Burned area mapping time series in Canada

Remote Sensing of Environment 117 (2012) 407–414

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Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse

Burned area mapping time series in Canada (1984–1999) from NOAA-AVHRR LTDR: A comparison with other remote sensing products and fire perimeters Jose A. Moreno Ruiz a, b, David Riaño a, c,⁎, Manuel Arbelo d, Nancy H.F. French e, Susan L. Ustin a, Michael L. Whiting a a

Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air and Water Resources, University of California, One Shields Avenue, Davis, CA 95616-8617, USA Universidad de Almería, Almería, Spain Instituto de Economía y Geografía, Centro de Ciencias Humanas y Sociales, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28006, Spain d Grupo de Observación de la Tierra y la Atmósfera (GOTA), Universidad de La Laguna, La Laguna, Tenerife, Spain e Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI, USA b c

a r t i c l e

i n f o

Article history: Received 6 June 2010 Received in revised form 19 October 2011 Accepted 21 October 2011 Available online 23 November 2011 Keywords: Boreal forests Burned area Canada NOAA-AVHRR

a b s t r a c t A new algorithm for mapping burned areas in boreal forest using AVHRR archival data Long Term Data Record (LTDR) (0.05°, ca. 5 km, version 3) was developed in Canada using burn records for the period between 1984 and 1999 and evaluated against AVHRR 1 km and AVHRR-PAL 8 km burned area map products. The algorithm combined 1) absolute and relative radiometric thresholds, 2) a Bayesian network classifier, and 3) neighborhood analysis for spatial fire coherence. Fire event records from Canadian Forest Service National Fire Database (CFSNFD) for western Canada were used to train the algorithm. LTDR and AVHRR 1 km burned area mapping were similar for the same area, and correlated well to CFSNFD annual fire event records for western Canada, r2 = 0.72 and 0.77, respectively. In addition, the LTDR mapping correlated well with fires for all of Canada in the CFSNFD database (r2 = 0.65). This mapping product was a significant improvement over an 8 km AVHRR-PAL burned area map product. For mapping boreal forests burned areas globally, this study demonstrates the potential accuracy for where LTDR represents the highest spatial and temporal resolution of daily images available since the 1980s. © 2011 Elsevier Inc. All rights reserved.

1. Introduction 1.1. Mapping boreal forest cover changes due to fire Boreal forest was selected for this study because of the major role boreal forest play in the carbon cycle. Boreal forests contribute to sequestration of carbon released to the atmosphere from combustion of sources such as fossil fuels, crop residues, and wildland vegetation. This ecosystem contains 88 Pg of carbon in the vegetation and 471 Pg in the soils, which represents 49% of biospheric carbon according to Dixon et al. (1994). However, due to the cold environment and slow vegetation regrowth, increased boreal forest fires may significantly alter the carbon balance (Balshi et al., 2009). Flannigan and Van Wagner (1991) combined three different general circulation models for six sites distributed across Canada that predicted a 50%

⁎ Corresponding author at: Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air and Water Resources, University of California, One Shields Avenue, Davis, CA 95616-8617, USA. Tel.: + 1 517 914 2842; fax: + 1 530 754 5491. E-mail addresses: [email protected] (J.A. Moreno Ruiz), [email protected] (D. Riaño), [email protected] (M. Arbelo), [email protected] (N.H.F. French), [email protected] (S.L. Ustin), [email protected] (M.L. Whiting). 0034-4257/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2011.10.017

increase in burned boreal forest area. The authors considered weather anomalies for each model by month and site that increased temperature by 0.1 to 10 °C and varied current precipitation by − 30 to +47%. Based on this 50% increase as a result of global warming within the next 50 to 100 years, Kasischke et al. (1995) estimated a release of 0.33 to 0.80 Pg/yr and a reduction in carbon sequestration of 2.3 to 4.4 kg/m 2.

1.2. Available daily imagery for global mapping The U.S. National Oceanic Atmospheric Administration (NOAA) Advance Very High Resolution Radiometer (AVHRR) satellite series constitutes a unique image source for mapping the extent and time of occurrence of large fires globally by providing systematic daily observations from the early 1980s to today. The AVHRR data is available at ground spatial distance (GSD, pixel size) of 1.1 km (nadir) either relayed directly to ground stations as High Resolution Picture Transmission (HRPT) data or a small portion is stored on board as Local Area Coverage (LAC) data for later downloading. In addition, Global Area Coverage (GAC) is available from an on-board generalized version of the original images with GSD of 4.4 km. GAC is resampled by selecting one in every three lines, averaging four samples per line,

