Recent trends in the timing of the growing season in New Zealand’s natural and semi-natural grasslands

We investigate the temporal dynamics of shifts in phenological responses of a range of key stages of the growing season in New Zealand’s three indigenous grassland types over the last 16 years (2001–2016). A near-daily Normalized Difference Vegetation Index (NDVI) time series from MODerate Resolution Imaging Spectroradiometer (MODIS) was used to extract five annual growth phenology indices, namely the Start, End, Length, Peak and Peak NDVI of a growing season. The start of the growing season advanced (i.e. happened earlier) by a median of 7.2, 6.0 and 8.8 days per decade in Alpine, Tall Tussock and Low Producing grassland, whereas the end of the season advanced by a median of 4.5, 0.4 and 0.4 days in the three types respectively. The length of growing season was extended by 3.2, 5.2 and 7.1 days per decade in these three grassland types. Over 86% of the investigated grassland areas showed an advancing (earlier) start of the growing season, and 74% of Alpine grassland showed a trend toward an earlier end of season. Over 63% of all grassland types showed an increase in growing season length. A trend toward earlier growing season peak and overall increasing NDVI in the three grassland types indicate a tendency for increasing vegetation vitality in grassland ecosystems in recent years. The start of growing season was correlated with atmospheric pressure (negatively) and precipitation (positively) changes in winter–spring months, while the timing of the season end is positively correlated with air temperature and solar radiation in summer–autumn months. Our study shows that different grassland types differ in magnitude–but not in direction–of their recent shifts in timing of key growing season stages with high-alpine grasslands showing the strongest response. This study highlights the usefulness of remote sensing for monitoring ecosystem-level phenological shifts over large areas and long time periods

Linking glacier annual mass balance and glacier albedo retrieved from MODIS data

Albedo is one of the variables controlling the mass balance of temperate glaciers. Multispectral imagers, such as MODerate Imaging Spectroradiometer (MODIS) on board the TERRA and AQUA satellites, provide a means to monitor glacier surface albedo. In this study, different methods to retrieve broadband glacier surface albedo from MODIS data are compared. The effect of multiple reflections due to the rugged topography and of the anisotropic reflection of snow and ice are particularly investigated. The methods are tested on the Saint Sorlin Glacier (Grandes Rousses area, French Alps). The accuracy of the retrieved albedo is estimated using both field measurements, at two automatic weather stations located on the glacier, and albedo values derived from terrestrial photographs. For summers 2008 and 2009, the root mean square deviation (RMSD) between field measurements and the broadband albedo retrieved from MODIS data at 250 m spatial resolution was found to be 0.052 or about 10% relative error. The RMSD estimated for the MOD10 daily albedo product is about three times higher. One decade (2000–2009) of MODIS data were then processed to create a time series of albedo maps of Saint Sorlin Glacier during the ablation season. The annual mass balance of Saint Sorlin Glacier was compared with the minimum albedo value (average over the whole glacier surface) observed with MODIS during the ablation season. A strong linear correlation exists between the two variables. Furthermore, the date when the average albedo of the whole glacier reaches a minimum closely corresponds to the period when the snow line is located at its highest elevation, thus when the snow line is a good indicator of the glacier equilibrium line. This indicates that this strong correlation results from the fact that the minimal average albedo values of the glacier contains considerable information regarding the relative share of areal surfaces between the ablation zone (i.e. ice with generally low albedo values) and the accumulation zone (i.e. snow with a relatively high albedo). As a consequence, the monitoring of the glacier surface albedo using MODIS data can provide a useful means to evaluate the interannual variability of the glacier mass balance. Finally, the albedo in the ablation area of Saint Sorlin Glacier does not exhibit any decreasing trend over the study period, contrasting with the results obtained on Morteratsch Glacier in the Swiss Alps

Repeat mapping of snow depth across an alpine catchment with RPAS photogrammetry

Being dynamic in time and space, seasonal snow represents a difficult target for ongoing in situ measurement and characterisation. Improved understanding and modelling of the seasonal snowpack requires mapping snow depth at fine spatial resolution. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial variability of snow depth is evaluated within an alpine catchment of the Pisa Range, New Zealand. Digital surface models (DSMs) at 0.15&thinsp;m spatial resolution in autumn (snow-free reference) winter (2 August 2016) and spring (10 September 2016) allowed mapping of snow depth via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by the propagation of check point residuals from the aero-triangulation of constituent DSMs and via comparison of snow-free regions of the spring and autumn DSMs. The accuracy of RPAS-derived snow depth was validated with in situ snow probe measurements. Results for snow-free areas between DSMs acquired in autumn and spring demonstrate repeatability yet also reveal that elevation errors follow a distribution that substantially departs from a normal distribution, symptomatic of the influence of DSM co-registration and terrain characteristics on vertical uncertainty. Error propagation saw snow depth mapped with an accuracy of ±0.08&thinsp;m (90&thinsp;% c.l.). This is lower than the characterization of uncertainties on snow-free areas (±0.14&thinsp;m). Comparisons between RPAS and in situ snow depth measurements confirm this level of performance of RPAS photogrammetry while also highlighting the influence of vegetation on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine-scale spatial variability. Despite limitations accompanying RPAS photogrammetry, which are relevant to similar applications of surface and volume change analysis, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological catchment ( ∼ 0.4&thinsp;km2) at very high resolution. Resolving snowpack features associated with redistribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data will enhance understanding of physical processes controlling spatial distributions of seasonal snow and their relative importance on varying spatial and temporal scales.</p

