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Assessing the impact of extreme climatic events on aspen defoliation usingMODIS imageryNate Currit a;Samuel B. St Clair b

a Department of Geography, Texas State University, San Marcos, Texas, USA b Department of Plantand Wildlife Sciences, Brigham Young University, Provo, Utah, USA

First published on: 18 June 2009

To cite this Article Currit, Nate andSt Clair, Samuel B.(2010) 'Assessing the impact of extreme climatic events on aspendefoliation using MODIS imagery', Geocarto International, 25: 2, 133 — 147, First published on: 18 June 2009 (iFirst)To link to this Article: DOI: 10.1080/10106040903051975URL: http://dx.doi.org/10.1080/10106040903051975

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Assessing the impact of extreme climatic events on aspen defoliation

using MODIS imagery

Nate Currita* and Samuel B. St Clairb

aDepartment of Geography, Texas State University, 601 University Drive, San Marcos,Texas 78666-4616, USA; bDepartment of Plant and Wildlife Sciences, Brigham Young

University, 293 WIDB, Provo, Utah 84602, USA

(Received 31 December 2008; final version received 14 May 2009)

Recent studies document the decline of quaking aspen across large geographicareas of North America. Extreme climatic events are possible contributors to thedecline, and drought is often cited as an important driver of aspen phenology.Little is known, however, about the effects of spring freeze events on aspenphenology, even though such events are projected to occur more frequently infuture. This study uses moderate resolution imaging spectrometer (MODIS)imagery to assess the spatial pattern and magnitude of damage to aspen forestsduring spring freeze and summer drought events that occurred in Utah in 2007.The analysis finds above normal Normalized Difference Vegetation Index(NDVI) in early spring, and below normal NDVI following the freeze eventand during the summer drought. Aspen damage is concentrated in certain terrainclasses, depending on the type of extreme climatic event. These findings suggestthere are predictable patterns of aspen defoliation that identify aspen standsvulnerable to extreme climatic events.

Keywords: aspen phenology; extreme climatic events; MODIS; terrain

1. Introduction

Quaking aspen (Populus tremuloides Michx.) is the most widely distributed treespecies in North America, extending across diverse topographic, edaphic,temperature and precipitation gradients. Its range across such diverse landscapesattests to its resilience and adaptability to a wide range of environmental conditions.However, widespread aspen decline and dieback have been observed from westernCanada (Hogg et al. 2002, Brandt et al. 2003) through the central and southernRocky Mountains (Rogers 2002, Worrall et al. 2008). These trends suggest thatcurrent management strategies and changing environmental conditions imposecritical constraints on aspen vigour across portions of its western range. Climateextremes are important selection forces on biological organisms (McDowell et al.2008) and can drastically alter plant community structure and ecosystem processes(Ciais et al. 2005, Holmgren et al. 2006). Apart from drought, however, little isknown about the impacts of extreme climatic events on aspen health (Frey et al.2004).

*Corresponding author. Email: currit@txstate.edu

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Vol. 25, No. 2, April 2010, 133–147

ISSN 1010-6049 print/ISSN 1752-0762 online

� 2010 Taylor & Francis

DOI: 10.1080/10106040903051975

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Evidence indicates that drought events have been a principal contributor towidespread forest mortality in western North America in recent decades (Breshearset al. 2005, van Mantgem et al. 2009). Research in Canada’s Boreal forest suggeststhat aspen is sensitive to drought stress (Hogg and Bernier 2005, Hogg et al. 2008)and that water deficit predisposes aspen to health decline (Grant et al. 2006, Hogget al. 2008). Researchers hypothesize that drought in the western US during 2000–2004 incited extensive aspen dieback in southwestern Colorado and Arizona(Fairweather et al. 2008; Worrall et al. 2008). Drought intensity and duration, whichare projected to increase in western North America under future climate scenarios(Easterling et al. 2000, IPCC 2007, Seager et al. 2007), will likely have a profoundinfluence on aspen stability in its western range. These patterns suggest thatmortality in western North American forests in general and aspen forest in particularwill intensify in the coming century.

