is therefore necessary to identify anything that perturbs this ecosystem, for example, fire. While it is possible to identify active fires operationally, the mapping of the areal extent, distribution and recovery of existing fire scars remains problematic. This paper extends the work of Eastwood at al. (1998) on fire scar detection through the segmentation VEGETATION data over the BOREAS experimental region. Imagery on a monthly time-step was acquired through the 1998 fire season (May-September). Segmentation was performed on 12 images to assess sensitivity of the approach to segmentation criteria and within a given month the variability of detection as a function of image geometry and atmospheric state. The results were compared against hot spot observations recorded for the time period in the FireM3 detection system ( (Li et al. 1997). Older fire scars were identified with reference to the Canadian Forestry Service GIS database of large fires covering the period Results indicate it is possible to reliably detect fire scars produced up to 19 years ago. Further work is focused on extending the detection system to handle data from the ATSR and AVHRR sensor series. Introduction The boreal ecosystem stretches across the Northern Hemisphere s circumpolar countries covering approximately 10% of the Earth s land surface ranking second in terms of total plant mass to the tropical forest belt. Because it contains approximately 40% of the terrestrial carbon it plays an extremely important role in the global carbon budget (Malhi et al. 1999, Schulze et al. 1999). It is therefore important to identify anything that perturbs this ecosystem, for example, fire. Tremendous fluctuations in annual area burned occur in the boreal zone and fires are typically of a high energy level. Despite fire management forest fires exert a significant influence on boreal ecosystem dynamics with 5-10m ha burnt annually (Cofer et al. 1996, Kasischke et al. 1999). However, global terrestrial carbon cycle models generally do not take into account loss of carbon through disturbance. Further, disturbance has a strong influence on succession in the boreal ecosystem through the effect on opportunity for change from boreal ecology to that typical of biomes to the south and north. Since climate change may alter the frequency and size of disturbance events monitoring changes in their spatial and temporal occurrence is vital for predicting the impacts of global environmental change. Typically the disturbance return interval is estimated to range from 50 to 240 years but inter-annual disturbance rates throughout the boreal system can vary widely. Currently there is no comprehensive database of disturbance across the entire boreal ecosystem and where efforts to collate information have been attempted it usually either spatially restricted or a snapshot in time. For the boreal forest the large area of individual burns makes coarse resolution remote sensing an attractive alternative although it is limited to the last 25 years since the launch of the first AVHRR sensor. Methods of mapping fire scars in boreal forests have been developed that utilise both microwave (Bourgeau-Chavez, et al. 1997) and optical data (Cahoon et al. 1992, Kasischke and French 1995, Li et al. 1997, Eastwood et al. 1998). Eastwood et al. (1998) tested three simple per-pixel methods of fire scar detection and a merging-using-moments segmentation algorithm (Cook et al. 1994) on simulated VEGETATION data. The simple methods were thresholding of NDVI, thresholding of the individual VEGETATION wavebands and unsupervised classification of the red,

2 and wavebands. This paper presents results that extend the work of Eastwood et al. (1998) on fire scar detection, through the segmentation of VEGETATION data acquired during the 1998 active fire season, over the BOREAS experimental region. Materials and Methods VEGETATION data The overall study area stretches from Hudson Bay in the east to Sasketchewan in the west (Latitude range: 57 to 51 N, Longitude range:116 to 76 W). A total of 24 VEGETATION P1 images were acquired for the region under study (Table 1) May June July August September V V V V V V V V V V V V V V V V V V V V V V V V V Table 1: VEGETATION images acquired for study The results presented here comprise a subset of the total area of approximately 700*700 km (Lat. Range: 56.5 to 53.5 N, Long. Range: 103 to 97 W). Thresholding of Previous results with simulated VEGETATION data indicated that thresholding of the Middle Infrared () waveband was a fast and effective method of delineating fire scars. Details of the method and its comparison with segmentation, unsupervised classification and NDVI thresholding are provided in Eastwood et al. (1998). Examination of this approach on VEGETATION data, however, revealed an overlap in the reflectance from known fire scars and cloud-shadow and agriculture. In addition the required threshold was image-specific and required manual intervention for each image. This approach was therefore abandoned in favour of segmentation. Vegetation Indices In addition to examination of individual wavebands indices using were constructed and compared with the and NDVI using the segmentation system. The three indices used were: NDVI: ND: NDR: Merging Using Moments Segmentation The Merging Using Moments (MUM) segmentation is a 'region-merging' algorithm (Cook et al. 1994). Region-merging attempts to grow large uniform regions by merging neighbouring small regions. The

