Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis

Pursuit and escape drive fine-scale movement variation during migration in a temperate alpine ungulate

Climate change reduces snowpack, advances snowmelt phenology, drives summer warming, alters growing season precipitation regimes, and consequently modifies vegetation phenology in mountain systems. Elevational migrants track spatial variation in seasonal plant growth by moving between ranges at different elevations during spring, so climate-driven vegetation change may disrupt historic benefits of migration. Elevational migrants can furthermore cope with short-term environmental variability by undertaking brief vertical movements to refugia when sudden adverse conditions arise. We uncover drivers of fine-scale vertical movement variation during upland migration in an endangered alpine specialist, Sierra Nevada bighorn sheep (Ovis canadensis sierrae) using a 20-year study of GPS collar data collected from 311 unique individuals. We used integrated step-selection analysis to determine factors that promote vertical movements and drive selection of destinations following vertical movements. Our results reveal that relatively high temperatures consistently drive uphill movements, while precipitation likely drives downhill movements. Furthermore, bighorn select destinations at their peak annual biomass and maximal time since snowmelt. These results indicate that although Sierra Nevada bighorn sheep seek out foraging opportunities related to landscape phenology, they compensate for short-term environmental stressors by undertaking brief up- and downslope vertical movements. Migrants may therefore be impacted by future warming and increased storm frequency or intensity, with shifts in annual migration timing, and fine-scale vertical movement responses to environmental variability.

Woody encroachment induced earlier and extended growing season in boreal wetland ecosystems

Woody plant encroachment (WPE), a widespread ecological phenomenon globally, has significant impacts on ecosystem structure and functions. However, little is known about how WPE affects phenology in wetland ecosystems of middle and high latitudes. Here, we investigated the regional-scale effects of WPE on the start (SOS), peak (POS), end (EOS), and length (GSL) of the growing season in boreal wetland ecosystems, and their underlying mechanisms, using remote sensing dataset during 2001-2016. Our results showed that WPE advanced the annual SOS and POS, while delaying EOS and extending GSL in boreal wetlands with these impacts increasing over time. When boreal wetland ecosystems were fully encroached by woody plants, the SOS and POS were advanced by 12.17 and 5.65 days, respectively, the EOS was postponed by 2.74 days, and the GSL was extended by 15.21 days. We also found that the impacts of WPE on wetland SOS were predominantly attributed to the increased degree of WPE (α), while climatic factors played a more significant role in controlling the POS and EOS responses to WPE. Climate change not only directly influenced phenological responses of wetlands to WPE but also exerted indirect effects by regulating soil moisture and α. Winter precipitation and spring temperature primarily determined the effects of WPE on SOS, while its impacts on POS were mainly controlled by winter precipitation, summer temperature, and precipitation, and the effects on EOS were mainly determined by winter precipitation, summer temperature, and autumn temperature. Our findings offer new insights into the understanding of the interaction between WPE and wetland ecosystems, emphasizing the significance of considering WPE effects to ensure accurate assessments of phenology changes.

Spatial Variation in Responses of Plant Spring Phenology to Climate Warming in Grasslands of Inner Mongolia: Drivers and Application

Plant spring phenology in grasslands distributed in the Northern Hemisphere is highly responsive to climate warming. The growth of plants is intricately influenced by not only air temperature but also precipitation and soil factors, both of which exhibit spatial variation. Given the critical impact of the plant growth season on the livelihood of husbandry communities in grasslands, it becomes imperative to comprehend regional-scale spatial variation in the response of plant spring phenology to climate warming and the effects of precipitation and soil factors on such variation. This understanding is beneficial for region-specific phenology predictions in husbandry communities. In this study, we analyzed the spatial pattern of the correlation coefficient between the start date of the plant growth season (SOS) and the average winter-spring air temperature (WST) of Inner Mongolia grassland from 2003 to 2019. Subsequently, we analyzed the importance of 13 precipitation and soil factors for the correlation between SOS and average WST using a random forest model and analyzed the interactive effect of the important factors on the SOS using linear mixing models (LMMs). Based on these, we established SOS models using data from pastoral areas within different types of grassland. The percentage of areas with a negative correlation between SOS and average WST in meadow and typical grasslands was higher than that in desert grasslands. Results from the random forest model highlighted the significance of snow cover days (SCD), soil organic carbon (SOC), and soil nitrogen content (SNC) as influential factors affecting the correlation between SOS and average WST. Meadow grasslands exhibited significantly higher levels of SCD, SOC, and SNC compared to typical and desert grasslands. The LMMs indicated that the interaction of grassland type and the average WST and SCD can effectively explain the variation in SOS. The multiple linear models that incorporated both average WST and SCD proved to be better than models utilizing WST or SCD alone in predicting SOS. These findings indicate that the spatial patterns of precipitation and soil factors are closely associated with the spatial variation in the response of SOS to climate warming in Inner Mongolia grassland. Moreover, the average WST and SCD, when considered jointly, can be used to predict plant spring phenology in husbandry communities.

