POSTFIRE REGROWTH TRAJECTORIES OF CHAMISE CHAPARRAL

BASED ON MULTI-TEMPORAL LANDSAT IMAGERY

_______________

A Thesis

Presented to the

Faculty of

San Diego State University

_______________

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

in

Geography

With a Concentration in

Geographic Information Science

_______________

by

Emanual A. Storey

Spring 2015

iii

Copyright © 2015

by

Emanual A. Storey

All Rights Reserved.

iv

ABSTRACT

Postfire Regrowth Trajectories of Chamise Chaparral

Based On Multi-temporal Landsat Imagery

by

Emanual A. Storey

Master of Science in Geography with a concentration in Geographic Information Science

San Diego State University, 2015

Assessments of postfire recovery outcomes for the chamise chaparral shrublands of

southern California provide a basis for land managers and ecologists to identify long-term

changes in this sensitive ecosystem. Postfire vegetation recovery assessments based on field-

plot vegetation sampling and aerial image analysis have proven to be limited in coverage and

inefficient for large areas of this landscape type. This study evaluates the potential of

remotely sensed regrowth trajectories based on multi-temporal Landsat 4, 5, 7, and 8 satellite

image observations for the postfire recovery assessment of chamise. Methods included: 1) an

a priori determination of postfire shrub fractional cover changes based on multi-date high

spatial resolution orthoimagery, 2) statistical testing to assess the sensitivity of regrowth

trajectories based on several spectral vegetation indices and applied metrics to the recovery

outcomes, and 3) an examination of regrowth trajectories which extend 19-29 years postfire

relative to field-based measurements from other studies.

Results provide a basis for interpretations about the sensitivities of the postfire

regrowth trajectories derived from Landsat surface reflectance data to changes in the shrub

matrix at various spatial and temporal scales. A primary finding was that several measures,

including the Regeneration Index and another proposed here which is termed the Scaled

Recovery Metric, enhanced the signals of postfire recovery derived from the multi-temporal

trajectories and increased their comparability. Findings indicate that several of the spectral

vegetation indices (NDVI, NDMI, NBR, and NBR2) were sensitive to long-term postfire

changes in chamise, and that these same indices were statistically significant indicators of

postfire recovery outcomes when certain metrics were applied. This study provides an

overview of some advantages, limitations, and technical considerations of deriving postfire

regrowth trajectories from Landsat imagery to assess postfire recovery outcomes of chamise.

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TABLE OF CONTENTS

PAGE

ABSTRACT ............................................................................................................................. iv

LIST OF TABLES ................................................................................................................... vi

LIST OF FIGURES ................................................................................................................ vii

ACKNOWLEDGEMENTS ................................................................................................... viii

CHAPTER

1 INTRODUCTION .........................................................................................................1

2 BACKGROUND AND LITERATURE REVIEW .......................................................4

Chamise Chaparral Fire Ecology .............................................................................4

Remote Sensing of Postfire Regrowth ...............................................................5

3 METHODS ....................................................................................................................9

Study Area ...............................................................................................................9

Data ....................................................................................................................9

Spatial and Temporal Data Sampling Approaches ....................................11

Metrics of Postfire Regrowth ...............................................................13

Analysis of Recovery Outcomes ....................................................15

Statistical Analysis and Interpretation .....................................16

4 RESULTS ....................................................................................................................17

Recovery Assessments Based On Orthoimagery ...................................................17

Regrowth Trajectory Sensitivity Analysis .......................................................21

Long-term Vegetation Changes and Comparison of SVI SVI

Sensitivities ................................................................................................26

5 DISCUSSION AND CONCLUSIONS .......................................................................29

Synthesis of Results and Findings .........................................................................29

Practical Context and Limitations ....................................................................31

Potential Applications and Future Work....................................................32

REFERENCES ........................................................................................................................34

vi

LIST OF TABLES

PAGE

Table 1. Accuracy assessment of orthoimage classifications. .................................................18

Table 2. Results from 1996 and 2012 orthoimage classifications of ten unburned sites .........19

Table 3. Analysis of variance (ANOVA) results ..................... Error! Bookmark not defined.

vii

LIST OF FIGURES

PAGE

Figure 1. Schematic depiction of regrowth trajectory metrics ................................................14

Figure 2. Map of the sites where multi-temporal Landsat data were extractedError! Bookmark not defined.

Figure 3. Example of growth form classifications. .................................................................20

Figure 4. Bar chart of postfire fractional cover change magnitudes and frequencies .............21

Figure 5. Selected regrowth trajectories from the regrowth trajectory sensitivity

analysis sites ............................................................................................................22

Figure 6. Scatter plot of coefficients of variance for the SVI- and RI-based

trajectories during postfire recovery and prefire phases .........................................23

Figure 7. Scatter plots depicting the SRM recovery metric based on NDVI-RI and

NBR2 relative to fractional cover changes at the RTSA sitesError! Bookmark not defined.

Figure 8. Averaged regrowth trajectories according to recovery class derived from

NDVI and NBR2 .....................................................................................................27

Figure 9. LTCA regrowth trajectories and field-based estimates of postfire recovery ..........28

viii

ACKNOWLEDGEMENTS

This Master’s thesis project was only possible with the many forms of academic and

personal support from a number of awesome people during the course of many years. I first

acknowledge the role of my mother, Florence Storey, who was responsible for my academic

education until age 17, and who has continued to support my endeavors during postsecondary

education and through many trials. The instructors who made extra efforts to impart valuable

knowledge and skills to me each enriched my educational background. Faculty at UNM who

may have taught me the most include: Grant Meyer, Paul Neville, Bruce Noll, Les

McFadden, John Geissman, and Laura Crossey. My senior thesis advisor, Gary Weissman,

mentored me during a crucial stage of my academic journey, and provided employment and

fellowship support when I had very limited means. I am very grateful to Dr. Garcia y Griego,

who mentored and supported me since 2010, first through a student internship and later by

giving me a staff position at UNM. He was a role model for me by demonstrating the

meaning of professional purpose and honorable conduct. Paul Neville, Gary Weissman, and

LM Garcia y Griego each wrote letters crucial to my acceptance to SDSU.

Dr. Douglas Stow has been the key supporter and collaborator for this Master’s

research. He has taught me an incredible amount about the art and science of remote sensing,

and invested many hours into the project to assist in its design and to edit my draft

documents. Most importantly, he has helped me to refine and organize my ideas, while

providing unwavering personal and logistical support. It was Dr. John O’Leary who

originally introduced me to the topic of postfire degradation in chamise chaparral, and later

served as a vital committee member for my thesis. I am grateful also for the assistance of Dr.

Tao Xie, who has supported this research by serving on the committee and providing his

insights. Staff at SDSU Geography were also key in the process by maintaining our facilities

and taking care of many practical things which are often unrecognized. My fellow students at

SDSU Geography have enriched my experience here and helped me in the most important of

ways, as supportive peers who have also brought empowering happiness into the experience.

1

CHAPTER 1

INTRODUCTION

Assessing rates and spatial patterns of vegetation recovery following wildland fire

events is important in shrubland communities that may risk mortality and possible type

conversion under certain fire and disturbance regimes. Chamise chaparral is a shrub

community that occurs mainly in southern California and is dominated by Adenostoma

fasciculatum, which has been observed to be non-resilient from fire in some areas (Zedler et

al. 1983; Haidinger and Keeley 1993; Lippitt et al. 2013). Processes of type conversion and

degradation in chamise may be linked to frequent and repeated burning (Zedler et al. 1983;

Haidinger and Keeley 1993; Lippitt et al. 2013), and exacerbated by drought during early

regrowth years and invasion by non-native plants (Keeley et al. 2009; Keeley and Brennan

2012). More information on the spatial extent, magnitude, and timing of postfire changes in

chamise would aid in understanding the causal mechanisms of its degradation more clearly.

Many studies on the topic of postfire recovery in chaparral have utilized repeated

field plot surveys for small areas (Zedler et al. 1983; Haidinger and Keeley 1993; Keeley et

al. 2005; Keeley and Brennan 2012), or observations based on visual field assessment and

mapping with the aid of aerial imagery at the stand scale (Lippitt et al. 2013). Field plot

sampling is not ideal for landscape-scale study of vegetation changes related to fire, due to its

limited spatial coverage and the requirement that fire occurs in areas that were already

surveyed to assess prefire vegetation cover. Assessment of postfire vegetation recovery based

on high spatial resolution aerial imagery is constrained by the costs and difficulties of image

processing and by the number of postfire observations (Wing et al. 2013). The archive of

Landsat 4, 5, 7, and 8 satellite imagery is potentially useful for assessing rates and large-scale

spatial patterns of postfire recovery in chamise, due to its multi-spectral coverage (six bands

in the visible, near infrared, and shortwave infrared portion of the electromagnetic spectrum),

2

30 m nominal spatial resolution, and 16-day potential observation interval spanning from

1984 to present.

