2013, Franklin et al. 2016). ...... Lentile, L. B., A. M. S. Smith, A. T. Hudak, P. Morgan, M. J. Bobbitt, S. A. Lewis, and P. R. ..... Chichester, UK: John Wiley & Sons.
12 A Review for Recent Advances
Remote Sensing of Fire Effects in Burned Area and Burn Severity Mapping Ran Meng and Feng Zhao
CONTENTS 12.1 Introduction ......................................................................................................................... 261 12.2 Remote Sensing of Burned Area......................................................................................... 265 12.2.1 Burned Area Estimation Using Active Fire Counts .............................................. 265 12.2.2 Image Classification Using Burned Areas’ Spectral Properties ...........................266 12.2.3 Burned Areas Estimation Using Changes in Canopy Cover ................................. 268 12.2.4 Burned Area Mapping Using Active Remote Sensing Sensors............................. 268 12.2.5 Burned Area Mapping Using Hybrid Algorithms ................................................. 269 12.3 Remote Sensing of Burn Severity ....................................................................................... 270 12.3.1 Representative Studies of Spectral Indices-Based Burn Severity ......................... 271 12.3.2 Representative Studies of Spectral Mixture Analysis-Based Burn Severity ........ 271 12.3.3 Representative Studies of Radiative Transfer Model-Based Burn Severity .......... 273 12.3.4 Supervised and Unsupervised Classification......................................................... 273 12.3.5 New Remote Sensing Techniques for Burn Severity Studies ................................ 274 12.4 Future Directions ................................................................................................................ 274 12.4.1 New Satellite Instruments for Remote Sensing of Fire Effects............................. 274 12.4.2 Scalable Burn Severity Maps for Improved Wildfire Monitor across Spatial Scales ......................................................................................................... 275 12.4.3 Toward Ecological Meaningful Characterization of Fire Effects ......................... 275 Acknowledgment ........................................................................................................................... 276 References ...................................................................................................................................... 276
12.1
INTRODUCTION
As a primary disturbance agent, fire significantly alters ecological processes and ecosystem services around the world, driving the changes in terrestrial carbon stocks; shaping the distribution and structure of vegetation; and influencing the temporal variability in carbon, water, and energy fluxes (Bowman et al. 2009, Scott et al. 2013, Franklin et al. 2016). For example, fire-related deforestation is a net CO2 source with a flux estimated to be 2.1 Pg C per year (Van der Werf et al. 2010), whereas postfire forest recovery is a CO2 sink and might be enhanced by proper management (Bowman et al. 2009); the water yield of river catchments was also found to be significantly influenced by fire effects and the postfire vegetation recovery process (Benda et al. 2003, Mayor et al. 2007). Due to the importance of fire on these fundamental ecosystem processes, accurately monitoring the effects of fire events (i.e., time, location, and severity) is thus one of the central questions in ecology and natural resource management. In addition, projection of fire behavior under potential future climate also relies on the proper characterization of fire effects at local, regional, and global levels. 261
262
Remote Sensing of Hydrometeorological Hazards
Burned area and burn severity are the two most widely used metrics for assessing fire effects (Turner et al. 1997 and 1999, Lentile et al. 2006, Meng et al. 2015) for calculating smoke generation and carbon consumption (Miller and Yool 2002, Randerson et al. 2012), for characterizing fire regimes (Morgan et al. 2001, Keane et al. 2003, Kasischke and Turetsky 2006), and for modeling the feedback between climate change and fire activity (Randerson et al. 2006, Westerling et al. 2006, Loehman et al. 2011, Smithwick et al. 2011, McKenzie and Littell 2016). Burned areas are usually composed of complex landscape mosaics of low, moderate, and high burn severity (Figure 12.1) because of variations in wind, topography, fuel conditions, and so on (Turner et al. 1994). The variable burn severity results in a heterogeneous pattern of fire effects including vegetation loss and soil alteration (Sugihara 2006, Lentile et al. 2006, Keeley 2009, Veraverbeke et al. 2011, Quintano et al. 2013). Burn severity refers to the degree in which an ecosystem has changed (e.g., vegetation removal, soil exposure, and soil color alteration), caused by fire disturbance. Although often used interchangeably nowadays (Keeley 2009), Lentile et al. (2006) discussed and clarified the distinctions between the term of burn severity and fire severity: fire severity refers to short-term (i.e., about within 1 year following the fire) effects on the local environment, whereas burn severity refers to both short-term and long-term (i.e., up to ten years) effects, including ecosystem response processes (e.g., vegetation recovery). Recently, Composite Burn Index (CBI, a generalized rating of postfire conditions in the field) and its variant GeoCBI have gradually become the standard protocol to measure field burn severity at landscape scale (Key and Benson 2006; De Santis et al.
