Verbyla, D.. 2008 The greening and browning of Alaska based on ...

Global Ecology <strong>and</strong> Biogeography, (Global Ecol. Biogeogr.) (<strong>2008</strong>) 17, 547–555 

Blackwell Publishing Ltd 

RESEARCH 

PAPER 

<strong>The</strong> <strong>greening</strong> <strong>and</strong> <strong>browning</strong> <strong>of</strong> <strong>Alaska</strong> 

<strong>based</strong> on 1982–2003 satellite data 

David <strong>Verbyla</strong> 

Department <strong>of</strong> Forest Sciences, University <strong>of</strong> 

<strong>Alaska</strong>, Fairbanks, AK 99775, USA 

ABSTRACT 

Aim To examine the trends <strong>of</strong> 1982–2003 satellite-derived normalized difference 

vegetation index (NDVI) values at several spatial scales within tundra <strong>and</strong> boreal 

forest areas <strong>of</strong> <strong>Alaska</strong>. 

Location Arctic <strong>and</strong> subarctic <strong>Alaska</strong>. 

Methods Annual maximum NDVI data from the twice monthly Global Inventory 

Modelling <strong>and</strong> Mapping Studies (GIMMS) NDVI 1982–2003 data set with 64-km 2 

pixels were extracted from a spatial hierarchy including three large regions: ecoregion 

polygons within regions, ecozone polygons within boreal ecoregions <strong>and</strong> 100-km 

climate station buffers. <strong>The</strong> 1982–2003 trends <strong>of</strong> mean annual maximum NDVI 

values within each area, <strong>and</strong> within individual pixels, were computed using simple 

linear regression. <strong>The</strong> relationship between NDVI <strong>and</strong> temperature <strong>and</strong> precipitation 

was investigated within climate station buffers. 

Results At the largest spatial scale <strong>of</strong> polar, boreal <strong>and</strong> maritime regions, the strongest 

trend was a negative trend in NDVI within the boreal region. At a finer scale <strong>of</strong> 

ecoregion polygons, there was a strong positive NDVI trend in cold arctic tundra 

areas, <strong>and</strong> a strong negative trend in interior boreal forest areas. Within boreal ecozone 

polygons, the weakest negative trends were from areas with a maritime climate or 

colder mountainous ecozones, while the strongest negative trends were from warmer 

basin ecozones. <strong>The</strong> trends from climate station buffers were similar to ecoregion 

trends, with no significant trends from Bering tundra buffers, significant increasing 

trends among arctic tundra buffers <strong>and</strong> significant decreasing trends among interior 

boreal forest buffers. <strong>The</strong> interannual variability <strong>of</strong> NDVI among the arctic tundra 

buffers was related to the previous summer warmth index. <strong>The</strong> spatial pattern <strong>of</strong> 

increasing tundra NDVI at the pixel level was related to the west-to-east spatial pattern 

in changing climate across arctic <strong>Alaska</strong>. <strong>The</strong>re was no significant relationship between 

interannual NDVI <strong>and</strong> precipitation or temperature among the boreal forest buffers. 

<strong>The</strong> decreasing NDVI trend in interior boreal forests may be due to several factors 

including increased insect/disease infestations, reduced photosynthesis <strong>and</strong> a change in 

root/leaf carbon allocation in response to warmer <strong>and</strong> drier growing season climate. 

Correspondence: David <strong>Verbyla</strong>, Department 

<strong>of</strong> Forest Sciences, University <strong>of</strong> <strong>Alaska</strong>, 

Fairbanks, AK 99775, USA. 

E-mail: D.<strong>Verbyla</strong>@uaf.edu 

Main conclusions <strong>The</strong>re was a contrast in trends <strong>of</strong> 1982–2003 annual maximum 

NDVI, with cold arctic tundra significantly increasing in NDVI <strong>and</strong> relatively warm 

<strong>and</strong> dry interior boreal forest areas consistently decreasing in NDVI. <strong>The</strong> annual 

maximum NDVI from arctic tundra areas was strongly related to a summer warmth 

index, while there were no significant relationships in boreal areas between annual 

maximum NDVI <strong>and</strong> precipitation or temperature. Annual maximum NDVI was 

not related to spring NDVI in either arctic tundra or boreal buffers. 

Keywords 

<strong>Alaska</strong>, boreal forest, <strong>browning</strong>, climate warming, drought, GIMMS, <strong>greening</strong>, 

NDVI, summer warmth index, tundra. 