Finding food in a changing world: Small-scale foraging habitat preferences of an insectivorous passerine in the Alps

Organisms living in high-elevation habitats are usually habitat specialists who occupy a narrow ecological niche. To envision the response of alpine species to a changing environment, it is fundamental to understand their habitat preferences on multiple spatial and temporal scales. However, information on small-scale habitat use is still widely lacking. We investigated the foraging habitat preferences of the migratory northern wheatear Oenanthe oenanthe during the entire presence at a breeding site in the central Alps. We repeatedly observed 121 adult and juvenile individuals. We applied Bayesian logistic regression models to investigate which habitat characteristics influenced foraging habitat selection on a fine spatial scale, and how habitat use varied temporally. Throughout their presence on the breeding grounds, northern wheatears showed a consistent preference for a mosaic of stones and bare ground patches with slow-growing, short vegetation. The proximity of marmot burrows was preferred, whereas dense and low woody vegetation was avoided. After arrival at the breeding site, short vegetation, preferably close to the snow, was favored. The preference for open habitat patches that provide access to prey underlines the critical role of small-scale habitat heterogeneity for northern wheatears. The strong and consistent preference for a habitat that is under pressure from land-use and climate change suggests that this alpine bird species may be sensitive to habitat loss, leading to a potential range contraction. We highlight the need to conserve habitat diversity on a small spatial scale to ensure the long-term availability of suitable habitat for northern wheatears in the Alps.

Spatial predictors and temporal forecast of total organic carbon levels in boreal lakes

Browning of Fennoscandian boreal lakes is raising concerns for negative ecosystem impacts as well as reduced drinking water quality. Declined sulfur deposition and warmer climate, along with afforestation, other climate impacts and less outfield grazing, have resulted in increased fluxes of Total Organic Carbon (TOC) from catchments to freshwater, and subsequently to coastal waters. This study assesses the major governing factors for increased TOC levels among several catchment characteristics in almost 5000 Fennoscandian lakes and catchments. Normalized Difference Vegetation Index (NDVI), a proxy for plant biomass, and the proportions of peatland in the catchment, along with surface runoff intensity and nitrogen deposition loading, were identified as the main spatial predictors for lake TOC concentrations. A multiple linear model, based on these explanatory variables, was used to simulate future TOC concentration in surface runoff from coastal drainage basins in 2050 and 2100, using the forecasts of climatic variables in two of the Shared Socio-economic Pathways (SSP): 1-2.6 (+2 °C) and 3-7.0 (+4,5 °C). These scenarios yield contrasting effects. SSP 1-2.6 predicts an overall decrease of TOC export to coastal waters, while SSP 3-7.0 in contrast leads to an increase in TOC export.

Vessels in a Rhododendron ferrugineum (L.) population do not trace temperature anymore at the alpine shrubline

INTRODUCTION

Mean xylem vessel or tracheid area have been demonstrated to represent powerful proxies to better understand the response of woody plants to changing climatic conditions. Yet, to date, this approach has rarely been applied to shrubs.

METHODS

Here, we developed a multidecadal, annually-resolved chronology of vessel sizes for Rhododendron ferrugineum shrubs sampled at the upper shrubline (2,550 m asl) on a north-facing, inactive rock glacier in the Italian Alps.