A useful technique for assessing postfire vegetation recovery using multi-temporal

satellite imagery is to derive time-sequential regrowth trajectories based on spectral

vegetation indices (SVIs) (Riaño et al. 2002; Quayle et al. 2005; Hope et al. 2007; Roder et

al. 2008; van Leeuwen et al. 2010, etc.). This approach provides greater temporal observation

frequency than two-date image differencing (Fraser et al. 2011), and thus more reliable

estimates of the timing and magnitude of vegetation changes. More research is needed to

determine how per-pixel regrowth trajectories should be analyzed to identify and compare

recovery states within compositionally heterogeneous vegetation types such as chamise

chaparral. It also remains unclear how postfire recovery signals from various SVIs may differ

temporally in relation to the actual biophysical process of recovery. Additionally, the

sensitivity of Landsat SVI-based regrowth trajectories to subtle postfire changes (e.g., partial

die-off, degradation, or type-conversion) in low-biomass stands of chamise chaparral elicits

evaluation. In this study, Landsat SVI-based regrowth trajectories were derived for chamise

sites of known recovery outcomes in San Diego County, California. Field observations and

comparisons of prefire-postfire aerial imagery provided validation of recovery states and

estimates of shrub fractional cover changes. Results were used to explore the scientific

utility, limitations, and technical considerations of using regrowth trajectories and metrics

based on several Landsat SVIs to assess postfire recovery and type-conversion in chamise.

Improved techniques for assessing spatial-temporal patterns of postfire recovery in

chamise chaparral are needed to support studies of how fire regimes and other environmental

factors are linked to long-term landscape change. San Diego County is a viable study area to

conduct this research because chamise chaparral is widespread there, occurring within a

patch-mosaic of stands where type-conversions are known to have occurred in recent

decades. Multi-temporal remote sensing based on Landsat imagery may be an effective

technique for assessing postfire recovery in chamise at decadal time scales across large areas.

Further insight should be gained, however, as to the effective use of remotely sensed

regrowth trajectories for the detection and analysis of long-term postfire effects including

degradation and type-conversion.

3

Changes in chamise cover may be challenging to detect or quantify with Landsat SVI

imagery at certain scales because A. fasciculatum is a low-biomass shrub species, and is

admixed with other vegetation (e.g., drought deciduous coastal sage scrub) at the 30 m

Landsat pixel scale. Additional work is needed to determine how reliable and appropriate

regrowth trajectories derived from Landsat SVI image data are to infer degrees of postfire

resilience in chamise. Evaluation and comparison of postfire regrowth trajectories at sites of

known recovery outcomes, based on a variety of SVIs and metrics, may help to address these

problems.

The above knowledge requirements are rationale for the following research questions:

1. At which spatial scales are Landsat-derived SVI trajectories sensitive to postfire

fractional cover declines of shrubs in chamise chaparral?

2. Which SVIs and regrowth trajectory metrics provide the strongest indication of

postfire recovery outcomes?

3. How sensitive are SVIs to long-term postfire vegetation changes, compared to

recovery trajectories based on empirical field studies?

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CHAPTER 2

BACKGROUND AND LITERATURE REVIEW

Chamise Chaparral Fire Ecology

Chamise is a chaparral shrubland type characterized by dominance of the shrub

species Adenostoma fasciculatum. Chamise occurs within a patch mosaic of other chaparral

communities and vegetation types in southern California (USA) and in northern portions of

Baja California, Mexico. Chaparral species are primarily woody shrubs which produce

evergreen schlerophyllous leaves, growing 1.5 m to 4 m in height in closely-spaced stands

(Minnich 1983; Keeley 2000). Chaparral consists of more than 100 shrub species; as few as

one to as many as twenty individual species can occur in a single stand. Ecosystem

complexity and species richness in chaparral are linked to the special climate and

environment of the southern Pacific coastal region of North America (Keeley and Davis

2007). Chaparral in San Diego County occurs in a Mediterranean-type climate with

moderate, moist winters and hot, dry summer and autumn seasons. Average annual

precipitation mainly ranges from 350 mm to 950 mm, depending elevation and location

across the southeast-trending Peninsular Range (Pryde 2013).

Chaparral is highly resilient to fire and is often dominated by species that depend on

disturbance by fire for reproduction (Keeley et al. 2011; Keeley and Brennan 2012). Fire

enables recruitment between chaparral and neighboring vegetation types, and compositional

changes within stands can occur in the postfire environment (Keeley et al. 2011). Fire return

intervals (FRIs) of 50 to 100 years characterize the historic fire regime in chaparral, but more

frequent fires have been observed in the second half of the 20th

century (Keeley et al. 1999).

Responses of shrub species to fire and to changes of fire regime depend strongly on

their reproductive strategies (Hanes 1971). A. fasciculatum regenerates after fire via

seedlings and by resprouting from underground lignotubers. Seedling regeneration may not

occur when this species has insufficient time between fires to attain reproductive maturity

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and replenish seed banks (Keeley et al. 2009), which may require a decade or more (Keeley

and Brennan 2012). Vegetation surveys indicate that A. fasciculatum cover decreased

substantially following a 2-year FRI compared to other shrub species (Zedler et al. 1983).

Seedling establishment following fire decreased substantially at sites that burned more than

once within six years (Haidinger and Keeley 1993). Similar findings indicate that A.

fasciculatum cover decreased substantially when assessing vegetation recovery at field plots

which burned with FRIs of 3 and 4 years (Keeley and Brennan 2012). At the landscape scale,

Lippitt et al. (2013) found that chamise stands showed the highest rates of degradation and

type-conversion (to herbs and coastal sage scrub (CSS)) in areas with FRIs of one to five

years. In the Lippitt et al. (2013) study, chamise recovery classes were defined as

‘unchanged’, ‘altered’, or ‘converted’, based on postfire fractional cover changes of

vegetation growth forms (shrubs, subshrubs, and herbaceous vegetation) compared to

vegetation cover defined by the prefire vegetation community mapping rules (Holland 1986).

The persistence and spatial extent of type-conversion in chamise is debated, however,

due to the small spatial scales and time-spans of postfire observation in previous studies (e.g.,

Zedler et al. 1983; Keeley and Brennan 2012; Lippitt et al. 2013), and uncertainty about the

influences of site factors on recovery (Meng et al. 2014). Type-conversion events are most

prevalent at low-elevation sites, and are suspected to occur within small areas rather than

across entire stands (Meng et al. 2014). Postfire observational timing and duration may be

equally important as spatial scale for the valid detection of type-converted areas. The most

substantial and rapid regrowth of A. fasciculatum occurs during the first five years following

fire and begins to plateau around 10-15 years postfire, according to field sampling (Horton

and Kraebel 1955; Keeley and Keeley 1981). The agreement between this estimate of

recovery timing and that of Hope et al. (2007) suggests that remotely sensed multispectral

imagery may be sensitive to most of the lateral expansion and height increase that occurs

during postfire regrowth of chamise.

Remote Sensing of Postfire Regrowth

The Normalized Difference Vegetation Index (NDVI) (Rouse et al. 1974) is a widely

used proxy for vegetation condition and abundance based on remotely sensed satellite

imagery, because of its strong relationship with above-ground biomass and foliar greenness

6

for a variety of vegetation types (Viedma et al. 1997; Henry and Hope 1998; van Leeuwen

2008; Clemente et al. 2009; Lhermitte et al. 2010):

NDVI = NIR − Red

NIR + Red (1)

where NIR is the near infrared band, and Red is the red band digital number, radiance or

reflectance value, recorded by electromagnetic radiation sensors. The NDVI is sensitive to

photosynthetic activity within ground resolution elements (associated with pixels), based on

the differential light absorption of chlorophyll in red and near infrared wavelengths (Glenn et

al. 2008). The NDVI has been related more closely to field-based estimates of postfire

vegetation cover in Mediterranean-type ecosystems than soil-adjusted vegetation indices

(SAVIs), due to their inability to account for variations in soil background brightness based

on single coefficients (Huete 1988; Baret and Guyot 1991; Qi et al. 1994; Clemente et al.

