Large uncertainties in the budget of atmospheric methane, an important greenhouse gas, limit the accuracy of climate change projections. Thaw lakes in North Siberia are known to emit methane, but the ...magnitude of these emissions remains uncertain because most methane is released through ebullition (bubbling), which is spatially and temporally variable. Here we report a new method of measuring ebullition and use it to quantify methane emissions from two thaw lakes in North Siberia. We show that ebullition accounts for 95 per cent of methane emissions from these lakes, and that methane flux from thaw lakes in our study region may be five times higher than previously estimated. Extrapolation of these fluxes indicates that thaw lakes in North Siberia emit 3.8 teragrams of methane per year, which increases present estimates of methane emissions from northern wetlands (< 6-40 teragrams per year; refs 1, 2, 4-6) by between 10 and 63 per cent. We find that thawing permafrost along lake margins accounts for most of the methane released from the lakes, and estimate that an expansion of thaw lakes between 1974 and 2000, which was concurrent with regional warming, increased methane emissions in our study region by 58 per cent. Furthermore, the Pleistocene age (35,260-42,900 years) of methane emitted from hotspots along thawing lake margins indicates that this positive feedback to climate warming has led to the release of old carbon stocks previously stored in permafrost.
We evaluated 13 remotely sensed indices across four wildfire burn sites in interior Alaska. The indices included single bands, band ratios, vegetation indices, and multivariate components. Each index ...was evaluated with post-burn and differenced pre/post-burn index values. The indices were evaluated by examining the correlation between each remotely sensed index and field-based Composite Burn Index (CBI) values. Radiant temperature was strongly correlated with field-based CBI when a post-fire image from autumn was used. Indices that used red and near-infrared bands performed poorly relative to indices that incorporated mid-infrared bands. The Normalized Burn Ratio (NBR), which incorporates near- and mid-infrared bands, was ranked within the top three indices for each of the four burns using post-burn images, and for three of the four burns using pre- and post-burn images. When indices were summed based on ranked correlations, the NBR was highest for both the post-burn and pre/post-burn approaches. The NBR had high correlations with the field-based CBI in closed needleleaf, mixed, and broadleaf forest classes. However, the NBR was useful as an index of burn severity only for forested sites. The correlation between NBR and field-based CBI was low in non-forested classes such as woodland, scrub, and herb land cover classes.
As part of a long-term moose browse/fire severity study, we used the Normalized Burn Ratio (NBR) with historic Landsat Thematic Mapper (TM) imagery to estimate fire severity from a 1983 wildfire in ...interior Alaska. Fire severity was estimated in the field by measuring the depth of the organic soil at 57 sites during the summer of 2006. Sites were selected for field sampling from five fire severity classes based on threshold NBR values. The linear relationship between post-fire NBR and organic soil depth among sites within the burn was weak (r
2
= 0.26), and improved substantially (r
2
= 0.66) when restricted to non-wetland black spruce sites. The relationship between NBR and aspen/willow counts was non-linear. Sites with high densities of aspen stems consistently occurred in the high fire severity classes, and sites with high willow stem densities consistently occurred in the moderate fire severity class. However, NBR varied substantially from sites with low aspen or willow reproduction and therefore predicting aspen or willow regeneration based on post-fire NBR values would be difficult.
The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) Product (MOD15A2) was evaluated for the growing seasons of 2000 through 2004 in Alaska. The LAI estimate may be ...affected by three factors not directly related to canopy leaf area: snow melt, cloud contamination and conifer forest versus broadleaf shrub canopy type. Increases in MODIS LAI values occurred during the snowmelt period, prior to leaf flush along an elevation gradient in central Alaska. This false LAI increase prior to broadleaf budburst could lead to an overestimate of growing season length based on the MODIS LAI product. During the spring greenup period, there were temporal dips in MODIS LAI estimates for up to 57% of the pixels. This decrease in MODIS LAI value was likely due to cloud contamination, despite use of the MODIS quality control information to select pixels that were cloud-free. The MODIS LAI algorithm may be sensitive to variation in near-infrared reflectance due to canopy type rather than leaf area. For example, coniferous boreal forests typically have a higher LAI than shrub tundra. However, the maximum seasonal LAI estimate from the MODIS product was consistently higher from shrub tundra areas compared to coniferous boreal forest areas. There was a strong correlation of the MODIS LAI estimate with MODIS near-infrared reflectance among conifer and broadleaf shrub frames. This could lead to overestimates of LAI in areas where coniferous forest is replaced by broadleaf shrub following wildfire in boreal forest regions.
