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

Fernerkundung der Vegetationsphänologie über MODIS NDVI Daten - Herausforderungen bei der Datenverarbeitung und -validierung mittels Bodenbeobachtungen zahlreicher Arten und LiDAR

Phenology, the cyclic events in living organisms is triggered by climatic conditions and indicators of climate change. They are important factors influencing species interactions and ecosystem functioning. This thesis deals with the estimation of phenological metrics (Land Surface Phenology or LSP) from MODIS based time series NDVI data. Results of data analysis emphasises the role of ground observations, topography and LiDAR characteristics of forest stand in describing the variability in LSP.Phänologie, die zyklischen Stadien von lebenden Organismen werden über klimatische Verhältnisse gesteuert und dienen als Indikatoren des Klimawandels. Diese Faktoren beeinflussen maßgeblich die Interaktionen zwischen Arten und sind für das Funktionieren von Ökosystemen ausschlaggebend. Diese Arbeit behandelt die Bestimmung von phänologischen Metriken (Phänologie der Landoberfläche oder LSP) unter Verwendung von MODIS basierten NDVI Zeitreihen. Die Ergebnisse der Datenanalyse hebt die Wichtigkeit von Bodenbeobachtungen, Topographie und LiDAR Merkmalen von Waldbeständen bei der Beschreibung der LSP Variabilität hervor

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

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

Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing

Phenological events are good indicators of the effects of climate change, since phenological phases are sensitive to changes in environmental conditions. Although several national phenological networks monitor the phenology of different plant species, direct observations can only be conducted on individual trees, which cannot be easily extended over large and continuous areas. Remote sensing has often been applied to model phenology for large areas, focusing mostly on pure forests in which it is relatively easier to match vegetation indices with ground observations. In mixed forests, phenology modelling from remote sensing is often limited to land surface phenology, which consists of an overall phenology of all tree species present in a pixel. The potential of remote sensing for modelling the phenology of individual tree species in mixed forests remains underexplored. In this study, we applied the seasonal midpoint (SM) method with MODIS GPP to model the start of season (SOS) and the end of season (EOS) of six different tree species in Slovenian mixed forests. First, substitute locations were identified for each combination of observation station and plant species based on similar environmental conditions (aspect, slope, and altitude) and tree species of interest, and used to retrieve the remote sensing information used in the SM method after fitting the best of a Gaussian and two double logistic functions to each year of GPP time series. Then, the best thresholds were identified for SOS and EOS, and the results were validated using cross-validation. The results show clearly that the usual threshold of 0.5 is not best in most cases, especially for estimating the EOS. Despite the difficulty in modelling the phenology of different tree species in a mixed forest using remote sensing, it was possible to estimate SOS and EOS with moderate errors as low as <8 days (Fagus sylvatica and Tilia sp.) and <10 days (Fagus sylvatica and Populus tremula), respectively

Migration behavior analysis of red deer (Cervus elaphus) and the influence of environmental covariates on migration timing

The red deer (Cervus elaphus) is a common ungulate in Switzerland, Austria and Italy. The different characteristics like a high climatic variability and various anthropogenic influences of these areas make it difficult to predict the movement of red deer. This thesis aims to analyze red deer migratory behavior concerning propensity and distance in eight different study areas. Furthermore, the timing of migration in spring and autumn is investigated in relation to environmental covariates. First, the migration pattern of 346 red deer were analyzed using MigrO, a QGIS plug-in based on the SeqScan algorithm. Defined criteria were applied to classify the red deer trajectories into migratory, resident, and disperser individuals. For the following analysis, the focus lied on the migratory and resident red deer. Next, the home range size (HRS), altitude, distance and timing were analyzed for all populations. These parameters are all outputs of MigrO. The results for all parameters of the eight study areas were then compared. Furthermore, intersexual differences were looked at. Influences of environmental factors such as snow, vegetation and temperature on migration timing were then investigated with the Cox Proportional Hazard Model. For snow, the Normalized Difference Snow Index (NDSI) was used, provided by MODIS Aqua. Vegetation was predicted by the Normalized Difference Vegetation Index (NDVI) supplied by MODIS Terra and for temperature data, the Land Surface Temperature (LST) by MODIS Aqua was used. The different results for the migration probability per study site showed that red deer behavior is dependent on various environmental factors (i.e. weather conditions and topology) and anthropogenic influences like hunting, supplementary forage and human made barriers. In addition, only moderate intersexual differences were observed for the behavior. Moreover, few red deer individuals (females and males) traveled to rutting grounds outside of their seasonal home ranges. Resident animals show a larger covered HRS than migratory individuals. Consequently, resident individuals likely roam larger areas within one larger annual range, while migratory animals fulfill their needs in two spatially separated smaller ranges. Stags cover a larger HRS than hinds and generally also have their home ranges at lower altitudes than female red deer. Generally, all migratory individuals traveled to lower elevations during winter and migrated to higher altitudes in summer. The traveled distances between the home ranges were evenly distributed between hinds and stags. Only on the study site in Tyrol, where supplementary feeding has a long tradition in winter, the traveled distances are significantly lower than compared to the other study sites, suggesting the effects of supplementary feeding in altering migration behavior. The spring migration starts earlier for stags than hinds, whereas in autumn the stags migrate all at once in a short time. This is assumed to be in relation to the rut or an event like for example the end of hunting or changing weather conditions. The results of the Cox Proportional Hazard Model show that the timing of spring migration is assumed to be determined by changes in vegetation and temperature, while the determinant driver for the autumn migration is snow and vegetation. Overall, the results show plastic responses of red deer to environmental and anthropogenic drivers. Differences in the studied populations show that various factors influence the migration behavior of red deer which need still more research to fully understand the mechanism

Alpine thermal dynamics and associated constraints on the behavior of mountain goats in Southeast Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2015Alpine Caprinae, including mountain goats (Oreamnos americanus), have been described to be sensitive to temperature changes within their summer range and consequently may be forced to select habitats that allow for the maintenance of a stable core temperature on warm days. Survival may be inhibited if warm ambient temperatures cause mountain goats to reduce time foraging or if too much time is spent on thermoregulatory habitat selection. I investigated mountain goat behavioral activity budgets across alpine temperature gradients in Southeast Alaska using focal animal sampling and scan sampling techniques. I tested the effects of temperature on mountain goat activity and mountain goat elevation. Coupled with the behavioral investigations, I simultaneously monitored elevational temperature gradients using an array of passive thermistors. By monitoring hourly temperatures and deriving near-surface lapse rates, I demonstrate the utility of downscaled, region-specific temperature-elevation profiles for ecological applications rather than making inferences based on broad spatial models. Except in winter, lapse rates within the study area were between -0.3°C 100m⁻¹ and -0.4°C 100m⁻¹, and were not inclusive of the global mean environmental lapse rate (-0.65°C 100m⁻¹). Mountain goats within the study area demonstrated behavioral conservation of their activity budgets by altering their orientation through space and time, rather than incurring thermal and/or nutritional deficits. In addition, the animals took advantage of cooler temperatures at high elevations to bolster thermoneutrality. I highlight the need for behavioral ecology research that links physiological mechanisms and mammalian life history in an effort to predict the fate of a sentinel wildlife species as it copes with a changing environment. Indeed, such indicator species are invaluable to understanding the dynamics of change in ecosystem structure, function, and phenology. Given current warming trends and projections of changing climate regimes being more pronounced at higher latitudes, there is a marked need to better understand thermoregulatory constraints on faunal behavior and the effect of changing landscapes on the distributions and survival of wildlife populations in Alaska