2009; Vila and Barbosa 2010). However, because chaparral stands also contain

heterogeneous mixtures of natural elements (green and dead foliage, branches, litter, and

shade) (Peterson and Stow 2003), different SVIs may each provide useful information and

have not been compared for their sensitivity to postfire changes in chaparral. The SVIs that

utilize shortwave infrared (sometimes called mid-infrared) wavelengths may support longer

detectible periods of postfire vegetation change than indices that rely only on visible and near

infrared wavelengths (Carreiras et al. 2006; Chen et al. 2011).

In determining the appropriate remote sensing data for postfire vegetation assessment,

a general trade-off exists between spatial resolution (detail) and temporal resolution

(frequency of image acquisitions) (Gitas et al. 2012). Two readily available sources of multi-

temporal satellite imagery spanning several decades (preceding the year 2015) are from

Advanced Very High Resolution Radiometer (AVHRR) and the Landsat sensors (Multi-

spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+),

and Operational Land Imager (OLI)). Although coarse resolution (250 m – 1 km) imagery

such as from NOAA AVHRR and Terra/Aqua MODIS systems is suitable to capture

widespread vegetation changes in the landscape, Landsat imagery provides spatial grains (30

– 90 m) which are more consistent with the scale of most localized vegetation changes (Skole

and Tucker 1993; Tucker and Townshend 2000; Wulder et al. 2008; Masek et al. 2013).

Landsat platforms also have ~16-day revisit intervals, providing greater temporal coverage

7

than high resolution aerial image archives (Masek et al. 2008; Hansen et al. 2010). Images

from Landsat 4-5 TM, Landsat 7 ETM+, and Landsat 8 OLI together provide a 30 year

archive of imagery of consistent multispectral band coverage and spatial resolution (30 m).

The advent of preprocessed Landsat imagery in surface reflectance form by the United States

Geologic Survey (USGS) greatly facilitates multi-temporal land change research that

previously required computationally intensive routines.

Multi-decadal remote sensing archives are useful for understanding processes of

vegetation canopy cover changes, which operate at decadal or sub-decadal time scales

(Schleeweis et al. 2012). Inter-annual postfire regrowth trajectories have been derived for

Mediterranean shrublands, using pixel-aggregated Landsat time series based on spectral

mixture analysis (Roder et al. 2008). Regrowth trajectories were represented by linear

regressions, the asymptotes of which indicated recovery levels by comparison with prefire

growth trajectories. Single pixel-tracking was used to assess the spatial distribution of

vegetation increase rates, average cover, and inter-annual variability during regrowth (Roder

et al. 2008). Pixel-tracking also provides an increased signal-to-noise ratio as compared to

two-date change detection (Fraser et al. 2011). Additionally, the complicating effects in inter-

site differences on vegetation growth are reduced by pixel-tracking (Hope et al. 2007;

Narasimhan and Stow 2010), in contrast to the chronosequence method used by Peterson and

Stow (2003) that results in substantial uncertainty due to the effects of inter-site variability.

A major challenge in using multi-temporal satellite data to track postfire regrowth

trajectories is that the magnitude of image brightness values are influenced by inter-annual

meteorological variability and differences in image data acquisition and processing (Diaz-

Delgado et al. 1998), as well as solar illumination and atmospheric optical effects. To reduce

the contribution of grasses and suffrutescent plants to SVI values for chaparral, researchers

have selected images from dry months of August and September when those plant types are

largely senescent (Henry and Hope 1998; McMichael et al. 2004; Hope et al. 2007).

Normalization based on control plots (that remain unburned during a study period, and are

mature at the time of fire in the burned plots) has proven effective in suppressing inter-annual

precipitation effects for the purpose of postfire regrowth analysis (Diaz-Delgado et al. 2002;

Diaz-Delgado et al. 2003; Idris et al. 2005; Li et al. 2008). One such approach is based on the

normalized Regeneration Index (RI) which has the following form:

8

RI𝑆𝑉𝐼 = SVIburned

SVIcontrol , (2)

where 𝑆𝑉𝐼𝑏𝑢𝑟𝑛𝑒𝑑 is the spectral vegetation index (SVI) for a burned plot and 𝑆𝑉𝐼𝑐𝑜𝑛𝑡𝑟𝑜𝑙 is the

SVI for an unburned control plot, each extracted from the same image and same vegetation

type. Broad scale application of this technique is limited by the difficulty of selecting

relevant control plots (Gitas et al. 2012). In addition, chaparral exhibits spatially disrupted

patterns of NDVI during dry periods (Hope et al. 2007), which may introduce uncertainty to

the RI normalization approach and to the interpretation of growth trajectories. Lhermitte et

al. (2010) suggested the use of unique associations between burned and unburned areas,

based upon temporal correlation of NDVI values in a prefire year and upon spatial proximity.

The applicability of RI normalization may be limited for burned sites (near the middle of

large fire scars) that lack unburned neighbor sites, and by land cover differences or

heterogeneity in the unburned landscape matrix (Gitas et al. 2012).

Visual analysis based on high spatial resolution aerial imagery enables the selection

of study sites and verification of vegetation cover and may be integrated with coarser-

resolution imagery to derive fine-scale estimates of land cover changes at broad scales.

Vegetation growth forms (e.g., trees, shrubs, subshrubs, suffrutescents and herbs) have been

mapped in Mediterranean-type ecosystems using high spatial resolution aerial imagery

supplemented by field observation (Shoshany 2000; Schneider et al. 2009; Lippitt et al.

2013). Two-date, growth form classifications from orthoimagery provided estimates of

postfire changes in chaparral shrub cover at the 2003 Cedar Fire site (Rachels et al. 2014).

The potential of using multi-date growth form maps derived by classification of

orthoimagery to interpret and assess the responses of Landsat SVI-based regrowth

trajectories to postfire resilience of chamise has not been explored.

9

CHAPTER 3

METHODS

Study Area

The study area is the less developed central zone (running north-south) of greater San

Diego County, California, USA. This county has a total area of 11,720 km², containing

varied topography and natural land surface features along the ~100 km east-west transect

which spans from the Pacific coastline to the Laguna Mountains, reaching the Sonoran

Desert near its eastern extent. Approximately 740 km² of chamise chaparral shrublands occur

across the eastern (less developed) two-thirds of the county, as classified under the Holland

(1986) code. Chamise in San Diego County occurs mainly on xeric, south- and west-facing

slopes in low-elevation (100 m ASL) to mid-elevation (1250 m ASL) areas within a patch

mosaic of other chaparral communities and vegetation types. Mean annual precipitation

received by chamise chaparral ranges from 250 mm to 650 mm (at high-elevation sites).

Approximately 93% of the mean annual precipitation occurs between the months of October

and April, which are followed by long, dry summer seasons characteristic of the

Mediterranean-type climate.

Data

Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images for WRS path 40/row 37

were obtained through the USGS Land Surface Reflectance Climate Data Record (LSRCDR)

(http://espa.cr.usgs.gov/). These image products obtained from USGS are in surface

reflectance form, based on Level-1 Landsat 5 TM and Landsat 7 ETM+ images (using

Landsat Ecosystem Disturbance Adaptive Processing System) and Landsat 8 OLI images

(using the L8SR algorithm) through radiometric calibration and correction for atmospheric

and illumination effects. Landsat 4-5 TM images are the primary data source for this study,

and were the only satellite image data from 1984 to 2011 (the archival period of the Landsat

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TM sensors) that were used, in order to maximize sensor consistency. Because Landsat 7

ETM+ images contain no-data pixels from the scan line corrector offset malfunction after

2003 (cf. Storey et al. 2005), they were used only for the year 2012 when they were the only

available source of Landsat data. Landsat 8 OLI images were used for years 2013 and 2014.

Time series of annual single-date images acquired during late August or early

September were used to derive postfire regrowth trajectories. The August-September season

is suitable for inter-annual remote sensing of shrub growth because admixed grasses which

proliferate in spring and early summer, suffrutescents, and subshrubs associated with coastal

sage scrub, are generally senescent during the dry season of late summer (McMichael et al.

2004; Hope et al. 2007). A total of 30 images were selected for the time period 1984-2014

based on the above criteria. Landsat datasets obtained through LSRCDR also include

nominal cloud-masking images derived from the Fmask software algorithm (Zhu and

Woodcock 2012), which were used to remove cloud-affected pixels from five of the Landsat

images. The other 25 Landsat images were cloud-free in the areas of interest. The LSRCDR

also provided the following SVI data in pre-processed form: Normalized Difference

Vegetation Index (NDVI) (Rouse et al. 1974), Enhanced Vegetation Index (EVI) (Huete et

al. 1999), Soil-Adjusted Vegetation Index (SAVI) (Huete 1988), Modified Soil-Adjusted

Vegetation Index 2 (MSAVI2) (Qi et al. 1994), Normalized Difference Moisture Index

(NDMI) (Hardisky et al. 1983), Normalized Burn Ratio (NBR) (Key and Benson 1999), and

Normalized Burn Ratio 2 (NBR2) (Key and Benson 2006).