Warmer spring and fall conditions associated with climate warming threaten toalter plant phenology across the globe, including early leaf flushing in the spring anddelayed senescence in the fall (Cleland et al. 2007). Warmer early spring conditionshave been shown to accelerate aspen leaf phenology in western Canada (Beaubienand Freeland 2000) and are projected to continue accelerating leaf phenologyregionally, leaving young foliage prone to late spring freeze damage (Cannell andSmith 1986, Hanninen 2006). Recently, a late spring freeze event in 2007 in theeastern United States caused millions of dollars worth of crop damage and severelyaltered the phenology of forest tree species (Gu et al. 2008). Cayford et al. (1959) andKorstian (1921) report that although freeze damage occurs across a wide range oftree species, a consistent pattern emerges in which deciduous species tend to be moresensitive than evergreen species and aspen exhibit the greatest freeze sensitivity of allthe tree species they examined. Lamontagne et al. (1998) additionally find that freezeevents impair the photosynthetic function of aspen leaves.

At more extreme temperatures or during sensitive stages of development, a freezeevent can cause aspen leaf necrosis leading to partial or complete canopy defoliation.Cayford et al. (1959) and Korstian (1921) report extensive forest defoliation in bothalpine and boreal forest systems due to late spring freezes following early leafflushing in response to warmer temperatures. Aspen tends to reflush several weekslater, but second flush leaves generally have altered morphology which variesdepending on freeze severity. Less severe freeze events may produce a full canopy atsecond flush, but the leaves tend to be smaller and thicker than normal (St. Clair,unpublished data). More severe freeze events can cause bud damage producingpatchy canopy reflushing (50–80% canopy loss is not uncommon) characterized byextremely large leaves (Korstian 1921, Cayford et al. 1959). Almost nothing isknown about how freeze induced defoliation events influence aspen vigour. Recentevidence suggests that a widespread freeze defoliation event in northern Arizona inthe spring of 1999 was an inciting factor in subsequent aspen dieback and mortality(Fairweather, unpublished). Similar aspen canopy defoliations caused by insectherbivores have been shown to reduce carbon gain (Hart et al. 2000) and incite aspendieback events (Hogg et al. 2002).

A regional analysis of aspen phenology will greatly enhance the presentunderstanding of the effects of extreme climatic events on aspen vigour anddefoliation. Remote sensing data and analysis is especially well-suited to such ananalysis because of the broad spatial, and fine temporal, coverage of satellite images.We suggest that a remote sensing analysis of aspen phenology, and the effects of

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extreme climatic events on aspen phenology, will contribute to current studies in twospecific ways. First, temporally frequent remote sensing analysis will allowexamination of intra- and inter-annual aspen phenologies, not simply before andafter snapshots of freeze damaged aspen forests. Second, a regional analysis willprovide a basis for integrating findings from multiple local case studies (Fisher et al.2006). A multi-site comparison will facilitate the discovery of general patterns ofclimatic impacts on aspen phenology across multiple sites. When general patterns areelusive, a regional, remote sensing-derived inventory of impacted aspen forests canbe integrated with other datasets to determine spatial patterns of aspen damage.Taken together, we suggest these two scientific contributions have the potential toenhance the current understanding and management of aspen forests.

The purpose of this study is to assess the impacts of extreme climatic events(a drought event and a freeze event) on aspen defoliation in Utah, USA. Followingan early spring, Utah experienced an extreme, discrete freeze event in late spring2007 (22–25 May). Severe defoliation at several sites was confirmed in the field(Figure 1). The spatial extent and magnitude of the defoliation currently remainunknown. The effects of multi-year drought appear evident during the followingsummer months. Therefore, we ask three primary questions about the spring freezeand summer drought events. First, what is the spatial extent of aspen defoliation?Second, what is the magnitude of aspen defoliation? Third, are there identifiablelandscape patterns of defoliation? Answers to these questions facilitate identificationof aspen stands that are most vulnerable to severe damage by extreme climaticevents. Identifying vulnerable stands will potentially improve aspen managementpractices.

2. Data and methodology

The most reliable method of measuring defoliation is by direct sampling, which isunreasonable in this case because of its cost, the time required for sampling, and theneed to collect data immediately before and after an extreme event. Satellite remotesensing provides an alternative that gives a view of an area before and after an event(Cleland et al. 2007). Remote sensing cannot provide measures of actual leaf loss, but

Figure 1. Severe defoliation occurred following an unusually late spring frost event on 22–25May 2007. Aspen defoliation is readily apparent in the photo on the left, taken 31 May 2007.The photo on the right was taken on 19 July 2007.