© <strong>2008</strong> <strong>The</strong> Author DOI: 10.1111/j.1466-8238.<strong>2008</strong>.00396.x 

Journal compilation © <strong>2008</strong> Blackwell Publishing Ltd www.blackwellpublishing.com/geb 547

D. <strong>Verbyla</strong> 

INTRODUCTION 

An increasing trend in the satellite-derived normalized difference 

vegetation index (NDVI) has been reported as a ‘<strong>greening</strong> trend’ 

at global (Myneni et al., 1997; Slayback et al., 2003) <strong>and</strong> regional 

scales (Hicke et al., 2002; Jia et al., 2003). <strong>The</strong> NDVI is correlated 

to the fraction <strong>of</strong> photosynthetically active radiation absorbed by 

plants, <strong>and</strong> thus to photosynthetic activity. A warming climate 

has led to earlier soil thaw, earlier green-up <strong>and</strong> longer growing 

seasons in boreal <strong>and</strong> tundra regions that may result in increased 

gross photosynthetic activity (Slayback et al., 2003) <strong>and</strong> net primary 

productivity (Kimball et al., 2007). 

At the continental scale <strong>of</strong> North America, NDVI trends in the 

boreal forest region have been weak or negative (Goetz et al., 

2005; Bunn & Goetz, 2006), while tundra regions have increased 

in NDVI with the warming climate (Bunn et al., 2005, 2007). 

At the circumpolar scale, Bunn & Goetz (2006) found trends from 

the global boreal forest to vary by season <strong>and</strong> cover type, with a 

<strong>greening</strong> trend in areas <strong>of</strong> sparse tree cover while more densely 

forested areas experienced a <strong>browning</strong> trend (decreasing NDVI), 

especially in late summer. <strong>The</strong>y hypothesized that temperatureinduced 

drought stress was likely to be influencing the decreasing 

trend in NDVI in some boreal forest areas. 

<strong>The</strong> objective <strong>of</strong> this paper is to examine the trends <strong>of</strong> 1982– 

2003 NDVI within <strong>Alaska</strong> <strong>and</strong> western Yukon Territory at several 

spatial scales. <strong>The</strong> study area consisted <strong>of</strong> a spatial hierarchy <strong>of</strong> 

regions with mean summer temperatures ranging from 4 to 10 °C 

<strong>and</strong> summer precipitation ranging from 75 to over 200 mm (Fig. 1). 

Mountain ranges such as the Brooks Range in northern <strong>Alaska</strong> 

<strong>and</strong> the <strong>Alaska</strong>, Chugach <strong>and</strong> Wrangell ranges in central <strong>Alaska</strong> act 

as topographic barriers influencing vegetation <strong>and</strong> climate. Cold 

arctic tundra <strong>and</strong> warmer boreal forest are separated by the Brooks 

Range, while a west to east gradient <strong>of</strong> maritime to continental 

climate within the boreal region is due to topographic barriers. 

METHODS 

Advanced Very High Resolution Radiometer (AVHRR) NDVI 

data were acquired from the NASA Global Inventory, Monitoring 

<strong>and</strong> Modelling project (GIMMS-G) for the period <strong>of</strong> 1982–2003 

from the University <strong>of</strong> Maryl<strong>and</strong> Global L<strong>and</strong> Cover Facility 

(http://www.l<strong>and</strong>cover.org/). <strong>The</strong>se data are available as 

maximum NDVI values for each 64-km 2 pixel from each 15-day 

composite period. By selecting the maximum NDVI during a 

15-day period, the non-vegetation effects, such as cloud or 

smoke contamination, <strong>and</strong> view geometry effects are reduced. 

<strong>The</strong> data are available globally <strong>and</strong> have been calibrated to 

correct for orbital drift <strong>and</strong> sensor degradation from a time series 

<strong>of</strong> five satellites (1982–85, NOAA-7; 1986–88, NOAA-9; 1989– 

93, NOAA-11; 1995–2000, NOAA-14; 2001–03, NOAA-16). <strong>The</strong> 

data were also processed to correct for atmospheric effects 

resulting from two major volcanic eruptions, El Chichon in 

1982, <strong>and</strong> Mount Pinatubo in 1991 (Tucker et al., 2005). 

In this paper, the maximum NDVI value from each year was 

selected for each 64 km 2 pixel. Annual maximum NDVI values 

can vary interannually <strong>and</strong> by vegetation type. For example, 

Hope et al., (2003) found annual maximum NDVI to vary from 

1–15 July to 1–15 August at an arctic tundra site in <strong>Alaska</strong>. Thus 

by using annual maximum NDVI, spatial variation within any 

composite period due to phenology (<strong>and</strong> also possible cloud 

contamination) was minimized. 