Field and aircraft measurements were acquired in April 1995 in central Alaska to map snow cover with MODIS Airborne Simulator (MAS) data, acquired from high-altitude aircraft. The Earth Observing ...System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) is a 36-channel system that will be launched on the EOS-AM-1 platform in 1999. A vegetation-density map derived from integrated reflectances (
R
i
), from MAS data, is compared with an independently-produced vegetation type and density map derived from Thematic Mapper (TM) and ancillary data. The maps agreed to within 13%, thus corroborating the effectiveness of using the reflectance technique for mapping vegetation density. Snow cover was mapped on a 13 April 1995 MAS image, using the original MODIS prototype algorithm and an enhanced MODIS prototype algorithm. Field measurements revealed that the area was completely snow covered. With the original algorithm, snow was mapped in 96% of the pixels having <50% vegetation-cover density according to the
R
i
map, while in the areas having vegetation-cover densities ⩾50%, snow was mapped in only 71% of the pixels. When the enhanced MODIS snow-mapping algorithm was employed, 99% of the pixels having <50% vegetation-cover density were mapped, and 98% of the pixels with ⩾50% vegetation-cover density were mapped as snow covered. These results demonstrate that the enhanced algorithm represents a significant improvement over the original MODIS prototype algorithm especially in the mapping of snow in dense vegetation. The enhanced algorithm will thus be adopted as the MODIS at-launch snow-cover algorithm. Using this simple method for estimating vegetation density from pixel reflectance, it will be possible to analyze the accuracy of the MODIS snow-cover algorithm in a range of vegetation-cover in places where information on vegetation-cover density is not available from ground measurements.
Vegetation on the Seward Peninsula, Alaska, which is characterized by transitions from tundra to boreal forest, may be sensitive to the influences of climate change on disturbance and species ...composition. To determine the ability to detect decadal-scale structural changes in vegetation, Change Vector Analysis (CVA) techniques were evaluated for Landsat Thematic Mapper (TM) imagery of the Seward Peninsula from 1986 to 1999. Scenes were geographically corrected to sub-pixel accuracy and then radiometrically rectified. Between the 1986 and 1992 satellite scenes, the CVA detected changes in direction and magnitude of the two indices (TM Band 4/TM Band 3, TM Band 5). For Row 14, change was detected for 135,518 ha and for Row 15, change was detected for 111,831 ha. Between the 1992 and 1999 scenes, change was detected by CVA for 93,278 ha. CVA results and photo interpretation together show that shrub advance is approximately 100 metres in valleys north of the Bendeleben Mountains and that shrubs have increased along riverbed bottoms. Across Path 78 Row 14 and 15, the unsupervised classification detected that 55% of the pixels changed between 1986 and 1992. Overall, approximately 759,610 ha changed to a class with a more developed canopy and only 268,132 ha changed to a class with a less developed canopy. Thus, the change detection analysis based on the unsupervised classification indicates that land-cover change on the Seward Peninsula was predominantly in the direction of increased shrubbiness. Taken together, our comparison of CVA results, unsupervised classification results, and visual interpretation of aerial photographs suggests that shrub cover may be increasing on the Seward Peninsula, which is consistent with results from experimental warming in tundra. The use of both CVA and unsupervised classification together provided a more powerful interpretation of change than either method alone in transitional regions between tundra and boreal forest.Original Abstract: La vegetation dans la Peninsule de Seward, en Alaska, caracterisee par des transitions de la toundra a la foret boreale, pourrait etre sensible aux influences du changement climatique au plan de la modification ou de la composition des especes. Pour determiner le potentiel de detection des changements structuraux dans la vegetation a l'echelle decadaire, des techniques d'analyse du vecteur de changement (AVC) ont ete evaluees pour des images Thematic Mapper (TM) de Landsat de la Peninsule de Seaward de 1986 a 1999. Les scenes ont ete corrigees geographiquement a une precision a l'echelle du souspixel et ensuite rectifiees radiometriquement. Entre les images de 1986 et 1992, l'analyse AVC a permis de detecter des changements dans la direction et l'amplitude des deux indices (bande 4 de TM/bande 3 de TM, bande 5 de TM). Pour la rangee 14, on a detecte un changement sur 135,518 ha et, pour la rangee 15, on a detecte un changement sur 111,831 ha. Entre les scenes de 1992 et 1999, base sur l'utilisation de l'analyse AVC, on a observe un changement sur 93,278 ha. Les resultats de l'AVC et de la photo-interpretation ensemble montrent que l'avancee des arbustes equivaut a 100 m dans les vallees au nord des Monts Bendeleben et que le couvert d'arbustes s'est accru le long des lits de rivieres. A travers la trajectoire 78, rangee 14 et 15, la classification non dirigee a permis de detecter que 55% des pixels ont subi un changement entre 1986 et 1992. Globalement, approximativement 759,610 ha ont change vers une classe caracterisee par un couvert mieux developpe et seulement 268,132 ha ont change vers une classe caracterisee par un couvert moins developpe. Ainsi, l'analyse de detection du changement basee sur la classification non dirigee indique que le changement du couvert dans la Peninsule de Seward s'est effectue principalement en fonction d'un accroissement dans la strate arbustive. Ensemble, notre comparaison des resultats AVC, des resultats de la classification non dirigee et de l'interpretation visuelle des photographies aeriennes suggere que le couvert arbustif pourrait etre en croissance dans la Peninsule de Seward, ce qui est coherent avec les resultats des experiences touchant le rechauffement dans la toundra. L'utilisation conjointe de l'analyse AVC et de la classification non dirigee represente un outil d'interpretation plus performant du changement que l'utilisation d'une seule de ces deux methodes dans les regions de transition, entre la toundra et la foret boreale.