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it can provide estimates of changes to vegetation ‘greenness’ through the use ofvegetation indices (Zhang et al. 2003). The Normalized Difference Vegetation Index(NDVI) has been used successfully to estimate ‘greenness’, above ground net primaryproductivity, leaf area index andbiomass for a variety of ecosystem types (Myneni et al.1995, Cohen et al. 2003, Wang et al. 2005). We use NDVI as an indicator of aspen leafgreenness and (de)foliation, and as a measure of aspen phenology.

The data sources we use for this study are (1) Moderate Resolution ImagingSpectrometer (MODIS) version 5 daily images for the week of Utah’s 2007 freezeevent, (2) MODIS version 5 sixteen-day NDVI composites for 2000–2004, and 2007,(3) a digital elevation model (DEM), (4) Utah Gap Analysis data to identify aspenforests1 (Jennings 2000) and (5) SNOTEL climate data for 32 sites located within3 km of aspen forests. The spatial resolution of the MODIS images and Utah StateDEM is 250 m, an appropriate resolution for large-scale vegetation analyses. Eachof these datasets is used to determine whether the extreme events of 2007 can bedetected via satellite and whether they are significantly different than normal. Wedefine normal as the 5-year NDVI average based on MODIS imagery from 2000 to2004. It is important to note that no freeze events occurred during the 2000–2004period. We use SNOTEL climate data for 2007, the 2000–2004 period and thenormal climate period from 1970 to 2000. Only the SNOTEL precipitation recordextends back to the 1970–2000 period. The DEM is used to assess the landscapevariability of defoliation across different terrain classes.

MODIS data are used for this study because of their high temporal resolutionand broad spatial coverage. The Terra satellite has a revisit cycle of 1.1 days,meaning that most of the globe is imaged daily.2 Multiple satellite passes overlap formuch of the globe, and are combined to produce daily images that minimize thenumber of outlier pixels caused by high view angles (Huete et al. 1999). These imagesare available from 2000 to the present. The main shortcoming of the daily images isthat they contain cloud cover.

The MODIS science team combines 16 daily images to produce a cloud freecomposite using a derivative of the maximum value compositing (MVC) algorithm(Huete et al. 1999). This algorithm first calculates NDVI for each of the 16 days.Cloud pixels have negative or low NDVI values. Vegetation has positive values thatare larger than cloud values. Next, for each pixel in an image, the compositingalgorithm selects the highest NDVI value from among the 16 days (i.e., the pixelvalue that is least likely a cloud) and maps that pixel to an output image. The pixelmapping process is used to create cloud-free reflectance image composites for eachspectral band. Cloud-free reflectance composites are used to produce a number ofbiophysical image products for the entire globe, including NDVI (Huete et al. 1999).These products contain pixels values from up to 16 different days.

We perform an additional preprocessing step on the daily images in order toexamine the effects of the freeze event on a daily basis. We identify clouds in the dailyNDVI images by applying a series of decision rules to the MODIS red and near-infrared reflectance bands, and NDVI calculated from the daily reflectance bands in amanner similar to previous studies (Ackerman et al. 2006, Jedlovec and Haines 2007).Jedlovec and Haines (2007) highlight limitations of using static, global thresholds todetermine regional and local cloud masks. As per their suggestions, we used scenedependent thresholds to mask clouds on the daily MODIS images. This procedureremoves obvious clouds andmost thin clouds.We additionally remove all pixels withina four pixel buffer of the clouds previously identified. This procedure produces

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a conservative estimate of non-cloud pixels that we consider more accurate for thisregion and period than the standard MODIS cloud mask product (MOD35).Nevertheless, our cloud masks are not error-free. The most apparent error is theinability to distinguish differences between the cloud-free salt flats west of the GreatSalt Lake from actual clouds in mountainous regions. Since aspen do not grow in thesalt flats we proceed without ameliorating this error in daily cloud identification.Instead, we remove all pixels that fall outside aspen polygons as identified byUtahGapAnalysis. This procedure produces cloud-free images where aspen grows, but does notinterpolate NDVI values where clouds are – cloud pixels have a no data value.