In addition, the maximum spring NDVI was extracted from 

each pixel to examine the relationship between spring NDVI <strong>and</strong> 

annual maximum NDVI. Maximum spring NDVI was extracted 

Figure 1 Polar, Boreal <strong>and</strong> Maritime regions 

<strong>and</strong> ecoregion polygons <strong>of</strong> boreal <strong>and</strong> arctic 

<strong>Alaska</strong>. Ecoregions: 1, Arctic Coastal Plain; 

2, Arctic Foothills; 3, Brooks Range; 4, Bering 

Tundra; 5, Western Taiga; 6, Western Interior; 

7, Eastern Interior; 8, <strong>Alaska</strong>–Chugach– 

Wrangell ranges; 9, Cook Inlet. Albers equal 

area map projection (st<strong>and</strong>ard parallels 

55° N, 65° N). (Source: Nowacki et al., 2001). 

© <strong>2008</strong> <strong>The</strong> Author 

548 Global Ecology <strong>and</strong> Biogeography, 17, 547–555, Journal compilation © <strong>2008</strong> Blackwell Publishing Ltd

<strong>Alaska</strong> NDVI trends 

Figure 2 Buffers <strong>of</strong> radius 100 km centred 

on first-order climate stations used in this 

study. Arctic tundra stations: Barrow, 

Kuparuk, Umiat. Bering tundra stations: 

Kotzebue, Nome, Bethel, King Salmon. Boreal 

forest stations: Bettles, McGrath, Fairbanks, 

Delta, Talkeetna, Gulkana. Only pixels within 

100-m elevation <strong>of</strong> each climate station were 

used in the analysis. Albers equal area map 

projection (st<strong>and</strong>ard parallels 55° N, 65° N). 

from the period 1–15 June for arctic tundra pixels, <strong>and</strong> the 

period 15–30 May for boreal forest pixels, since these composite 

periods correspond to the spring green-up period for tundra <strong>and</strong> 

boreal forest regions in <strong>Alaska</strong>. 

Simple linear regression was used to summarize the trend in 

annual maximum NDVI within each region during the 22-year 

period. A spatial hierarchy <strong>of</strong> ecoregion polygons was used as 

defined by Nowacki et al. (2001). At the largest spatial scale <strong>of</strong> 

analysis, the trends within polar, boreal <strong>and</strong> maritime regions 

(Fig. 1) were investigated. <strong>The</strong> polar region was predominantly a 

tundra zone with cold summer temperatures. <strong>The</strong> boreal region 

was predominantly boreal forest within the rain shadow <strong>of</strong> major 

mountain ranges. <strong>The</strong> maritime region was a wet region <strong>of</strong> 

coastal areas influenced by the Pacific Ocean. 

Within the polar <strong>and</strong> boreal regions, a smaller scale <strong>of</strong> ecoregion 

polygons was used because there is a west to east gradient <strong>of</strong> 

maritime to continental climate within the <strong>Alaska</strong>n boreal forest 

<strong>and</strong> a coastal temperature gradient within the polar zone. Each 

ecoregion was a polygon defined by physiography (Table 1, 

Fig. 1). At a smaller scale, ecozone polygons (Nowacki et al., 

2001) within the boreal ecoregions were also examined to further 

assess the effect <strong>of</strong> the east to west climatic gradient within the 

boreal region. Areas that were burned during 1973–2003 were 

excluded from the analysis since wildfire is common within 

boreal <strong>Alaska</strong> <strong>and</strong> can influence NDVI values. 

First-order climate stations within the polar <strong>and</strong> boreal 

regions <strong>of</strong> <strong>Alaska</strong> were used to investigate the interannual 

relationship between annual maximum NDVI <strong>and</strong> temperature/ 

precipitation indices. A buffer was created around the location <strong>of</strong> 

each climate station, as all pixels within 100 m elevation <strong>and</strong> 

within 100 km <strong>of</strong> the climate station. <strong>The</strong>re were few climate 

stations in <strong>Alaska</strong> with records from the period 1982–2003: three 

Table 1 Linear trends in annual maximum normalized difference 

vegetation index (NDVI) (1982–2003) by ecoregion (n = 22 years). 

Slopes represent changes in NDVI (unitless) per year (n = 22). 

Ecoregion r 2 Slope P-value 

1. Arctic Coastal Plain 0.63 +0.005 < 0.01 

2. Arctic Foothills 0.52 +0.003 < 0.01 

3. Brooks Range 0.09 +0.0008 0.17 

4. Bering Tundra 0.04 –0.0008 0.37 

5. Western Taiga 0.17 –0.002 0.05 

6. Western Interior 0.38 –0.003 < 0.01 

7. Eastern Interior 0.53 –0.003 < 0.01 

8. Wrangell/<strong>Alaska</strong> ranges 0.04 –0.0008 0.38 

9. Cook Inlet 0.16 –0.002 0.07 

stations from the arctic tundra region, four stations from the 

Bering tundra region <strong>and</strong> six stations from the interior boreal 

region (Fig. 2). Climate data for the period 1982–2003 were 

downloaded from the Western Regional Climate Center website 

(http://www.wrcc.dri.edu/). An annual summer warmth index 

(Jia et al., 2003) was computed for tundra climate stations as the 

sum <strong>of</strong> monthly mean temperatures above 0 °C. 