RESULTS AND DISCUSSION

Over the 1960-1989 period, the vessel size chronology shares 64% of common variability with summer temperatures, thus confirming the potential of wood anatomical analyses on shrubs to track past climate variability in alpine environments above treeline. The strong winter precipitation signal recorded in the chronology also confirms the negative effect of long-lasting snow cover on shrub growth. By contrast, the loss of a climate-growth relation signal since the 1990s for both temperature and precipitation, significantly stronger than the one found in radial growth, contrasts with findings in other QWA studies according to which stable correlations between series of anatomical features and climatic parameters have been reported. In a context of global warming, we hypothesize that this signal loss might be induced by winter droughts, late frost, or complex relations between increasing air temperatures, permafrost degradation, and its impacts on shrub growth. We recommend future studies to validate these hypotheses on monitored rock glaciers.

Remote sensing hydrological indication: Responses of hydrological processes to vegetation cover change in mid-latitude mountainous regions

Hydrological processes in mid-latitude mountainous regions are greatly affected by changes in vegetation cover that induced by the climate change. However, studies on hydrological processes in mountainous regions are limited, because of difficulties in building and maintaining basin-wide representative hydrological stations. In this study, a new method, remote sensing technology for monitoring river discharge by combining satellite remote sensing, unmanned aerial vehicles and hydrological surveying, was used for evaluating the runoff processes in the Changbai Mountains, one of the mid-latitude mountainous regions in the eastern part of Northeast China. Based on this method, the impact of vegetation cover change on hydrological processes was revealed by combining the data of hydrological processes, meteorology, and vegetation cover. The results showed a decreasing trend in the monitored river discharge from 2000 to 2021, with an average rate of -5.13 × 10⁵ m³ yr^-1. At the monitoring section mainly influenced by precipitation, the precipitation-induced proportion of changes in river discharge to annual average river discharge and its change significance was only 6.5 % and 0.23, respectively, showing the precipitation change was not the cause for the decrease in river discharge. A negative impact of evapotranspiration on river discharge was found, and the decrease in river discharge was proven to be caused by the increasing evapotranspiration, which was induced by the drastically increased vegetation cover under a warming climate. Our findings suggested that increases in vegetation cover due to climate change could reshape hydrological processes in mid-latitude mountainous regions, leading to an increase in evapotranspiration and a subsequent decrease in river discharge.

Intermediate snowpack melt-out dates guarantee the highest seasonal grasslands greening in the Pyrenees

In mountain areas, the phenology and productivity of grassland are closely related to snow dynamics. However, the influence that snow melt timing has on grassland growing still needs further attention for a full understanding, particularly at high spatial resolution. Aiming to reduce this knowledge gap, this work exploits 1 m resolution snow depth and Normalized Difference Vegetation Index observations acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees. During two snow seasons (2019-2020 and 2020-2021), 14 NDVI and 17 snow depth distributions were acquired over 48 ha. Despite the snow dynamics being different in the two seasons, the response of grasslands greening to snow melt-out exhibited a very similar pattern in both. The NDVI temporal evolution in areas with distinct melt-out dates reveals that sectors where the melt-out date occurs in late April or early May (optimum melt-out) reach the maximum vegetation productivity. Zones with an earlier or a later melt-out rarely reach peak NDVI values. The results obtained in this study area, suggest that knowledge about snow depth distribution is not needed to understand NDVI grassland dynamics. The analysis did not reveal a clear link between the spatial variability in snow duration and the diversity and richness of grassland communities within the study area.

Alpine Grassland Reviving Response to Seasonal Snow Cover on the Tibetan Plateau

Season snow cover plays an important role in vegetation growth in alpine regions. In this study, we analyzed the spatial and temporal variations in seasonal snow cover and the start of the growing season (SOS) of alpine grasslands and preliminarily studied the mechanism by which snow cover affects SOS changes by modifying the soil temperature (ST) and soil moisture (SM) in spring. The results showed that significant interannual trends in the SOS, snow end date (SED), snow cover days (SCD), ST, and SM existed over the Tibetan Plateau (TP) in China from 2000 to 2020. The SOS advanced by 2.0 d/10 a over the TP over this period. Moreover, the SOS showed advancing trends in the eastern and central parts of the TP and a delayed trend in the west. The SED and SCD exhibited an advancing trend and a decreasing trend in high-elevation areas, respectively, and the opposite trends in low-elevation areas. The ST showed a decreasing trend in low-elevation areas and an increasing trend in high-elevation areas. The SM tended to increase in most areas. The effects of the seasonal snow cover on the ST and SM indirectly influenced the SOS of alpine grasslands. The delayed SEDs and more SCD observed herein could provide increasingly wet soil conditions optimal for the advancement of the SOS, while less snow and shorter snow seasons could delay the SOS of alpine grasslands on the TP.

Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.

The tempo of greening in the European Alps: Spatial variations on a common theme

The long-term increase in satellite-based proxies of vegetation cover is a well-documented response of seasonally snow-covered ecosystems to climate warming. However, observed greening trends are far from uniform, and substantial uncertainty remains concerning the underlying causes of this spatial variability. Here, we processed surface reflectance of the moderate resolution imaging spectroradiometer (MODIS) to investigate trends and drivers of changes in the annual peak values of the Normalized Difference Vegetation Index (NDVI). Our study focuses on above-treeline ecosystems in the European Alps. NDVI changes in these ecosystems are highly sensitive to land cover and biomass changes and are marginally affected by anthropogenic disturbances. We observed widespread greening for the 2000-2020 period, a pattern that is consistent with the overall increase in summer temperature. At the local scale, the spatial variability of greening was mainly due to the preferential response of north-facing slopes between 1900 and 2400 m. Using high-resolution imagery, we noticed that the presence of screes and outcrops locally magnified this response. At the regional scale, we identified hotspots of greening where vegetation cover is sparser than expected given the elevation and exposure. Most of these hotspots experienced delayed snow melt and green-up dates in recent years. We conclude that the ongoing greening in the Alps primarily reflects the high responsiveness of sparsely vegetated ecosystems that are able to benefit the most from temperature and water-related habitat amelioration above treeline.

Early Breeding Conditions Followed by Reduced Breeding Success Despite Timely Arrival in an Alpine Migratory Songbird

Timing reproduction to coincide with optimal environmental conditions is key for many organisms living in seasonal habitats. Advance in the onset of spring is a particular challenge to migratory birds that must time their arrival without knowing the conditions on the breeding grounds. This is amplified at high elevations where resource availability, which is linked to snowmelt and vegetation development, shows much annual variation. With the aim of exploring the effects of variability in the onset of local resource availability on reproduction, we compared key life history events in an Alpine population of the Northern Wheatear (Oenanthe oenanthe) between years of contrasting timing of snowmelt. Based on remote sensed images, we identified 2020 as an exceptionally early snowmelt and green-up year compared to the preceding year and the long-term average. Individuals tracked with light-level geolocators arrived well before the snowmelt in 2020 and clutch initiation dates across the population were earlier in 2020 compared to 2019. However, observations from a citizen science database and nest monitoring data showed that the arrival-breeding interval was shorter in 2020, thus the advance in timing lagged behind the environmental conditions. While hatching success was similar in both years, fledging success was significantly reduced in 2020. A trophic mismatch in early 2020 could be a possible explanation for the reduced reproductive success, but alternative explanations cannot be excluded. Our results show that, despite the timely arrival at the breeding grounds and a contraction of the arrival-breeding interval, Wheatears were not able to advance breeding activities in synchrony with environmental conditions in 2020. Earlier reproductive seasons are expected to become more frequent in the future. We show that the negative effects of changing seasons in Alpine migratory birds might be similar to birds breeding at high latitudes, despite their shorter migratory distance.

Scale gaps in landscape phenology: challenges and opportunities

Phenology, or the timing of life history events, can be heterogeneous across biological communities and landscapes and can vary across a wide variety of spatiotemporal scales. Here, we synthesize information from landscape phenology studies across different scales of measurement around a set of core concepts. We highlight why phenology is scale dependent and identify gaps in the spatiotemporal scales of phenological observations and inferences. We discuss the consequences of these gaps and describe opportunities to address the inherent sensitivities of phenological metrics to measurement scale. Although most studies we review and discuss are focused on plants, our work provides a broadly relevant overview of the role of observation scale in landscape phenology and a general approach for measuring and reporting scale dependence.