The first analysis step is to extract mean NDVI values for each region within3 km of a SNOTEL site and intersecting one of the aspen polygons. These meanvalues are extracted for each 16-day period from 2000 to 2004 and 2007. We thencalculate annual mean (‘normal’) and standard deviation values for each SNOTELsite for 2000–2004. We calculate the number of standard deviations; the 2007 valuesdiffer from the 2000 to 2004 ‘normal’ values for each site. The mean standarddeviation difference (SDdifj) is calculated using the following equation:

SDdifj ¼Pn

i¼0 ½2007ij �Xij=SDij�n

where i is the ith of n SNOTEL sites, j is the Julian day of the year, 2007ij and Xij arethe 2007 NDVI values and normal NDVI mean values, and SDij is the normal NDVIstandard deviation.

Plotting these values produces a graph that identifies the elevated greennessassociated with early spring warming (JDs 81, 97), the drops in greenness associatedwith the freeze (JD 145) and drought (JD 193) events, and elevated greenness late inthe year (JD 321) (Figure 2). Finally, NDVI and climate trends are compared.

The second step in our analysis is to visualize the spatio-temporal NDVIdeviations from normal for each period in 2007. The 16-day cloud-free compositesare used to analyse the significant spring freeze (JD 145) and summer drought (JD193) events. First, we create normal aspen NDVI images for each 16-day period from2000 to 2004. We then calculate the difference between each 2007 16-day compositeand each 16-day normal composite. These steps allow us to visualize spatio-temporalpatterns of deviation from normal for an entire year and to identify regions wheregreenness has decreased most dramatically during extreme events. We use sevencloud-free daily images (JD 139–145) to analyse NDVI variations associated with thefreeze event. Because standardized daily cloud-free images are not available, there isno means of comparing our daily images to ‘normal’ daily values. Instead, wecompare our daily images to normal JD 145 – the nearest 16-day cloud-freecomposite. This comparison is not exact because the MVC technique producesexpectedly higher NDVI values in each 16-day composite. Additionally, clouds mayalso block the view of aspen forests. Therefore, our evaluations of above and belownormal daily NDVI should be viewed as general patterns of variation from aproximate known value, and not exact deviations from normal.

The third step is to determine damage to aspen forests. All decreases inphotosynthetic activity will lead to decreases in NDVI. Bud damage or defoliationwill lead to relatively large decreases in NDVI. Therefore, we define damage as asizable decrease in NDVI associated with an extreme climatic event. A sizabledecrease is determined by first calculating the percent change from normal NDVI for

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each image pixel, and then selecting those pixels that represent the largest changes.The magnitude of the largest changes varies by extreme event and period. For thesummer drought event, we use the following two criteria to identify regions of largeNDVI decreases from normal. First, we identify a pixel with a large decrease inNDVI when its JD 193, 2007 value is at least 10% less than normal. Second, weidentify a pixel with a large decrease in NDVI when its summer mean value (June,July and August) is at least 5% less than normal. All pixels that meet either criterionare labelled as drought damaged aspen. For the freeze event, we identify a pixel witha large decrease in NDVI when any daily value is at least 40% less than JD 145,2007. All pixels meeting this criterion are labelled as freeze damaged aspen.

The fourth step is to determine how damage is related to landscape patterns andcharacteristics. Specifically, we examine whether or not aspen damage is uniformlydistributed across elevation, aspect and slope classes. First, we determine the percentof each terrain class in each aspen polygon. Second, we determine the percent of totaldamage in each terrain class for the spring freeze and summer drought events. In aperfectly uniform distribution, the percent of total damage in each class will be equalto the percent of that class in the aspen polygons. Other distributions indicate aconcentration of damage in particular terrain classes.

3. Results and discussion

The average 2000–2004 normal NDVI values and the average 2007 NDVI values forall SNOTEL sites are plotted in Figure 2. The large circles indicate 2007 NDVI

Figure 2. Mean annual NDVI values for 32 SNOTEL sites for 16-day periods during 2000–2004 and 2007. The large circles indicate 16-day periods when the 2007 mean is greater than orequal to 2 standard deviations above or below the 2000–2004 mean. The x-axis indicates theJulian day of the year.

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values that are greater than or equal to two standard deviations away from the 2000to 2004 normal values, meaning that there is less than a 5% chance these valueswould occur in a normal year. An earlier than normal increase in NDVI is apparentin JDs 81 and 97. There is an equally apparent drop in NDVI at JD 193 and anextension of high NDVI values into the fall at JD 321. Surprisingly, significant is thatthe JD 145 value (i.e., the closest date to the freeze event) is unlikely to occur in anormal year, even though it appears relatively close to the 2000–2004 normal value.The reason for this is that many of the SNOTEL sites had small JD 145, 2000–2004standard deviations and so moderate decreases in 2007 NDVI values are significantlydifferent than normal.

