Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California

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1 Graham Emde GEOG 3230 Advanced Remote Sensing February 22, 2013 Lab #1 Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California Introduction Wildfires are a common disturbance in the forests and shrublands of southern California while being an integral part of these ecosystems. The California Department of Forestry and Fire Protection report that more than 5,600 fires burn more than 1720,000 acres each year in the state (CAL FIRE 2012). The Chaparral shrubland of southern California are particularly prone to wildfires, especially during the hot and dry months between May and October. Fires are destructive in many cases, but they are crucial for the maintenance of semi-arid ecosystems, such as California s chaparral region (Rogan and Yool 2001, Thomas & Davis 1989). This report responds to the call by the US Forest Service (California) for an assessment of the ability to use remote sensing technology for wildfire monitoring in San Diego County. This report compares two satellite sensors, Landsat TM-5 and IKONOS, and analyzes each sensor s ability to map the spatial extent and severity of post-wildfire burn scars in the county. 1) What is the feasibility of remote sensing technology to assess post-wildfire damage in southern California to assist in habitat monitoring, post-fire regeneration, site restoration and prevention of soil-erosion? Several studies have shown the feasibility of using remotely sensed imagery to map the extent and severity of post-wildfire damage (Rogan and Yool 2001, Rogan and Franklin 2001). Scientists and forest managers rely on remotely sensed data to provide information about wildfires that would otherwise be difficult to collect because of the remote location of the postwildfire sites (Rogan and Franklin 2011). Remotely sensed data of post-wildfire damage provide important information about wildfire extent and severity but also help scientists and managers monitor the impacts on habitats, soil erosion risk, vegetation restoration, and locations that are particularly at risk of wildfires in the future. Studies show that high-resolution satellite imagery can be used to produce highly accurate measurements of post-wildfire burn scar spatial extents. Compared to the coarse resolution (68 x 83 m) of the MSS sensor used on early Landsat satellites, the Landsat TM-5 sensor produces images with 30 m spatial resolution. Landsat TM-5 produces more accurate measurements of 1

2 burn scar extents than low-resolution sensors, but sensors with even higher resolutions, such as IKONOS, can produce even more accurate maps of burn scar spatial extents. It is more complicated to map burn severity, but using a scene model and an error matrix can help increase the accuracy of a burn severity assessment. 2) How accurately can vegetation damage be detected? Are there verifiable ways to determine that remote sensing imagery are doing a good job? Rogan and Franklin (2001) cite studies that show that low-resolution sensors are approximately 80% accurate when mapping the spatial extent of post-wildfire burn scars. They also reference studies that show that high-resolution imagery, such as Lansdat TM-5, is more than 90% accurate. Mapping burn scar severity is less accurate. In studies that use three classifications for ground cover, accuracy is only 64%. Studies that use more classifications for ground cover are even less accurate. With four classifications, accuracy reaches only 46%, and with five classifications, accuracy is a mere 38% (Rogan and Franklin 2001). The best way to assess the accuracy of wildfire maps derived from remote sensing imagery is to collect ground truth points from the field and compare these points to the maps (Rogan and Yool 2001). An error matrix is a useful tool for comparing the results of a classified map of burn severity with ground truth points. Points on the classified burn severity map are accurate when they match the corresponding ground truth points and inaccurate when they do not match. An error matrix of these matched and unmatched points can be used to produce an overall estimate of the accuracy of the map. Studies show that remote sensing imagery can be used to a high degree of accuracy when mapping the spatial extent of post-wildfire burn scars but that remote sensing imagery is less accurate at mapping burn severity. Though accuracy of burn severity mapping is little more than 60%, this report concludes that remote sensing technology is an important tool for collecting data on post-wildfire damage. 3) Given the great variety of imagery available (based on cost, areal coverage, resolutions etc) which remotely sensed data sources would be best to map fire damage in San Diego County (California) for one fire season (May-October)? A scene model can be a useful tool for determining the ideal specifications for a remotely sensed dataset. Table 1 outlines the scene model for post-wildfire extent and severity assessment in San Diego County. The specifications in this scene model help with the selection of remotely sensed data for this study. 2

3 Table 1: Scene model showing specifications for post-wildfire extent and severity assessment in San Diego County. Information required Spatial scale Temporal scale Environment type Components and hierarchy Spatial dimensions Temporal dimensions Spectral dimensions Extent of post-wildfire burn scar Severity of post-wildfire burn scar. For example, the % cover in each burn severity classification category: (1) shade, (2) green vegetation, (3) non-photosynthetic vegetation, (4) bare soil, and (5) burned vegetation (char and ash) Grain = smallest vegetation patch, m Extent = approximate extent of San Diego County, 11,720 km 2 Minimum requirement = one pre-fire and one post-fire image Chaparral shrubland Constraint = shrubland complex Focus = vegetation patches Mechanism = individual plants H-resolution Grain = m Extent = 11,720 km 2 Optimal date = One pre-fire and one post-fire image Solar conditions = as close to noon as possible Red = nm NIR = nm SWIR = nm Radiometric dimensions Grain (quantization) = 0.01 (reflectance) Extent (dynamic range) = green (0.04), red (0.07), and NIR (0.14)* Error tolerance levels Kappa statistic must be > 0.6 * The radiometric parameters are taken from Phinn (2003). The information required for this study is the extent and severity of the post-wildfire burn scar. The extent of the burn scar may be measured along its borders, and the severity of the burn scar may be measured using a classification technique. The ideal spatial scale for this project is a grain of between 0.5 and 1.0 m since this represents the smallest vegetation patch that may or may not be burned in the project area. The extent of the project area is the approximate extent of San Diego County, which is 11,720 km 2. The temporal scale for this study is at least one pre-fire and one post fire image. The environment type is chaparral shrubland, which is also the constraint of the project. The focus is the vegetation patches, and the mechanism is the individual plants. The spatial dimensions required for this study are H-resolution imagery, and the ideal solar conditions is as close to noon as possible to minimize shadows. Red and Near Infrared are the most important spectral dimensions since these wavelengths provide the most information about vegetation change, but SWIR is also important for showing the moisture in the scene, which can be useful for mapping dried-out burn scars. The radiometric parameters 3

