UNIVERSITY OF CALIFORNIA

Santa Barbara

Multi-temporal Remote Sensing of Vegetation Regrowth After a Large Wildfire

A Thesis submitted in partial satisfaction of the

requirements for the degree Master of Arts

in Geography

by

Christopher Linscott Kibler

Committee in charge:

Professor Dar A. Roberts, Chair

Professor Stuart H. Sweeney

Professor Carla M. D’Antonio

June 2019

The thesis of Christopher Linscott Kibler is approved.

______

Stuart H. Sweeney

______

Carla M. D’Antonio

______

Dar A. Roberts, Committee Chair

June 2019

Multi-temporal Remote Sensing of Vegetation Regrowth After a Large Wildfire

Copyright © 2019

by

Christopher Linscott Kibler

iii

ACKNOWLEDGEMENTS

I would like to thank the people whose guidance and support made this thesis possible. I would like to thank my advisor, Professor Dar Roberts, for his valuable mentorship and guidance over the last three years. Professors Stuart Sweeney and Carla

D’Antonio also provided valuable advice about how to design and implement this study. In addition to my committee members, several other people made substantial contributions to this research. Anne-Marie Parkinson worked tirelessly to organize the fieldwork trips and analyze the field survey data. She was assisted by field technicians Monica Goodwin and

Payton Thomas, whose diligence and perseverance enabled us to collect excellent data. Seth

Peterson provided valuable advice about how to design and implement the remote sensing analyses. Members of the VIPER laboratory also provided useful guidance on many occasions. Finally, I would like to thank my girlfriend, Claire, for her encouragement and enthusiasm as I completed this project. I would also like to thank my parents for their unwavering love and support during all of my endeavors. To everyone who supported me as I created this thesis, thank you.

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ABSTRACT

Multi-temporal Remote Sensing of Vegetation Regrowth After a Large Wildfire

by

Christopher Linscott Kibler

Large wildfires occur regularly in southern California and disturb a variety of vegetation communities that exist in different environmental conditions across the landscape.

Different plant species have different functional adaptations to fire that affect their recovery.

Chaparral shrublands are well adapted to high intensity crown fires and will recover rapidly in the years following a fire. Other vegetation types, such as coniferous forests, are less adapted to severe fires and will take longer to regenerate in areas of canopy mortality. As large wildfires become more frequent across the western United States, it is important to monitor how these fires affect different vegetation communities. Changes in the species composition of individual patches affect carbon and nutrient cycling, local hydrology, and other aspects of ecosystem function. In some cases, patches of vegetation may not return to their pre-fire conditions.

This study examines how patches of chaparral shrubland and mixed conifer forest recovered from the 2007 Zaca wildfire in Santa Barbara County, California. It combines multi-temporal remote sensing imagery and field survey data to identify characteristic recovery signals for different vegetation types. Landsat imagery from 2000-2018 was used to

v compute the relative differenced normalized burn ratio, green vegetation fractions, and shade fractions for the entire burn scar. Land cover data for the chaparral shrublands were collected from 82 field survey transects in the summer of 2018. Land cover data for the mixed conifer forests were created by manually classifying high resolution aerial imagery. The resulting remote sensing trajectories were used to compare recovery behavior across different vegetation types. The conifer land cover data was also used to develop a statistical model that identified the environmental predictors of conifer mortality during the fire. Finally, I quantified the fractional cover of standing dead wood in Quercus chrysolepis crowns to determine if standing dead wood affects remote sensing estimates of green vegetation regrowth.

The chaparral shrub species recovered rapidly from 2007-2011, but recovery stalled from 2011-2017 because of a severe drought. Cercocarpus betuloides was the most resilient species during the drought. C. betuloides is well adapted to dry conditions, which suggests that droughts may affect which species successfully regenerate after wildfires. Both the remote sensing data and the field surveys revealed widespread mortality of mixed conifer forest. In general, the conifer stands did not recover to the levels of vegetation greenness seen before the fire. In total, 34.6% of the stands analyzed experienced partial or complete canopy loss as a result of the fire. A statistical model of conifer mortality revealed that insolation, maximum temperature, and local topographic position were important environmental determinants of post-fire canopy cover. These variables all control water availability on the landscape, which suggests that conifers in relatively wet areas are more likely to survive large wildfires. Field surveys indicated that many areas of conifer mortality are converting to shrubland. Some shrub species, such as Ceanothus palmeri, may even be inhibiting conifer

vi regeneration. The mean fractional cover of standing dead wood in Quercus chrysolepis crowns was 0.25. As a result, remote sensing models may be underestimating green vegetation regrowth by as much as 25% for some vegetation types.

vii

TABLE OF CONTENTS

1 Introduction ...... 1

2 Methods...... 5

2.1 Study Area ...... 5

2.2 Land Cover Reference Data ...... 5

2.2.1 Conifer Reference Data ...... 6

2.2.2 Shrub and Herbaceous Plant Reference Data ...... 7

2.2.3 Standing Dead Wood Cover Data ...... 10

2.3 Remote Sensing ...... 12

2.3.1 Imagery Data ...... 12

2.3.2 Normalized Burn Ratio ...... 13

2.3.3 Spectral Mixture Analysis ...... 14

2.3.4 Multiple Endmember Spectral Mixture Analysis...... 17

2.3.5 Spectral Library Development ...... 18

2.3.6 Characteristic Recovery Trajectories ...... 23

2.4 Environmental Modeling of Type Conversion ...... 23

3 Results ...... 25

3.1 Land Cover Reference Data ...... 25

3.2 Characteristic Recovery Trajectories...... 27

3.2.1 RdNBR Trajectories ...... 29

3.2.2 Green Vegetation Trajectories ...... 31

3.2.3 Shade Trajectories ...... 34

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3.3 Fractional Cover of Standing Dead Wood ...... 37

3.4 Predictors of Conifer Mortality ...... 38

4 Discussion ...... 43

4.1 Regrowth of Green Vegetation ...... 43

4.2 Remote Sensing of Vegetation Structure ...... 47

4.3 Type Conversion of Mixed Conifer Forests ...... 49

4.4 Implications for Management and Future Research ...... 53

5 Conclusion ...... 55

Appendices ...... 57

Bibliography ...... 64

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LIST OF FIGURES

Figure 1: Map showing the burn scar in relation to California county boundaries...... 4

Figure 2: Flowchart of the process used to develop spectral libraries for this study. ..19

Figure 3: PCA biplot of species cover for species that were dominant (>25% cover) in at least

one field survey transect...... 27

Figure 4: Diagram of a hypothetical recovery trajectory demonstrating ΔVIshort, ΔVIrecovery,

and ΔVIlong. I use this framework to quantify changes in vegetation spectral indices, but

it can also be used to analyze other metrics of change after disturbance...... 28

Figure 5: Mean RdNBR trajectories for conifer stands with different combinations of pre-fire

and post-fire stand densities. The stands with no canopy cover before the fire (bottom

left subplot) appear to have been misclassified by CALVEG. They contain mixtures of

shrubs, grasses, and barren ground...... 30

Figure 6: Mean RdNBR trajectories for six common chaparral species. RdNBR trajectories

were extracted from the locations of the field survey transects. The transects were

grouped by dominant species, and the mean trajectory was calculated for each species.

...... 31

Figure 7: Validation of remote sensing GV fractions from 2018 using the estimates of green

vegetation cover from the transect photographs (n = 80, RMSE = 0.096)...... 32

Figure 8: Mean GV trajectories for conifer stands with different combinations of pre-fire and

post-fire stand densities. The stands with no canopy cover before the fire (bottom left

subplot) appear to have been misclassified by CALVEG. They contain mixtures of

shrubs, grasses, and barren ground...... 33

x

Figure 9: Mean GV trajectories for six common chaparral species. GV trajectories were

extracted from the locations of the field survey transects. The transects were grouped by

dominant species, and the mean trajectory was calculated for each species...... 34

Figure 10: Mean shade trajectories for conifer stands with different combinations of pre-fire

and post-fire stand densities. The stands with no canopy cover before the fire (bottom

left subplot) appear to have been misclassified by CALVEG. They contain mixtures of

shrubs, grasses, and barren ground...... 35

Figure 11: Mean shade trajectories for six common chaparral species. Shade trajectories were

extracted from the locations of the field survey transects. The transects were grouped by

dominant species, and the mean trajectory was calculated for each species...... 36

Figure 12: Predictions of post-fire conifer canopy cover based on different values of pre-fire

canopy cover, insolation, TPI, and maximum temperature. High, medium, and low

values of insolation and maximum temperature are equal to the 75th, 50th, and 25th

percentiles of the observed values, respectively. A TPI value of one means that the

elevation of a stand is one meter above the average elevation of the surrounding 50 m by

50 m area...... 41

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1 INTRODUCTION

Wildfire plays a critical role in shaping and reshaping the distribution of plant species across southern California. While plant species distributions are broadly influenced by environmental variables such as climate and topography (Franklin, 1995), extreme disturbance events such as wildfires create opportunities to rapidly transform the structure and composition of vegetation patches. Intense crown fires are a defining characteristic of

California’s chaparral ecosystem (Keeley and Fotheringham, 2001). After wildfires, chaparral shrub species either resprout from surviving burl tissue, recruit from seeds, or both

(James, 1984). Resprouting species are extremely resilient after wildfires, while obligate seeders require a dormant seed bank to regenerate. As a result, patches dominated by resprouting species change little after fires, while patches dominated by seeding species can change in both structure and species composition (Keeley et al., 2005). Fires also initiate a succession of non-dominant herbaceous species in the interstices between dominant shrubs and trees (Keeley et al., 1981; Keeley et al., 2005).

Small patches of conifer forest also occur across southern California. They typically occur on mountain slopes and are separated by large areas of chaparral (Stephenson and

Calcarone, 1999). Conifer forests are prone to local extirpation after wildfires because individual trees are slow to reach reproductive maturity and because conifer species as a whole are slow to colonize new areas. Intense crown fires destroy mature trees and cone sources, which can eliminate propagule sources from the landscape. As a result, large stand- replacing fires that are typical in chaparral can destroy conifer stands and significantly increase the distance to a seed source for post-fire recolonization. This prevents new conifer seedlings from germinating in areas of high canopy mortality (Shive et al., 2018; Haffey et

1 al., 2018) and facilitates the establishment of shrubs and herbaceous species in areas previously dominated by conifers (Franklin, 2010). Long-term changes in species composition, known as type conversion (e.g., Keeley, 2002), can have broad impacts on ecosystem function, including altered fire regimes, alien plant invasions, and modifications to carbon and nutrient cycling (D’Antonio and Vitousek, 1992; Keeley, 2006; Gonzalez et al.,

2015).