High spatial resolution orthoimagery from USGS (earthexplorer.usgs.gov) was used

to visually assess vegetation growth form distributions and to estimate postfire fractional

cover changes. A set of 1 m spatial resolution, three band (G-R-NIR) color infrared (CIR)

digitalized aerial photographs (acquired September 1996) supported prefire vegetation cover

assessment. A set of 0.5 m spatial resolution, four band (B-G-R-NIR) digital orthoimage tiles

(acquired May 2012) provided information on recent (postfire) vegetation cover. More recent

(2013) high resolution three band (B-G-R) satellite imagery of 0.3 m spatial resolution, a

seamless DigitalGlobe product displayed by ArcGIS Imagery Only Base Map (IOBM),

supported study site selection based on preliminary, visual assessment of vegetation features.

Vegetation community type maps (produced in 1995) which follow the Holland (1986)

classification code were used to delineate chamise chaparral areas (http://www.sandag.org/).

11

Historic fire perimeter maps were obtained from California Fire Resource and Protection

(FRAP) (http://frap.cdf.ca.gov). Overlay of historic fire perimeter and vegetation community

type maps provided a means of determining the appropriate stands and time periods from

which to select analysis sites.

Spatial and Temporal Data Sampling Approaches

Areas of interest (AOIs) were selected as spatial subsets for which growth form

classifications, and postfire regrowth trajectories based on the multi-temporal Landsat SVI

image data were derived. The initial phase of the AOI selection process involved the overlay

of historic fire perimeter and vegetation community maps in ArcGIS 10.2, in order to

delineate stands of chamise chaparral by most recent burn year and prefire burn history. After

the chamise stands of interest were selected, a process of selecting hectare-scale AOIs based

on several phases of orthoimage analysis (described below) was conducted.

Stands of chamise that burned after 1996 were selected to enable prefire shrub canopy

cover assessment based on visual interpretation of high spatial resolution CIR orthoimagery

(first acquired in 1996). Stands with fire-free periods of ten years or more preceding the first

fire in each study period were necessary in order to ensure that substantial maturation of the

shrubs occurred by the time of prefire canopy cover estimation. Based on the same rationale,

stands that had not burned since 2003 were considered suitable for recovery assessment using

2013 IOBM imagery. Comparison of postfire shrub fractional cover changes (based on

orthoimagery) with postfire regrowth trajectories from Landsat SVIs was termed a regrowth

trajectory sensitivity analysis (RTSA). In order support a long-term change analysis (LTCA)

based on the regrowth trajectories, a second set of chamise stands were delineated that last

burned during the time period 1985-1994. A minimum of fifteen years of postfire regrowth

was suitable in order to assess how Landsat SVI-based trajectories differ longitudinally, and

how they compare to field-based estimates of postfire recovery timing (Horton and Kraebel

1955, Keeley and Keeley 1981).

The RTSA and LTCA stands were screened by visual inspection of IOBM imagery,

in order to mask out areas that contained human development features such as roads,

buildings, and land cover changes. For the RTSA stands, visual comparison of prefire (1996)

orthoimagery with postfire (2013 IOBM) orthoimagery was conducted in order to identify

12

every possible site that seemed to exhibit postfire decline of shrub canopy cover. Color

infrared (2012) orthoimagery was then acquired for these potential AOIs in order to support

vegetation feature interpretation and growth form classifications. Following re-inspection

based on the 2012 imagery, the number of potentially degraded AOIs was reduced from 261

to 55, because the high spatial resolution of the IOBM imagery revealed much more exposed

soil/rock cover between shrub canopy gaps than the 2012 or 1996 orthoimagery. The spatial

resolution of the 1996 orthoimagery (1 m) was more similar to that of the 2012 orthoimagery

(0.5 m) than the IOBM imagery (0.3 m), thus providing a more viable comparison of prefire-

postfire vegetation features. Only sites where vegetation changes were identified by

comparing the 1996 and 2012 orthoimagery were selected. The 55 AOIs exhibiting postfire

degradation were all located in areas which burned in 2003; while no such sites were

identified in areas that burned between 1997 and 2002. For comparison, 19 additional sites

that appeared recovered were selected from areas near to the degraded sites.

Following general identification of the 74 AOIs associated with the RTSA, square

polygons were delineated for each AOI to coincide with 3 x 3 arrays of 30 m Landsat pixel

elements, representing plots of ~0.81 ha. All Landsat data used in this study were aggregated

as average values of pixels within each AOI, following the rationale that errors caused by

differences in geometric registration of pixel elements between images are reduced by

sampling from larger areas (cp. Hope et al. 2007). The RTSA AOI polygons were also used

to extract subsets from the 1996 and 2012 orthoimages. This approach allowed comparison

of Landsat SVI-based postfire regrowth trajectory data with detailed estimates of prefire-

postfire shrub fractional cover changes (SFCC) based on the CIR orthoimagery (procedures

described below). A separate set of AOIs were used to delineate subsets from Landsat SVI

images for the LTCA component of this study. The LTCA did not include validation of

recovery outcomes, as high spatial resolution (color-infrared) orthoimagery is not available

for the 1984-1993 time period.

In order to reduce the effects of inter-annual precipitation variability on Landsat SVI-

based postfire regrowth trajectories of the RTSA and LTCA AOIs, normalization based on

control sites was implemented. Control sites are Landsat pixel samples selected from older

(not recently burned) chamise stands that are proximal to postfire recovery analysis sites.

Selecting proximal sites for the RTSA AOIs was challenging given the constraints posed by

13

the large perimeters associated with the 2003 fires. Several control sites were selected from

unburned chamise areas within the 2003 fire perimeters, which were identified based on a

Landsat 5 (TM) NBR image acquired 19 November 2003, and resulted in less inter-annual

variability in the regrowth trajectories than control sites farther away. Factors for selecting

control sites also included size of the stand, shrub canopy homogeneity, and similarity of

prefire Landsat NDVI values between analysis and control sites.

Metrics of Postfire Regrowth

Regrowth trajectories were derived for each of the AOIs, based on the multi-temporal

Landsat SVI values. One set of trajectories was based on the original SVI values, and a

separate set was based on SVI values that were normalized according to unburned control

sites at each image observation (i.e., the RI). Potentially meaningful postfire recovery

information is represented by differences between the SVI or RI median value for the prefire

years, the first postfire value, and the asymptotic value of the postfire regrowth trajectory, as

depicted in Figure 1. Based on the prefire median (RI or SVI) index values (𝐼𝑝𝑟𝑒𝑓𝑖𝑟𝑒) and

postfire asymptote index values (𝐼𝑟𝑒𝑔𝑟𝑜𝑤𝑡ℎ) two basic recovery metrics were implemented:

the Difference Recovery Metric (DRM) and the Ratio Recovery Metric (RRM):

DRM = Iregrowth − Iprefire (3)

RRM =Iregrowth

Iprefire (4)

The DRM represents an arithmetical subtraction, associated with letter c in Figure 1. The

RRM represents the ratio of a postfire (RI or SVI) asymptote value to a prefire median value

and is proportional to c from Figure 1, for a given site.

Differences in the prefire median SVI values or RI values (not always 1.0) and

differences in range between the index values associated with first postfire observations

(𝐼𝑏𝑢𝑟𝑛𝑒𝑑) and the 𝐼𝑝𝑟𝑒𝑓𝑖𝑟𝑒 values of the RTSA sites, however, may limit the comparability of

the sites based on the DRM and the RRM. To account for such differences a normalized

version of the DRM, termed the Scaled Recovery Metric (SRM), was formulated and

evaluated:

SRM =Iregrowth − Iburned

Iprefire− Iburned (5)

14

Figure 1. Schematic depiction of regrowth trajectory metrics. A prefire growth and

postfire regrowth trajectory based on the Regeneration Index (computed from NDVI)

for one of the RTSA sites, used to illustrate potential recovery metrics. The prefire

median, the first postfire observation, and the postfire asymptote (maximum of a best-

fit regression line derived for years 2008-2013) are potential sources of postfire

recovery information, marked by double-arrow separations (a), (b), and (c).

