Tree mortality caused by outbreaks of the bark beetle Ips typographus (L.) plays an important role in the natural dynamics of Norway spruce (Picea abies L.) stands, which could cause far-reaching changes in the occurrence and duration of vegetation phenology. Field-based early detection of tree disturbances is hampered by logistic, terrain, and technical shortcomings, and by the inability to continuously monitor disturbances over large areas. Despite achievements in remote mapping of bark-beetle-induced tree mortalities, early warning has been mostly unsuccessful mainly because of the lack of spectral sensitivity and discrepancies in definitions of field- and image-based disturbance classes. Here we applied a method based on inter-annual phenology of Norway spruce stands derived from synthetic multispectral data to part of the Bavarian Forest National Park in Germany. We fused temporally continuous Moderate Resolution Imaging Spectroradiometer and discrete RapidEye data using a flexible spatiotemporal data fusion method to achieve validated 8-day RapidEye-like composites of normalized difference vegetation index for 2011. We assumed that the dead trees delineated on 2012 aerial photographs were those in which bark beetle infestations were initiated in 2011. Samples were drawn with variable-sized buffering to represent the areas prone to infestations and their surroundings. We applied a conditional inference random forest to select the best image date among the entire 46 synthetic datasets to best discriminate between the core infestation patches and their surroundings from the subsequent year. Of the discrete time points identified, day 281 of the year represented the highest discrepancy between aerial image-based dead trees and their surroundings. Classification results were significantly correlated with beetle count data obtained using pheromone traps. Our method provided valuable information for management purposes and enabled wall-to-wall mapping of stands prone to infestation and its uncertainty. The results offer potential implications for rapid and cost-effective monitoring of bark beetle outbreaks using satellite data, which would be of great benefit for both management and research tasks.

Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species–environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data‐driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross‐validation modeling scheme. Moreover, it responds to dynamic, site‐specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks (Ciconia ciconia) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze species–environment interactions. As it standardizes site‐dependent behavioral and environmental characteristics, it can be used in the comparison of intra‐ and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.

Information on the timing of phenological events and their changes are critical parameters for quantifying the impact of climate alterations and of major relevance for terrestrial ecosystem models. Spatially consistent phenological information can be derived from remote sensing based vegetation indices by calculating phenological metrics such as Start of Season (SOS), End of Season and Length of Season (LOS). Different approaches exist for preprocessing of time series from medium resolution satellite data as well as for derivation of phenological metrics, but these methods often lack any biophysical or ecological meaning and qualitative comparisons between different methods are rare. This study aims at (1) deriving SOS as an indicator for the onset of spring in Germany, (2) comparing the phenological layers with phenological ground observations and the global MODIS “Land Surface Dynamics” product and (3) developing value added phenological layers from the data to (4) characterize spatiotemporal variations.

Changes in the timing of phenological events are a critical parameter for quantifying the impact of climate alterations on vegetation development that in reverse alters patterns of biosphere-atmosphere interactions. By the means of remote sensing, numerous approaches have been developed for deriving Land Surface Phenology. This study aims at identifying the best combination of data quality integration, filtering and threshold choice for the Start of Season (SOS) in Germany using MODIS data. The resulting remote sensing based SOS is related to phenological ground observations. For the year 2010, SOS data was produced by 5 different methods and the best method was identified by the comparison of the SOS dates to ground observations of the leaf unfolding of Broad-Leaved forest and beginning of turning green for Pastures, respectively. The results show that the impact of time series data quality screening, filter choice and threshold setting differs among the selected land cover classes.

Thermal and optical remote sensing data with the Surface Energy Balance Algorithm for Land (SEBAL) provide an appropriate basis for modelling energy fluxes at regional scale. This study compares SEBAL applied to ASTER data with in-situ-measurements. The results, especially those based on data corrected for atmospheric influences found close agreement with in-situ-measurements.

Vor dem Hintergrund des Klimawandels ist das Forschungsfeld der Phanologie immer mehr in den Fokus interdisziplinarer Fragestellungen geruckt. Fernerkundliche Ansatze mittelaufgeloster Daten schaffen dabei mit ihrer flachenhaften und repetitiven Abdeckung eine unverzichtbare Datenbasis fur vielseitige Anwendungen, sofern sie a) hinreichend validiert sind sowie b) phanologische Veranderungen der Vegetation adaquat widerspiegeln. In diesem Beitrag werden Ansatze zur Ableitung fernerkundlicher phanologischer Mase (z.B. Start of Season, End of Season) aus verschiedenen NDVI-Zeitserien fur Deutschland prasentiert. Am Beispiel der MODIS-Datensatze der Jahre 2001-2012 werden die Schwierigkeiten bei der Anwendung dieser Methoden und Moglichkeiten zum Umgang mit verrauschten Zeitserien diskutiert. Mit Hilfe statistischer Analysen wird die Plausibilitat dieser kunstlichen Zeitpunkte unter Verwendung der real beobachteten phanologischen Daten des DWD untersucht. Weiterhin wird vorgestellt, inwieweit sich potenzielle phanologische Kenngrosen aus den Zeitserien fur verschiedene Landbedeckungsklassen ableiten lassen. Dazu werden CORINE-Daten und weitere raumbeschreibende Variablen wie beispielsweise geographische Breite, Hohe, Hangneigung sowie Exposition herangezogen. Die Arbeiten sind Beitrag zum BMWI-Projekt „Multisaisonale Fernerkundung fur das Vegetationsmonitoring“, in dem diese Quantifizierungen der intra-annuellen Vegetationsdynamik entlang unterschiedlicher Gradienten als zusatzliche Eingangsgrose fur die Artverbreitungsmodellierung und Klassifikation von Grunlandflachen dienen.