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J.A. Moreno Ruiz et al. / Remote Sensing of Environment 117 (2012) 407–414

and skipping every fifth sample (http://www.class.ngdc.noaa.gov/ data_available/avhrr/index.htm, last accessed July 26, 2011). While the Canadian ground stations have received full resolution data since the beginning of the AVHRR transmissions (Latifovic et al., 2005), this is not the case for other parts of the world, such as Siberia. NASA installed a Siberian ground station only in the 1990s (Sukhinin et al., 2004), making GAC the only daily images covering Siberia for the 1980s. To fill this gap worldwide for global terrestrial process analysis, NOAA-NASA spatially resampled GAC from AVHRR instruments 7, 9, 11, and 14 data for the period of 1981 to 2000 to generate the Pathfinder AVHRR Land (PAL) 8 km global dataset (Agbu & James, 1994). Recently, the Long Term Data Record project (LTDR), also funded by NASA, created a 0.05° (~5 km GSD) global daily dataset from the GAC. This LTDR version 3 covers years 1981 to 2000, and includes improved atmospheric correction and inter-calibration between sensors (Nagol et al., 2009; Pedelty et al., 2007; Vermote & Saleous, 2006). For all of Canada, Latifovic et al. (2005) generated a cross-calibrated atmospherically corrected HRPT and LAC AVHRR (1 km) dataset, referred to within this article as LAC. This product included daily and clear sky 10 day composites for the years 1981 to 2006. A second version of this dataset is under production (A. Trishchenko, personal communication). Table 1 shows the available AVHRR products for Canada. The reliability of the satellite series to map fires depends on accurate radiometric inter-calibration between satellite sensors and spatial co-registration of the images. Creating a 20 year time series with AVHRR data is challenging because of discrepancies within each AVHRR instrument, such as positional variation at image acquisition and solar-sensor geometry due to orbital drift, and spectral sensitivity deterioration without on-board calibration capability (Jiang et al., 2008; Koslowsky, 1997; Trishchenko et al., 2002). These discrepancies of positional variation and spectral sensitivity also exist between AVHRR instruments (Jiang et al., 2008). In addition, the correction of bidirectional reflectance distribution function (BRDF) effect is complicated for low illumination and sensor viewing angles sampling rates (Los et al., 2005). Vicarious calibration over invariant targets, such as deserts, has been widely applied and partially addresses these issues (Koslowsky, 1997; Vermote & Saleous, 2006). 1.3. Study objective Despite these calibration issues, AVHRR constitutes the longest series of global satellite daily observations. In our study we calculate the area burned annually within Canadian boreal forest from the LTDR dataset for the years 1984 to 1999 with the idea of evaluating a potential algorithm for mapping boreal forest fires globally using LTDR. Our algorithm was calibrated with fire events from the Canadian Forest Service National Fire Database (CFSNFD) (Little, 2009). Canada is a unique development site for multi-temporal algorithms due to the availability of several existing burned area map datasets from satellite imagery at various scales for validation, such as SPOT Vegetation (Fraser & Landry, 2000), LAC (Pu et al., 2007), PAL (Riaño et al., 2007), and extensive fire records and consistently mapped polygons nationwide in the CFSNFD (Little, 2009).

2. Method 2.1. Study site The Canadian boreal forest was selected for this study because of the availability of reliable long term CFSNFD records of fire incidence. The boreal forest dominating Canada burns at least once approximately every 280 years. The number and area of local fires per year greatly depends on latitude and distance to the ocean. After fire recovery period is approximately 9 years in terms of net primary productivity, but may be two years before recovery is apparent (Hicke et al., 2003). Of the average 19,000 km 2 of Canadian boreal forest burned annually, the class of large fires greater than 200 ha represents only 5% of the total number of fires, and accounts for 99% of the total area burned. While the average size is 6708 ha within this large fire class, one third of these fires are greater than 2500 ha and account for 92% of all Canadian boreal forest fires. Lighting caused fires account for 82% of the total area burned, that represent 72% of all large fires. Within the April to September fire season, June and July months have the greatest fire incidence of 39% and 33% of the total burned area, respectively. The incidences in the large fire class during these months account for 31% and 33% of burned area, respectively. The Canadian fire regime described above was generated from CFSNFD point data (version “20101210”) for the years 1959 to 2006. CFSNFD records were gathered by fire management agencies within each province dating back to 1917 for fires as small as 1 ha (Little, 2009). While this product contains fire ignition location, time and total area burned, it does not contain mapped fire perimeter. Additional descriptive statistics are found in Stocks et al. (2003) for fires in Canada from 1959 to 1997 using an earlier version of this product. CFSNFD also contains another product with polygon fire parameter data also for the years 1959 to 2006 (version “20090929”), and provided one validation source for the LTDR generated burned area maps in this study. The fire perimeter mapping and total burned area are not complete for the years before AVHRR because some provincial fire management agencies did not create fire perimeter maps at the time (J. Little, personal communication). The fire agencies mapped fire perimeters using as a source available aerial photos, Landsat satellite images, and GPS records. Only 55% of the CFSNFD fires include the source in their records, but account for 34% of total burned area. Unburned islands within fire perimeters were not recorded in all cases. For training our LTDR mapping algorithm, we selected a region of western Canada (UL: 62°16′N, −122°20′E; LR: 53°N, −96°30′E) because it provided a greater number of fires than the average for Canada, burning every 131 years, and several comparable fire mapping studies for this region. 2.2. Mapping algorithm To accurately class burned areas using LTDR data, we propose the following empirical approach described in detail below: 1) within each pixel, difference thresholds for specific spectral bands and indexes between one year and those for previous and following years