Detecting areas of fire-induced type conversion is thus an important part of monitoring landscape dynamics in southern California, especially as large fires become more frequent in the western United States (Dennison et al., 2014). Previous studies have typically relied on field surveys to detect type conversion (e.g., Franklin et al., 2006; Goforth and

Minnich, 2008), but field surveys are prohibitively expensive and time consuming for continuous landscape-scale monitoring. Remote sensing is a more effective method of monitoring land cover change that provides broad spatial and temporal coverage.

Multispectral satellite imagery covering the entire globe has been collected regularly for over

35 years and is freely available for public use (Wulder et al., 2012). Yet, its successful use depends on identifying remote sensing signals that are characteristic of different vegetation types. To date, few studies (Meng et al., 2014; Park et al., 2018) have attempted to use remote sensing imagery to detect type conversion.

Spectral indices are the most common method of assessing burn severity and post-fire vegetation regrowth using remote sensing data. Many studies have relied on the normalized burn ratio and its variants (Key and Benson, 2006; Miller and Thode, 2007) for these purposes (e.g., Hudak et al., 2007; Keeley et al., 2008; Storey et al., 2016). Other studies have used spectral mixture analysis to estimate the fractional cover of different materials

2 inside of burn scars (e.g., Rogan and Franklin, 2001; Riaño et al., 2002; Quintano et al.,

2013; Fernandez-Manso et al., 2016). The spectral indices used in post-fire applications primarily measure changes in greenness compared to pre-fire conditions. While these indices are able to detect the destruction and regrowth of aboveground biomass, they may not be sensitive to changes in the structure and composition of vegetation patches that can result from fire. The quantity of biomass destroyed during a fire is correlated with many ecosystem responses to the disturbance, but cannot reliably predict any specific response (Keeley,

2009). As a result, the most common spectral indices are effective at quantifying burn severity but may be ineffective at detecting specific changes in ecosystem structure and function.

Many recent studies have used multi-temporal trajectories of spectral indices to monitor ecosystem recovery after disturbance (e.g., Kennedy et al., 2007, 2010; Meigs et al.,

2011; Cohen et al., 2017; Frazier et al., 2018). While these studies have identified broad- scale recovery patterns for different disturbance agents, few studies have examined how recovery patterns vary between different plant species and functional types for a given type of disturbance. Hope et al. (2007) and Storey et al. (2016) demonstrated that fire recovery trajectories are sensitive to the species composition of chaparral patches. Viedma et al.

(1997) demonstrated that these trajectories are also sensitive to vegetation structure, including stand density and plant functional types. That being said, it is not clear what drives this variability and whether these remote sensing signals can be used to reliably retrieve patch characteristics without ground reference data. Understanding the causes of this variability may enable researchers to exploit multi-temporal data sets to detect type conversion and other structural changes to vegetation patches after disturbance.

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In this study, I combine field survey data with multi-temporal remote sensing data to identify post-fire recovery trajectories for chaparral shrub species and stands of mixed conifer forest. I also examine determinants of conifer mortality during wildfires. In doing so,

I answer the following research questions:

1. What drives variability in post-fire recovery trajectories between different species and plant functional types?

2. How structurally and compositionally heterogeneous are patches of mixed conifer forest and chaparral shrubland? How does this heterogeneity affect post-fire recovery trajectories?

3. To what extent does standing dead wood affect remote sensing estimates of green vegetation regrowth after wildfires?

4. What are the environmental predictors of conifer mortality after wildfires in southern California?

Figure 1: Map showing the burn scar in relation to California county boundaries.

4

2 METHODS

2.1 Study Area

This study examines vegetation regrowth following the 2007 Zaca wildfire in Santa

Barbara County, California (Figure 1). The Zaca fire burned 97,334 ha starting on July 4,

2007. It was fully contained on September 2, 2007. The majority of the burn scar is located inside of Los Padres National Forest at elevations ranging from 254 m to 2,079 m. Mean annual precipitation ranges from 324 mm to 1260 mm, with 95% of the precipitation occurring between November and March (Davis and Michaelsen, 1995). The majority of vegetation cover inside the burn scar is chaparral shrubland, which is characterized by the presence of species such as Ceanothus spp., Arctostaphylos spp., and Adenostoma fasciculatum Hook. & Arn. Coastal sage scrub species such as Eriogonum fasciculatum

Benth. occur in more xeric locations (Taylor, 2004). Oak species such as Quercus berberidifolia Liebm., Quercus chrysolepis Liebm., and Quercus wislizenii A. DC. are also prevalent across the landscape. Stands of conifers occur intermittently throughout the fire scar, especially at higher elevations. These include species such as Pinus coulteri D. Don,

Pinus jeffreyi Grev. & Balf., Pseudotsuga macrocarpa (Vasey) Mayr, Abies concolor

(Gordon & Glend.) Hildebr., and Calocedrus decurrens (Torr.) Florin. Nomenclature follows

Baldwin et al. (2012). A full list of species discussed in this study is included in Appendix 1.

2.2 Land Cover Reference Data

Characterizing the recovery trajectories of different species and plant functional types requires definitive land cover data for use as reference data during remote sensing analysis. I developed two land cover data sets for this purpose. The first data set recorded the canopy cover of mature conifer stands before and after the fire. It was developed by manually

5 interpreting high resolution aerial imagery. The second data set recorded the fractional cover of shrubs and herbaceous species after the fire. Individual shrubs and herbaceous plants generally cannot be resolved using high resolution aerial imagery, so this data set was developed by conducting field surveys inside of the burn scar.

2.2.1 Conifer Reference Data

Land cover data for conifer forests were created by extracting conifer stand boundaries from CALVEG, a GIS database of California land cover maintained by the U.S.

Forest Service (2018). Each stand in the CALVEG database contains an attribute for dominant vegetation type, organized by species alliances. I extracted the 756 stands within the burn scar dominated by the “Mixed Conifer - Pine” alliance. This alliance was selected because the locations of stands coincided with areas of known canopy mortality after the

Zaca fire (N. Molinari, personal communication). It also contains a diverse mix of species that represent most of the conifer species found within the burn scar. Previous literature

(Franklin et al., 2006; Goforth and Minnich, 2008; Franklin and Bergman, 2011) has documented the type conversion of mixed conifer forests after other wildfires in southern

California, so the structure and composition of these stands are likely to have changed as a result of the fire.

The conifer stand boundaries were overlaid onto high resolution aerial imagery available through the National Agriculture Imagery Program and Google Earth (Google,

Mountain View, California, USA). This imagery covered the entire time series of the study

(2000-2018). Examination of the CALVEG data revealed that many stands classified as being dominated by conifers before the fire actually had extremely sparse canopy cover. In areas with open canopies, the spectral response of the understory can dominate the remote

6 sensing signal (Franklin, 1986), so it was necessary to characterize the actual tree cover of each stand through manual interpretation of the high resolution aerial imagery.

Fractional canopy cover from before and after the fire was manually recorded for each stand. Canopy cover was classified using four binned classes: no trees (0% canopy cover), sparse canopy (1-25% canopy cover), open canopy (25-75% canopy cover), and dense canopy (75-100% canopy cover). Thresholds in 25% increments were selected to minimize subjectivity and error during the manual interpretation process (Powell et al.,

2004). They are similar to thresholds used by Canada’s National Forest Inventory (2017; 0-

10%; 10-25%, 25-60%, and 60-100%) and previous studies (e.g., Stenback and Congalton,

1990; 0-30%, 30-70%; and 70-100%). While recording canopy cover on a continuous scale would have been ideal, the aerial imagery had various spatial resolutions and illumination conditions, which made it difficult to make precise estimates of canopy cover. A more precise classification scheme would not necessarily have increased the accuracy of the data.

It is worth noting that some stands were classified as having no canopy cover before the fire.

Based on the high resolution aerial imagery, these stands appear to have been misclassified by CALVEG.

2.2.2 Shrub and Herbaceous Plant Reference Data

Land cover data for shrubs and herbaceous plants in the post-fire environment were collected by conducting field surveys in the burn scar in the summer of 2018. A 45 m by 5 m transect was established at each surveyed site, and species cover data was recorded within each transect.

Potential transects were identified using GIS and remote sensing. In order to control for the effect of variable burn severity on vegetation recovery, only areas with high burn

7 severity were considered for transects. These areas were identified by calculating the differenced normalized burn ratio (dNBR) using Landsat images captured in September 2006 and September 2007. Burn severity classifications were based on thresholds reported by Key and Benson (2006).

Plots needed to be accessible by the field crew without unnecessary risk, so areas farther than 150 meters from a well-maintained road or trail were not considered. Areas with slopes steeper than 30 degrees were also not considered. Slopes were calculated using a 30- meter digital elevation model (DEM) from the U.S. Geological Survey (2017). Even when these criteria were used to identify potential plot locations, a large number of transects were not safely accessible when conducting the field surveys. The 30-meter DEM vastly underestimated slope in many areas and did not resolve many narrow but impassable terrain features. Dense stands of chaparral were also impassable in some locations. Nonetheless, 82 transects were located and surveyed.

Different plant species exhibit different post-fire response strategies (Keeley et al.,

2006), so species composition is an important control on post-fire recovery behavior. In order to capture some of this variability in the field survey data, potential transects were stratified by species alliance using the CALVEG data. Based on the CALVEG classifications, 50% of the potential transects were dominated by mixed chaparral (e.g., Ceanothus spp.,

Arctostaphylos spp., and A. fasciculatum), 25% of the potential transects were dominated by

Quercus spp., and 25% of the potential transects were dominated by conifers.1 Even though

1 As noted above, areas classified as being dominated by conifers often had sparse canopy cover in reality. Thus, a majority of the land cover in these transects was actually shrubs and herbaceous species. None of the transects had dominant conifer overstories. 8 many of the potential transects were ultimately inaccessible, an effort was made to ensure that the completed transects reflected this stratification.

For practical reasons, it is often not possible to place transects so that they align exactly with image pixel boundaries when conducting field surveys. In order to address this, exact locations for the ends of the transects were not determined ahead of time. Instead, I identified 60 m by 60 m plots (two by two Landsat pixels) that exhibited homogenous recovery trajectories based on dNBR images from 2007-2018. The final locations for the transects were determined in the field once the field crew was able to examine the plots in person. The ends of the transects were allowed to be anywhere within the 60 m by 60 m homogeneous area. To the extent possible, transects were placed so that they had a consistent slope and aspect across the transect.

Species cover data within each transect was collected by mounting a digital camera with a remote trigger to a 5 m tall boom (Wonder Pole, Salem, Oregon, USA). The camera was adjusted so that the lower border of each image aligned with the transect tape and the upper border of the image extended 5 m downslope of the transect tape. Photographs were taken every 5 m along the transect tape, effectively creating a 45 m by 5 m photographic transect. Every plant species within the transect was identified and extensive notes were taken recording the locations of each species within the transect.