Use of GIS and Remote Sensing Data to Understand the Impacts of Land Use/Land Cover Changes (LULCC) on Snow Leopard (Panthera uncia) Habitat in Pakistan

Habitat degradation and species range contraction due to land use/land cover changes (LULCC) is a major threat to global biodiversity. The ever-growing human population has trespassed deep into the natural habitat of many species via the expansion of agricultural lands and infrastructural development. Carnivore species are particularly at risk, as they demand conserved and well-connected habitat with minimum to no anthropogenic disturbance. In Pakistan, the snow leopard (Panthera uncia) is found in three mountain ranges—the Himalayas, Hindukush, and Karakoram. Despite this being one of the harshest environments on the planet, a large population of humans reside here and exploit surrounding natural resources to meet their needs. Keeping in view this exponentially growing population and its potential impacts on at-risk species like the snow leopard, we used geographic information systems (GIS) and remote sensing with the aim of identifying and quantifying LULCC across snow leopard range in Pakistan for the years 2000, 2010, and 2020. A massive expansion of 1804.13 km2 (163%) was observed in the built-up area during the study period. Similarly, an increase of 3177.74 km2 (153%) was observed in agricultural land. Barren mountain land increased by 12,368.39 km2 (28%) while forest land decreased by 2478.43 km2 (28%) and area with snow cover decreased by 14,799.83 km2 (52%). Drivers of these large-scale changes are likely the expanding human population and climate change. The overall quality and quantity of snow leopard habitat in Pakistan has drastically changed in the last 20 years and could be compromised. Swift and direct conservation actions to monitor LULCC are recommended to reduce any associated negative impacts on species preservation efforts. In the future, a series of extensive field surveys and studies should be carried out to monitor key drivers of LULCC across the observed area.

NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau

In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.

Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia

Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world’s protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability, we analyzed the potential of several state-of-the-art climate datasets (CHIRPS, CRU, ERA5-Land, GPCC-Monitoring-Product, IMERG-GPM, MERRA-2, MODIS-MOD10A1) for assessing NDVI anomalies (MODIS-MOD13Q1) in two particularly suitable remote conservation areas. We calculated anomalies of 156 climate variables and seasonal periods during 2001–2018, correlated these with vegetation anomalies while taking the multiple comparison problem into consideration, and computed their spatial performance to derive suitable parameters. Our results showed that four datasets (MERRA-2, ERA5-Land, MOD10A1, CRU) were suitable for vegetation analysis in both regions, by showing significant correlations controlled at a false discovery rate < 5% and in more than half of the analyzed areas. Cross-validated variable selection and importance assessment based on the Boruta algorithm indicated high importance of the reanalysis datasets ERA5-Land and MERRA-2 in both areas but higher differences and variability between the regions with all other products. CHIRPS, GPCC and the bias-corrected version of MERRA-2 were unsuitable and not important in both regions. We provide evidence that reanalysis datasets are most suitable for spatiotemporally consistent environmental analysis whereas gauge- or satellite-based products and their combinations are highly variable and may not be applicable in peripheral areas.

Impacts of Agroclimatic Variability on Maize Production in the Setsoto Municipality in the Free State Province, South Africa

The majority of people in South Africa eat maize, which is grown as a rain-fed crop in the summer rainfall areas of the country, as their staple food. The country is usually food secure except in drought years, which are expected to increase in severity and frequency. This study investigated the impacts of rainfall and minimum and maximum temperatures on maize yield in the Setsoto municipality of the Free State province of South Africa from 1985 to 2016. The variation of the agroclimatic variables, including the Palmer stress diversity index (PSDI), was investigated over the growing period (Oct–Apr) which varied across the four target stations (Clocolan, Senekal, Marquard and Ficksburg). The highest coefficients of variance (CV) recorded for the minimum and maximum temperatures and rainfall were 16.2%, 6.2% and 29% during the growing period. Non-parametric Mann Kendal and Sen’s slope estimator were used for the trend analysis. The result showed significant positive trends in minimum temperature across the stations except for Clocolan where a negative trend of 0.2 to 0.12 °C year−1 was observed. The maximum temperature increased significantly across all the stations by 0.04–0.05 °C year−1 during the growing period. The temperature effects were most noticeable in the months of November and February when leaf initiation and kernel filling occur, respectively. The changes in rainfall were significant only in Ficksburg in the month of January with a value of 2.34 mm year−1. Nevertheless, the rainfall showed a strong positive correlation with yield (r 0.46, p = < 0.05). The overall variation in maize production is explained by the contribution of the agroclimatic parameters; the minimum temperature (R2 0.13–0.152), maximum temperature (R2 0.214–0.432) and rainfall (R2 0.17–0.473) for the growing period across the stations during the study period. The PSDI showed dry years and wet years but with most of the years recording close to normal rainfall. An increase in both the minimum and maximum temperatures over time will have a negative impact on crop yield.