Table 1 AVHRR products available for Canada. Product

Spatial resolution

Spatial coverage

Temporal resolution

Temporal coverage

Bands available

AHVRR raw source

References

PAL

0.1° (~ 8 km) 0.1° (~ 8 km) 0.05° (~ 5 km) 0.01° (~ 1 km)

Global

Daily and 10 day composite

1981–2000

All bands

GAC

Global

15 day composite

1981–2006

NDVI

GAC

Global

Daily

1981–2000

All bands

GAC

Canada, northern USA, Alaska and Greenland

10 day composite

1981–2006

All bands

HRPT and LAC

Agbu and James (1994), James and Kalluri (1994) Pinzon et al. (2005), Tucker et al. (2004), Tucker et al. (2005) Nagol et al. (2009), Pedelty et al. (2007), Vermote and Saleous (2006) Khlopenkov and Trishchenko (2007), Latifovic et al. (2005)

GIMMS LTDR v2 LAC


identifies potential fire events; 2) a Bayesian network trained with known burned locations from the CFSNFD to determine fire event probabilities for classing; 3) a neighborhood criteria in the post processing stage to increase accuracy of pixels classed as burned where neighboring pixels have a high burned probability; and lastly, 4) assess the accuracy of annual burned area maps by comparing to other burned area products. 2.2.1. Generating difference thresholds for specific spectral bands and indexes As part of the pre-processing evaluation, we compared vicarious calibration results for the most recent LTDR dataset version 3 released May 15th, 2010 (http://ltdr.nascom.nasa.gov/ltdr/productSearch.html, last accessed 10/07/2010) to that of a previous version. Moreno Ruiz et al. (2009) analyzed the data quality of version 2 of LTDR by comparing the calibration between sensors over invariant targets. They reported 0.04 and 0.06 reflectance factor biases for channels ρ1 (0.63 μm) and ρ2 (0.83 μm), respectively, from the AVHRR 14 to previous satellite instruments for an invariant target in the Libyan desert. Our analysis of version 3 of LTDR data indicated that the biases were reduced to 0.01 and 0.03 reflectance factor for ρ1 and ρ2, respectively, for the same targets. Ten day composites from daily data were generated from 1983 to 2000 for the entire Canadian study area using the maximum brightness temperature of channel T3 (3.75 μm) criterion following Chuvieco et al. (2005). Composites for complete years were included in the mapping algorithm, however, to eliminate snow cover, pixels with ρ1 values greater than 0.15 and T3 brightness temperatures lower than 288 were eliminated before processing the images with the algorithm. These spectral thresholds were selected after empirical analysis of snow versus snow free pixels. Kangas et al. (2001) applied the same ρ1 threshold to eliminate bright snow pixels in Finland. The threshold for T3 was set assuming pixels appear warmer than 288 K to burn. For a different study region, these thresholds should be re-evaluated. The algorithm relies on identifying pixels with high T3 and low p2 values to trigger calculation of the differences between spectral variables within pre-fire and post-fire 10 day composite periods of potential fire dates. Smoldering fires reach a temperature of 500 K, whereas flaming fires may exceed 1000 K (Li et al., 2001). The band best suited to detect active fires is T3 at 3.75 μm. According to Wien's displacement law, this region is the radiative energy peak for temperatures near 773 K reached by fires. Signal saturation in this band is common when a fire occurs, although it also saturates with other high temperature and bright surfaces, such as soils or clouds. This confusion is less common in the boreal region with generally low temperatures and low solar elevation (Li et al., 2001). Fire scars are best detected in the optical region with a decrease in reflectance after vegetation combustion leaves very dark charcoal surfaces (Chuvieco et al., 2006). Exploratory analysis of image timing for burned areas indicated the times of maximum T3 values, when fire is active, and those of minimum reflectance of ρ2, when the vegetation is mostly charcoal, did not always coincide. To evaluate both channels simultaneously, we defined a new index to detect the potential fire events called Burned Boreal Forest Index (BBFI): BBFI ¼