In order to estimate the fractional cover of each species within each transect, 42 pixels were sampled from each photograph (i.e., 420 pixels from each transect) using a regular sampling scheme. The species cover within each pixel was identified based on field

9 notes and manual interpretation. The proportion of pixels assigned to each species was used as an estimate of its fractional cover within the transect (Liu and Treitz, 2016; Liu, n.d.).2

The fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil were also estimated using the same method. These estimates were used to validate remote sensing products, as described below. Because these cover types vary at a finer spatial resolution than species cover, a larger number of pixels were sampled from each photograph.

100 pixels were sampled from each photograph, totaling 1,000 pixels for each transect. The proportion of pixels assigned to each cover type was used as an estimate its fractional cover within the transect.

2.2.3 Standing Dead Wood Cover Data

Many plant species with large amounts of woody biomass will leave skeletons of standing dead wood on the landscape after a fire (Appendix 2). More intense fires will burn larger diameter trunks and branches (Moreno and Oechel, 1989), so the amount of standing dead wood present after a fire is a function of pre-fire woody biomass and fire intensity.

Resprouting species such as Q. chrysolepis and Q. berberidifolia will produce new green vegetation at the base of these skeletons once the plants begin to recover. Preliminary analysis for this study revealed that standing dead wood was likely obscuring the view of green vegetation for nadir-viewing sensors, causing remote sensing analyses to underestimate the extent of green vegetation regrowth.

In order to quantify the magnitude of this effect, nadir-viewing photographs were taken directly over skeletons of standing dead wood (Appendix 3). A digital camera was mounted to a telescoping boom and the photographs were taken using a remote trigger

2 This process was completed using the “Photo Grid Classifier” software written by Nanfeng Liu. The software was operated by field technicians Anne-Marie Parkinson, Monica Goodwin, and Payton Thomas. 10

(Appendix 4). Q. chrysolepis skeletons were selected for this analysis because they consistently had a large amount of standing dead wood but were short enough that is was usually feasible to hold the camera over the center of the skeleton. Whenever possible, photographs were taken so that the entire skeleton was visible in the photo. Very tall skeletons were photographed by standing on a hillside adjacent to the plant and holding the boom horizontally over the crown (Appendix 5). Skeletons were selected randomly and opportunistically while trying to capture the entire range of skeleton sizes and heights.

Skeletons were selected from several different stands in different parts of the burn scar. The final sample of 58 skeletons was deemed to be representative of the skeletons on the landscape.

For each skeleton, the heights of the standing dead wood and the resprouting green vegetation were recorded. The diameter at breast height (DBH) of the largest trunk and the number of dead trunks stemming directly from the base of the skeleton were also recorded.

Each photo was cropped so that the boundaries of the photo aligned with the edge of the skeleton crown. Approximately 100 pixels were sampled from each photograph using a regular sampling scheme. Each pixel was manually labeled as dead wood, resprouting vegetation, other vegetation, or substrate. The proportion of pixels assigned to each cover type was used as an estimate its fractional cover within the crown. A 95% confidence interval for the mean fractional cover of dead wood was created by performing 100,000 bootstrapping iterations on the fractional cover data. This provided an estimate of how much land cover was obscured by woody skeletons in areas dominated by Q. chrysolepis and similar species.

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2.3 Remote Sensing

2.3.1 Imagery Data

This study utilized repeat-date Landsat images from 2000-2018. The Level 2 surface reflectance products for these images (path 42, row 36) were acquired from the U.S.

Geological Survey (Masek et al., 2006). All images but one were captured in September. The

2015 image was captured in August because there was no cloud-free imagery in September.

September imagery was selected in order to maximize vegetation senescence at all elevations across the burn scar. September is the end of the dry season in Santa Barbara County, so this helped minimize topographically-derived variability in phenology. All scenes were clipped to the extent of the burn scar before further processing. The 2000-2006 images were used to calibrate baseline metrics of variability in mature vegetation cover. Most of the burn scar had not burned in at least 50 years, so the pre-fire images represent mature vegetation. The 2007 image was captured after the fire had been contained but before it was technically declared extinguished. The 2007-2018 images were used to monitor vegetation regrowth inside of the burn scar.

This study also used imagery from the Airborne Visible Infrared Imaging

Spectrometer (AVIRIS) to build spectral libraries for spectral mixture analysis. AVIRIS is a

224 channel imaging spectrometer typically flown on an ER-2 (Green et al., 1998). While endmembers can be extracted directly from Landsat imagery, the higher spectral resolution of AVIRIS data makes it possible to identify narrow absorption features, which may reveal the contents of the pixel in more detail. AVIRIS flightlines have been flown over the burn scar regularly since 2013 as part of the HyspIRI airborne campaign (Lee et al., 2015). This study utilized five AVIRIS flightlines from August 2015 and June 2016. The five flightlines

12 covered the majority of the burn scar. The Level 2 surface reflectance products for these images were acquired from NASA (Thompson et al., 2015). Additional pre-processing was completed by Dr. Susan Meerdink (Meerdink, 2018). NPV and soil spectra were extracted from the August 2015 images. GV spectra were extracted from the June 2016 images. The spectral library development is described below.

2.3.2 Normalized Burn Ratio

The normalized burn ratio (NBR) and its variants are the most commonly used spectral indices for monitoring burn severity and post-fire vegetation regrowth. NBR is derived from Landsat imagery using the formula

푁퐼푅 − 푆푊퐼푅2 푁퐵푅 = 푁퐼푅 + 푆푊퐼푅2 where NIR is the near-infrared band and SWIR2 is the second shortwave infrared band (Key and Benson, 2006). The near-infrared band is sensitive to vegetation greenness, while the shortwave infrared band is sensitive to the presence of soil and NPV. The denominator helps control for variability in illumination. Positive NBR values indicate healthy vegetation while negative NBR values indicate the presence of exposed soil and char. NBR values near zero indicate the presence of senesced vegetation (Key and Benson, 2006).

Wildfires cause a significant decrease in NBR values inside of burn scars. The magnitude of this change is often used as a metric of burn severity and vegetation regrowth

(Key and Benson, 2006). This spectral index, known as the differenced NBR (dNBR), is calculated using the formula

푑푁퐵푅 = 푁퐵푅푝푟푒푓푖푟푒 − 푁퐵푅푝표푠푡푓푖푟푒 where NBRprefire can be the NBR value from a single date or a multi-date average.

Calculating NBRpostfire from an image captured immediately after the fire provides an

13 estimate of burn severity. Calculating NBRpostfire from subsequent images provides a metric of vegetation regrowth.

One shortcoming of dNBR is that its values are correlated with the amount of green vegetation present before the fire. Because NBR is sensitive to the amount of green vegetation in a pixel, the largest dNBR values will occur in pixels that had large amounts of green vegetation before the disturbance. As a result, dNBR trajectories are not directly comparable between vegetation patches with different amounts of pre-fire vegetation cover.

This trend was observed in the data for this study (results not shown). In order to address this, Miller and Thode (2007) proposed the relative dNBR (RdNBR), which is calculated using the formula

푑푁퐵푅 푅푑푁퐵푅 =

√|푁퐵푅푝푟푒푓푖푟푒/1000| where NBR values are scaled by 1,000. RdNBR addresses the shortcoming of dNBR by normalizing dNBR values by pre-fire vegetation cover. This is a more reliable metric for comparing disturbance and recovery patterns across disparate sites.

RdNBR was calculated using the 2007-2018 Landsat images. The median NBR value for 2000-2006 was used as NBRprefire for all calculations. This provided an RdNBR recovery trajectory for each pixel inside of the burn scar. Average RdNBR trajectories were computed for different vegetation types using the land cover reference data.

2.3.3 Spectral Mixture Analysis

Spectral mixture analysis (SMA) estimates the fractional cover of materials inside of a pixel (Roberts et al., 1993). While spectral indices such as NBR provide relative measures of vegetation regrowth, SMA can produce an absolute estimate of green vegetation cover. As

14 a result, SMA fractions are physically-meaningful values that can be directly compared with observations by field ecologists. This has obvious advantages for studies that integrate remote sensing and field ecology data. Fire effects also vary at a scale that is smaller than the spatial resolution of spaceborne sensors (Key, 2006). Spectral indices that assign a single value to a pixel do not capture this subpixel variability, but SMA can decompose the remote sensing signal in order to determine a pixel’s composition. As a result, SMA is more sensitive to the variability that occurs within individual vegetation patches after fires.

SMA assumes that the reflectance of a pixel at each wavelength is a linear combination of the reflectances of the materials inside of that pixel, weighted according to the fractional cover of each material (Smith et al., 1990). SMA estimates the fractional cover of different materials by decomposing the observed spectrum using reference spectra of pure materials, known as endmembers. The decomposition is based on the equations

푁

푆푖휆 = ∑ ƒ푘푖 ∗ 퐸푘휆 + 휀푖휆 푘=1

푁

∑ ƒ푘푖 = 1 푘=1 where 푆 is an observed spectral mixture for location 푖 at wavelength 휆. 퐸 is the set of 푁 reference endmembers, which are weighted by the fractions ƒ푘푖. The linear combination of endmembers that produces the smallest root mean squared error (RMSE) is selected as the final model for an observed spectrum. RMSE is calculated using the equation:

1 휆 2 2 (∑(휀푖휆) /푁) 푘=1

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Endmembers for SMA can be extracted from images or collected in situ using a field spectrometer. Field spectra can be captured at the leaf scale, branch scale, or stand scale.

Image spectra are typically stand scale measurements (Roberts et al., 2004). Endmember selection is an important step in the analysis process that determines the accuracy of the final product (Tompkins et al., 1997). In practice, most SMA analyses use a spectral library that consists of one GV, one NPV, and one soil endmember. A virtual photometric shade endmember with zero reflectance at all wavelengths is generally added to the library before unmixing. This enables SMA to model observed spectra that are proportionally darker than mixtures of endmembers because of illumination variability. Because the sum of fractional covers within each pixel must equal one, the fractional cover allocated to this virtual shade endmember can be proportionally reallocated to the other materials in the pixel to estimate subpixel land cover (Smith et al., 1990). This process is known as shade normalization.

For this study, though, I exploited shade fractions from “simple” SMA in order to monitor changes in vegetation structure. Put simply, shade fractions quantify the shadows being cast by objects within a pixel. If an object is significantly larger than its surroundings, it will cast large shadows. This reduces the illumination of the surrounding area and forces

SMA to increase the shade fraction in order to model the observed spectra. As a result, shade fractions are larger in areas where there is significant variability in vegetation height, such as forests and mature shrublands (Kane et al., 2008). Several studies have demonstrated that shade fractions are positively correlated with vegetation structure and aboveground biomass

(Adams et al., 1995; Roberts et al., 1997; Sabol et al., 2002; Lu et al., 2003; Kane et al.,

2008). Simple SMA was used for the shade analysis because multiple endmember spectral mixture analysis (MESMA, described below) selects endmembers in part based on their

16 brightness. Thus, MESMA effectively compensates for variation in illumination and makes it difficult to retrieve physically-meaningful shade fractions.