Land cover change may be overestimated due to positional error in multi-temporal images. To assess the potential magnitude of this bias, we introduced random positional error to identical classified ...images and then subtracted them. False land cover change ranged from less than 5% for a 5-class AVHRR classification, to more than 33% for a 20-class Landsat TM classification. The potential for false change was higher with more classes. However, false change could not be reliably estimated simply by number of classes, since false change varied significantly by simulation trial when class size remained constant. Registration model root mean squared (rms) error may underestimate the actual image co-registration asccuracy. In simulations with 5 to 50 ground control locations, the mean model rms error was always less than the actual population rms error. The model rms error was especially unreliable when small sample sizes were used to develop second order rectification models. We introduce a bootstrap resampling method to estimate false land cover change due to positional error. Although the bootstrap estimates were unbiased, the precision of the estimates may be too low to be of practical value in some land cover change applications.
The biodiversity-productivity relationship (BPR) is foundational to our understanding of the global extinction crisis and its impacts on ecosystem functioning. Understanding BPR is critical for the ...accurate valuation and effective conservation of biodiversity. Using ground-sourced data from 777,126 permanent plots, spanning 44 countries and most terrestrial biomes, we reveal a globally consistent positive concave-down BPR, showing that continued biodiversity loss would result in an accelerating decline in forest productivity worldwide. The value of biodiversity in maintaining commercial forest productivity alone-US$166 billion to 490 billion per year according to our estimation-is more than twice what it would cost to implement effective global conservation. This highlights the need for a worldwide reassessment of biodiversity values, forest management strategies, and conservation priorities.
Three satellite fire detection models (threshold, contextual, and fuel mask) were compared and evaluated using National Oceanographic and Atmospheric Administration (NOAA)-11, NOAA-12, and NOAA-14 ...Advanced Very High Resolution Radiometer sensor data from interior Alaska. The fixed threshold model compared the radiant temperature of each pixel to predetermined threshold values. The contextual model compared the radiant temperature of each pixel to its surrounding (background) pixels. The fuel mask model is similar to the contextual model, but pixels were tested for fuel availability according to pre-fire vegetation index values. Fire location data from the Alaska Fire Service was used to assess the accuracy of the fire detection models. Fire detection accuracy: (a) was highest using the fuel mask model; (b) was lowest using the fixed threshold model; (c) increased as fire size increased; (d) was considerably greater in afternoon images than morning or night images. Fire detection methods may be less accurate in taiga/tundra regions such as interior Alaska due to landscape heterogeneity and relatively low aboveground fuel.
Arctic vegetation distribution is largely controlled by climate, particularly summer temperatures. Summer temperatures have been increasing in the Arctic and this trend is expected to continue. ...Arctic vegetation has been shown to change in response to increases in summer temperatures, which in turn affects arctic fauna, human communities and industries. An understanding of the relationship of existing plant communities to temperature is important in order to monitor change effectively. In addition, variation along existing climate gradients can help predict where and how vegetation changes may occur as climate warming continues. In this study we described the spatial relationship between satellite-derived land surface temperature (LST), circumpolar arctic vegetation, and normalized difference vegetation index (NDVI). LST, mapped as summer warmth index (SWI), accurately portrayed temperature gradients due to latitude, elevation and distance from the coast. The SWI maps also reflected NDVI patterns, though NDVI patterns were more complex due to the effects of lakes, different substrates and different-aged glacial surfaces. We found that for the whole Arctic, a 5 °C increase in SWI along the climate gradient corresponded to an increase in NDVI of approximately 0.07. This result supports and is of similar magnitude as temporal studies showing increases of arctic NDVI corresponding to increases in growing season temperatures over the length of the satellite record. The strongest positive relationship between NDVI and SWI occurred in partially vegetated and graminoid vegetation types. Recently deglaciated areas, areas with many water bodies, carbonate soil areas, and high mountains had lower NDVI values than predicted by SWI. Plant growth in these areas was limited by substrate factors as well as temperature, and thus is likely to respond less to climate warming than other areas.
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