T3 1 þ ρ2 2

ð1Þ

During an active fire, BBFI tracks an increase in T3. As the fire cools, T3 decreases, while ρ2 reflectance, due to the presence of charcoal, also decreases and holds BBFI high. We compared the performance of BBFI to detect the timing of a fire event to several other variables, such as minimum albedo, and individual channels of minimum ρ2, maximum T3, maximum T4 (10.8 μm) and maximum T5 (12.0 μm), as well as, the minimum of two vegetation indexes applied to detecting burned pixels, Global Environment Monitoring Index (GEMI) (Pinty & Verstraete, 1992) used by Chuvieco et al.

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(2008) and channel 3 brightness Temperature Vegetation Index (VI3T) (Barbosa et al., 1999). Of these, BBFI maximum values correlated best (r2 = 0.49) to the CFSNFD training site for 1998 fire events. Maximum T3 brightness values were the next best with an r2 = 0.19. Before applying the algorithm, the maximum and minimum values, and pre-fire and post-fire median values were computed for each variable: single channels ρ1, ρ2 and T3, and spectral indexes BBFI and GEMI within each pixel for each 10 day composite of daily images (Fig. 1). Green, red and brown boxes represent statistics calculated for the year before (Y− 1), fire event year (Y0) and year after (Y+ 1), respectively. In the first of multiple steps in Fig. 1, the thresholds for maximum BBFI values selected potential burned pixels, and set the date (Tx), fire event year, and year after. From the single date, the five 10 day composites pre-fire (T[x − 5, x − 1]) and five in the post-fire (T[x + 1,x + 5]) were also specified for calculating median values for variables applied later in the algorithm. Lower thresholds for maximum BBFI in the year before, selected pixels were likely not burned and had active vegetation. Thresholds for differences between median GEMI values from pre-fire and post-fire periods within each of three years (in the blue outlined boxes) were used to confirm the selection of potential burned pixels. In the last steps, thresholds shown in red and brown boxes also confirm the potential burned pixels by detecting the burned signal during the fire year and year after based on high maximum T3 values, low minimum ρ2 values, and a small difference in median GEMI values from the pre-fire (T[x − 5, x − 1]) and post-fire (T[x + 1,x + 5]) periods (shown in the blue outlined box). The median, a non-parametric statistic, was preferred to the mean for “averaging” the index values within five 10 day composite periods because the spectral data was not normally distributed (i.e., temporally collinear and non-independent). The composite that represents the fire event was excluded in computing the median for the fire year. Longer and shorter periods for calculating the medians were also evaluated, but five 10 day composites generated the most consistent statistics before and after the fire. Shorter periods were subject to greater variability in pre-fire and post-fire statistics, and longer periods muted the fire scar signal. The pre-fire and post-fire statistics were computed for several indexes but the GEMI median was the best detector of differences between both periods.

2.2.2. Bayesian network classifier trained with known burned locations Once the algorithm thresholds detected candidate pixels to be burned, a Bayesian network classifier calculated the probability of each pixel to be burned. The Bayesian network classifier within the machine learning package WEKA (http://www.cs.waikato.ac.nz/~ml/ index.html) was applied to a training set for the region in western Canada for a single year 1998 from the CFSNFD. Conditional probabilities, or cost of classifying a pixel as burned when the correct is really unburned and vice-versa, determined by a Bayesian network classifier are based on how a training dataset with known variables behaves (Langseth & Nielsen, 2006). The same variables to define the algorithm thresholds were used here to train the Bayesian network. Output from the classifier software included the probability density functions for the selected variables within the network for both classes, burned and unburned as separate images. One option was to assign each pixel to the class with the highest probability; however, to handle data uncertainty and avoid false detections, we computed the normalized probability, resulting in values between −1 and 1. Following Bayes' theorem (Bayes, 1763), the joint probability density function for a given class was written as a product of individual density functions. While other investigators use zero as the reference value to separate burned from unburned classes, our values distribution was dependent on the number of variables submitted to the classifier, and the relationships among them. In order to minimize errors, we evaluated other reference thresholds based on the best kappa statistic between the resulting burned maps and reference maps

410


10 day composites

Daily LTDR max. T3

ρ2, T3, BBFI, GEMI Year BeforeY-1 154