The SRM normalizes for the relationships between 𝐼𝑝𝑟𝑒𝑓𝑖𝑟𝑒, 𝐼𝑏𝑢𝑟𝑛𝑒𝑑, and 𝐼𝑟𝑒𝑔𝑟𝑜𝑤𝑡ℎ,

effectively inferring the value of c based on values a and b (Figure 1) in a site-specific

manner. The SRM was evaluated for the potential that it is less sensitive than the DRM and

the RRM to differences in soil background and other site specific landscape influences on

SVI values, less sensitive to differences in SVI values relative to control sites, and may

provide a means of comparing regrowth between sites of differing vegetation abundance.

The SVI-based and RI-based regrowth trajectories were characterized using statistical

measures. Best-fit lines were derived for each regrowth trajectory using a linear regression

function in Excel 2013 (© Microsoft). A linear model was chosen to represent the regrowth

trajectory trends from 2008 to 2013 because the RI trends (based on NDVI and several other

SVIs) appeared linear during this time period in the X-Y data plots. The first four years

postfire were excluded from the linear regressions because growth of postfire annual and

suffrutescent plants is potentially dominant during this period (McMichael et al. 2004; Hope

et al. 2007). Calculations based on the DRM, RRM, and SRM metrics utilized median SVI

and RI values from 1998-2003 (six years prefire) and the asymptotes of best-fit lines

representing the regrowth trajectories. After viewing the trajectories, it was decided that

averages of years 2009-2011 and 2012-2014 would be tested as alternate postfire asymptote

measures because declines (related to drought-like conditions) were observed in the

15

trajectories from 2012 to 2014. The slopes of the best-fit lines – potentially associated with

recovery rate (cf. Roder et al. 2008) – were also evaluated as a postfire recovery metric. In

order to assess the effectiveness of the RI normalization, a coefficient of variation was

computed for each prefire growth trajectory (1998-2003) and for the regrowth trajectories

(2008-2013) relative to the best-fit lines, for both the SVI and RI values.

Analysis of Recovery Outcomes

Changes in the fractional cover of growth forms within the 74 RTSA AOIs were first

assessed visually by interpreting and comparing prefire (1996) and postfire (2012)

orthoimages. NDVI images were then derived from the orthoimage sets, based on un-

calibrated digital number values, which are different than NDVI (and other SVIs) that were

derived from the Landsat surface reflectance values. Fractional cover changes were estimated

based on per-pixel classifications of the high spatial resolution NDVI images, using a

density-slice (thresholding) approach. Surface cover was classified into the following

categories (referred to hereafter as growth forms): true shrub, subshrub/herbaceous, and

soil/rock. The NDVI thresholding approach to classifying growth forms was selected because

it proved more efficient and accurate than supervised and unsupervised image classification

techniques in several pilot studies. This approach relies mainly on the assumption that areas

covered by true shrubs (e.g., A. fasciculatum) exhibit distinctly higher NDVI values than

nearby subshrubs and herbaceous plants, and that exposed rock and soil exhibit even lower

NDVI values. The NDVI thresholds used for each RTSA AOI were chosen by interactive

sampling of NDVI values in many different pixels, while overlaying the CIR orthoimages to

visually assess the spatial arrangement of vegetation types. The thresholds varied slightly

between sites, but were generally from 0.09 to 0.10 and from 0.18 to 0.20 for the

‘subshrub/herbaceous’-‘soil/rock’ and ‘true shrub’-‘subshrub/herbaceous’ divisions,

respectively. Classifications were performed iteratively (three to 12 times) until close

agreement between the classifications and orthoimage interpretations was achieved.

Thematic rasters resulting from this process provided numeric estimates of growth form

fractional cover for 1996 and 2012.

Ten of the RTSA AOIs were randomly selected for accuracy assessment of the 1996

and 2012 growth form maps. Forty points were generated in a random spatial distribution

16

within each of these AOIs. A growth form class was then assigned to each of the (400 total)

points, based on careful visual image interpretation and assessment of vegetation spatial

distributions and spectral properties from the orthoimages in green-red-near infrared display.

Thematic values from the growth form maps were then extracted to the points and compared

numerically. An additional assessment was conducted to determine the potential effects of

spatial resolution and illumination differences between the orthoimage sets on the growth

form classifications. These factors are known to cause errors in vegetation cover change

estimates (Fensham and Fairfax, 2002). Sites which appeared unchanged and were unburned

from 1996 to 2012 (and aged ten years or more as of 1996) were selected for this purpose.

Ten AOIs of dimensions 150 m x 150 m, representing a range of true shrub fractional covers

(from sparse to dense) were delineated. Growth form maps for each site were then produced

independently based on the 1996 and 2012 orthoimages, using the NDVI thresholding

approach. Fractional cover estimates were then compared between the 1996 and 2012 maps.

Statistical Analysis and Interpretation

A one-way analysis of variance (ANOVA) was conducted in order to assess how

datasets derived from the Landsat SVI regrowth trajectories and metrics are related to

recovery states of the RTSA sites, based on differences in the mean values and relative

variance within defined recovery classes. The R² statistical results from the ANOVAs

represent the fraction of overall variance which is attributable to membership in the recovery

classes. Postfire recovery classes were defined as recovered (+7.5% to -7.5% SFCC),

moderately degraded (-7.5% to -22.5% SFCC), and severely degraded (-22.5% to -37.5%

SFCC). It is acknowledged that growth form fractional cover estimates from 2012 are not

directly, chronologically comparable with Landsat datasets from 2013 or 2014, and that some

additional fractional cover increase was possible after this time period. However, based on

the findings of Keeley and Keeley (1981), rates of cover increase of A. fasciculatum during

postfire regrowth decrease substantially after three years. The ANOVA tests were conducted

using the XLSTAT (© Addinsoft) extension for Excel 2013, based on a 95% confidence level.

Combinations of recovery metrics, normalizations, and postfire asymptotes that showed

statistical significance in the ANOVA tests, based on NDVI regrowth trajectories, were then

applied to regrowth trajectories based on the other SVIs and tested using ANOVA.

17

CHAPTER 4

RESULTS

Recovery Assessments Based On Orthoimagery

A range of chamise sites with particular prefire and postfire vegetation cover

characteristics were identified and examined in detail based on high spatial resolution aerial

imagery. These RTSA sites are dispersed across several parts of San Diego County which

burned in 2003 (Figure 2) and represent a range of prefire shrub matrix distributions, from

sparse to dense. The number and spatial distribution of these sites was limited to those which

could be identified as degraded based on interpretation of prefire and postfire orthoimagery.

Figure 2. Map of the sites where multi-temporal Landsat data were extracted. Sites

include long-term change analysis and regrowth trajectory sensitivity analysis sites,

along with the associated (non-burned) control sites used to minimize inter-annual

precipitation effects, and 2003 fire perimeters in San Diego County, California.

18

Accuracy assessment of the growth form maps associated with the ten randomly

selected RTSA sites (individual sample areas) indicates that the classification accuracies

were generally higher for the 2012 maps (88.8%) than for the 1996 maps (76.3%), based on

the median summary statistics (Table 1). There are a greater range and standard deviation of

accuracy outcomes among the 1996 maps than the 2012 maps. Error rates are the greatest for

the subshrub/herbaceous class, followed by the true shrub class for both map sets. Because

errors of commission and omission in each class are nearly equivalent, and due to the small

number of errors per class which are identified for each site, errors are described here as

absolute numeric totals (Table 1), rather than by a confusion matrix.

Table 1. Accuracy assessment of orthoimage classifications. Statistics are associated

with 1996 and 2012 growth form classifications of ten regrowth trajectory sensitivity

analysis (RTSA) sites.