Simple SMA was performed on the Landsat imagery from 2000-2018 using the spectral library described below. SMA was completed using VIPER Tools version 2.0

(Roberts et al., 2019), an extension for ENVI (Harris Geospatial Solutions, Broomfield,

Colorado, USA). The shade fractions from each image date were extracted to create a shade trajectory for each pixel inside of the burn scar. Average shade trajectories were computed for different vegetation types using the land cover reference data.

2.3.4 Multiple Endmember Spectral Mixture Analysis

MESMA, another variant of SMA, allows the number and type of endmembers to vary by pixel (Roberts et al., 1998). The minimum and maximum allowable fractions for each endmember are constrained by user-defined thresholds. The maximum allowable RMSE is also constrained by a user-defined threshold; a pixel will not be modeled if the smallest possible RMSE exceeds that value. Because the number and type of endmembers varies by pixel, MESMA spectral libraries can contain multiple endmembers for each class and endmembers for classes that are not present across the entire scene. As a result, both the size of the spectral library and the number of potential models for each pixel can be very large. To address this, several techniques have been developed to select potential endmembers and choose the ones that form the best possible spectral library (Roth et al., 2012; Tane et al.,

2018).

For this study, MESMA GV fractions were used to monitor vegetation regrowth inside of the burn scar. The MESMA spectral library was developed using the process described below. Once the spectral library was finalized, it was convolved to the Landsat

17 spectral resolutions and used to unmix the Landsat imagery from 2000-2018. Endmember fractions were constrained between 0 and 1, shade fractions were constrained between 0 and

0.8, and the maximum allowable RMSE was 0.025 (Wetherley et al., 2017). All fractions were then shade normalized. The shade-normalized GV fractions were extracted for each image date in order to create a GV trajectory for each pixel inside of the burn scar. Average

GV trajectories were computed for different vegetation types using the land cover reference data.

The GV fractions for 2018 were validated using the estimates of green vegetation cover from the transect photographs. The remote sensing GV fractions were extracted from the locations where the transect photographs were taken, and the two sets of GV fractions were directly compared. This is one benefit of using spectral mixing models for ecology studies; this type of validation is not possible for spectral indices such as NDVI, dNBR, or

RdNBR.

2.3.5 Spectral Library Development

Separate spectral libraries were developed for MESMA and simple SMA (Figure 2).

MESMA requires a larger spectral library than simple SMA, so the MESMA spectral library was developed first. Endmembers for simple SMA were then selected from the MESMA spectral library. Potential endmembers were extracted from the AVIRIS images. CALVEG was used to identify areas dominated by different vegetation alliances. High resolution aerial imagery from Google Earth was also used to identify different land cover types. The goal of the MESMA analysis was to quantify the fractional cover of GV, NPV, and soil within each pixel, so potential endmembers were stratified across those classes. An ash class was also added to the MESMA spectral library for the 2007 image.

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Figure 2: Flowchart of the process used to develop spectral libraries for this study. 2.3.5.1 Endmember Extraction

GV spectra were extracted from the June 2016 flightlines in order to maximize vegetation greenness. They were selected from areas within the burn scar and stratified across six

CALVEG alliances representing different species and plant functional types: the Ceanothus chaparral alliance, the Chamise alliance, the Scrub Oak alliance, the Coast Live Oak alliance, the Bigcone Douglas-fir alliance, and the Black Cottonwood alliance. Extracting GV spectra from different vegetation types helped ensure that the GV spectra captured the spectral variability that exists across the landscape. Ultimately, however, the GV spectra were not

19 used to discriminate between vegetation types, so the spectral library did not need to represent every species present in the burn scar. GV spectra were manually selected on a pixelwise basis in order to maximize vegetation greenness and pixel purity. Approximately

15 spectra were selected from each flightline for each alliance, totaling 480 candidate GV spectra.

NPV spectra were extracted from the August 2015 flightlines in order to maximize vegetation senescence. They were selected from areas dominated by annual grasses and forbs according to CALVEG. Approximately 100 spectra were selected from each flightline, totaling 495 candidate NPV spectra. Soil spectra were also extracted from the August 2015 flightlines. They were selected from areas where the land cover was agricultural soil, streambed alluvium, or barren ground. Approximately 35 spectra were selected from each flightline for each land cover type, totaling 559 candidate soil spectra.

2.3.5.2 Iterative Endmember Selection

A preliminary set of endmembers was selected from the extracted spectra using iterative endmember selection (IES; Schaaf et al., 2011; Roth et al., 2012). IES first selects the pair of spectra that has the largest kappa coefficient when compared to the set of all extracted spectra. IES then iteratively adds and removes spectra from this selection in order to increase the value of the kappa coefficient. Endmember selection is completed when it is not possible to increase the value of the kappa coefficient by adding or removing any more spectra from the library. I implemented a version of IES that requires at least one spectrum from each class (or subclass) to be included in the output library (Roth et al., 2012). If no spectra from a class are selected, count-based endmember selection (Roberts et al., 2003) and

20 endmember average RMSE (Dennison and Roberts, 2003) are used to select the best possible spectrum within the class; the selected spectrum is added to the output library.

All spectra were categorized as GV, NPV, or soil. GV spectra were then classified by life form and then species. Soil spectra were classified by land cover type. All NPV spectra were collected from areas dominated by annual grasses and forbs, so they were placed into a single class. IES was forced to include at least one spectrum from each GV life form, one spectrum from each soil land cover type, and one NPV spectrum in the output library. This yielded an output library of 46 endmembers, including 26 GV endmembers, 17 soil endmembers, and 3 NPV endmembers. Every species of GV was included in the output library, even though the IES parameterization only required that every life form of GV be included.

2.3.5.3 Manual Endmember Selection

Preliminary unmixing using the IES endmembers revealed that many vegetated areas were being modeled by a single GV endmember (i.e., their shade-normalized GV fraction was 1). This occurred in vegetation types that typically don’t form closed canopies, which suggests that the endmembers themselves are mixed and are not pure representations of the class. In order to address this, all spectra were manually inspected and obviously mixed spectra were removed. Spectra that were frequently included in single-endmember models were also removed. In total, 21 endmembers (18 GV endmembers, 2 soil endmembers, and 1

NPV endmember) were manually removed from the library.

Many Landsat pixels containing soil and NPV were not being modeled by the

AVIRIS-derived endmembers because their spectra were brighter than the endmember spectra due to illumination variability. In order to address this, brighter spectra were

21 extracted from the WestUSA spectral library, which is a reference spectral library of plant species in the western United States that was created using a field spectrometer (Roberts et al., 2004). MESMA was used to determine which WestUSA spectra best modeled the

Landsat pixels that were not being modeled by the image-derived endmembers. One soil spectrum and two NPV spectra were selected from WestUSA and were added to the spectral library.

The September 2007 Landsat image was collected 12 weeks after the fire was ignited, so a large portion of the landscape was covered in white ash when the image was collected.

In order to model these areas, three ash endmembers were extracted from the August 2009

AVIRIS flightline, which was flown three months after the Jesusita fire in Santa Barbara

County, California (S. Peterson, unpublished data). An additional ash endmember was extracted from the 2007 Landsat image. The Landsat endmember helped ensure that the brightness of the ash endmembers was consistent with the brightness of the ash in the image.

Ash tends to be relatively short-lived on the landscape after wildfires (Pérez-Cabello et al.,

2012; Bodí et al., 2014), so the four ash endmembers were only included in the spectral library for the 2007 image.

The final MESMA spectral library contained 28 endmembers, including 8 GV endmembers, 16 soil endmembers, and 4 NPV endmembers. All six GV classes and three soil classes were represented in the final library. The four ash endmembers were also added to the library for the 2007 image. The final spectral libraries were convolved to Thematic

Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager

(OLI) spectral resolutions before unmixing.

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2.3.5.4 Simple SMA Spectral Library

Shade fractions from simple SMA account for the difference in brightness between the observed spectrum and the mixture of endmembers with the same spectral shape. Thus, the most pronounced shade signal occurs when the brightest endmembers comprise the spectral mixture model. For this reason, the brightest GV, NPV, and soil endmembers were selected from the MESMA spectral library to form the spectral library for simple SMA. This library was convolved to TM, ETM+, and OLI spectral resolutions before unmixing.

2.3.6 Characteristic Recovery Trajectories

Time-series data sets for RdNBR, GV fractions, and shade fractions were computed for the entire burn scar. The RdNBR data set included the Landsat image dates from 2007-

2018. The GV and shade fraction data sets also included the pre-fire years of 2000-2006. In order to determine whether different species or plant functional types exhibited distinct recovery trajectories, values for each remote sensing product were extracted from the locations associated with the two land cover data sets (i.e., the transects and the manually digitized conifer stands). For the conifer reference data, stand-level metrics were created by calculating the median pixel value of each stand. The stands were then grouped based on pre- fire and post-fire stand densities and the mean trajectories were computed for each group. For the shrubs and herbaceous plants, pixel values were extracted from the centroid of each 45 m by 5 m transect. The transects were then grouped by dominant species and the mean trajectories were computed for each group.

2.4 Environmental Modeling of Type Conversion

In order to determine whether environmental variables predicted conifer mortality during the fire, statistical models were developed to analyze the relationships between

23 environmental variables and the conifer reference data. Both topographic and climatological variables were considered for inclusion in the final model.

Topographic variables were derived from a 1/3 arc-second DEM acquired from the

U.S. Geological Survey (2017). The DEM was projected and resampled to a 10 m spatial resolution. It was used to compute elevation, slope, aspect, topographic wetness index (TWI), topographic position index (TPI), and annual insolation. TWI estimates soil moisture based on the slope of a location and the upslope area that drains through that location (Beven and

Kirkby, 1979). Gómez-Plaza et al. (2001) reported that TWI is less effective in semi-arid environments than it is in mesic environments because there are fewer water inputs and because evapotranspiration plays a larger role in controlling soil moisture. TPI was calculated as an alternative to TWI because it also models the accumulation of water on the landscape. TPI represents the difference in elevation between a pixel and its surrounding pixels. A positive TPI value indicates that the pixel is higher than the mean elevation of its surrounding pixels, and vice versa. For this study, the TPI of each 10 m pixel was calculated based on the mean elevation of the surrounding 50 m by 50 m area. Based on field observations, this scale captures the local topographic variability that affects water movement on the landscape. Annual insolation was calculated using ArcGIS (ESRI, Redlands,

California, USA). I also acquired 30-year average climatology data for January minimum temperature, August maximum temperature, annual mean temperature, and total annual precipitation (PRISM Climate Group, 2016; Daly et al., 2008). These data were provided in a gridded format with an 800 m spatial resolution. They were reprojected and resampled to the spatial resolution of the DEM.