In-situ pictures at May 2015
U
0.10 m aerial ortho-photos on May 3, 2012
2m Worldview-2 imagery on September 12, 2012
30 m Landsat-7 Enhanced Thematic Mapper Plus (ETM+) on April 28, 2012
Center 7.5 m Buffer 0 7 14 21 28 35 42 m
L
M
H
FIGURE 12.1 Plot examples of unburned (first row), low (second row), moderate (third row) and high (fourth row) burn severity across in situ (first column) photos at May, 2016, 0.10 m aerial ortho-photos (second column) at May, 2012, 2 m WorldView-2 imagery at September, 2012, and 30 m Landsat-7 imagery spatial scales at September, 2012 in a Pine Barrens ecosystem in the Eastern United States.
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Remote Sensing of Fire Effects
2009). Specifically, as an integrated metric, (Geo)CBI averages the magnitude of change by fire across five strata from soil to vegetation canopies, then within each strata four or five variables are visually assessed and assigned a value from zero (unburned) to three (highest severity). Remote sensing has provided a convenient and consistent way to monitor fire events and quantity fire effects across spatial scales. Remote sensing sensors measure reflected energy within specified regions of the electromagnetic spectrum, which is known as a band or bandwidth. Each band responds differently to surficial characteristics such as water, soil, and vegetation. A common practice to enhance information from target features is to combine brightness values of multiple bands, such as the red, near-infrared (NIR), and shortwave near-infrared bands. Unique spectral signatures of vegetation and burn residuals become the foundation for detecting vegetation change by fires (Figure 12.2). Since the late-1970s and 1980s, remote sensing technique has been widely used to assess how severe is the fire. Different variables have been measured as ground reference readily to assess burn severity from remotely sensed measurements (Morgan et al. 2014). Fire effects lead to the changes in spectral response and make the remote sensing of burn severity possible. After a fire, a dramatic reduction in visible to NIR surface reflectance (i.e., 0.4–1.3 µm) associated with the charring and removal of vegetation is the dominant signals detected by pre- and postfire sensors at moderate–coarse spatial resolution; at fine spatial scales (< 5 m), an increase in surface reflectance is likely detected, due to the deposition of white ash, as an indicator of combustion completeness (higher burn severity). With the increase in wildfire’s size, severity, and frequency over recent decades, there are increasing interests in remote sensing of fire effects and the potential impact of climate change on wildfire activities. We did a series of searches in Web of Science to examine the current research on remote sensing of fire effects, with keywords such as burned area remote sensing and burn severity remote sensing. Results from such investigation show that the number of publications on fire effects has been increasing over the past decade, especially after the year 2002, in consistent with the increase in large wildfire events around the world (Figure 12.3).
Spectral profile
250
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150
150
Value
Value
200
100
100
50 0
(a)
Spectral profile
250
50 1
2
3 4 Band number
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FIGURE 12.2 Vegetation spectral reflectance of Landsat bands 1–6 for (a) prefire and (b) postfire conditions. The spectral profile shows the reflectance value for the center pixel at the crosshair mark.
264
Remote Sensing of Hydrometeorological Hazards 80 Number of publications
70 60
Burned area Burn severity
50 40 30 20 0
1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
10
FIGURE 12.3 Number of publications related to remote sensing of burned area and burn severity in the Web of Science database from 1991 to 2015.