Differences between the 1996 and 2012 growth form classification results for

chamise sites which did not burn during the study period are slight, as shown in Table 2. The

greatest margins of error are in the subshrub/herbaceous class, followed by the true shrub

class (Table 2), as found in the accuracy assessment results (Table 1). The median difference

among the 10 unburned sites is less than 2% for each class, suggesting that there is no

substantial, systematic bias in the estimation of any class between the 1996 and 2012 growth

form classifications for these sites. Absolute differences are generally less than 5%

2012 Accuracy statistics Errors per growth form class (#) 1996 Accuracy statistics Errors per growth form class (#)

Site ID

Number of

sample points

correct

(of 40 total)

Percent

correctSoil/rock

Subshrub/

herbs

True

shrubSite ID

Number of

sample

points

correct

Percent

correctSoil/rock

Subshrub/

herbs

True

shrub

1-A 36 90.0 0 1 2 1-A 26 65.0 2 8 3

2-A 34 85.0 0 4 2 2-A 35 87.5 1 4 0

3-A 37 92.5 0 0 3 3-A 35 87.5 0 5 0

4-A 37 92.5 0 2 1 4-A 27 67.5 1 8 4

5-A 34 85.0 1 3 2 5-A 32 80.0 2 5 1

6-A 33 82.5 6 1 0 6-A 32 80.0 3 3 2

7-A 35 87.5 0 5 0 7-A 29 72.5 1 7 3

8-A 38 95.0 1 1 0 8-A 29 72.5 1 5 5

9-A 36 90.0 1 2 1 9-A 32 80.0 0 6 2

10-A 35 87.5 2 1 2 10-A 27 67.5 5 5 3

Median: 88.8 0.5 1.5 1.5 Median: 76.3 1 5 2.5

Mean: 88.8 1.1 2.0 1.3 Mean: 76.0 1.6 5.6 2.3

Standard Dev.: 4.0 - - - Standard Dev.: 8.2 - - -

Maximum: 95.0 - - - Maximum: 87.5 - - -

Minumum: 82.5 - - - Minumum: 65.0 - - -

Sum: - 11 20 13 Sum: - 16 56 23

19

considering all classes, and only four of the 10 classifications exhibit differences greater than

5% in any class.

Table 2. Results from 1996 and 2012 orthoimage classifications of ten unburned sites.

Numeric data represent growth form fractions in percentages.

Estimation of growth form fractional cover based on the orthoimage NDVI

thresholding technique proved an effective approach for the RTSA sites. It was possible to

differentiate portions of the shrub canopy matrix which recovered following fire from

portions which changed to subshrub/herbaceous cover or soil/rock (Figure 3). Postfire

changes to the spatial arrangement of shrub cover were also detectible. Spatial re-

arrangement of shrub cover is evident in prefire and postfire images for certain discrete areas,

although the prefire arrangements were mainly reestablished at the RTSA sites, as illustrated

in one case by Figure 3. Postfire spatial arrangements of exposed soil/rock cover were

generally expressed as expansion or as no change from the prefire soil/rock patches.

Subshrub/herbaceous cover generally replaced cover of true shrubs in degraded areas, and

expanded into soil/rock patches in the postfire environment at a few of the RTSA sites.

Postfire SFCC is between -10% and +10% for approximately half of the RTSA sites,

as shown in Figure 4. Twenty-seven of the sites exhibit -10% to -30% SFCC, while seven of

the most severely degraded sites exhibit -30% to -40% SFCC. Only three of the sites exhibit

substantial increases (+10% to +30%) in shrub cover. The frequency of fractional cover

2012 1996 Differences

Soil/rockSubshrub/

herbaceousTrue shrub Soil/rock

Subshrub/

herbaceousTrue shrub Soil/rock

Subshrub/

herbaceousTrue shrub

11.1 28.0 60.8 8.5 30.3 61.2 -2.6 2.3 0.4

6.3 52.5 41.2 5.5 52.9 41.6 -0.7 0.4 0.3

2.8 49.6 47.6 1.9 51.0 47.1 -0.8 1.3 -0.5

5.8 67.7 26.5 7.2 72.8 20.0 1.4 5.2 -6.5

5.8 90.8 3.4 4.4 91.7 3.8 -1.4 0.9 0.5

11.0 58.4 30.6 13.4 63.2 23.5 2.4 4.7 -7.1

13.6 71.5 14.9 10.5 83.6 5.9 -3.1 12.1 -9.0

2.4 9.9 87.7 4.4 7.8 87.8 2.0 -2.1 0.1

3.8 54.5 41.7 5.0 45.4 49.5 1.3 -9.1 7.8

1.8 76.1 22.1 6.7 74.5 18.8 4.9 -1.6 -3.3

Median: 1.3 0.9 -0.5

Mean: 0.7 1.3 -2.0

St. Dev.: 2.4 5.8 5.1

Maximum: 4.9 12.1 7.8

20

changes in the subshrub/herbaceous class follows a similar trend as the true shrub class, yet

exhibits more frequent positive changes and fewer negative changes. The soil/rock class

exhibits the greatest frequency of positive changes, mainly between +10% and +20%, as

shown in Figure 4.

Figure 3. Example of growth form classifications. Frames a and c are NDVI

images based on the 1996 and 2012 orthoimagery, respectively (histogram

equalization stretches). Corresponding frames (b and d) display growth form

classifications for 1996 and 2012, respectively. Postfire true shrub cover

decrease is 5%, and increase of soil/rock cover is 4% at this RTSA site.

21

Figure 4. Bar chart of postfire fractional cover change magnitudes and frequencies.

This is based on two-date (1996 and 2012) growth form classifications derived from

high spatial resolution orthoimagery for the 74 RTSA sites. Negative values

represent postfire decline in cover.

Regrowth Trajectory Sensitivity Analysis

Regrowth trajectories associated with the RTSA sites were derived based on inter-

annual NDVI and NDVI-RI values, as shown in Figure 5. Of the SVIs used in this study,

NDVI was chosen to evaluate the utility of the RI normalization for reducing inter-annual

variability related to precipitation, because regrowth trajectories based on NDVI seemed

particularly effected by inter-annual precipitation differences. Inter-annual variability of the

trajectories based on NDVI-RI values are low for the prefire period (1998-2003), compared

to the NDVI values which show a declining trend during that time and co-vary with the

declining trend in annual precipitation. Similarly, NDVI co-varies with annual precipitation

during the postfire regrowth period (2004-2014), most notably during the wet period of 2010-

2011, and the dry years of 2007 and 2012-2014. Postfire regrowth trajectories based on the

NDVI-RI values exhibit trends that appear more consistent with the expected pattern of

vegetation regrowth, characterized by substantial and incremental increases in the first five

years and gradual changes in subsequent years (Horton and Kraebel 1955; Keeley and

Keeley 1981). Figure 5 shows that some of the NDVI-RI trajectories also decline from 2011

to 2014, which coincides with a trend of declining annual precipitation and may suggest that

22

the recovering sites were more impacted by the dry conditions than their respective control

sites during that time.

Figure 5. Selected regrowth trajectories from the regrowth trajectory sensitivity

analysis sites. Recovery classes are indicated by color in the legend. These trajectories

from the same sites are based on (a) original NDVI values and (b) control-site-

normalized values. (c) Annual precipitation by water year from NWS station at Lake

Cuyamaca (1998-2014) shown to illustrate inter-annual co-variation with NDVI.

Coefficients of variation (standard deviation/mean) computed for regrowth

trajectories from NDVI versus NDVI-RI, as a measure of the relative amount of scatter

(primarily ‘noise’) associated with the prefire (1998-2003) and postfire (2008-2013) periods

are shown in Figure 6. In principle, the prefire period is characterized by very little inter-

annual variability in actual vegetation abundance due to the mature, and likely stable state of

the RTSA sites during this time. The NDVI trajectories generally exhibit greater coefficients

of variation during the prefire period than in the postfire period, while the NDVI-RI

coefficients of variation are generally similar between the two time periods. With few

exceptions, coefficients of variation are substantially lower for the RI-normalized trajectories

23

relative to the NDVI trajectories of the same sites, in both prefire and postfire time periods

(Figure 6), which is beneficial.

Figure 6. Scatter plot of coefficients of variance for the SVI-

and RI-based trajectories during postfire recovery and prefire

phases. Prefire years are 1998-2003 and postfire years are

2008-2013. Postfire standard deviations were computed

relative to expected values for each year based on best-fit

regression lines, and prefire standard deviations were

computed relative to the prefire trajectory median values.

Regrowth trajectories based on NDVI and NDVI-RI were used to compute recovery

metrics including the slopes of the 2008-2013 best-fit lines, DRM, RRM, and the SRM,

based on several postfire asymptote types (2009-2011 average, 2012-2014 average, and

2008-2013 regression line maxima). Datasets generated based on the various metrics and

postfire sample types were examined for general co-variation with postfire shrub fractional

cover changes using X-Y scatter plots (not reported). This preliminary assessment suggested

that eight of these datasets were potentially correlated with SFCC, including combinations

from each postfire asymptote type using the DRM, SRM, and best-fit line slopes, some of

which were based on the NDVI-RI and others on the NDVI. These dataset types were then

evaluated using ANOVA statistical tests for all SVIs.