Climate data for these periods explain the patterns of NDVI deviation fromnormal. Besides length of day, which is invariant year-to-year, temperature is themost important driver of aspen phenology. The temperature record (Figure 3)indicates a warmer than normal daily average temperature in the spring and fall of2007 for the SNOTEL sites found within aspen forests. The early arrival of warmertemperatures in the spring and the extension of warmer temperatures in the fallcorrelate with higher than normal NDVI values during the spring and fall. That is,higher than normal spring temperatures led to early leaf flushing and higher thannormal fall temperatures led to delayed leaf senescence. The freeze event (Figure 3) isevident in the daily average temperature record near JD 145. The mean dailyminimum temperature for all sites from 22 to 25 May 2007 was 3.48C below zero.The coldest day was 23 May 2007 with a mean daily minimum temperature

Figure 3. Mean daily average temperatures for 32 SNOTEL sites for each 16-day periodduring 2000–2004 and 2007. Spring freeze daily average temperatures are shown for 22–25May 2007, plus 2 days before and after the freeze. The x-axis indicates the Julian day of theyear.

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of 75.18C. The absolute daily minimum temperatures for each day were 78.0,710.9, 78.1 and 76.4. Although specific temperature thresholds for aspen freezedamage are not known, these temperatures are all within the range likely to delayaspen phenology.

The cumulative precipitation record for the 2007 water year (Figure 4) explainsthe pattern of below normal mid-summer NDVI. A water year is the 12-monthperiod beginning in October when precipitation contributes to the following year’svegetation growth season. Cumulative precipitation is the sum of precipitationduring a water year and is the total amount of precipitation that contributes to plantgrowth in a calendar year. The precipitation record indicates that the 2007precipitation was less than the 2000–2004 precipitation normal from mid-March tothe end of the water year. Precipitation was nearly 33 mm below normal on JDs 97and 177. What is more significant than these comparisons, however, is thecomparison of the 1970–2000 precipitation normal to the 2000–2004 and 2007precipitation periods. The trends of recent periods are significantly below the 1970–2000 normal from nearly the beginning of the water year. Precipitation in 2007reaches a maximum deficit of 170 mm below normal on JD 177. Taken as a whole,these trends indicate that aspen forests have experienced prolonged drought and soare under severe physiological stress. The significant drop in NDVI occurredon JD 193, the 16-day period directly after the lowest precipitation point andsuggests aspen forests reached a critical threshold in long-term stress due to wateravailability.

Figure 4. Differences between the 1970–2000 cumulative precipitation normal and the meancumulative precipitation for 32 SNOTEL sites for each 16-day period during 2000–2004 and2007 water years. The x-axis indicates the Julian day of the year.

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Spatio-temporal patterns of deviation indicate how much each pixel is above orbelow normal at 16-day intervals for a normal year.3 The early spring above normalNDVI pattern prior to the freeze event begins in the east and gradually moves west.The largest above normal deviations tend to occur in central Utah. During the freezeevent (JD 145), above normal NDVI values exist in small pockets within a matrix ofbelow normal NDVI values. The most severe below normal NDVI values are innorth-central Utah. Above average values are found dispersed in northern, centraland southern Utah. These patterns indicate that there were heterogeneous outcomesof the freeze event – not all aspen forests were affected equally. The daily data mayprovide a clearer picture of the freeze event than the composite data since up to 16days, including non-freeze days, comprise the composite. The early summer (JD 193)drop in NDVI occurs almost uniformly across space – nearly all pixels are belownormal. The most severe drops below normal occur in central and northern Utah.Only in the far eastern reaches of aspen forest are there a few pixels that are near orslightly above normal.

Spatio-temporal patterns in daily NDVI deviations are also apparent.1 Over the7-day period examined (JD 139–145), the largest decreases occur in central andnorthern Utah. Near normal pixels are found more frequently in southern Utah.Throughout Utah only a few pixels were greater than JD 145. For the SNOTELsites, the largest decreases from JD 145 occur on JDs 141 and 144. JDs 139, 140 and145 appear close to expected, on average. These findings suggest a patchy pattern ofdefoliation on a daily scale due to the freeze event.