Spring budburst <strong>and</strong> green-up typically occur in early June in 

tundra areas <strong>and</strong> early May in boreal areas <strong>of</strong> <strong>Alaska</strong>. A tundra 

spring warmth index was computed for tundra buffers as the 

sum <strong>of</strong> monthly mean temperatures for May plus June <strong>of</strong> each 

year. A boreal spring warmth index was computed for boreal 

buffers as the sum <strong>of</strong> monthly mean temperatures for April plus 

May <strong>of</strong> each year. 

<strong>The</strong> finest grain size used in this study was the pixel level. <strong>The</strong> 

linear trend for each 64-km 2 pixel was computed using a linear 


Global Ecology <strong>and</strong> Biogeography, 17, 547–555, Journal compilation © <strong>2008</strong> Blackwell Publishing Ltd 549


Table 2 Linear trends in annual maximum normalized difference vegetation index (NDVI) (1982–2003) by ecozone polygon within western 

<strong>and</strong> eastern boreal ecoregions. Slopes represent changes in NDVI (unitless) per year (n = 22). Mean temperature <strong>and</strong> precipitation values were 

extracted from each ecozone polygon <strong>based</strong> on climate data from Fleming et al. (2000). Climate <strong>and</strong> wildfire burn data were downloaded from 

http://agdc.usgs.gov/data/. 

Ecozone 

Including burns: 

Excluding burns: 

r 2 Slope P-value r 2 Slope P-value 

Mean May–Aug. 

temp. (°C) 

Total May–Aug. 

precip (mm) 

Davidson Mountains 0.14 –0.001 0.09 0.12 –0.001 0.11 4.2 129 

Kobuk Ridges <strong>and</strong> Valleys 0.18 –0.002 0.05 0.18 –0.002 0.05 8.7 150 

Lower Yukon Lowl<strong>and</strong>s 0.54 –0.004 < 0.01 0.54 –0.004 < 0.01 11.1 171 

Kuskokwim Upl<strong>and</strong>s 0.36 –0.003 < 0.01 0.33 –0.003 < 0.01 10.5 207 

Lime Hills 0.06 –0.001 0.27 0.06 –0.001 0.28 9.3 239 

Kuskokwim Lowl<strong>and</strong>s 0.28 –0.003 0.01 0.30 –0.003 < 0.01 10.6 192 

Cook Inlet 0.16 –0.002 0.06 0.16 –0.002 0.06 10.3 203 

Copper River Basin 0.30 –0.002 < 0.01 0.30 –0.002 < 0.01 9.2 263 

Tanana Lowl<strong>and</strong>s 0.51 –0.003 < 0.01 0.52 –0.003 < 0.01 11.1 196 

Ray Mountains 0.41 –0.003 < 0.01 0.41 –0.003 < 0.01 10.1 192 

Yukon–Tanana Upl<strong>and</strong>s 0.45 –0.003 < 0.01 0.45 –0.003 < 0.01 9.3 203 

Yukon–Old Crow Basin 0.52 –0.002 < 0.01 0.53 –0.002 < 0.01 8.2 108 

North Ogilvie Mountains 0.31 –0.002 < 0.01 0.30 –0.002 < 0.01 7.5 174 

regression. Linear trends were computed for over 28,000 pixels 

<strong>and</strong> outputted as rasters <strong>of</strong> P-values <strong>and</strong> slope from each pixel’s 

regression. 

RESULTS 

<strong>The</strong>re were contrasting linear trends from 1982–2003 between 

the polar <strong>and</strong> boreal regions, with the polar region increasing 

(r 2 = 0.13, slope = +0.0011, P = 0.10), <strong>and</strong> the boreal region 

decreasing (r 2 = 0.41, slope = –0.0024, P = 0.002) in mean 

annual maximum NDVI. <strong>The</strong>re was no significant trend in 

the maritime region (r 2 < 0.01, slope = –0.0003, P = 0.817). All 

regions had a decrease in NDVI in 1992, presumably due to the 

stratospheric aerosols <strong>and</strong> subsequent cooling resulting from the 

1991 Pinatubo eruption (Lucht et al., 2002). 

At the ecoregion polygon scale, the only significant (P > 0.05) 

positive trends in mean annual maximum NDVI were from the 

arctic tundra ecoregions north <strong>of</strong> the Brooks Range, with the 

strongest trend from the Arctic Coastal Plain (Table 1). <strong>The</strong>re 

were significant decreasing NDVI trends in boreal forest 

ecoregions, with the strongest trend from the Eastern Interior. 