24

The ANOVA results used to determine the statistical significance of regrowth

trajectory-based datasets as indicators of postfire recovery, as defined by recovery classes

(SFCC ranges) are given in Table 3. All except two of the potential recovery metrics yielded

p-values of less than 0.05 based on NDVI, which suggests that the mean values are

significantly different between the recovery classes in statistical terms. The ANOVA tests

from the same dataset types as applied to the other SVIs indicated statistical significance for

only one dataset (particular combination of postfire asymptote type and recovery metric)

based on NBR and NDMI, and three datasets based on NBR2. Based on the p-values (Pr > F)

as well as the R² values, the datasets which appear as the strongest indicators of postfire

SFCC categories are the NDVI-RI (based on the SRM for the period 2012-2014), and NBR2

and NBR2-RI (based on the SRM in the periods 2009-2011 and 2012-2014, respectively)

(Table 3).

Table 3. Analysis of variance (ANOVA) results. Table rows show the results for several

SVIs based on selected postfire metrics/sample types relative to three postfire recovery

classes, with a 95% confidence level and 65 degrees of freedom (n=65). All results for

NDVI are shown, and for other SVIs where (Pr > F) < 0.05 (formatted in bold).

Input dataset characteristics: Goodness-of-fit: ANOVA summary:

Dataset

Basis

Postfire asymptote

type

Recovery

Metric R²

Adj.

R² RMSE

Sum of

Squares

Mean

Squares F Pr > F

NDVI 2009-2011 average SRM 0.161 0.161 0.20 0.33 0.11 2.88 0.046

NDVI 2008-2013 RL* max. SRM 0.173 0.118 0.17 0.27 0.09 3.13 0.035

NDVI 2008-2013 RL slope RL slope 0.074 0.012 0.31 0.35 0.12 1.20 0.320

NDVI-RI 2009-2011 average DRM 0.218 0.166 0.10 0.13 0.04 4.19 0.011

NDVI-RI 2008-2013 RL max. DRM 0.222 0.170 0.10 0.12 0.04 4.28 0.010

NDVI-RI 2009-2011 average SRM 0.162 0.162 0.19 0.32 0.11 2.90 0.045

NDVI-RI 2012-2014 average SRM 0.252 0.202 0.16 0.37 0.12 5.05 0.004

NDVI-RI 2008-2013 RL max. SRM 0.148 0.091 0.18 0.24 0.08 2.60 0.063

NBR-RI 2009-2011 average DRM 0.169 0.113 0.06 0.03 0.01 3.04 0.038

NBR2 2009-2011 average SRM 0.272 0.223 0.26 1.16 0.39 5.59 0.002

NBR2 2008-2013 RL max. SRM 0.249 0.198 0.29 1.22 0.41 4.96 0.005

NBR2-RI 2009-2011 average DRM 0.169 0.113 0.06 0.03 0.01 3.04 0.038

NBR2-RI 2012-2014 average SRM 0.187 0.164 0.31 1.56 0.78 8.07 0.001

NDMI-RI 2009-2011 average DRM 0.169 0.113 0.06 0.03 0.01 3.04 0.038

* RL refers to best-fit regression lines derived from SVI or RI values (2008-2013).

25

Scatter plots of the SRM datasets based on NDVI-RI and NBR2, provided in order to

visualize their co-variability with SFCC, are portrayed in Figure 7. Although the NDVI-RI

exhibits a trend with fewer outliers in relation to SFCC than NBR2, the higher R² value for

NBR2 is likely a result of the weaker distinction of the recovered category (+7.5% to -7.5%)

from the moderately degraded category (-7.5% to -22.5%) that results from NDVI-RI.

Because postfire fractional cover changes in the subshrub/herbaceous class may contribute to

inter-annual changes in the recovery indicator datasets, values representing change in this

class were arithmetically added to the SFCC values with a coefficient of 0.63 (which

produced the greatest fit based on iterative testing). The SRM dataset based on the NDVI-RI

visibly co-varies with this combined estimate of fractional cover change. The greatest

outliers are in the middle range of the fractional cover change values, as shown by Figure 7.

Figure 7. Scatter plots depicting the SRM recovery metric based on NDVI-RI

and NBR2 relative to fractional cover changes at the RTSA sites. Plots (a) and

(b) show fractional cover change of true shrub, while (c) and (d) show a

combination of (true shrub) + 0.63*(subshrub/herbaceous) on the X-axes.

26

Postfire regrowth trajectories based on the SRM relationship for each year during

the study period (before derived only from the postfire asymptote values) from NDVI,

NDVI-RI, NBR2, and NBR2-RI are displayed in Figure 8 and provide an alternate means

of evaluating the sensitivity of these data sources to postfire recovery outcomes. The

separability between the recovery classes based on NDVI-SRM is generally comparable to

that of the NDVI-RI-SRM during the latter years of the postfire monitoring period.

However, given the effects of inter-annual moisture differences on the NDVI trends (Figure

5), the SRM datasets derived from NDVI-RI seem to provide more reliable recovery

information for any given year. In contrast, the NBR2 trajectories are much more similar to

their RI-normalized counterparts than those based on NDVI, and far less effected by inter-

annual moisture differences. The magnitude of separation between the regrowth trajectories

of the recovered and severely degraded classes based on the SRM is higher for NDVI than

for NRB2, and for their RI-normalized counterparts, respectively. The NBR2-RI

trajectories provide the clearest distinction between the recovery classes overall,

considering both the standard deviation values and the separation between the recovered

and moderately degraded classes shown by Figure 8.

Long-term Vegetation Changes and Comparison of SVI

Sensitivities

Multitemporal Landsat SVI data were extracted to represent the regrowth

trajectories of 14 chamise plots which burned during the period 1984-1994 and which

exhibit un-interrupted postfire recovery periods until 2014. The LTCA sites are dispersed

throughout San Diego County (Figure 2) and represent a variety of sites ranging from low

to high elevation and from sparse to dense vegetation cover. The postfire regrowth

trajectory time-spans for these sites range from 19 to 29 years. For each SVI, the median

value of the RI-based trajectories relative to the final three years in each trajectory is shown

in Figure 9. For comparison, rates of postfire surface cover and height increase of A.

fasciculatum based on previous field studies are given (Figure 9). This may indicate that

the NDVI and the NBR are more sensitive to long-term changes in chamise associated with

postfire regrowth than the other SVIs.

27

Figure 8. Averaged regrowth trajectories according to recovery class derived

from NDVI and NBR2. Regrowth trajectories are based on mean scaled

recovery metric values and (1.0) standard deviations bars for the RTSA sites

within each of the recovery classes, derived from (a) NDVI, (b) NDVI-RI, (c)

NBR2, and (d) NBR2-RI. These sites burned in 2003.

28

Field data represented in Figure 9 indicate that the rate of horizontal expansion of A.

fasciculatum declines dramatically after three years postfire, whereas increases in height may

persist for 20 years postfire in re-sprouts and likely longer for seedlings (Horton and Kraebel

1955; Keeley and Keeley 1981). Although it is beyond the scope of this study to determine

the relative contributions of postfire surface cover increase and height increase for re-sprouts

and seedlings to the Landsat SVI trends, these results may suggest that NDVI, NBR, and

NBR2 are sensitive to a combination (i.e., three-dimensional physiognomy) of those changes.

Figure 9. LTCA regrowth trajectories and field-based estimates of postfire

recovery. Regrowth trajectories from: (a) NDVI, NBR, NBR2, NDMI, EVI,

SAVI, and MSAVI based on median values from 15 sites during 19-29 years

postfire. Regeneration Index values are relative to the mean of the final three

years of each trajectory. Labeled arrows indicate times that trajectories reach

100%. (b) Field data of (absolute) surface cover increase rates of A. fasciculatum,

averaged for 16 sites in San Diego County (from Keeley and Keeley 1981). (c)

Field data showing rates of postfire height increase for A. fasciculatum, based on

repeated measurements of one or two seedlings and resprouts over 25 years

(from Horton and Kraebel 1955).

29

CHAPTER 5

DISCUSSION AND CONCLUSIONS

Synthesis of Results and Findings

A primary objective in this study is to assess how metrics derived from regrowth

trajectories based on multi-temporal Landsat imagery might be used to evaluate postfire

recovery outcomes in chamise chaparral. Methods utilized to address the main research

questions included: 1) a validation of recovery outcomes from orthoimage-based vegetation

change assessments, 2) ANOVA testing to evaluate various SVIs and applied metrics as

potential recovery indicators, and 3) a comparison of long-term trajectories from the SVIs to

field-based data on postfire recovery of A. fasciculatum. Results generated using this

framework help to clarify the possible relationships between the regrowth trajectory-based

datasets and vegetation changes at various spatial and temporal scales. This research has

enabled a synopsis of the potential viability, limitations, and considerations of extracting

regrowth trajectories to assess postfire recovery of chamise at a landscape scale.