The damage classes are created from the spatio-temporal patterns just describedand the criteria outlined in the methodology (Figure 5). The combined summer (JD193) and sustained summer (June, July and August) damage covers an area of

Figure 5. Regions of high magnitude aspen damage associated with summer drought (left)and spring freeze (right) events. High magnitude damage is shown in black within aspen forestpolygons.

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49,543 ha and is concentrated in central Utah. The spring freeze damage covers anarea of 65,100 ha and is most concentrated in central and northern Utah. Minimalfreeze damage is dispersed throughout southern Utah. Though specific aspen siteswere field checked almost immediately after the freeze event, the site selection did nothave the benefit of these findings and so patterns of damage classes cannot becompletely verified. The extensive summer damage was not field checked. Thesedamage regions should be field checked over the next years to determine long-termeffects of the extreme events. We consider these regions to be likely regions of aspendieback.

The distribution of spring freeze and summer drought damage is not uniformlydistributed across terrain classes. We describe the distributional patterns of damagewithin elevation (Table 1), aspect (Table 2) and slope (Table 3) classes. Damage isuniformly distributed when the ‘actual distribution’ column values are equal to the‘uniform distribution’ column values. The ‘uniform distribution’ column indicatesthe exact percentage of each terrain class in the aspen forests. Differences betweenactual and uniform distributions are shown in the ‘difference’ columns. Positive(negative) differences indicate a concentration (deconcentration) of damage in aparticular class. Interestingly, the landscape patterns for summer drought damageand spring freeze damage are distinct, suggesting different possible processes ofdamage to aspen forests.

Summer drought damage is most prevalent at relatively high elevations, and onsouth-facing, moderately steep slopes. Specifically, summer drought damageconcentration is greatest at the 2633–2968 m elevation class – it is 14.23% aboveexpected for a uniform distribution (Table 1). It is surprisingly deconcentrated at the1962–2297 m, and 2297–2633 m elevation classes, with differences of 73.02%and 78.93%, respectively. The distribution in the lowest and highest elevationclasses is near uniform. East, southeast, south and southwest facing slopes havegreater than expected summer damage concentrations (Table 2). The differences are1.64%, 2.48%, 1.96, and 2.35%, respectively. Summer damage is deconcentrated onnorth and northwest facing slopes, and nearly uniformly concentrated on west and

Table 1. Aspen damage by elevation classes.

Class definitionUniform

distribution*

Summer damagecomposite Freeze damage composite

Actualdistribution Difference

Actualdistribution Difference

1627–1962 m 0.85% 0.52% 70.33% 2.01% 1.16%1962–2297 m 8.29% 5.27% 73.02% 12.80% 4.51%2297–2633 m 42.16% 33.23% 78.93% 42.90% 0.74%2633–2968 m 38.35% 52.58% 14.23% 38.80% 0.45%2968–3303 m 10.35% 8.39% 71.96% 3.50% 76.85%

A disproportionately large percentage of the summer damage occurs at the 2633–2968 m class, while adisproportionately large percentage of the freeze damage occurs at the 1962–2297 m class.

*The percentage of the study area found in each terrain class is indicated by the ‘Uniform Distribution’columns. If the summer and freeze damage regions were uniformly distributed across terrain classes, theiractual distributions would equal the uniform distributions. Differences between the actual and uniformdistributions are indicated for each damage class. Positive (negative) differences indicate a concentration(deconcentration) of damage in a class.

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northeast facing slopes. Surprisingly, summer damage is nearly uniformly distributedacross all slope classes (Table 3). Only in the 19.6–42.5% slope class is the con-centration of damage much less than expected. Significantly, damage concentrationgradually increases with increasing slope and reaches maximum concentration at the6.7–8.2% slope class. Afterward, damage concentration gradually decreases withincreases in slope.

The landscape patterns of summer drought damage have a number of likelycauses. First, higher elevation aspen forests experience more direct exposure to

Table 2. Aspen damage by aspect classes.