Trends from all other ecoregions were not significant (Table 1). 

Within smaller ecozone polygons from boreal ecoregions, the 

strongest negative trends were from physiographic basins such as 

the Lower Yukon Lowl<strong>and</strong>s, the Tanana Lowl<strong>and</strong>s <strong>and</strong> the 

Yukon–Old Crow Basin (Table 2). Physiographic regions with 

the weakest negative trends were from areas with a maritime 

climate (Lime Hills <strong>and</strong> Cook Inlet) <strong>and</strong> colder mountainous 

regions (Davidson Mountains, Kobuk Ridges <strong>and</strong> Valleys). 

<strong>The</strong> 1982–2003 trends from climate station buffers were similar 

to regional trends from the ecoregion polygon analysis. <strong>The</strong>re 

were no significant trends (P > 0.05) in annual maximum NDVI 

among climate station buffers from the Bering tundra region <strong>of</strong> 

western <strong>Alaska</strong>. All arctic tundra buffers had significant positive 

trends <strong>and</strong> all boreal stations had significant negative trends in 

annual maximum NDVI (Table 3). 

In arctic <strong>Alaska</strong>, the interannual patterns <strong>of</strong> NDVI (both 

annual maximum <strong>and</strong> 1–15 June maximum NDVI) from the 

climate station buffers were strongly correlated (Pearson’s 

r > 0.80) with the interannual patterns NDVI from the Arctic 

Coastal Plain ecoregion. If the interannual NDVI from buffers 

<strong>and</strong> the ecoregion were not strongly correlated, then local factors 

such as variable cloud cover or differing disturbances among 

climate station might dominate the NDVI trend relative to the 

ecoregion NDVI trend. 

Over the 22-year period, the most rapid increase in NDVI in 

arctic tundra areas occurred during the 1–15 June composite 

period, probably due to snowmelt, budburst <strong>and</strong> leaf flush. <strong>The</strong> 

1–15 June maximum NDVI within each climate station buffer 

was related to the tundra spring warmth index from each climate 

station (r 2 ranging from 0.33 to 0.66, P < 0.01). However, the 

relationship between 1–15 June maximum NDVI <strong>and</strong> annual 

maximum NDVI was weak <strong>and</strong> not significant (r 2 ranging from 

0.05 to 0.03, P > 0.33), indicating that early growing season conditions 

may not control annual maximum NDVI in the <strong>Alaska</strong>n 

arctic tundra. 

Jia et al. (2003) reported a significant linear relationship in 

arctic <strong>Alaska</strong> between maximum NDVI <strong>and</strong> a summer warmth 

index, expressed as the sum <strong>of</strong> monthly mean temperatures 

above 0 °C. In this study, there was also a significant linear 

relationship (P < 0.01, r 2 = 0.58) between annual maximum 

NDVI <strong>and</strong> summer warmth index. However, the relationship 

between annual maximum NDVI values <strong>and</strong> the previous 

year’s summer warmth index values was stronger (P < 0.01, 

r 2 = 0.66). 

Among boreal climate station buffers, the early spring NDVI 

(1–15 May) was linearly related to the boreal spring warmth 

index (r 2 ranging from 0.48 to 0.58, P < 0.01). <strong>The</strong>re were no 




Table 3 Linear trend in annual maximum normalized difference vegetation index (NDVI) (1982–2003) within climate station 100-km buffers. 

Slopes represent changes in NDVI (unitless) per year (n = 22). 

Climate station r 2 Slope P-value No. <strong>of</strong> pixels 

Mean May–Aug. 

temp. ( o C) 

Mean annual 

precipitation (mm) 

Bering tundra region 

Bethel 0.09 –0.002 0.16 455 10 453 

Kotzebue 0.007 0.0003 0.72 219 8 282 

Nome 0.01 +0.0005 0.59 145 8 438 

King Salmon 0.01 –0.0005 0.63 291 11 497 

Arctic tundra region 

Barrow 0.56 +0.005 < 0.01 419 1 111 

Kuparuk 0.65 +0.005 < 0.01 235 3 97 

Umiat 0.55 +0.004 < 0.01 278 6 126 

Boreal forest region 

Bettles 0.33 –0.002 < 0.01 122 12 380 

Delta 0.67 –0.004 < 0.01 118 13 309 

Fairbanks 0.46 –0.003 < 0.01 180 14 278 

Gulkana 0.30 –0.002 < 0.01 49 11 294 

McGrath 0.43 –0.004 < 0.01 185 12 465 

Talkeetna 0.19 –0.002 0.04 131 13 738 

significant (P > 0.16) linear relationships between annual 

maximum NDVI as a function <strong>of</strong> early spring NDVI for boreal 

buffers (r 2 ranging from < 0.01 to 0.09) Unlike the arctic tundra 

stations, there were no significant linear relationships between 

annual maximum NDVI <strong>and</strong> current or lagged summer warmth 

index values (P > 0.30, r 2 < 0.10). 