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Binomial logistic regression was used to model conifer mortality. Post-fire canopy cover was predicted using pre-fire canopy cover and the environmental variables as potential explanatory variables. Post-fire canopy cover was recorded in binned classes, so it was converted to a pseudo-continuous variable by assigning each class the midpoint value of its bin. For example, the open canopy class included stands with 25-75% canopy cover, so it was assigned a value of 0.5 for the logistic regression. This made it possible to predict post- fire canopy cover using binomial logistic regression. Pre-fire canopy cover was treated as a categorical variable, which is how the data were originally collected. The mean values of the environmental variables were calculated for each conifer stand.

3 RESULTS

3.1 Land Cover Reference Data

Pre-fire and post-fire canopy cover were manually labelled for all 756 CALVEG stands dominated by the “Mixed Conifer - Pine” alliance (Table 1). Saplings could not be resolved in the imagery, so the classifications were based on the fractional cover of mature trees. Twenty-one stands were classified as having no canopy cover before the fire. Based on the high resolution aerial imagery, these stands appear to have been misclassified by

CALVEG and will be labeled as such in subsequent figures. The misclassified stands are not included in the calculations reported below.

In total, 254 stands (34.6%) experienced partial or complete canopy mortality as a result of the fire (i.e., they changed cover class). 481 stands (65.4%) did not change cover class. No stand had more canopy cover after the fire than it did before the fire.

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Table 1: Number of mixed conifer stands with different combinations of pre-fire and post-fire canopy cover based on manual interpretation of high-resolution aerial imagery. Pre-fire Canopy Cover

No Trees Sparse Canopy Open Canopy Dense Canopy

Dense Canopy n.d. n.d. n.d. 139

Post-fire Open Canopy n.d. n.d. 254 84 Canopy Cover Sparse Canopy n.d. 88 63 45

No Trees 21 15 21 26

Species cover data for shrubs and herbaceous plants were collected from 82 vegetation transects between June and August of 2018. Sixty-nine plant species and four other land cover types were observed within the transects. Grass species were not identified individually and were assigned to a single class. There were 12 dominant species of shrubs and herbaceous plants, defined as species that had a fractional cover greater than 25% in at least one transect (Jennings et al., 2009).

Principal components analysis (PCA) of the transect data revealed that the fractional cover of the dominant species often had low or negative correlations with the fractional cover of other dominant species (Figure 3). This was particularly true for the four most prevalent species: Q. berberidifolia, C. cuneatus, A. fasciculatum, and A. glandulosa. This suggests that these species tend not to compete with each other and may be diagnostic (sensu Jennings et al., 2009) of different vegetation alliances that exist across the landscape. For this reason, I selected these four species for further analysis. I also selected Q. chrysolepis and C. betuloides. Q. chrysolepis occurs as a shrub when it is young but as a tree when it is mature

(Minnich, 1980), so its canopy architecture is distinct from that of the other species. C. betuloides had very low mortality during the 2011-2017 California drought (Venturas et al.,

2016). The six selected species also have different functional responses to fire. Q.

26 berberidifolia, Q. chrysolepis, and C. betuloides are obligate resprouters that leave skeletons of standing dead wood on the landscape after fires. A. fasciculatum and A. glandulosa are facultative seeders. C. cuneatus is an obligate seeder (Minnich, 1980; Keeley, 1992; Keeley et al., 2006).

Figure 3: PCA biplot of species cover for species that were dominant (>25% cover) in at least one field survey transect. 3.2 Characteristic Recovery Trajectories

Three metrics can be used to quantify how a disturbance affects a given metric of change. Several recent studies have used this framework to quantify changes in vegetation spectral indices such as NBR and NDVI (e.g., Storey et al., 2016). These metrics are: the change between pre-disturbance values and the value immediately after the disturbance

(ΔVIshort), the change between pre-disturbance values and the value several years after the

27 disturbance (ΔVIlong), and the change between the value immediately after the disturbance and the value several years after the disturbance (ΔVIrecovery). When this notation is used, VI is the vegetation spectral index. Negative values indicate a decrease in the value of the index and positive values indicate an increase in the value of the index (Figure 4). By definition,

RdNBR does not have pre-fire values, so this notation is not used to describe the RdNBR trajectories.

Figure 4: Diagram of a hypothetical recovery trajectory demonstrating ΔVIshort, ΔVIrecovery, and ΔVIlong. I use this framework to quantify changes in vegetation spectral indices, but it can also be used to analyze other metrics of change after disturbance. For the green vegetation fractions, the median of the 2000-2006 values was used as the pre-fire baseline. The 2007 value was used to calculate ΔGVshort and the 2018 value was used to calculate ΔGVlong. The 2007 and 2018 values were used to calculate ΔGVrecovery.

The shade fractions for 2007 were anomalous because the landscape was covered in bright ash when the 2007 Landsat image was collected but the simple SMA spectral library did not contain an ash endmember. The simple SMA spectral library only contained GV,

NPV, and soil endmembers. Shade fractions represent the difference in brightness between

28 the observed and modeled spectra. If the number and types of endmembers vary by pixel, the inherent brightness of the mixtures will differ. Thus, the same endmembers must be used to model each image in order to ensure that the shade fractions are calculated from a consistent baseline. As a result, the 2007 shade fractions are not comparable with the other shade fractions, and they were not included in these calculations. The median of the 2000-2006 values was used as the pre-fire baseline. The 2008 value was used to calculate ΔShadeshort.

The 2018 value was used to calculate ΔShadelong. The 2008 and 2018 values were used to calculate ΔShaderecovery.

3.2.1 RdNBR Trajectories

RdNBR was inversely correlated with the severity of canopy loss following fire. In stands that experienced complete canopy mortality (n = 62), the mean 2007 RdNBR was

1,114. Partial canopy mortality leading to sparse (n = 108) or open (n = 84) canopies resulted in RdNBR values of 701 and 351, respectively. For stands that did not experience canopy mortality (n = 481), the mean 2007 RdNBR value was 302.

For stands that experienced complete canopy mortality (i.e., no mature trees survived), the RdNBR values decreased rapidly during the first four years of recovery and then more slowly after that. Other stands had more linear recovery trajectories. Mean 2018

RdNBR values were relatively invariant across different combinations of pre-fire and post- fire stand densities. The mean values for those groups only ranged from 150 to 272.

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Figure 5: Mean RdNBR trajectories for conifer stands with different combinations of pre-fire and post-fire stand densities. The stands with no canopy cover before the fire (bottom left subplot) appear to have been misclassified by CALVEG. They contain mixtures of shrubs, grasses, and barren ground. For the six dominant species of shrubs, mean 2007 RdNBR values ranged from 1,275 to 1,423. All six species recovered rapidly between 2007 and 2011, but recovery stalled between 2013 and 2017 (Figure 6). These dates coincided with a period of extreme drought

(Swain et al., 2018), suggesting that the drought delayed post-fire vegetation regrowth. Q. chrysolepis (n = 5 transects) generally had the highest mean RdNBR values throughout the time series, followed by A. glandulosa (n = 15 transects). The 2018 RdNBR values for those species were 366 and 285, respectively. C. betuloides (n = 2 transects) had the lowest mean

RdNBR values after 2011, suggesting that it was more resilient during drought than the other species. The 2018 RdNBR value for C. betuloides was 95. Trajectories for A. fasciculatum (n

30

= 19 transects), C. cuneatus (n = 7 transects), and Q. berberidifolia (n = 12 transects) had intermediate RdNBR values and were fairly similar throughout the time series.

Figure 6: Mean RdNBR trajectories for six common chaparral species. RdNBR trajectories were extracted from the locations of the field survey transects. The transects were grouped by dominant species, and the mean trajectory was calculated for each species. 3.2.2 Green Vegetation Trajectories

The GV fractions for 2018 were validated using the estimates of green vegetation cover from the transect photographs. The two sets of GV fractions showed strong agreement

(RMSE = 0.096), indicating that the MESMA model produced reliable estimates of green vegetation cover across the burn scar (Figure 7).

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Figure 7: Validation of remote sensing GV fractions from 2018 using the estimates of green vegetation cover from the transect photographs (n = 80, RMSE = 0.096). Generally speaking, GV fractions declined as a result of the fire between 2006 and

2007 and subsequently increased between 2007 and 2018 as vegetation regrew. Conifer stands that experienced partial canopy mortality had linear recovery trajectories, while stands that experienced complete crown loss had logarithmic recovery trajectories that approached asymptotes four to five years after the fire (Figure 8). Presumably, this regrowth signal was driven by shrubs or grasses and not trees.

The maximum possible canopy mortality occurred in conifer stands that had dense canopies before the fire and no trees after the fire. In those stands (n = 26), the mean value of

ΔGVshort was -0.92. In conifer stands that did not experience significant canopy mortality (n

= 481), the mean value of ΔGVshort was -0.20. GV fractions in 2018 were lower than the pre- fire baseline values across all combinations of pre-fire and post-fire stand densities. In stands that had dense canopies before the fire and no trees after the fire (n = 26), the mean value of

32

ΔGVlong was -0.24. In stands that that did not experience significant canopy mortality (n =

481), the mean value of ΔGVlong was -0.12.

Figure 8: Mean GV trajectories for conifer stands with different combinations of pre-fire and post-fire stand densities. The stands with no canopy cover before the fire (bottom left subplot) appear to have been misclassified by CALVEG. They contain mixtures of shrubs, grasses, and barren ground. The six shrub species had similar GV trajectories from 2000-2018 (Figure 9). The greenness of all six species fluctuated before the fire because of inter-annual variability in precipitation during the wet season. There were significant declines in vegetation greenness in 2002 and 2004, which were years that had abnormally low rainfall in Santa Barbara

County. Q. chrysolepis generally had the highest GV fractions before the fire.

Practically all green vegetation in the chaparral shrubland was destroyed by the fire.

Mean 2007 GV fractions for the six species ranged from 0 to 0.01. GV fractions increased

33 rapidly in the four years after the fire. Mean 2011 GV fractions ranged from 0.52 for Q. chrysolepis to 0.76 for C. betuloides. GV fractions declined after 2011 as a result of the drought. C. betuloides had the highest GV fractions during the drought years, while GV fractions for the other species were substantially lower. In 2018, C. betuloides still had the highest GV fractions (0.71), while A. fasciculatum had the lowest GV fractions (0.51).

Figure 9: Mean GV trajectories for six common chaparral species. GV trajectories were extracted from the locations of the field survey transects. The transects were grouped by dominant species, and the mean trajectory was calculated for each species.