Land surface phenology and greenness in Alpine grasslands driven by seasonal snow and meteorological factors

Snow accumulation and melt have multiple impacts on Land Surface Phenology (LSP) and greenness in Alpine grasslands. Our understanding of these impacts and their interactions with meteorological factors are still limited. In this study, we investigate this topic by analyzing LSP dynamics together with potential drivers, using satellite imagery and other data sources. LSP (start and end of season) and greenness metrics were extracted from time series of vegetation and leaf area index. As explanatory variables we used snow accumulation, snow cover melt date and meteorological factors. We tested for inter-annual co-variation of LSP and greenness metrics with seasonal snow and meteorological metrics across elevations and for four sub-regions of natural grasslands in the Swiss Alps over the period 2003-2014. We found strong positive correlations of snow cover melt date and snow accumulation with the start of season, especially at higher elevation. Autumn temperature was found to be important at the end of season below 2000 m above sea level (m asl), while autumn precipitation was relevant above 2000 m asl, indicating climatic growth limiting factors to be elevation dependent. The effects of snow and meteorological factors on greenness revealed that this metric tends to be influenced by temperatures at high elevations, and by snow melt date at low elevations. Given the high sensitivity of alpine grassland ecosystems, these results suggest that alpine grasslands may be particularly affected by future changes in seasonal snow, to varying degree depending on elevation.

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

Daily MODIS Snow Cover Maps for the European Alps from 2002 onwards at 250 m Horizontal Resolution Along with a Nearly Cloud-Free Version

Snow cover dynamics impact a whole range of systems in mountain regions, from society to economy to ecology; and they also affect downstream regions. Monitoring and analyzing snow cover dynamics has been facilitated with remote sensing products. Here, we present two high-resolution daily snow cover data sets for the entire European Alps covering the years 2002 to 2019, and with automatic updates. The first is based on moderate resolution imaging spectroradiometer (MODIS) and its implementation is specifically tailored to the complex terrain, exploiting the highest possible resolution available of 250 m. The second is a nearly cloud-free product derived from the first using temporal and spatial filters, which reduce average cloud cover from 41.9% to less than 0.1%. Validation has been performed using an extensive network of 312 ground stations, and for the cloud filtering also with cross-validation. Average overall accuracies were 93% for the initial and 91.5% for the cloud-filtered product using the ground stations; and 95.3% for the cross-validation of the cloud-filter. The data can be accessed online and via the R and python programming languages. Possible applications of the data include but are not limited to hydrology, cryosphere and climate.

Climatic Drivers of Greening Trends in the Alps

Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Numerous studies have focused on spatial patterns and mechanisms of this phenomenon, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We studied the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000–2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. The objectives of this study were to (i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. We show that 63% of vegetated areas experienced significant trends during the 2000–2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts (>1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps.

Greening Implication Inferred from Vegetation Dynamics Interacted with Climate Change and Human Activities over the Southeast Qinghai–Tibet Plateau