Bunn et al. (2005) found that the previous spring minimum 

temperature was an important variable in predicting summer 

NDVI in coniferous <strong>and</strong> broadleaf boreal areas <strong>of</strong> Canada. In this 

study, the linear relationships <strong>of</strong> annual maximum NDVI as a 

function <strong>of</strong> previous spring temperature were weak (r 2 ranging 

from < 0.01 to 0.22). <strong>The</strong> linear relationships <strong>of</strong> annual maximum 

NDVI as a function <strong>of</strong> August through July precipitation were 

also weak (r 2 ranging from 0.06 to 0.24). 

<strong>The</strong> 1982–2003 pattern <strong>of</strong> an increasing NDVI trend in 

northern arctic <strong>Alaska</strong> <strong>and</strong> a decreasing trend in interior <strong>Alaska</strong> 

also was evident from the pixel-level linear regressions. <strong>The</strong> 

highest rate <strong>of</strong> increase occurred along the central <strong>and</strong> eastern 

Arctic coastal plain, while the highest rate <strong>of</strong> decrease occurred in 

basins <strong>of</strong> interior <strong>Alaska</strong> (Fig. 3). 

DISCUSSION 

NDVI trends across the <strong>Alaska</strong>n tundra 

<strong>The</strong> growing season <strong>of</strong> the Arctic is now at its warmest relative to 

at least the past 400 years (Overpeck et al., 1997). Arctic <strong>Alaska</strong> is 

undergoing a system-wide response to an altered climatic state 

(Hinzman et al., 2005). <strong>The</strong> summer warming in arctic <strong>Alaska</strong> 

may be due to a lengthening <strong>of</strong> the snow-free season, with early 

sensible heating <strong>of</strong> the lower atmosphere (Chapin et al., 2005). 

Vegetation responses have included delayed senescence (March<strong>and</strong> 

et al., 2004) <strong>and</strong> increased broadleaf shrub abundance across the 

<strong>Alaska</strong>n arctic tundra (Sturm et al., 2001; Tape et al., 2006). <strong>The</strong> 

increase in annual maximum NDVI may be due to an increase 

in height <strong>and</strong> cover <strong>of</strong> shrubs <strong>and</strong> graminoids (Walker et al., 

2006). 

Within the arctic climate station buffers there was a significant 

linear relationship between annual maximum NDVI <strong>and</strong> annual 

summer warmth index values, consistent with the results <strong>of</strong> 

March<strong>and</strong> et al. (2004), who found infrared heating <strong>of</strong> tundra 

plots significantly increased NDVI within a few weeks <strong>of</strong> heating. 

A stronger linear relationship was observed between annual 

maximum NDVI <strong>and</strong> the previous year’s summer warmth index. 

For example, the highest summer warmth index at Barrow, 

Kuparuk <strong>and</strong> Umiat occurred in 1989 <strong>and</strong> the highest residual 

from the NDVI trend lines occurred the next year. Spring greenup, 

expressed as maximum NDVI from the 1–15 June composite 

period, was a poor predictor <strong>of</strong> annual maximum NDVI at the 

climate station buffer scale <strong>and</strong> at the ecoregion scale. Thus it 

appears that annual maximum NDVI was not dependent on 

whether spring was early or late for any given year. 

<strong>The</strong>re were significant positive temporal trends in NDVI from 

1982 to 2003 at all spatial scales examined in the arctic tundra 

region. However, there were no significant temporal trends in 

NDVI at any spatial scale in the shrub tundra region <strong>of</strong> western 

<strong>Alaska</strong>. This may be due to the much colder climate <strong>of</strong> the arctic 

ecoregions relative to the Bering tundra ecoregion (Table 3), with 

warming occurring at a faster rate in the arctic region. In general, 

there has been less warming <strong>and</strong> drying in western <strong>Alaska</strong> relative 

to central <strong>and</strong> eastern arctic <strong>Alaska</strong>, which has been described as 

a steepened gradient in continentality over the past 20 years 

(Thompson et al., 2006). <strong>The</strong> pattern <strong>of</strong> increasing NDVI over 

the period 1982–2003 in tundra regions <strong>of</strong> <strong>Alaska</strong> followed this 

west–east trend, with the northern arctic trend strongest east <strong>of</strong> 

160° W longitude (Fig. 3). 