The general approach of extracting postfire recovery information from regrowth

trajectories based on multi-temporal satellite image observations, as explored in this study, is

based on findings from similar research (Henry and Hope 1998; Diaz-Delgado et al. 2002;

Diaz-Delgado et al. 2003; McMichael et al. 2004; Hope et al. 2007; Roder et al. 2008; van

Leeuwen et al. 2010, etc.). Most of this previous work has focused on either the temporal

trends of postfire regrowth or the environmental factors which control it. Roder et al. (2008)

uniquely applied metrics to regrowth trajectories on a per-pixel basis in order to assess

postfire recovery in a spatially explicit manner. This study builds upon the work of Roder et

al. (2008) by linking the regrowth trajectories to detailed estimates of vegetation change, and

by comparing results from several SVIs and regrowth trajectory metrics. This study is a

contribution to the body of remote sensing research which has focused on postfire recovery

of chaparral in particular (Henry and Hope 1998; Peterson and Stow 2003; McMichael et al.

30

2004; Hope et al. 2007; Lippitt 2013; Lippitt et al. 2013; Meng et al. 2014), by applying a

relatively simple yet effective method of postfire recovery assessment and evaluating its

sensitivity for the chamise chaparral community type.

The degree of correlation between the recovery datasets based on the regrowth

trajectories and the SFCC estimates suggests that is not feasible to reliably detect small

incremental changes in SFCC recovery based on the regrowth trajectories or metrics. Rather,

it was determined from the ANOVA tests that several of the regrowth trajectory periods and

metrics may be used to differentiate recovered sites from moderately degraded and severely

degraded sites in a relative or broad interval class manner. Most of the sites that were

classified as severely degraded underwent substantial postfire SFCC of 20-30%, yet it is

unclear whether this magnitude of change is common or characteristic of chamise sites that

degrade following fire. Rachels et al. (submitted) recorded mean postfire shrub cover

declines of 13-21% at several chaparral sites covering an area of 36 km² in San Diego

County, which represents a potentially detectible level of change based on the recovery

datasets utilized in this study. It is possible that more extreme postfire degradation occurs at

chamise sites which experience frequent and repeated fires (cf. Lippitt et al. 2013). As may

be expected, the closest explanation of postfire changes in data from the SVIs and applied

metrics would result from a combination of shrub and subshrub/herbaceous cover changes.

Multiple endmember spectral mixture analysis (MESMA) is an alternative, and a

potentially more appropriate technique for comprehensively estimating growth form

fractional cover changes because SVIs appear sensitive to changes in subshrub/herbaceous

growth form fractional cover (which conflates the signal associated with shrub cover

change). The MESMA technique enables the distinction of herbaceous cover from shrub

cover in disturbed chaparral, at fractional cover accuracies of 90% and greater (Lippitt 2013).

Although differentiation or estimation of changes in subshrub/herbaceous growth form

fractions based on the SVIs and metrics utilized here is not likely to be reliable, the results of

this study suggest that type-converted chamise (commonly defined as greater than 50%

herbaceous cover) could be identified and distinguished from well-recovered chamise if the

type-conversion event resulted in shrub cover decline of 15% or greater.

Evaluation of regrowth trajectory datasets based on several SVIs, postfire recovery

metrics and time periods, and RI normalization revealed that a few combinations of these

31

elements provided viable indications of postfire recovery outcomes. The NDVI and NBR2

(and to a lesser degree, NDMI and NBR) spectral indices yielded regrowth trajectories that

were significantly correlated with postfire recovery classes based on several metrics. The

SAVIs (along with EVI) yielded regrowth trajectories that were less sensitive to recovery

outcomes. This finding reflects those of other studies which suggest that SAVI and SAVI-

like indices are less sensitive than NDVI to changes in Mediterranean-type shrublands (Baret

and Guyot 1991; Clemente et al. 2009; Vila and Barbosa 2010, etc.). Findings from the

LTCA indicate that the NBR2 is more sensitive to gradual increases of chamise abundance

during postfire recovery than other SVIs (cf. Carreiras et al. 2006; Chen et al. 2011),

followed by NDVI. The favorable performance of the NBR2 reflects the sensitivity of the

shortwave infrared (2.09-2.35 µm) and near infrared (1.55-1.75 µm) spectral band

combination to the postfire regrowth dynamics of chamise. Although the NBR2 showed

sensitivity to subtle changes of chamise from 12 to 19 years postfire, most of the postfire

regrowth (as detected by NBR2 and NDVI) occurs within eight to 12 years postfire. This

finding is generally consistent with the available field data (Horton and Kraebel 1955;

Keeley and Keeley 1981), although additional field data would be required in order to make

substantive comparisons with the SVI trends during postfire recovery.

The RI normalization proved effective for reducing the inter-annual variability

(‘noise’) in the NDVI-based regrowth trajectories that is mostly associated with inter-annual

precipitation differences (cf. Diaz-Delgado et al. 2002; Diaz-Delgado et al. 2003; Idris et al.

2005; Li et al. 2008). The novel SRM effectively increased the comparability of the regrowth

trajectories: two of the strongest recovery indicator datasets were based on the SRM. The

optimal postfire asymptote type and time period varied between the recovery metrics and

SVIs. Maxima of best-fit regression lines which span a period of steady increase in the

regrowth trajectories, along with averages during wet and dry years (following seven years of

postfire recovery) were each useful for extracting postfire recovery signals.

Practical Context and Limitations

The quantity and spatial distribution of the RTSA study sites were limited by the low

prevalence of degraded chamise, which reflects the findings of Meng et al. (2014). This

scarcity of degraded sites may be linked to the fact that fire return intervals (< five years)

32

which tend to cause type-conversions in chamise (Lippitt et al. 2013) did not occur within the

region and time frame of this study. This constraint on the fire history criteria was necessary

in order to obtain prefire estimates of shrub fractional cover. The general accuracy of the

growth form classifications seems sufficient for the purposes of this study. However, the

NDVI thresholding approach is time-expensive and requires much visual image

interpretation, which further amplifies the need for an alternative method of postfire recovery

assessment such as the Landsat SVI trajectory approach. A small number of sites (~20)

where the levels of postfire shrub cover decline were beyond the margins of error in the

classifications were identifiable. These sites could be reasonably defined as degraded and

compared with the datasets derived from the multi-temporal Landsat imagery. The small

spatial dimensions of the chamise sites that were deemed as degraded (generally less than 1

ha) suggest that deriving postfire regrowth trajectories based on pixel aggregate averages of

larger areas would yield misleading results for chamise landscapes in southern California.

Postfire recovery assessment at the landscape scale, based on SVI trajectories derived

from Landsat surface reflectance time-sequential data that incorporate RI normalization,

would require the interactive selection of an equivalent number of unburned control sites.

This was a major challenge in this study due to the distance and dissimilarity between sites

and the inaccuracy and imprecision of the historic fire perimeter data. Although the RI

normalization effectively reduced the inter-annual effects of precipitation for the purposes of

recovery assessment based on NDVI, results from this study indicate that comparable results

may be attained by computing the SRM based on NBR2, potentially eliminating the

requirement for control plots. Postfire recovery data acquired using the methods proposed

here provide no direct estimate of any particular biophysical quantity, and thus may require

empirical association with some type of biophysical change (e.g., shrub fractional cover

change) in order to retain practical scientific or ecological meaning.

Potential Applications and Future Work

The most important finding from this study is that inter-annual (August-September)

NDVI and NBR2 trajectories, when enhanced by the RI normalization and especially the

SRM metric, are strong indicators of relative postfire recovery states in chamise chaparral.

Obtaining postfire recovery information based on these methods involves far less complexity

33

and data processing than by aerial orthoimage interpretation or automated classification or

applying MESMA to moderate spatial resolution satellite imagery. If properly applied, the

Landsat trajectory approach may be used to assess the influences of environmental and fire

regime factors on postfire recovery of chamise by acquiring data samples from many

different sites. Using an appropriate spatial sampling unit (e.g., smaller than 1 ha), frequency

distribution plots of postfire vegetation regrowth would provide a basis for comparing

recovery between chamise stands. In potential application for land management practice,

spatial patterns of postfire degradation in chamise could be identified a posteriori.

Retrospective information on postfire recovery at broad spatial scales may not directly

facilitate the arrest of processes such as type-conversion at disturbed sites, but could

elucidate the conditions that cause vulnerability and serve to inform future land management

decisions for chamise stands that are relatively undisturbed. In future studies, it could be

insightful to compare postfire regrowth trajectories of chamise within stands of various fire

histories, landscape positions and time periods, in order to determine how fire regime,

microclimate, and drought affect postfire recovery.

34

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