The Browning of Alaska’s Boreal Forest

Parent, Mary Beth; Verbyla, David L.

doi:10.3390/rs2122729

Cited by 52 publications

(48 citation statements)

References 28 publications

(28 reference statements)

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“…A comparison between the GIMMS dataset and a Canadian dataset shows weaker post-fire recovery trends and more negative NDVI trends in unburned forests in the GIMMS dataset [20]. Other studies confirm trend estimates based on the GIMMS dataset: Despite of some regional differences in areas at very high latitudes with low vegetation cover, NDVI trends from the GIMMS dataset agree with trends from MODIS data (Moderate Resolution Imaging Spectrometer) [17,18,21]. Trends from the GIMMS dataset compare well with trends computed from Landsat imagery [22].…”

Section: Introductionsupporting

confidence: 70%

“…Previous studies reported negative NDVI trends especially in eastern boreal Alaska [21]. These browning trends are associated with wildfires [13], occur mainly in evergreen needle-leaf forests [17] and are related to temperature-induced drought stress and insect disturbances [21,54]. Browning trends in western boreal Alaska based on a previous version of the GIMMS NDVI dataset are more doubtful because such GIMMS NDVI had only a weak agreement with MODIS NDVI in this often cloud-affected region [21].…”

Section: Plausibility Of Trend and Breakpoint Estimates In Alaskamentioning

confidence: 84%

“…Browning trends were detected by all methods in some parts of boreal Alaska. Previous studies reported negative NDVI trends especially in eastern boreal Alaska [21]. These browning trends are associated with wildfires [13], occur mainly in evergreen needle-leaf forests [17] and are related to temperature-induced drought stress and insect disturbances [21,54].…”

Section: Plausibility Of Trend and Breakpoint Estimates In Alaskamentioning

confidence: 99%

“…These browning trends are associated with wildfires [13], occur mainly in evergreen needle-leaf forests [17] and are related to temperature-induced drought stress and insect disturbances [21,54]. Browning trends in western boreal Alaska based on a previous version of the GIMMS NDVI dataset are more doubtful because such GIMMS NDVI had only a weak agreement with MODIS NDVI in this often cloud-affected region [21]. In contrast to previous results of [35], we detected fewer trend changes in boreal Alaska and more monotone browning trends over this 30 year period.…”

Section: Plausibility Of Trend and Breakpoint Estimates In Alaskamentioning

confidence: 99%

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Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

et al. 2013

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Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends OPEN ACCESSRemote Sens. 2013, 5 2114 is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

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Section: Introductionsupporting

confidence: 70%

Section: Plausibility Of Trend and Breakpoint Estimates In Alaskamentioning

confidence: 84%

Section: Plausibility Of Trend and Breakpoint Estimates In Alaskamentioning

confidence: 99%

Section: Plausibility Of Trend and Breakpoint Estimates In Alaskamentioning

confidence: 99%

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Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

et al. 2013

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“…Many areas of boreal forest have experienced productivity declines (high confidence; Goetz et al, 2007;Parent and Verbyla, 2010;Beck and Goetz, 2011), related to warming-induced drought, specifically the greater drying power of air (Williams et al, 2012), inducing photosynthetic down-regulation of boreal tree species not adapted to the warmer conditions (Welp et al, 2007;Bonan, 2008). Conversely, productivity has increased along the boreal-tundra ecotone where more mesic (moist) conditions may be generating the expected warming-induced positive growth response (McGuire et al, 2007;Goldblum and Rigg, 2010;Beck and Goetz, 2011).…”

Section: Impacts On Major Systemsmentioning

confidence: 99%

Detection and Attribution of Observed Impacts

Crämer¹,

Yohe²,

Auffhammer³

et al.

Climate Change 2014 Impacts, Adaptation, and Vulnerability

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Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges

et al. 2016

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Terrestrial hydrology is central to the Arctic system and its freshwater circulation. Water transport and water constituents vary, however, across a very diverse geography. In this paper, which is a component of the Arctic Freshwater Synthesis, we review the central freshwater processes in the terrestrial Arctic drainage and how they function and change across seven hydrophysiographical regions (Arctic tundra, boreal plains, shield, mountains, grasslands, glaciers/ice caps, and wetlands). We also highlight links between terrestrial hydrology and other components of the Arctic freshwater system. In terms of key processes, snow cover extent and duration is generally decreasing on a pan-Arctic scale, but snow depth is likely to increase in the Arctic tundra. Evapotranspiration will likely increase overall, but as it is coupled to shifts in landscape characteristics, regional changes are uncertain and may vary over time. Streamflow will generally increase with increasing precipitation, but high and low flows may decrease in some regions. Continued permafrost thaw will trigger hydrological change in multiple ways, particularly through increasing connectivity between groundwater and surface water and changing water storage in lakes and soils, which will influence exchange of moisture with the atmosphere. Other effects of hydrological change include increased risks to infrastructure and water resource planning, ecosystem shifts, and growing flows of water, nutrients, sediment, and carbon to the ocean. Coordinated efforts in monitoring, modeling, and processing studies at various scales are required to improve the understanding of change, in particular at the interfaces between hydrology, atmosphere, ecology, resources, and oceans.

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The Browning of Alaska’s Boreal Forest

Cited by 52 publications

References 28 publications

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

Detection and Attribution of Observed Impacts

Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges

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