Figure 3 Linear trends in annual (1982–2003) maximum normalized difference vegetation index (NDVI) values for each 64-km 2 pixel in the 

study region. Only pixels with significant (P < 0.05) linear regression slopes are displayed. Albers equal area map projection (st<strong>and</strong>ard parallels 

55° N, 65° N). 

NDVI trends across the <strong>Alaska</strong>n boreal forest 

Starting around 1976, the climate <strong>of</strong> boreal <strong>Alaska</strong> shifted to a 

regime with warmer winter <strong>and</strong> summer temperatures (Hartman 

& Wendler, 2005). <strong>The</strong> shift in climate regime coincided with a 

shift in the phase <strong>of</strong> the Pacific Decadal Oscillation (Mantua 

et al., 1997). Based on analyses <strong>of</strong> Fairbanks tree rings <strong>and</strong> carbon 

isotope data, the warmest summers in the past 200 years have 

occurred in the period since the mid-1970s (Barber et al., 2004). 

With warmer temperatures <strong>and</strong> no significant trend in precipitation, 

an increase in potential evapotranspiration has occurred in 

boreal <strong>Alaska</strong> (Riordan et al., 2006). Decreases in NDVI in boreal 

<strong>Alaska</strong> may be associated with several factors, including tree 

drought stress <strong>and</strong> mortality, wildfire dynamics, insect <strong>and</strong> 

disease infestations <strong>and</strong> changes in leaf/root allocation. 

In boreal <strong>Alaska</strong>, shrinking ponds have occurred due to 

increased drainage associated with warming permafrost 

(Yoshikawa & Hinzman, 2003) <strong>and</strong> increased evaporation (Klein 

et al., 2005). Analysis <strong>of</strong> tree rings has demonstrated reduced 

growth associated with climate warming <strong>and</strong> attributed to 

drought stress (Barber et al., 2000). <strong>The</strong> declining trend <strong>of</strong> NDVI 

may be due to a reduction <strong>of</strong> photosynthesis during soil moisture 

deficits (Angert et al., 2005, Ueyama et al., 2006), although the 

response may lag by several years <strong>of</strong> drought on wet black spruce 

sites (Kljun et al., 2006). A longer-term response that also would lead 

to declining NDVI is a change in carbon allocation in response to 

drier conditions with decreasing foliage production <strong>and</strong> increasing 

root production (Runyon et al., 1994, Lapenis et al., 2005). 

Although wildfire is the major large-scale disturbance agent in 

boreal <strong>Alaska</strong>, the area burned between 1973 <strong>and</strong> 2003 was a 

small proportion <strong>of</strong> any ecoregion polygon <strong>and</strong> excluding 

burned areas from the analysis did not substantially affect the 

trends (Table 2). However, at a pixel level (Fig. 3), two <strong>of</strong> the 

hotspots <strong>of</strong> strongest decline in 1982–2003 NDVI were from 

areas that burned during this period in boreal <strong>Alaska</strong>. 

Climate warming in boreal <strong>Alaska</strong> has resulted in increased 

tree <strong>and</strong> shrub mortality from insects <strong>and</strong> disease (Soja et al., 

2007), with some insect species shortening their life cycle by half 

(Berg et al., 2006). Over the past 20 years, there have been 

substantial increases in insects <strong>and</strong>/or diseases infesting 

hundreds <strong>of</strong> thous<strong>and</strong>s <strong>of</strong> hectares <strong>of</strong> shrubl<strong>and</strong> <strong>and</strong> forests in 

boreal <strong>Alaska</strong> including spruce budworm <strong>and</strong> spruce bark beetle 

in white spruce (Malmstrom & Raffa, 2000; Berg et al., 2006), 

larch sawfly (Malmstrom & Raffa, 2000), leafblotch miner in 

willows (Furniss et al., 2001), aspen leaf miner (Doak et al., 

2007), birch leaf mining sawflies (Snyder et al., 2007) <strong>and</strong> alder 




woolly sawfly/stem cankers in alders (Ruess et al., 2006). <strong>The</strong> area 

affected by insect <strong>and</strong> disease infestations exceeds wildfire in 

boreal <strong>Alaska</strong> (Malmstrom & Raffa, 2000) <strong>and</strong> would probably 

cause a decrease in NDVI. 