3.2.3 Shade Trajectories

Shade fractions for the stands of mixed conifer forest increased slightly from 2006 to

2008 and then decreased steadily from 2008 to 2018. The mean value of ΔShadeshort for all stands (n = 735) was 0.04. This increase was relatively invariant across all combinations of

34 pre-fire and post-fire stand densities. The mean values of ΔShadeshort for those groups only ranged from 0.03 to 0.05.

Figure 10: Mean shade trajectories for conifer stands with different combinations of pre-fire and post-fire stand densities. The stands with no canopy cover before the fire (bottom left subplot) appear to have been misclassified by CALVEG. They contain mixtures of shrubs, grasses, and barren ground. Shade fractions for the conifer stands were correlated with canopy cover both before and after the fire (Appendix 6 and Appendix 7). As a result, stands that experienced significant canopy mortality had large declines in shade fractions after the fire. Thus,

ΔShaderecovery was proportional to the amount of canopy mortality that occurred in a stand, regardless of its pre-fire stand density.

In stands that did not experience canopy mortality, shade fractions returned to their pre-fire values but did not continue to decline. The mean value of ΔShaderecovery for those

35 stands (n = 481) was -0.04 and the mean value of ΔShadelong was 0.005. Stands that moved to the next lowest canopy cover class as a result the fire (e.g., open canopy to sparse canopy) had a mean ΔShaderecovery value of -0.08 (n = 162). Stands that moved down two canopy cover classes (e.g., open canopy to no trees) had a mean ΔShaderecovery value of -0.12 (n =

66). Stands that moved down three canopy cover classes, the largest possible decline, had a mean ΔShaderecovery value of -0.14 (n = 26).

Figure 11: Mean shade trajectories for six common chaparral species. Shade trajectories were extracted from the locations of the field survey transects. The transects were grouped by dominant species, and the mean trajectory was calculated for each species. The shade trajectories for shrubs were diametrically different than the shade trajectories for conifer stands: the fractions decreased significantly after the fire and then increased over time as vegetation regrew. This trend was most apparent in C. cuneatus, A. glandulosa, and A. fasciculatum, which are obligate and facultative seeders. The mean values of ΔShadeshort for those species were -0.07, -0.16, and -0.1, respectively. The mean values of

ΔShaderecovery were 0.02, 0.11, and 0.06. This trend was less pronounced for C. betuloides

36 and Q. berberidifolia, which are obligate resprouters that leave skeletons of standing dead wood after fires. For those species, the mean values of ΔShadeshort were -0.06 and -0.03, respectively. The mean values of ΔShaderecovery were 0.011 and 0.002.

Q. chrysolepis, an obligate resprouter that occurs as a tree when it is mature, was an exception to this trend. Its shade fractions increased slightly after the fire and then declined over time. The mean value of ΔShadeshort for Q. chrysolepis was 0.02, and the mean value of

ΔShaderecovery was -0.02. In this sense, the shade trajectories for Q. chrysolepis behaved similarly to the trajectories of the conifer stands. This suggests that the direction and magnitude of shade trajectories are sensitive to vegetation structure with trees behaving differently than shrubs. It is likely that the initial increase in shade fractions are caused by shading from tall standing dead wood and char on the landscape. The subsequent decline may be caused by the regrowth of shrubs and foliage and the loss of char over time. These hypotheses are discussed in more detail below.

3.3 Fractional Cover of Standing Dead Wood

Fifty-eight Q. chrysolepis skeletons were photographed to estimate the fractional cover of standing dead wood within their crowns. The height of the skeleton, the DBH of the largest trunk, and the number of dead trunks stemming directly from the base were recorded for all 58 skeletons. The height of the resprouting green vegetation was recorded for 50 of the skeletons. The mean fractional cover of standing dead wood was 0.25 (n = 58) with a bootstrapped 95% confidence interval of [0.219, 0.282] based on 100,000 bootstrapping iterations. Skeleton height (m) was the structural attribute that best predicted fractional cover of dead wood (Table 2). Vegetation height (m), DBH (cm), and the ratio of vegetation height

37 to skeleton height were also significant predictors. The number of trunks was not a significant predictor (not shown).

Table 2: Linear regression models that predict the fractional cover of standing dead wood within Q. chrysolepis crowns. Based on measurements of 58 Q. chrysolepis skeletons within the burn scar.

Dependent variable: Fractional Cover of Standing Dead Wood (1) (2) (3) (4) Skeleton Height 0.050*** (0.007) Vegetation Height 0.078*** (0.019) Height Ratio -0.405*** (0.112) DBH 0.011*** (0.002) Constant 0.017 0.038 0.487*** 0.106*** (0.035) (0.054) (0.067) (0.034)

Observations 58 50 50 58 R2 0.474 0.258 0.213 0.271 Adjusted R2 0.465 0.242 0.197 0.258 0.090 0.110 0.113 0.106 Residual Std. Error (df = 56) (df = 48) (df = 48) (df = 56) 50.535*** 16.680*** 13.005*** 20.830*** F Statistic (df = 1; 56) (df = 1; 48) (df = 1; 48) (df = 1; 56) Note: ***p < 0.01

3.4 Predictors of Conifer Mortality

Binomial logistic regression was used to predict the proportion of post-fire canopy cover using pre-fire canopy cover and several environmental variables as potential explanatory variables. The potential models were over-dispersed, so quasi-binomial models were used to ensure that the estimates for the standard errors were not distorted (Gelman and

Hill, 2007). Variable selection was largely guided by domain knowledge of the environmental processes that affect fire behavior and vegetation regrowth. Many of the

38 variables were mechanistically related. I selected a group of variables that were not mechanistically related in order to avoid multicollinearity and other unexpected interactive effects.

In order to select variables for the final model, principal components analysis was performed on the environmental variables to identify groups of variables that explained the same environmental processes. Across the entire burn scar, the first principal component was strongly influenced by variables that varied along an elevational gradient, including precipitation, mean temperature, and maximum temperature. The second principal component was strongly influenced by variables that were related to local topographic position, including TWI and TPI. However, the stands of mixed conifer forest only occur at the highest elevations within the burn scar, and the elevational gradient was not as influential on environmental conditions at that scale. At the scale of the conifer stands, three groups of environmental variables emerged: variables related to solar insolation, variables related to topographic position, and variables that varied along an elevational gradient. Variables within each group were generally moderately correlated with each other, so an effort was made to select variables from different groups in order to avoid multicollinearity.

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Table 3: Binomial logistic regression model of post-fire canopy cover. Insolation is scaled to units of 100,000 WH/m2 and maximum temperate is reported in degrees Celsius. Dense canopy is the reference level for pre-fire canopy cover.

Dependent variable: Post-fire Canopy Cover Sparse Pre-fire Canopy Cover -2.304*** (0.155) Open Pre-fire Canopy Cover -0.589*** (0.075) Insolation -0.222*** (0.025) Topographic Position Index -0.493*** (0.058) Maximum Temperature -0.127*** (0.029) Constant 6.754*** (1.055)

Observations 735 Note: ***p<0.01

The first group of environmental variables included slope, aspect (calculated as deviation from due south), and insolation. Because slope and aspect control insolation, insolation was selected for inclusion in the model. The second group included TWI, TPI, and minimum temperature. TWI models water availability based on the upslope area that drains through a location and the slope of that location. TWI values are especially high in gullies near the bottom of hillslopes. TPI is a similar metric that functions on a very local scale. It models water drainage and cold air pooling based on the elevation of a point relative to the elevation of its immediate surroundings. TPI is widely used in fire refugia literature (e.g.,

Bradstock et al., 2010; Leonard et al., 2014; Krawchuk et al., 2016). TWI and TPI behaved similarly in the model and explained similar environmental phenomena, albeit at different spatial scales. TPI was selected for inclusion in the model because it produces more intuitive

40 values that are easier for land managers and other stakeholders to interpret. The minimum temperature data were acquired from the PRISM Climate Group, which produces interpolated climatic data sets. The PRISM algorithm explicitly uses TPI to interpolate minimum temperature values because it models cold air pooling (Daly et al., 2008). In order to avoid unexpected interactions between minimum temperature and TPI, minimum temperature was not included in the model. The third group of environmental variables included elevation, precipitation, mean temperature, and maximum temperature. These variables all vary along an elevational gradient, although this relationship was weaker at the scale of the mixed conifer forests than it was across the entire burn scar. All four of these variables were tested in models that also included the pre-fire canopy cover, insolation, and

TPI. Maximum temperature was the only variable from this group that was statistically significant, so it was included in the final model.

Figure 12: Predictions of post-fire conifer canopy cover based on different values of pre-fire canopy cover, insolation, TPI, and maximum temperature. High, medium, and low values of insolation and maximum temperature are equal to the 75th, 50th, and 25th percentiles of the observed values, respectively. A TPI value of one means that the elevation of a stand is one meter above the average elevation of the surrounding 50 m by 50 m area.

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The final model included pre-fire canopy cover, insolation (x̅ = 13.35, s = 1.77), TPI

(x̅ = -0.13, s = 0.69), and maximum temperature (x̅ = 28.74, s = 1.45) as predictor variables

(Table 3). Insolation was scaled to units of 100,000 WH/m2. Maximum temperate is reported in degrees Celsius. This indicates that insolation, maximum temperature, and local topographic position were important determinants of conifer crown loss, as discussed below.

This model was used to predict post-fire canopy cover using different values of the predictor variables. TPI values for the predictions ranged from -1 to 1. Insolation and maximum temperature values were set to the 25th, 50th, and 75th percentiles of the observed values (low, medium, and high, respectively). All three classes of pre-fire canopy cover were included in the predictions (Figure 12).

Odds ratios were also calculated using the model coefficients (Table 4). Odds ratios represent the ratio of probabilities for two events with different interventions. An odds ratio of one indicates that the intervention does not change the modeled post-fire canopy cover. An odds ratio greater than one indicates that an intervention will increase modeled canopy cover, and an odds ratio less than one indicates that an intervention will decrease modeled canopy cover. Based on the results below, post-fire tree cover will decline by approximately 39% if the TPI increases by one. Likewise, a stand with sparse pre-fire canopy cover will have 90% less post-fire canopy cover than a stand with dense pre-fire canopy cover.

Table 4: Odds ratios for environmental predictors of post-fire canopy cover. Insolation is scaled to units of 100,000 WH/m2 and maximum temperate is reported in degrees Celsius. Dense canopy is the reference level for pre-fire canopy cover.

