Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data

Monitoring and understanding climate-induced changes in the boreal and arctic vegetation is critical to aid in prognosticating their future. Weused a 33 year (1982-2014) long record of satellite observations to robustly assess changes in metrics of growing season (onset: SOS, end: EOS and length: LOS) and seasonal total gross primary productivity. Particular attention was paid to evaluating the accuracy of these metrics by comparing them to multiple independent direct and indirect growing season and productivity measures. These comparisons reveal that the derived metrics capture the spatio-temporal variations and trends with acceptable significance level (generally p < 0.05). We find that LOS has lengthened by 2.60 d dec(-1) (p < 0.05) due to an earlier onset of SOS (-1.61 d dec(-1), p < 0.05) and a delayed EOS (0.67 d dec(-1), p < 0.1) at the circumpolar scale over the past three decades. Relatively greater rates of changes in growing season were observed in Eurasia (EA) and in boreal regions than in North America (NA) and the arctic regions. However, this tendency of earlier SOS and delayed EOS was prominent only during the earlier part of the data record (1982-1999). During the later part (2000-2014), this tendency was reversed, i.e. delayed SOS and earlier EOS. As for seasonal total productivity, we find that 42.0% of northern vegetation shows a statistically significant (p < 0.1) greening trend over the last three decades. This greening translates to a 20.9% gain in productivity since 1982. In contrast, only 2.5% of northern vegetation shows browning, or a 1.2% loss of productivity. These trends in productivity were continuous through the period of record, unlike changes in growing season metrics. Similarly, we find relatively greater increasing rates of productivity in EA and in arctic regions than in NA and the boreal regions. These results highlight spatially and temporally varying vegetation dynamics and are reflective of biome-specific responses of northern vegetation during last three decades

Latitudinal gradient of spruce forest understory and tundra phenology in Alaska as observed from satellite and ground-based data

The latitudinal gradient of the start of the growing season (SOS) and the end of the growing season (EOS) were quantified in Alaska (61°N to 71°N) using satellite-based and ground-based datasets. The Alaskan evergreen needleleaf forests are sparse and the understory vegetation has a substantial impact on the satellite signal. We evaluated SOS and EOS of understory and tundra vegetation using time-lapse camera images. From the comparison of three SOS algorithms for determining SOS from two satellite datasets (SPOT-VEGETATION and Terra-MODIS), we found that the satellite-based SOS timing was consistent with the leaf emergence of the forest understory and tundra vegetation. The ensemble average of SOS over all satellite algorithms can be used as a measure of spring leaf emergence for understory and tundra vegetation. In contrast, the relationship between the ground-based and satellite-based EOSs was not as strong as that of SOS both for boreal forest and tundra sites because of the large biases between those two EOSs (19 to 26 days). The satellite-based EOS was more relevant to snowfall events than the senescence of understory or tundra. The plant canopy radiative transfer simulation suggested that 84–86% of the NDVI seasonal amplitude could be a reasonable threshold for the EOS determination. The latitudinal gradients of SOS and EOS evaluated by the satellite and ground data were consistent and the satellite-derived SOS and EOS were 3.5 to 5.7 days degree− 1 and − 2.3 to − 2.7 days degree− 1, which corresponded to the spring (May) temperature sensitivity of − 2.5 to − 3.9 days °C− 1 in SOS and the autumn (August and September) temperature sensitivity of 3.0 to 4.6 days °C− 1 in EOS. This demonstrates the possible impact of phenology in spruce forest understory and tundra ecosystems in response to climate change in the warming Artic and sub-Arctic regions

Mapping the Birch and Grass Pollen Seasons in the UK Using Satellite Sensor Time-series

Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15-20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK

Glacier surface velocity estimation using SAR interferometry technique applying ascending and descending passes in Himalayas

In this study ascending and descending passes interferometric synthetic aperture radar (InSAR) techniques are used for glacier surface velocity estimation in the Himalaya. Single-track interferometric measurements are sensitive to only a single component of the three dimensional (3-D) velocity vectors. European Remote Sensing satellites (ERS-1/2) tandem mission data in ascending and descending tracks provide an opportunity to resolve the three velocity components under the assumption that glacier flow is parallel to its surface. Using the surface slope as an essential input in this technique the velocity pattern of Siachen glacier in Himalaya has been modelled. Glaciers in the Himalayan region maintain excellent coherence of SAR return signals in one-day temporal difference. As a result we could obtain spatially continuous surface velocity field with a precision of fraction of radar wavelength. The results covering the main course of glacier are analysed in terms of spatial and temporal variations. A maximum velocity of 43 cm/day has been observed in the upper middle portion of the glacier. This technique has been found accurate for monitoring the flow rates in this region, suggesting that routine monitoring of diurnal movement Himalayan glaciers would be immensely useful in the present day context of climate change. (C) 2011 Elsevier B.V. All rights reserved

Estimation and validation of glacier surface motion in the northwestern Himalayas using high-resolution SAR intensity tracking

We estimate two-dimensional (2D) glacier surface motion using synthetic aperture radar (SAR) X-band intensity tracking. It has been observed that the viability of SAR interferometry (InSAR) is often limited by coherence loss over glaciers in landlocked regions using SAR data pairs of more than 1 day temporal baseline. An alternative to InSAR is the intensity-tracking approach, which relies on intensity cross-correlation for the estimation of subpixel surface motion in range and azimuth direction. In this work, we apply this approach for 2D glacier surface motion estimation in the north-western (NW) Himalayas, using TerraSAR-X (TS-X) spotlight mode high-resolution data pairs of 11, 22, and 33 day temporal separation. The results are in good agreement with total station surveying measurements synchronous with the satellite data acquisition period. The technique is found to be highly appropriate for monitoring the flow rate of glaciers in the Himalayas on a multitemporal basis

Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI

Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35 × 162 km2, 68° N 23° E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology

A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula

An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005. To create the dataset, (1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel-specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and (2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor-quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km×5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates (root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula

The onset of spring and timing of migration in two arctic nesting goose populations: the pink-footed goose Anser bachyrhynchus and the barnacle goose Branta leucopsis

An earlier onset of spring has been recorded for many parts of Eurasia in recent decades. This has consequences for migratory species, both in changing the conditions encountered by individuals on reaching migratory sites and in affecting cues regulating the timing of migration where decisions to migrate are influenced by local environmental variables. Here we examine the timing of spring migration for two arctic goose populations, the pink-footed goose Anser brachyrhynchus (during 1990-2003) and barnacle goose Branta leucopsis (during 1982-2003), which both breed on Svalbard. The satellite-derived Normalised Difference Vegetation Index (NDVI) was used to express the onset of spring at their wintering and spring staging sites. Pink-footed geese use several sites during spring migration, ranging from the southernmost wintering areas in Belgium to two spring staging areas in Norway, and distances between sites used along the flyway are relatively short. There was a positive correlation in the onset of spring between neighbouring sites, and the geese migrated earlier in early springs. Barnacle geese, on the other hand, have a long overseas crossing from their wintering grounds in Britain to spring staging areas in Norway. Although spring advanced in both regions, there was no corresponding correlation in the timing of onset of spring between their wintering and spring staging sites, and little evidence for barnacle geese migrating earlier over the whole study period. Hence, where geese can use spring conditions at one site as an indicator of the conditions they might encounter at the next, they have responded quickly to the advancement of spring, whereas in a situation where they cannot predict, they have not yet responded, despite the advancement of spring in the spring staging area