<strong>The</strong> lack <strong>of</strong> simple correlations between interannual maximum 

NDVI <strong>and</strong> precipitation among boreal climate station buffers is 

not surprising. Spatial variation <strong>of</strong> precipitation is high relative 

to temperature (Simpson et al., 2002). For example, the Fairbanks 

<strong>and</strong> Delta climate stations both occur on the Tanana River 

floodplain with overlapping 100-km climate buffers (Fig. 2), 

have strongly correlated annual maximum NDVI values (Pearson’s 

r = 0.91, 1982–2003), <strong>and</strong> mean monthly temperature (Pearson’s 

r = 0.99, 1982–2003). However, the correlation <strong>of</strong> monthly total 

precipitation between these two stations is substantially lower 

(Pearson’s r = 0.61, 1982–2003), <strong>and</strong> the actual mean precipitation 

within 100 km <strong>of</strong> each climate buffer is likely to vary 

substantially relative to precipitation at a climate station. 

<strong>The</strong> short-term response <strong>of</strong> boreal trees to precipitation may 

also be species- <strong>and</strong> site-specific. For example, Yarie & Van Cleve 

(2006) found reduced diameter growth <strong>of</strong> floodplain white 

spruce <strong>and</strong> balsam poplar trees, but not in most upl<strong>and</strong> species 

in a rain-exclusion experiment. Barr et al. (2004) found a decline 

in aspen maximum leaf area index during a 4-year drought, but 

no concomitant decline from understorey hazelnut canopies. 

<strong>The</strong> long-term decreasing trend in NDVI in boreal <strong>Alaska</strong> is in 

contrast to an increasing trend in the boreal forest <strong>of</strong> the 

Komi Republic <strong>of</strong> north-west Russia (Lopatin et al., 2006). Both 

regions have experienced warming since the early 1980s. 

However, much <strong>of</strong> boreal <strong>Alaska</strong> is semi-arid with potential 

evapotranspiration exceeding precipitation (Barber et al., 2000; 

Gower et al., 2001), while precipitation exceeds potential 

evapotranspiration in the Komi Republic (Lopatin et al., 2006). 

In this study, the NDVI regression slopes were most negative <strong>and</strong> 

the R 2 values were greatest from the warmest <strong>and</strong> driest boreal 

ecozones (Table 2). <strong>The</strong> strongest negative trends among boreal 

climate station buffers were also from relatively warm/dry areas 

with an annual precipitation <strong>of</strong> less than 500 mm <strong>and</strong> mean 

summer temperature above 12 °C (Table 3). 

Although the GIMMS-NDVI data have been corrected for 

major volcanic eruptions, sensor calibration <strong>and</strong> orbital drift 

over time, confounding factors leading to residual error are 

possible, including reduction <strong>of</strong> NDVI due to cloud <strong>and</strong> cloud 

shadow, extreme viewing angles, low solar elevation early or late 

in the growing season <strong>and</strong> atmospheric effects. <strong>The</strong> strategy <strong>of</strong> 

selecting the maximum NDVI during a composite period lessens 

the effect <strong>of</strong> these confounding factors, but does not completely 

eliminate these problems. However, the NDVI trends in this 

study consistently occurred at a variety <strong>of</strong> spatial scales. For 

example, cold artic tundra had significant increasing trends in 

annual maximum NDVI at the ecoregion <strong>and</strong> 100-km buffer 

scale, while warmer shrub tundra from western <strong>Alaska</strong> had no 

significant trends at these spatial scales. Boreal forest had significant 

decreasing trends at all spatial scales examined <strong>and</strong> the 

trends were consistently strongest in the eastern interior where 

growing seasons are the warmest <strong>and</strong> driest relative to other areas 

in this study. 

ACKNOWLEDGEMENTS 

I thank Scott Goetz, Martha Raynolds, John Yarie <strong>and</strong> the 

anonymous referees for their useful suggestions that helped 

improved the manuscript. This research was supported by the 

Bonanza Creek LTER (Long-Term Ecological Research) program 

(funded jointly by NSF grant DEB-0423442 <strong>and</strong> USDA Forest 

Service, Pacific Northwest Research Station grant PNW01- 

JV11261952-231). 

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BIOSKETCH 

David <strong>Verbyla</strong> is a pr<strong>of</strong>essor in the Department <strong>of</strong> 

Forest Sciences, University <strong>of</strong> <strong>Alaska</strong> Fairbanks. His 

research interests include l<strong>and</strong>scape-level changes in the 

<strong>Alaska</strong>n boreal forest associated with climate warming, 

estimating wildfire severity from remote sensing, <strong>and</strong> 

validation <strong>of</strong> remote sensing products. 

Editor: Martin Sykes

Verbyla, D.. 2008 The greening and browning of Alaska based on ...

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