3 decision to merge depends on the statistical criterion adopted, for example the Student t test. The following example of its operation is provided by Oliver and Quegan (1998): Initialise by subdividing image into arbitrary small regions (e.g. single pixel grid). For each pair of adjacent sub-regions A and B calculate the pooled variance as: var{ A + B}= N A var A + N B var B N A + N B N A N B var are individual variances and pooled variance and N is number of pixels Normalised difference is: t A B { } var A + B Apply Student t test for N A +N B 2 degrees of freedom to evaluate probability for merging. The two regions are considered significantly different if the t is larger than tabulated values. This is applied over all A and B and a list generated in order of priority for merging pairs of adjacent sub-regions. Regions with the highest priority are then merged and the operation repeated for the new larger regions. Experimentation with a variety of combinations of thresholds established a visually acceptable probability of false alarm of Since the MUM segmentation system used is implemented in the Erdas-Imagine Radar Module a number-of-looks parameter, which equates to variance tolerance (degree of smoothness within a segment), of 12 for the single band () and 100 (Vegetation Indices) produced a generally comparable number of image segments. Cloud and water masking Cloud and water masks were created for each image for use in the fire scar probability analysis. Standard band and NDVI thresholding and band comparison were used to detect cloud and water. These generally worked well but the threshold reflectance range had to be adjusted to accommodate approximately half the images. Th e test methods were: Water: reflectance < reflectance or NDVI < 0.25 and reflectance < Water: reflectance > X and reflectance > Y and NDVI < 0.25 (X varies between and 0.15, Y varies between 0.1 and 0.15) Cloud: NDVI > 0.35 & reflectance > 0.25 Validation and Fire Scar Probability Determination While visual comparison of the segmentations against: mapped fire scar polygons, generated from aerial reconnaissance (provided by Canadian Forest Service)

4 active fire hotspot observation derived from AVHRR thermal saturation (FireM3) is a reasonable qualitative test there is a requirement for a more objective means of determining fire scar location and spatial extent. Segmented images were therefore re-analysed in Arc/Info to assess individual image quality and cumulative image quality by comparison with the two datasets above. The probability of a segment being a fire scar was based on co-location with fire map and fire hotspots. After experimentation empirical threshold values of 15% overlap between mapped polygon and a segment and greater than 1% of total number of hotspots in a given segment were devised as acceptance criteria. The key steps in the evaluation are as follows: 1 segmentations are applied to available multi-temporal image sets; 2 the fire scar / hot spot maps are intersected with each of the resulting segmentations; 3 within each image segmentation, an individual segment is regarded as burnt if it has more than 15% overlap with a mapped fire scar polygon; 4 segments affected by cloud or water are rejected, using a rule-based cloud and water mask, based on simple band thresholds; 5 a burn probability is computed for each remaining segment, based on the proportion of the image segmentations in which the polygon has been recorded as burnt. Results Twelve images of the 24 acquired were chosen for segmentation (highlighted ion Table 1). The remainder were rejected primarily because cloud cover in the sub-area was greater then 50%. These images were acquired because they were clear in the Eastern part of the full area adjacent to Hudson Bay. The images from 2 nd July 1998 show the sub-area in the study (with water and arable region masked out), as depicted from left in the and by the three vegetation indices ND, NDVI and NDR. Fire scars are clearly visible in as bright patches and in ND as dark patches. The NDVI does not reveal scars while NDR is intermediate between and NDVI. The resulting MUM segmentations on the images above demonstrate the capability of and ND to pick up fire scars. The NDVI is not effective while the NDR generally picks the scars but tends to merge individual scars. ND and show discrepancies with tending to smooth the internal structure of the scars. However in this instance it outperforms ND in detecting 1981 scars.

