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

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