4 are a reflectance of 0.01 and a dynamic range for green of 0.04, red of 0.07, and NIR of The error tolerance levels for this study will be a Kappa statistic of greater than 0.6. Table 2: Compliance matrix comparing scene model parameters for post-wildfire extent and severity assessment in San Diego County with sensor specifications of Landsat TM-5 and IKONOS. Parameter Scene Model Landsat TM- 5 Landsat TM-5 Level of Match IKONOS IKONOS Level of Match Position of bands Red nm NIR nm SWIR nm Radiometric Quantization 0.01 (reflectance) Dynamic range Temporal Date green (0.04), red (0.07), and NIR (0.14) One pre-fire and one postfire image Band 1 (Blue) µm Band 2 (Green) µm Band 3 (Red) µm Band 4 (NIR) µm Spatial Pixel Size m 30 m un 4 m Scene Extent 11,720 km 2 31,820 km km 2 un H/L resolution H H H Spectral No. of bands 3 4 partially- 4 partially- Band 1 (Blue) µm Band 2 (Green) µm Band 3 (Red) µm Band 4 (NIR) µm 11 bits/pixel to 2.6 bits/pixel for transmission 8-bit Collected as 11-bit, delivered as 16-bit Every 16 days Every three to five days Solar time Noon Depends N/A Depends Error Levels Types Kappa > 0.6 Not N/A Not processed N/A processed Magnitude Kappa > 0.6 Not processed N/A Not processed N/A 4

5 This study analyzes two remote sensing images of the same post-wildfire scene in San Diego County. Four bands (blue, green, red, and NIR) are provided, and 234 false-color composites (see figures 1 and 2) visualize the vegetation (in red) and burn scar areas. Neither image has the ideal pixel size of m, but IKONOS has a more resolution of 4m than the 30 m of Landsat TM-5. The ideal spatial extent for this project is 11,720 km 2, which is the entire area of San Diego County, and Landsat TM-5 has a more extent at 31,820 km 2 than IKONOS, which only has an spatial extent of 121 km 2. Though Landsat s spatial extent makes it simpler to use, IKONOS images could still be used since images can easily be mosaicked together, and since it would be rare to require an image of the entire county. It would be more likely that only a couple of IKONOS images would be required to capture the entire wildfire area, and the only prohibiting factor would be the cost of the images. (Also, it is important to note that Landsat images are free). The scene model requires H-resolution imagery, and though Landsat TM-5 is often considered H-resolution, it is less for this scene model than the IKONOS imagery, which resolves greater detail. The spectral parameters require red, NIR, and SWIR wavelengths, and both the Landsat TM-5 and IKONOS imagery provide red and NIR, but lack the SWIR wavelengths. These two sets of imagery are only partially, because SWIR wavelengths would be useful for showing scene moisture differences between pre- and post-fire images. This study was unable to determine the quantization levels of Landsat TM-5, but IKONOS s 11-bit dynamic range is more than Landsat s 8-bit dynamic range because it provides a great level of detail in brightness values. Both images meet the temporal scale requirements of the scene model. Landsat produces images of the same location every 16 days and IKONOS does the same in 3 to 5 days. This temporal scale is more than enough to capture one pre-fire and one post-fire image, as required. The compliance matrix demonstrates that IKONOS is more for mapping the spatial extent and severity of wildfire dame in San Diego County. IKONOS s 4 m pixel size and higher resolution allows it to resolve more spatial detail in the images, which produces a more accurate spatial representation of the burn scar. Though the spatial extent of IKONOS is smaller than the scene parameters, multiple IKONOS images can be acquired at a reasonable cost (between $10 and $50 per image). Also, IKONOS has higher resolution radiometric specifications than Landsat, which allows IKONOS to resolve greater detail in the level of burning and ash present in the post-fire image. Conclusion Previous studies have shown that remote sensing imagery can be useful for monitoring wildfires. This report confirms the findings of these studies through the development of a scene model and a comparison of the suitability of Landsat TM-5 and IKONOS datasets with a 5

6 compliance matrix. I would recommend using remote sensing imagery to assist in the mapping and monitoring of wildfires in San Diego County, and I would particularly recommend using satellite imagery such as IKONOS, which provides better high spatial and radiometric detail. The high level of detail in satellite imagery and the ability of satellites to collect data from otherwise inaccessible locations make remote sensing technology a highly valuable and potentially vital tool for wildfire monitoring. References CAL FIRE (2012). The California Department of Forestry and Fire Protection. Retrieved from Phinn, S.R., Stow D.A., Franklin J., Mertes L.A.K., and Michaelsen, J. (2003). Remotely sensed data for ecosystem analyses: Combining hierarchy theory and scene models. Environmental Management 31(3): Rogan, J., and Yool S.R. (2001). Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico. Int. J. of Remote Sensing, 22(16), Rogan, J., and Franklin J. (2001). Mapping wildfire burn severity in southern California forests and shrublands using enhanced thematic mapper imagery. Geocarto International, 16(4), Thomas, C. M., and S. D. Davis. (1989). Recovery patterns of three chaparral shrub species after wildfire. Oecologia 80(3):

7 Figures Figure 1: IKONOS image of a post-wildfire burn scar in a chaparral region of San Diego County. Figure 2: Landsat TM-5 image of a post-wildfire burn scar in a chaparral region of San Diego County. 7