5 Cumulative probability-of-detection maps compared to mapped fire scar polygons in 1989 (left) and hotspots in 1995 (right). The difference in area covered is a function of the common areas shared by hotspot, polygon and images. Red tones represent % probability, brown 50-90% and yellow 10-50%. Values below 10% are considered not burnt. Although there are minor discrepancies between the mapped and the estimated extent of burn, there is good overall agreement. The larger burn areas, with simpler shapes are those which remote sensing methods map as fire scars with the highest levels of probability. Combining these fire polygons with the same 12 segmentations as in the previous slides reveals the maximum probability that an area has been burnt in those years tested (1981, 1989, 1994 or 1995). Examination of other years and areas picked as high probability fire scars reveals good agreement. Conclusions Thresholding based on, while successful in specific cases, requires manual intervention. Segmentation generally proves robust; both and ND are reasonably successful. fails to detect recent fires. Vegetation data appear particularly useful for scar mapping; since they only date from 1998, similar methods are needed to ingest data from for example ATSR-2 and AVHRR (Steyaert et al. 1997). This work, in combination with fire dynamic models (e.g. Johnson and Gutsell 1994), has the potential for use in carbon accounting approaches (for example GTOS/TCI).

6 References Bourgeau-Chavez, L. L., Harrell, P., Kasischke E. S., and French, N. H. F., 1997, The detection and mapping of Alaskan wildfires using a spaceborne imaging radar system., International Journal of Remote Sensing, 18, Cahoon, D.R., Stocks, B.J., Levine, J.S., Cofer, W.R and Chung, C.C., 1992, Evaluation of a technique for satellite-derived area estimation of forest fires, Journal of Geophysical Research, 97, Cofer, W.R., Winstead, E.L., Stocks, B.J., Overbay, L.W., Goldammer, J.G., Cahoon, D.R., and Levine, J.S., 1996, Emissions from boreal forest fires: Are the atmospheric impacts underestimated? in Levine, J.S., (ed.) Biomass Burning and Global Change, MIT Press, Cambridge, MA, pp Cook, R., McConnell, I. and Oliver, C.J., 1994, MUM (Merging Using Moments) segmentation for SAR images, Proc. Europto Conference on SAR Data Processing for Remote Sensing, SPIE 2316, Eastwood, J., Plummer, S.E., Stocks, B.J. and Wyatt, B., The potential of SPOT-Vegetation data for fire scar detection in boreal forests. International Journal of Remote Sensing. 19, Johnson, E.A. and Gutsell, S.L., 1994, Fire frequency models, methods and interpretations, Adv in Ecol Research, 25, Kasischke, E. S., French, N. H. F., Harrell, P., Christensen, N. L., Ustin, S. L., and Barry, D., 1993, Monitoring of wildfires in boreal forests using large area AVHRR NDVI composite image data, Remote Sensing of Environment, 45, Kasischke, E. S., French, N. H. F., Bourgeau-Chavez, L. L., and Christensen, N. L.,1995, Estimating release of carbon from 1990 and 1991 forest fires in Alaska, Journal of Geophysical Research, 100, Kennedy, P. J., Belward, A. S., and Gregoire, J.-M., 1994, An improved approach to fire monitoring in West Africa using AVHRR data, International Journal of Remote Sensing, 15, Malhi, Y., Baldocchi, D. and Jarvis, P. G., 1999, The carbon balance of tropical, temperate and boreal forests, Plant, Cell and Environment, 22, Oliver, C. J., McConnell, I., and Stewart, D., 1996, Optimum texture segmentation of SAR clutter. In Eusar'96: European Conference On Synthetic Aperture Radar, Edited By R. Klemm, W. Keydahl and J. Ender (Berlin: Vde-Verlag Gmbh), pp Oliver, C. And Quegan, S., 1998, Understanding Synthetic Aperture Radar Images, Artech House, London, pp Pereira, J. M. C., A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping. IEEE Transactions on Geoscience and Remote Sensing, 37(1), Pereira, M. C., and Setzer, A. W., 1993, Spectral characteristics of fire scars in Landsat-5 TM images of Amazonia, International Journal of Remote Sensing, 14, Schulze, E. D., Lloyd, J., Kelliher, F. M., et al., 1999, Productivity of forests in the Eurosiberian boreal region and their potential to act as a carbon sink a synthesis, Global Change Biology, 5, Steyaert, L. T., Hall, F.G., and Loveland, T. R., Land cover mapping, fire regeneration and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data, JGR, 102 (D24), Papers generated as a function of the grant Eastwood, J., Plummer, S.E., Stocks, B.J. and Wyatt, B., The potential of SPOT-Vegetation data for fire scar detection in boreal forests. International Journal of Remote Sensing. 19, Plummer, S.E., In Press, Perspectives on combining ecological process models and remotely sensed data, Ecological Modelling