Variable Odds Ratio Sparse Pre-fire Canopy Cover 0.100 Open Pre-fire Canopy Cover 0.555 Insolation 0.801 Topographic Position Index 0.611 Maximum Temperature 0.881

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4 DISCUSSION

The RdNBR values and GV fractions indicated rapid vegetation regrowth in the first four years after the fire, followed by a decline in vegetation health because of the 2011-2017

California drought. The shade fractions revealed changes in vegetation structure during the

10 years after the fire. These results demonstrate that recovery trajectories derived from multi-temporal remote sensing data sets are sensitive to changes in the structure and composition of vegetation patches and yield valuable insights into post-fire vegetation regrowth. That being said, it is not clear that the recovery trajectories are distinct enough that the models can be inverted to retrieve patch characteristics without ground reference data.

4.1 Regrowth of Green Vegetation

Inter-annual and inter-specific variability in the RdNBR and GV trajectories was largely driven by sensitivity to environmental conditions. The greenness of all six species fluctuated before the fire because of inter-annual variability in precipitation. All six species regrew rapidly in the first four years after the fire, followed by a decline in vegetation health during the 2011-2017 California drought. In 2018, all six species had recovered to the levels of greenness seen in the dry years of 2002 and 2004, but had not recovered to levels of greenness seen in wetter years. C. betuloides was the most resilient species after the fire, especially during the drought. Q. chrysolepis and A. glandulosa had the poorest recovery compared to pre-fire conditions.

C. betuloides had the highest GV fractions and lowest RdNBR after 2011. C. betuloides is extremely drought tolerant and had low mortality during the 2011-2017 drought

(Venturas et al., 2016), which explains why its greenness was less sensitive to the reduced precipitation than the other species. This was the most severe drought event in California in

43 the last 1,200 years (Griffin and Anchukaitis, 2014). Anthropogenic forcing increased the severity of the drought and has increased the likelihood of future droughts (Williams et al.,

2015; Diffenbaugh et al., 2015). If severe droughts persist in California, they may begin to have a selective effect on the mortality and recruitment of plant species. More strongly drought-adapted species such as C. betuloides may become more prevalent across the landscape, especially if fires create the opportunity for widespread type conversion. The exact species transitions that occur would depend on the physiological responses of individual species to fire and drought, among other factors.

Q. chrysolepis generally had the highest GV fractions before the fire, but its GV fractions were similar to those of other species after the fire. Because of this, it had the highest RdNBR values from 2008-2018. Q. chrysolepis is an obligate resprouter that occurs as a shrub when it is young and as a tree when it is mature. The Zaca fire burned many mature stands of Q. chrysolepis, leaving large skeletons of standing dead wood on the landscape. The results presented in Section 3.3 reveal that approximately 25% of the ground area was covered by standing dead wood within Q. chrysolepis crowns. It is likely that the standing dead wood obscured the sensor’s view of resprouting green vegetation, causing the remote sensing model to underestimate vegetation regrowth. The pre-fire median GV fraction for Q. chrysolepis was 0.94, and the 2018 GV fraction was 0.6. Part of this difference might be attributable to standing dead wood, and not to poor recovery. The remote sensing model may have also underestimated the regrowth of other resprouters that leave skeletons, such as

Q. berberidifolia, A. fasciculatum, and C. betuloides. That being said, Q. chrysolepis is the only species that occurs as a tree when it is mature, so its skeletons have more vertical structure than skeletons from the other species. The results in Section 3.3 reveal that skeleton

44 height and the ratio of skeleton height to green vegetation height are significant predictors of ground area covered by standing dead wood. This suggests that Q. chrysolepis skeletons likely obscure more green vegetation than skeletons from the other species. This may explain why the remote sensing data indicated that Q. chrysolepis had poorer recovery than the other species compared to pre-fire conditions.

Extracting trajectories for individual species based on post-fire cover data also assumes that the species composition of vegetation patches did not change as a result of the fire. If the species composition did change, then the pre-fire species cover would have been different from what was observed during the field surveys. This would cause the pre-fire baseline values to be erroneous. This assumption likely holds true for obligate resprouters, which survive the fire and reemerge from underground plant tissue. It may not hold true for obligate seeders, which die during the fire and then recruit from dormant seed banks.

Facultative seeders can both resprout and recruit new seedlings from dormant seed banks.

While the distribution of obligate resprouters is unlikely to change as a result of the fire, the post-fire distributions of obligate and facultative seeders depend on the locations of seed banks at the time of the fire. Without pre-fire species cover data, it is difficult to determine where and to what extent the species composition changed as a result of the fire.

Even after the fire, the species composition of individual patches were not homogenous over time or space. Herbaceous plants and subshrubs dominate burn scars in the first years after a fire, but decline in prevalence as shrubs regrow (Keeley et al., 1981). The recovery trajectories were likely influenced by these transient species for the first 3-4 years after the fire. At the time of the field surveys, the species composition within each transect was also highly heterogeneous. Between 3 and 19 species were observed in each transect,

45 with a mean diversity of 8.4 species. Grass species were not identified individually and were assigned to a single class. While the transects were classified based on their dominant species, many non-dominant species undoubtedly influenced the recovery trajectories.

The field survey data also revealed that some dominant species occur in relatively homogeneous patches, while others occur more heterogeneous patches (i.e., Figure 3). Thus, our ability to attribute a trajectory to a single overstory species likely depends on how it relates to other species on the landscape. In the Zaca burn scar, for example, Q. berberidifolia, A. fasciculatum, C. cuneatus, and A. glandulosa effectively partitioned the landscape and tended to dominate relatively homogenous patches. Species such as Q. chrysolepis and Q. wislizenii were found in more heterogeneous patches with diverse overstories (Figure 3).

A similar challenge emerges when extracting trajectories for stands of mixed conifer forest. In stands without closed canopies, the remote sensing signal is strongly influenced by shrubs and herbaceous plants growing in the understory (Franklin, 1986). Thus, the recovery trajectories for conifer stands are influenced by both trees and understory plants. For stands that experienced complete canopy mortality, the recovery signal is entirely controlled by understory plants. This is seen in the shape of the recovery trajectories. Stands that experienced complete canopy mortality had logarithmic recovery trajectories that were qualitatively similar to the trajectories of the chaparral shrub species. The other conifer stands had relatively linear trajectories, presumably because surviving trees influenced the regrowth signal.

This highlights the need for compositional and structural homogeneity when characterizing the signal for different land cover types using multi-spectral or multi-temporal

46 remote sensing data. Countless remote sensing studies have extracted spectral signatures from imagery to train land cover classifiers. In this study, I assessed whether similar signatures could be extracted from multi-temporal data sets of vegetation indices. Extracting spectral or temporal endmembers from an area within an image implicitly assumes that the land cover of that area is compositionally and structurally homogeneous. This is not an easy assumption to test, and many studies rely on off-the-shelf land cover products (e.g.,

CALVEG) without the benefit of ground reference data. The field survey data from this study demonstrates that vegetation patches are, in fact, highly heterogeneous. Researchers should be cognizant of how this heterogeneity affects remote sensing signals, and they should be wary of false precision when classifying land cover or characterizing the spectral response of different land cover types.

4.2 Remote Sensing of Vegetation Structure

An alternative method of monitoring changes in vegetation structure using remote sensing is the use of shade fractions derived from spectral mixture analysis. Vegetation patches with large plants and variable canopy height will have high shade fractions. Thus, shade fractions might be more effective at detecting type conversion than indices of vegetation greenness. This is a novel application of a technique that has received relatively little attention in remote sensing literature.

Shade fractions for stands of mixed conifer forest were proportional to canopy cover both before and after the fire, and stands that experienced significant canopy mortality had large declines in shade fractions. Interestingly, the shade fractions did not decrease to their post-fire levels immediately after the fire. Instead, they increased slightly after the fire and then decreased slowly over a period of ten years. Because shade fractions were proportional

47 to canopy cover, stands that experienced more canopy mortality had larger declines in shade fractions during those ten years. Stands that did not experience canopy mortality returned to their pre-fire shade fractions.

I propose two explanations for why it took forest shade fractions several years to reach their post-fire levels. Conifers can experience delayed mortality after wildfires

(Franklin et al., 2006), so the full effect of the fire might not have been observed until the second or third year after the fire. Once the trees have been killed, standing dead wood from large conifers can take several years to fall. During field surveys in 2018, many dead trunks were still standing vertically in areas of canopy mortality (Appendix 8). Fallen trunks were also observed on the ground. The standing trunks cast shadows on the landscape, even after the trees have died. Presumably, shade fractions declined over several years because more dead trunks or branches fell each year. If this explanation is true, shade fractions will continue to decline after 2018 because many trunks had not fallen at the time of the field surveys.

Understory vegetation may also influence forest shade fractions. Many seeding shrub species germinated in the understory in areas of canopy mortality, forming large stands of shrubs with a relatively even age structure. As understory shrubs regrow, they often reach a closed canopy with relatively homogeneous height. This would reduce height variability in the understory and may cause the shade fractions to decline as shrubs regrow.

Shade fractions for the non-forested shrubland were also sensitive to vegetation structure. With the exception of Q. chrysolepis, shade fractions for the shrub species decreased after the fire and then increased over time as the shrubs regrew. This is likely because there was less standing dead wood left by the shrub species than the conifer species.

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Smaller diameter trunks will burn more easily during a fire (Moreno and Oechel, 1989). A. glandulosa, A. fasciculatum, and C. cuneatus had the largest declines in shade fractions after the fire. These species are obligate and facultative seeders whose woody biomass was mostly destroyed during the fire. Shade fractions for C. betuloides, Q. berberidifolia, and Q. chrysolepis did not decrease substantially after the fire. These species are obligate resprouters. Based on field observations, these species often left large skeletons of standing dead wood on the landscape. This may explain why these species had smaller declines in shade fractions after the fire. With the exception of Q. chrysolepis, shade fractions for the shrub species increased over time as their foliage and woody biomass regrew. Q. chrysolepis occurs as a tree when it is mature, and its shade trajectory mimicked that of the conifer stands.

Based on this, we can conclude that the direction and magnitude of shade trajectories are sensitive to vegetation structure after wildfires. This could potentially be leveraged to develop a historical record of type conversion using archival remote sensing imagery, even for locations that lack land cover data.

4.3 Type Conversion of Mixed Conifer Forests

Creating spatially explicit data of tree cover before and after the fire makes it possible to identify environmental predictors of conifer mortality and subsequent type conversion.

Binomial logistic regression was used to predict post-fire canopy cover using pre-fire canopy cover and several environmental variables as potential explanatory variables. The final model included four variables: pre-fire canopy cover, insolation, August maximum temperature, and

TPI. Pre-fire canopy cover was positively correlated with post-fire canopy cover, and it had the largest effect on the dependent variable. The three selected environmental variables all

49 model water availability on the landscape. Insolation and temperature control potential evapotranspiration (Campell and Norman, 1998). TPI models whether water accumulates in an area or flows away from an area based on whether it is higher or lower than its immediate surroundings. It also models cold air pooling on the landscape. All three environmental variables had negative correlations with post-fire canopy cover, which suggests that areas with more water availability experienced lower conifer mortality.