Perennial snow and ice variations (2000–2008) in the Arctic circumpolar land area from satellite observations

Perennial snow and ice (PSI) extent is an important parameter of mountain environments with regard to its involvement in the hydrological cycle and the surface energy budget. We investigated interannual variations of PSI in nine mountain regions of interest (ROI) between 2000 and 2008. For that purpose, a novel MODIS data set processed at the Canada Centre for Remote Sensing at 250 m spatial resolution was utilized. The extent of PSI exhibited significant interannual variations, with coefficients of variation ranging from 5% to 81% depending on the ROI. A strong negative relationship was found between PSI and positive degree‐days (threshold 0°C) during the summer months in most ROIs, with linear correlation coefficients (r) being as low as r = −0.90. In the European Alps and Scandinavia, PSI extent was significantly correlated with annual net glacier mass balances, with r = 0.91 and r = 0.85, respectively, suggesting that MODIS‐derived PSI extent may be used as an indicator of net glacier mass balances. Validation of PSI extent in two land surface classifications for the years 2000 and 2005, GLC‐2000 and Globcover, revealed significant discrepancies of up to 129% for both classifications. With regard to the importance of such classifications for land surface parameterizations in climate and land surface process models, this is a potential source of error to be investigated in future studies. The results presented here provide an interesting insight into variations of PSI in several ROIs and are instrumental for our understanding of sensitive mountain regions in the context of global climate change assessment

Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand : methodology and accuracy assessment

International audienceThis study describes a comprehensive method to produce routinely regional maps of seasonal snow cover in the Southern Alps of New Zealand (upper Waitaki basin) on a subpixel basis, and with the MODerate Resolution Imaging Spectroradiometer (MODIS). The method uses an image fusion algorithm to produce snow maps at an improved 250 m spatial resolution in addition to the 500 m resolution snow maps. An iterative approach is used to correct imagery for both atmospheric and topographic effects using daily observations of atmospheric parameters. The computation of ground spectral reflectance enabled the use of image-independent end-members in a constrained linear unmixing technique to achieve a robust estimation of subpixel snow fractions. The accuracy of the snow maps and performance of the algorithm were assessed carefully using eight pairs of synchronic MODIS/ASTER images. ‘Pixel-based' metrics showed that subpixel snow fractions were retrieved with a Mean Absolute Error of 6.8% at 250 m spatial resolution and 5.1% after aggregation at 500 m spatial resolution. In addition, a ‘feature-based' metric showed that 90% of the snowlines were depicted generally within 300 m and 200 m of their correct position for the 500-m and 250-m spatial resolution snow maps, respectively. A dataset of 679 maps of subpixel snow fraction was produced for the period from February 2000 to May 2007. These repeated observations of the seasonal snow cover will benefit the ongoing effort to model snowmelt runoff in the region and to improve the estimation and management of water resources

A century of ice retreat on Kilimanjaro: the mapping reloaded

A new and consistent time series of glacier retreat on Kilimanjaro over the last century has been established by re-interpreting two historical maps and processing nine satellite images, which removes uncertainty about the location and extent of past and present ice bodies. Three-dimensional visualization techniques were used in conjunction with aerial and ground-based photography to facilitate the interpretation of ice boundaries over eight epochs between 1912 and 2011. The glaciers have retreated from their former extent of 11.40 km2 in 1912 to 1.76 km2 in 2011, which represents a total loss of about 85% of the ice cover over the last 100 yr. The total loss of ice cover is in broad agreement with previous estimates, but to further characterize the spatial and temporal variability of glacier retreat a cluster analysis using topographical information (elevation, slope and aspect) was performed to segment the ice cover as observed in 1912, which resulted in three glacier zones being identified. Linear extrapolation of the retreat in each of the three identified glacier assemblages implies the ice cover on the western slopes of Kilimanjaro will be gone before 2020, while the remaining ice bodies on the plateau and southern slopes will most likely disappear by 2040. It is highly unlikely that any body of ice will be present on Kilimanjaro after 2060 if present-day climatological conditions are maintained. Importantly, the geo-statistical approach developed in this study provides us with an additional tool to characterize the physical processes governing glacier retreat on Kilimanjaro. It remains clear that, to use glacier response to unravel past climatic conditions on Kilimanjaro, the transition from growth to decay of the plateau glaciers must be further resolved, in particular the mechanisms responsible for vertical cliff development