Understanding spatial patterns of tree mortality in a California coastal pine forest
Sara Baguskas1, Bodo Bookhagen1 , Seth Peterson1, Christopher Still1 , and Gregory Asner2
1Department
2
of Geography, University of California at Santa Barbara, California: baguskas@geog.ucsb.edu
Department of Global Ecology, Carnegie Institution for Science, Stanford University, California
Introduction
Study Site
In recent decades, trees have been dying at alarming rates in forests
across the western United States. Many studies have demonstrated
that widespread tree mortality is induced by drought-stress. If the
western U.S. becomes drier, as current climate models project, rates
of trees mortality will likely persist. Our current knowledge of forest
sensitivity to drought is limited to areas with continental, montane
climates; we know less about coastal forests.
Following extreme drought in southern
California (2007-2009), many Bishop
pines on Santa Cruz Island (SCI) died.
The Bishop pine is a relict and endemic
plant species restricted to the fog-belt of
coastal California and northern Baja
California (Fig. 1); therefore, a major
reduction of existing populations on SCI
would greatly reduce the distribution of
Figure 1. Range map of
the species as a whole.
Bishop pine populations.
Santa
S
t C
Cruz IIsland,
l d Channel Islands National Park, CA
Results: Spatial Pattern of Tree Mortality and Explanatory Environmental Variables
A
B
C
D
Figure 3. SCI is
located 25 km off the
coastline of Santa
Barbara (top).
Bishop pines outlined
in red (bottom).
Background
2005
93.82 cm
2006
56.99 cm
2007
16.28 cm
2008
44.75 cm
Figure 2. Normalized Difference Vegetation
Index (NDVI) for the Bishop pine stand on
SCI during the month of October, for 30 m
Landsat Thematic Mapper imagery. NDVI is
a commonly used metric of vegetation
growth and vigor, and it is used to assess
relative levels of vegetation water stress.
This 2005-2009 time series spans the
drought period, indicated by below average
rainfall between water years 2007 and 2009
(long-term mean rainfall on adjacent Santa
Barbara mainland is ~46 cm). Vegetation
progressed from less stressed (high NDVI
values) to high plant stress (or low NDVI
values) by 2009.
NDVI Value Range
2009
30.04 cm
0.40-0.45
Methods
To isolate the dead Bishop pines in the largest stand, we
first used a 2005 georectified 1m aerial photo (DOQQ) to
determine the extent of healthy vegetation. We calculated
the Visible Atmospherically Resistant Index (VARI) for a
2009 DOQQ and found that a threshold of -0.08 identified
the crowns of dead Bishop pines, which were formerly
healthy vegetation. False positives were removed by only
retaining clusters of 3 pixels or more.
Figure 5. The spatial pattern of Bishop pine mortality overlaid on the four environmental variables considered in this research. (a) The first figure
shows that tree mortality is clustered at high elevations at the east end of the stand. (b) Clusters of dead trees are found where the Topographic Index
(TI) is most negative. Negative values of the TI are associated with dry parts of the landscape, such as hillslopes, while positive values of TI indicate
stream channels. (c) Similarly, clusters of dead trees appear to be on divergent parts of the landscape, i.e., ridges, indicated by negative kappa values.
Positive kappa values indicate convergent parts of the landscape that are often channelized. (d) Dead trees tend to have canopy heights of 3m or less,
but the relationship is weak.
0.55-0.60
0.45-0.50
0.65-0.70
0.50-055
0.70-0.75
Objectives
The objective of our research is to advance basic understanding of the
physiological mechanisms that connect drought-stress to mortality in
a coastal forest ecosystem. Spatial patterns of tree mortality can help
elucidate important environmental controls on tree mortality, we
addressed the following questions:
1) What is the current spatial distribution of tree mortality in the
Bishop pine stand?
2) Aside from climate variables, do landscape and soil attributes
help explain this mortality pattern?
By addressing this objective, and these questions, my research will
enable more accurate mechanistic modeling and prediction of mortality
risk in coastal forest ecosystems.
Figure 4. 2009 DOQQ with an area of Bishop pine mortality
outlined in red (left). Hash marks identify dead trees (right).
Spatial clustering of dead Bishop pines was quantified using
the GizScore metric, where low values indicate no spatial
clustering (congruent with the null hypothesis) and high
values indicate clustering.
Three environmental variables were generated;
(1)Topographic Index is calculated from the 1.5m resolution
LiDAR DEM as upslope catchment area/ln(slope), and
represents where subsurface water would accumulate; (2)
Curvature was calculated by taking the second derivative of
the slope from the LiDAR DEM. The convergent and
divergent areas are expressed in positive and negative kappa
values, respectively; (3) Canopy height was calculated by
subtracting the bare earth LiDAR DEM from the first return
values.
Figure 6. Box and Whisker plots show the relationship between clustering of dead Bishop pines and TI, curvature, and canopy height.
Discussion
Remote sensing proved to be a powerful tool for identifying the spatial extent of tree mortality in this rugged and remote location. Current tree
mortality shows relationships with environmental variables, such as variability in soil moisture due to topography, especially in areas where mortality
was highly clustered. The relationship between tree mortality and canopy height, a proxy for age structure of the forest, can help predict the impact of
mortality on future demography as well as the age class most susceptible to mortality. Future work will focus on how tree mortality relates to the
interaction between climatic variables and geomorphic attributes.
Acknowledgements
This research is funded by the Kearney Foundation for Soil Science for years 2010-2012. The Carnegie Airborne Observatory is supported by the W.M. Keck Foundation,
Gordon and Betty Moore Foundation, and William Hearst III. The science data collection, processing and analysis for this project was supported by the Carnegie Institution,
The Nature Conservancy, and NASA. Literature Cited: Griffin, J.R., and W.B. Critchfield. 1972. The distribution of Forest Trees in California. Research Paper PSW-82.
Berkeley: USDA Forest Service. Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for estimation of vegetation fraction. Remote Sensing
of Environment, 80, 76−87. Santa Barbara Historic Rainfall Record: http://www.countyofsb.org/pwd/water/downloads/hydro/234mdd.pdf