Trees with greater water availability likely had higher live fuel moisture at the time of the fire. Live fuel moisture is an important control on flammability and fire spread

(Dimitrakopoulos and Papaioannou, 2001) that can buffer trees from the most severe fire impacts. Higher live fuel moisture likely reduced conifer mortality in some areas. These variables may also control water availability to conifer seedlings during post-fire recovery

(Romme and Knight, 1981), although seedlings could not be resolved in the imagery used for this analysis.

These findings are consistent with qualitative observations during field surveys and manual image interpretation. Gullies and other topographic depressions appeared to serve as refugia for conifers in areas of widespread canopy mortality. Lower mortality was also observed on north-facing slopes, which have lower evaporative demand. Similar refugia have been documented after other fires and have been attributed to increased soil moisture, decreased evaporation, decreased wind exposure, and cooler air temperatures (Romme and

Knight, 1981; Bradstock et al., 2010; Leonard et al., 2014; Krawchuk et al., 2016).

Fire refugia play an important role in preserving ecosystem function during post-fire recovery. They protect seed sources and facilitate the regeneration of species that might otherwise be extirpated from the landscape. They also preserve compositional heterogeneity

50 within burn scars, which helps ensure plant and animal biodiversity (Meddens et al., 2018).

Conifer forests in southern California are especially vulnerable to extirpation because they occur in discrete patches that are separated by large areas of chaparral. Remnant seed sources are necessary for conifer patches to persist after fires because it is unlikely that seeds will be dispersed across wide swaths of shrubland.

The recovery of conifer stands to pre-fire conditions depends on the successful recruitment of conifer seedlings in areas of canopy mortality. Water availability to seedlings is an important determinant of post-fire recruitment success. Drought stress facilitates the type conversion of conifer stands to shrubland, and it is expected that anthropogenic climate change will exacerbate this trend (Stevens-Rumann et al., 2018). Odion et al. (2010) argued that chaparral shrubland and conifer forests are alternate stable states that are maintained by self-reinforcing feedbacks. They argued that conifer forests will only reemerge if there is a long interval between fires.

Seeding shrubs can also outcompete conifer seedlings in burn scars, potentially creating a positive feedback that accelerates type conversion to shrubland. During field surveys, we observed extensive patches of Ceanothus palmeri Trel. in areas previously dominated by conifers (e.g., Appendix 8). Previous studies have revealed that C. palmeri suppresses conifer regeneration in southern California burn scars (Goforth and Minnich,

2008; Franklin and Bergman, 2011). C. palmeri also reaches reproductive maturity faster than conifer seedlings (Goforth and Minnich, 2008; Bonner and Karrfalt, 2008), so it is unlikely that conifer species will regain dominance in the short term once C. palmeri has become established.

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In order to examine the relationship between conifer seedlings and C. palmeri, we conducted preliminary field surveys on four hillslopes where there was widespread conifer mortality. Two hillslopes were dominated by C. palmeri and did not have any conifer seedlings. One hillslope was dominated by C. palmeri and had established conifer seedlings.

One hillslope had established conifer seedlings and did not have any C. palmeri. We counted the whorls on randomly selected conifer seedlings in order to estimate their age and year of recruitment (Borchert, 1985). On the hillslope where both C. palmeri and conifer seedlings were present, the age structure of the conifer seedlings was relatively homogenous; successful conifer recruitment was restricted to the first few years after the fire. On the hillslope that had established conifer seedlings but did not have any C. palmeri, the age structure of the conifer seedlings was more heterogeneous; successful conifer recruitment occurred over many years following the fire. I hypothesize that C. palmeri began to outcompete the conifer seedlings once it became established on the first hillslope, preventing future conifer recruitment. C. palmeri reaches reproductive maturity after three growing seasons (Goforth and Minnich, 2008). This is consistent with our estimate of when conifer recruitment stopped at the first site. More robust field surveys are needed to validate these preliminary findings, so the quantitative results are not shown here. It was often impossible to identify the conifer species, so cone serotiny and other factors may have affected recruitment at these sites. I present this as an opportunity for future research.

The patches of C. palmeri may be creating a “landscape trap” – a paradigm in which feedbacks prevent an ecosystem from recovering from a disturbed state (Lindenmayer et al.,

2011; Meddens et al., 2018). The shrubs may also serve as ladder fuels in the event of a future wildfire, increasing the susceptibility of remnant conifer stands to crown fire and

52 hence fire-induced mortality. Based on these findings, it is clear that the remnant conifer stands are still vulnerable to extirpation, especially as anthropogenic climate change increases fuel aridity (Abatzoglou and Williams, 2016) and fire risk across the western

United States.

4.4 Implications for Management and Future Research

These findings have implications for management and restoration efforts after large wildfires in southern California. The combined disturbances of fire and drought will accelerate changes in ecosystem structure and function in the coming decades. While this study focused on type conversion from mixed conifer forest to chaparral shrubland, similar conversions can occur when exotic grasses invade chaparral ecosystems (D’Antonio and

Vitousek, 1992). Land managers must be cognizant of these changes when planning conservation efforts that protect biodiversity or other ecosystem properties. Certain species will be resilient to these changes, while others are vulnerable to extirpation. For example, our findings reveal that shrub species adapted to dry conditions, such as C. betuloides, are the most resilient of the shrub dominants we evaluated when drought occurs in the years after a fire. These species may become more prevalent if drought conditions persist. Likewise, our findings reveal that areas with increased water availability serve as refugia for conifers both during and after fires. These areas should be closely monitored because their resilience under changing environmental conditions will determine the long-term prognosis for the region’s mixed conifer forests. Fire supression efforts to protect these refugia would increase ecosystem resilience after future wildfires, although ecological considerations typically do not guide burned area emergency response (Meddens et al., 2018).

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These findings also inform the design of future remote sensing studies that analyze post-fire recovery. Species composition is an essential variable to study in order to monitor and understand changes in ecosystem structure and function after wildfires. While indices of vegetation greenness provide a general metric of plant cover, they fail to capture specific interactions between plant species and compositional shifts that can determine an ecosystem’s longterm functional response to disturbance. As a result, spectral indices such as

NDVI, dNBR, and RdNBR are of limited use when monitoring post-fire recovery at stand to landscape scales. This is a shortcoming of previous studies that have retrieved recovery trajectories inside of burn scars. MESMA represents an improvement over these indices because it is able to estimate subpixel land cover and can map plant species when applied to hyperspectral data (e.g., Roth et al., 2012). While hyperspectral sensors have generally been limited to airborne platforms, there are several planned or proposed spaceborne imaging spectrometers that will provide hyperspectral data with global coverage and relatively short revisit times. This will significantly improve researchers’ ability to map species cover and retrieve other ecosystem properties after wildfires.

Future studies might also develop innovative ways to map type conversion using existing remote sensing data sets. Recent literature has focused on retrieving post-fire recovery trajectories from multi-temporal data sets of common spectral indices (e.g., Storey et al., 2016). In this study, I extended that work by determining if individual species or plant functional types exhibited distinct recovery trajectories and determining what drove that variability. GV trajectories for the shrub species were similar, although Q. chrysolepis exhibited a distinct trajectory because of its canopy architecture and its retention dead trunks and branches.Likewise, C. betuloides exhibited a distinct trajectory because it is very

54 resilient during drought. There were also differences in the shapes of the recovery trajectories between shrubs and trees. Shrub species exhibited logarithmic recovery trajectories, while surviving conifers exhibited linear recovery trajectories. Shade fraction trajectories were sensitive to vegetation structure across the different species and functional types. Future studies could expand on this research by examining NPV trajectories, which were not considered in this study but could provide valuable insights into vegetation structure. They could also combine GV, NPV, and shade trajectories in a single multi-temporal data set, which might be more effective at distinguishing between species or retrieving other ecosystem properties.

5 CONCLUSION

Multi-temporal trajectories of spectral indices are often used to monitor ecosystem recovery after disturbance, although few studies have retrieved trajectories for different species or plant functional types. Plant species composition is an important control on ecosystem response to disturbance. This study combined remote sensing and field survey data to identify the sources of variability in post-fire recovery trajectories across these groups. Vegetation structure and sensitivity to environmental conditions are the dominant sources of variability in recovery trajectories. Compositional and structural heterogeneity also affected the recovery trajectories, although it may be harder to identify and control for this during remote sensing analyses. These findings may help develop novel methods of land cover classification based on multi-temporal data sets. This study also analyzed the environmental predictors of conifer mortality after the Zaca wildfire. Environmental variables that controlled water availability on the landscape were the best predictors of post- fire tree cover. Areas of water accumulation, such topographic depressions and gullies,

55 served as refugia for conifers during the fire. The sensitivity of these refugia to changing environmental conditions will play an important role in determining the long-term resilience of mixed conifer forests in southern California.

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APPENDICES

Appendix 1: Species discussed in this study. Nomenclature follows Baldwin et al. (2012). Abies concolor (Gordon & Glend.) Hildebr. Adenostoma fasciculatum Hook. & Arn. Arctostaphylos glandulosa Eastw. Calocedrus decurrens (Torr.) Florin. Ceanothus cuneatus (Hook.) Nutt. Ceanothus leucodermis Greene Ceanothus oliganthus var. oliganthus Ceanothus palmeri Trel. Cercocarpus betuloides Nutt. Eriogonum fasciculatum Benth. Hosckia crassifolia var. crassifolia Pinus coulteri D. Don Pinus jeffreyi Grev. & Balf. Pseudotsuga macrocarpa (Vasey) Mayr Quercus berberidifolia Liebm. Quercus chrysolepis Liebm. Quercus wislizenii A. DC.

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Appendix 2: A Q. chrysolepis plant inside of the Zaca burn scar. The plant has a skeleton of standing dead wood and green vegetation resprouting from its base.

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Appendix 3: A nadir-viewing photograph of a Q. chrysolepis plant inside of the Zaca burn scar. Fifty-eight Q. chrysolepis plants were photographed in this way to quantify the fractional cover of standing dead wood inside of their crowns.

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Appendix 4: The author photographing a small Q. chrysolepis plant using a camera mounted to a telescoping boom. Photo credit: Anne-Marie Parkinson.

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Appendix 5: The author photographing a large Q. chrysolepis plant by standing on a hillside adjacent to the plant. Photo credit: Anne-Marie Parkinson.

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Appendix 6: Jitter plot showing the 2006 shade fractions for 756 mixed conifer stands grouped by pre-fire canopy cover.

Appendix 7: Jitter plot showing the 2018 shade fractions for 756 mixed conifer stands grouped by post-fire canopy cover.

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Appendix 8: Trunks from dead conifer trees on the summit of Big Pine Mountain in 2018. The shrubs in the understory are primarily C. palmeri.

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