As fire effects can vary at different scales, one spatial or temporal scale may not be appropriate to address all objectives for assessing burn severity (Morgan et al. 2014). Over the past three decades, various satellite remote sensing-based approaches have been developed to monitor fire events at coarse, moderate, and fine resolutions. A few studies have already reviewed the application of moderate–coarse resolution remotely sensed measurement in mapping large-scale fire characteristics (Lentile et al. 2006, Chu and Guo 2013, Roy et al. 2013, Morgan et al. 2014), so here we will focus on remote sensing of fire effects at moderate and high spatial resolution in this review. A number of studies with different types of satellite imagery and approaches have been conducted for burn severity assessment. Moderate Resolution Imaging Spectroradiometer (MODIS) fire product is one of the most popular datasets for wildfire studies across the globe. The Landsat sensors provide one of the longest and widely used imagery collections for wildfire monitoring, especially for burn severity applications (Eidenshink et al. 2007); while images from newer launched sensor with high spatial resolution, such as WorldView-2 and QuickBird, also incur interest in very high spatial resolution (VHR) fire mapping (Holden et al., 2010; Meng et al., 2017). In addition to the type and resolution of imagery used, image acquisition date, in relation to field data collection and time since fire, also plays an important role in remote sensing of burn severity: interannual phonological change of vegetation, the interaction of long-term climate patterns (i.e., drought), and regeneration trends might confuse varying fire effects. What is more, challenges still exist in the repeatable and transferable assessment of burn severity across spatial scales or fire regimes, given the limited mechanistic and predictive power of widely used but subjective descriptors of burn severity (unburned, low, moderate and high severity): thresholds on the widely used Normalized Burn Ratio (NBR)-based burn severity measurements are arbitrary and often vary between fires within the same ecoregion (Kolden et al. 2015). A new paradigm in burn severity assessment, based on a consistent and transferable quantification of burn severity (e.g., changes in carbon, water and energy fluxes), has been discussed and explored in the community recently (Morgan et al. 2014; Smith et al. 2016; Sparks et al. 2016; Meng et al. 2017). With the development of remote sensing techniques (i.e., light detection and ranging [LiDAR], hyperspectral, and VHR imagery) these years, fire measurements with high temporal, spatial, and spectral resolution become increasingly available and provide new opportunities in remote sensing of fire effects studies (Montealegre et al. 2014, Schepers et al. 2014, McCarley et al. 2017, Meng et al. 2017). Accurate characterization of fire effects is critical for postfire forest management. Effective fire management is reliant on reliable information on which to base appropriate decisions and actions. With projected increasing occurrences of wildfires under the current climate change scenarios, there are urgent needs to better characterize the impact of fires on ecosystem dynamics and processes.
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Remote Sensing of Fire Effects
In this chapter, we discuss the recent advances of remote sensing applications in monitoring burned areas and burn severity at local to regional and global scales. In the following sections, we summarize key mapping techniques for both burned areas and burn severity, respectively, and also discuss the potential future directions in characterizing burned areas and burn severity.
12.2
REMOTE SENSING OF BURNED AREA
One of the key remote sensing measurements of fire effects is the burned area. Fires produce a significant change in the structure and the reflectance of vegetation and the soil properties within the burned area that are noticeable in the microwave, visible, and especially the infrared part of the electromagnetic spectrum (Leblon et al. 2012). In this section, we will discuss the remote sensing of burned areas, by techniques. A variety of techniques have been employed for burned area mapping. These techniques can be grouped into five types of approaches: 1. 2. 3. 4. 5.