Classdefinition

Uniformdistribution*

Summer damagecomposite Freeze damage composite

Actualdistribution Difference

Actualdistribution Difference

Flat 0.07% 0.05% 70.02% 0.07% 0.00%North 10.53% 4.83% 75.70% 11.54% 1.01%Northeast 11.90% 11.48% 70.41% 12.99% 1.09%East 14.44% 16.09% 1.64% 16.21% 1.77%Southeast 14.18% 16.66% 2.48% 13.38% 70.80%South 12.12% 14.09% 1.96% 10.53% 71.59%Southwest 11.58% 13.93% 2.35% 10.32% 71.26%West 13.14% 13.23% 0.09% 12.65% 70.49%Northwest 12.04% 9.66% 72.39% 12.31% 0.27%

The summer and freeze damage classes have opposing patterns: the summer damage tends to concentrateon south-facing slopes, while the freeze damage tends to concentrate on north-facing slopes.

*See Table 1 for an explanation of this column.

Table 3. Aspen damage by slope classes.

Classdefinition

Uniformdistribution*

Summer damagecomposite Freeze damage composite

Actualdistribution Difference

Actualdistribution Difference

0.0–3.4% 9.70% 8.89% 70.81% 6.19% 73.51%3.4–5.2% 10.61% 12.08% 1.47% 9.07% 71.54%5.2–6.7% 10.16% 11.96% 1.79% 9.89% 70.27%6.7–8.2% 10.81% 12.74% 1.93% 10.69% 70.12%8.2–9.7% 10.56% 11.97% 1.40% 11.12% 0.56%9.7–11.3% 10.20% 11.01% 0.82% 11.06% 0.86%11.3–13.3% 9.81% 10.03% 0.22% 10.93% 1.12%13.3–16.0% 9.43% 8.55% 70.89% 10.59% 1.16%16.0–19.6% 9.41% 7.63% 71.78% 10.34% 0.93%19.6–42.5% 9.30% 5.15% 74.15% 10.12% 0.82%

Summer and freeze damage are deconcentrated at low slopes, but gradually become concentrated as slopeincreases. Maximum summer damage concentration occurs at the 6.7–8.2% slope class and decreasesthereafter, becoming deconcentrated at higher slopes. Maximum freeze damage concentration occurs atthe 13.3–16.0% slope class and decreases thereafter.

*See Table 1 for an explanation of this column.

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insolation and sustained wind than lower elevations. These conditions tend to reducesoil moisture and thereby limit aspen productivity, leading to reduced summerNDVI values. Second, northern hemisphere summers are long and south-facingslopes receive more insolation than other aspects. Greater insolation on south-facingslopes tends to dry soils more quickly than on other aspects, leading to reducedaspen productivity. Reduced aspen productivity manifests itself in lower summerNDVI values. During periods of drought, the aforementioned conditions areexacerbated. Multiple years of drought conditions likely lead to sustainedreductions in productivity as evidenced by significant drops in summer NDVI.Such long-term conditions may adversely impact aspen longevity. These explana-tions are supported by the climate data. An explanation for the distribution ofsummer aspen damage across slope classes is not clear, however, and will requirefurther investigation.

The spring freeze damage pattern is distinct from summer drought damage. It ismost prevalent at relatively low elevations, and on north-facing, moderate-to-verysteep slopes. Specifically, freeze damage concentration is greatest in the 1962–2297 m elevation class where the difference between actual and uniform is 4.51%(Table 1). Damage is much less than expected at the 2968–3303 m elevation class,and only slightly concentrated for the intermediate elevation classes. Freeze damageis nearly uniformly concentrated across aspect classes (Table 2). Interestingly, it isslightly concentrated on northwest, north, northeast and east facing slopes. Freezedamage is also nearly uniformly distributed across slope classes (Table 3). Only inthe 0.0–3.4% slope class is the concentration of damage much less than expected.Especially surprising, given the summer damage slope class findings, is that freezedamage concentration gradually increases with increases in slope and reaches amaximum at the 13.3–16.0% slope class. Thereafter damage is decreasinglyconcentrated.