Vegetation dynamics are sensitive to climate change and human activities, as vegetation interacts with the hydrosphere, atmosphere, and biosphere. The Yarlung Zangbo River (YZR) basin, with the vulnerable ecological environment, has experienced a series of natural disasters since the new millennium. Therefore, in this study, the vegetation dynamic variations and their associated responses to environmental changes in the YZR basin were investigated based on Normalized Difference Vegetation Index (NDVI) and Global Land Data Assimilation System (GLDAS) data from 2000 to 2016. Results showed that (1) the YZR basin showed an obvious vegetation greening process with a significant increase of the growing season NDVI (Zc = 2.31, p < 0.05), which was mainly attributed to the wide greening tendency of the downstream region that accounted for over 50% area of the YZR basin. (2) Regions with significant greening accounted for 25.4% of the basin and were mainly concentrated in the Nyang River and Parlung Tsangpo River sub-basins. On the contrary, the browning regions accounted for <25% of the basin and were mostly distributed in the urbanized cities of the midstream, implying a significant influence of human activities on vegetation greening. (3) The elevation dependency of the vegetation in the YZR basin was significant, showing that the vegetation of the low-altitude regions was better than that of the high-altitude regions. The greening rate exhibited a significantly more complicated relationship with the elevation, which increased with elevated altitude (above 3500 m) and decreased with elevated altitude (below 3500 m). (4) Significantly positive correlations between the growing season NDVI and surface air temperature were detected, which were mainly distributed in the snow-dominated sub-basins, indicating that glaciers and snow melting processes induced by global warming play an important role in vegetation growth. Although basin-wide non-significant negative correlations were found between precipitation and growing season NDVI, positive influences of precipitation on vegetation greening occurred in the arid and semi-arid upstream region. These findings could provide important information for ecological environment protection in the YZR basin and other high mountain regions.

Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas

In this study, we evaluated three different downscaling approaches to enhance spatial resolution of thermal imagery over Alpine vegetated areas. Due to the topographical and land-cover complexity and to the sparse distribution of meteorological stations in the region, the remotely-sensed land surface temperature (LST) at regional scale is of major area of interest for environmental applications. Even though the Moderate Resolution Imaging Spectroradiometer (MODIS) LST fills the gap regarding high temporal resolution and length of the time-series, its spatial resolution is not adequate for mountainous areas. Given this limitation, random forest algorithm for downscaling LST to 250 m spatial resolution was evaluated. This study exploits daily MODIS LST with a spatial resolution of 1 km to obtain sub-pixel information at 250 m spatial resolution. The nonlinear relationship between coarse resolution MODIS LST (CR) and fine resolution (FR) explanatory variables was performed by building three different models including: (i) all pixels (BM), (ii) only pixels with more than 90% of vegetation content (EM1) and (iii) only pixels with 75% threshold of homogeneity for vegetated land-cover classes (EM2). We considered normalized difference vegetation index (NDVI) and digital elevation model (DEM) as predictors. The performances of the thermal downscaling methods were evaluated by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) between the downscaled dataset and Landsat LST. Validation indicated that the error values for vegetation fraction (EM1, EM2) were smaller than for basic modelling (BM). BM model determined averaged RMSE of 2.3 K and MAE of 1.8 K. Enhanced methods (EM1 and EM2) gave slightly better results yielding 2.2 K and 1.7 K for RMSE and MAE, respectively. In contrast to the EMs, BM showed a reduction of 22% and 18% of RMSE and MAE respectively with regard to Landsat and the original MODIS LST. Despite some limitations, mainly due to cloud contamination effect and coarse resolution pixel heterogeneity, random forest downscaling exhibits a large potential for producing improved LST maps.

Deriving Regional Snow Line Dynamics during the Ablation Seasons 1984–2018 in European Mountains

Snowmelt in the mid-latitude European mountains is undergoing significant spatiotemporal changes. Regional snow line elevation (RSLE) is an appropriate indicator for assessing snow cover variations in mountain areas. To derive regional snow line dynamics during the ablation seasons 1984–2018, the present study unprecedentedly introduced a readily applicable framework. The framework constitutes four steps: atmospheric and topographic correction, snow classification, RSLE retrieval, and regional snow line retreat curve (RSLRC) derivation. The developed framework has been successfully applied to 8641 satellite images acquired by Landsat, ASTER, and Sentinel-2. The results of the intra-annual regional snow line variations show that: (1) regional snow lines in the Alpine catchments preserve the longest; (2) RSLEs are lower in the northern Pyrenees than in the southern part; (3) regional snow lines persist the shortest in the Carpathian catchments; and (4) during the end of the ablation season 2018, intermediate snowfall events in the catchments Adda, Tagliamento, and Uzh are observed. In terms of the long-term inter-annual variations, significantly accelerating snow line recession is detected in the northern Pyrenean catchment Ariege. In the Alpine catchment Alpenrhein and Drac, RSLRCs are shifting towards lower accumulated air-temperature (AT) significantly, with the magnitude of −3.77 °C·a−1 (Alpenrhein) and −3.99 °C·a−1 (Drac).