Burned area estimation using active fire counts Image classification using the spectral properties of burned residues Burned areas estimation using changes in canopy cover Burned areas classification using active remote sensing sensors Burned area mapping using hybrid classification approach (Table 12.1)
12.2.1
BURNED AREA ESTIMATION USING ACTIVE FIRE COUNTS
Active fire count products capture the location and timing of fire burning at the time of the satellite overpass, usually as swath-based fire masks or lists of fire pixel locations and dates (Giglio et al. 2006). Globally, long-term observations of active fires made with coarse- and mediumresolution spaceborne sensors are readily available. Selected examples of these observations
TABLE 12.1 Summary of Major Burned Area Mapping Methods and Selected References Burned Area Mapping Methods
Types
2.1 Active fire counts
Aggregate active fire detections
2.2 Spectral change detection approach
Multitemporal composites Spectral indices (SIs) Spectral mixture analysis (SMA) Machine learning classification
2.3 Canopy cover change detection 2.4 Active remote sensing
2.5 Hybrid approach
Time series change detection Changes in Leaf Area Index Forest cover loss Synthetic aperture radar (SAR)
PALSAR SIs + thermal Time series change detection + machine learning classification
Selected References Giglio et al. (2006) Oliva and Schroeder (2015) Chuvieco et al. (2008) Key and Benson (2006) Quintano et al. (2006) Petropoulos et al. (2010, 2011) Hudak and Brockett (2004) Goodwin and Collett (2014) Boer et al. (2008) Potapov et al. (2008) Siegert and Hoffman (2000) Gimeno et al. (2004) Kasischke et al. (2008) Polychronaki et al. (2013) Roy et al. (1999) Zhao et al. (2015) Kennedy et al. (2015) Schroeder et al. (2015)
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Remote Sensing of Hydrometeorological Hazards
TABLE 12.2 Name, Equation, and References for Major Vegetation Spectral Indices (SIs) in Burned Area Mapping Index Normalized Difference Vegetation Index (NDVI) Enhanced Vegetation Index (EVI) Soil Adjusted Vegetation Index (SAVI) Normalized Burn Ratio (NBR)
Equation (ρ4 − ρ3)/(ρ4 + ρ3) (ρ5 − ρ4)/(ρ5 + 6 * ρ4 − 7.5 * ρ2 + 1) ((ρ5 − ρ4)/(ρ5 + ρ4 + 0.5)) * 1.5 (ρ4 − ρ7)/(ρ4 + ρ7)
Reference Tucker et al. (1986) Gao et al. (2000) Huete (1988) Key and Benson (2006)
Note: ρ3, ρ4, and ρ7 represent the surface spectral reflectances as measured in Bands 3 (red band, 0.3–0.69 µm), 4 (nearinfrared band, 0.76–0.90 µm), and 7 (Shortwave infrared band, 2.08–2.35 µm) of the Landsat Thematic Mapper and Enhanced Thematic Mapper Sensors
include the advanced very high resolution radiometer (AVHRR) active fire product (Li et al. 2001), along track scanning radiometer (ATSR) nighttime fire product (Schultz 2002), the MODIS global active fire product (Giglio 2010), and the visible infrared imaging radiometer suite (VIIRS) global active fire product (Schroeder et al. 2014). Although these fire count products capture many aspects of the spatial and temporal distribution of burning, it is difficult to relate them to actual area burned due to inadequate spatial and temporal resolutions, variability in cloud cover and fuel conditions, and differences in fire behavior (Giglio et al. 2009, Oliva and Schroeder 2015). In addition, the probability of active fire detection is dependent on the fire temperature and size: small- and/or low-intensity fires may not be detected at the time of satellite overpass (Boschetti et al. 2015). Cumulative active fire detection algorithms usually underestimate the area burned in grassland and savanna ecosystems where the fires front progresses rapidly across the landscape (Roy et al. 2008, Oliva and Schroeder 2015). Conversely, active fire detection methods may overestimate the area burned for isolated fire points that are detected but very hot and smaller than the pixel dimension, for example, in certain forest ecosystems where the fuel conditions can sustain high energy fires and where the fire spread is slow relative to the satellite overpass frequency (Boschetti et al. 2015). Several studies reported burned areas mapped from aggregated active fire detections for large fires. Many studies underestimated the burned areas due to cloud contamination and difficulty in detecting small fires with coarse satellite images. For example, Sukhinin et al. (2004) used aggregated active fire detection data from the AVHRR to estimate burned areas in Russia in 2000, underestimating the total area measured by 27% (Sukhinin et al. 2004). Oliva and Schroeder (2015) assessed the performance of the VIIRS 375 m active fire detection product for direct burned area mapping. Fire detection rates were lower for small fires (