The landscape patterns of spring freeze damage have a number of likely causes.Two likely causes exist to explain the patterns of freeze damage across elevationclasses. First, leaf flushing initially occurs at lower elevations where the temperaturesare warmer, and decreases with elevation gain and cooler temperatures. The latespring freeze event caused a greater than expected drop in NDVI in regions whereleaf flush was greatest – at relatively lower elevations. Higher elevation aspen forestshad not yet flushed to the extent of the lower elevation forests and had more nearnormal NDVI values during the freeze event. This pattern and explanations werealso reported by Gu et al. (2008) in their analysis of the 2007 spring freeze in theeastern US. Second, cold air drainage occurs when temperatures drop belowfreezing. Therefore, the coldest temperatures were in the lower elevations, leading todecreased NDVI values at lower elevations. Similarly, a late onset of greenness in thenortheast US was attributed to cold air drainage in specific microclimates, lendingsupport to our explanation (Fisher et al. 2006). The pattern of freeze damageconcentration on north-facing slopes likely occurs because north-facing slopes aregenerally colder than south-facing slopes. They are colder because they do notreceive direct insolation, like south-facing slopes do. This pattern leads to greaterdefoliation and subsequent decreases in NDVI values. Multiple freeze events,especially in combination with multiple summer drought events, will likely havecumulative effects on aspen phenology and longevity. Productivity will likelydecrease and result in significantly lower than expected NDVI values. Theseexplanations and their outcomes make intuitive sense. An explanation for the

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distribution of aspen freeze damage across slope classes is not clear, however, andwill require further investigation.

4. Conclusions

Recent aspen defoliation and dieback are major concerns in the Rocky Mountainregion of the US. Multiple causes likely lead to the loss of aspen forests. Drought isoften cited as a major cause of defoliation, in addition to a number of predisposingfactors. The effects of freeze events on aspen phenology are not well understood. Ourpurpose in this study was to assess the impact of extreme climatic events on aspenphenology in Utah, USA. Utah experienced an early spring 2007, followed by aspring freeze event at the end of May. Site visits confirmed that severe defoliationresulted from the freeze event, but the spatial extent and magnitude of damage wasnot known until now. A drought later occurred in the summer months.

Using MODIS-based NDVI, we assess spatio-temporal patterns of NDVIdeviations from normal. We identify damage to aspen forests caused by the latespring freeze and a summer drought. Central and northern aspen forests appear tohave suffered the most damage from the climatic events. We then examine thedistributions of spring freeze and summer drought damage across different landscapeclasses. The relationships between aspen damage and certain terrain classes indicatethat summer damage is found disproportionately (1) at higher elevations, (2) onsouth-facing slopes and (3) on moderately steep slopes. Of these three factors, thegreatest (least) concentrations of damage occur at higher elevations (on moderatelysteep slopes). These findings suggest reduced soil moisture is a major driver ofsummer damage, not just reduced precipitation. Surprisingly, each of these patternsis opposite that found for the spring freeze damage event. The terrain relationshipsindicate that freeze damage is found disproportionately (1) at lower elevations, (2) onnorth-facing slopes and (3) on moderate to steep slopes. These findings suggestvariations in micro-climate are major drivers of freeze damage, not simply freezingtemperatures.

Taken as a whole, these findings demonstrate that extreme climatic events do notuniformly damage aspen forests. Instead, there are predictable spatial patterns ofaspen damage across different landscape classes. Moreover, these findings suggestwhich aspen forests are most vulnerable to spring freeze and summer drought events.Proper identification of these aspen stands should facilitate improved aspen forestmanagement. Future work must include long-term monitoring of aspen sites,including their land-use history, climate variations and extreme events. Continuedmonitoring will facilitate a tighter coupling of in situ and remote sensing analysis,allowing us to assess appropriate thresholds for decreases in NDVI that identifyphenologically damaging changes to aspen forests. Additionally, effort should bemade to improve metrics for identifying freeze damage that may be more sensitivethan NDVI (Jurgens 1997).

Notes

1. The overall classification accuracy of the Utah GAP Analysis land-cover data was 75.3%.Errors of omission and commission will influence the NDVI response within aspenpolygons and the selection of SNOTEL sites near aspen polygons. Nevertheless, GAPdata is the best available data for identifying aspen forests. We assume aspen polygonsidentify pixels that are predominantly aspen.

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2. We chose to use only Terra images (as opposed to supplementing with Aqua images) inorder to avoid unnecessary complications when interpreting findings. The major concernis in trying to interpret daily freeze findings produced by combining morning (Terra) andafternoon (Aqua) satellite passes.

3. The number of image composites in a year (23) and daily images in a week (7) areprohibitively large to include as figures. These paragraphs, therefore describe patterns seenover a year in 23 images, and over a week in 7 images.

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