@article{KhareLatifiKhare2021,
  author    = {Khare, Suyash and Latifi, Hooman and Khare, Siddhartha},
  title     = {Vegetation growth analysis of UNESCO World Heritage Hyrcanian forests using multi-sensor optical remote sensing data},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {19},
  issn      = {2072-4292},
  doi       = {10.3390/rs13193965},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-248398},
  year      = {2021},
  abstract  = {Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility.},
  language  = {en}
}
@article{VillagomezNuernbergerRequieretal.2021,
  author    = {Villagomez, Gemma N. and N{\"u}rnberger, Fabian and Requier, Fabrice and Schiele, Susanne and Steffan-Dewenter, Ingo},
  title     = {Effects of temperature and photoperiod on the seasonal timing of Western honey bee colonies and an early spring flowering plant},
  series = {Ecology and Evolution},
  volume    = {11},
  journal   = {Ecology and Evolution},
  number    = {12},
  doi       = {10.1002/ece3.7616},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258770},
  pages     = {7834-7849},
  year      = {2021},
  abstract  = {Temperature and photoperiod are important Zeitgebers for plants and pollinators to synchronize growth and reproduction with suitable environmental conditions and their mutualistic interaction partners. Global warming can disturb this temporal synchronization since interacting species may respond differently to new combinations of photoperiod and temperature under future climates, but experimental studies on the potential phenological responses of plants and pollinators are lacking. We simulated current and future combinations of temperature and photoperiod to assess effects on the overwintering and spring phenology of an early flowering plant species (Crocus sieberi) and the Western honey bee (Apis mellifera). We could show that increased mean temperatures in winter and early spring advanced the flowering phenology of C. sieberi and intensified brood rearing activity of A. mellifera but did not advance their brood rearing activity. Flowering phenology of C. sieberi also relied on photoperiod, while brood rearing activity of A. mellifera did not. The results confirm that increases in temperature can induce changes in phenological responses and suggest that photoperiod can also play a critical role in these responses, with currently unknown consequences for real-world ecosystems in a warming climate.},
  language  = {en}
}
@article{ZieglerPollingerBoelletal.2020,
  author    = {Ziegler, Katrin and Pollinger, Felix and B{\"o}ll, Susanne and Paeth, Heiko},
  title     = {Statistical modeling of phenology in Bavaria based on past and future meteorological information},
  series = {Theoretical and Applied Climatology},
  volume    = {140},
  journal   = {Theoretical and Applied Climatology},
  issn      = {0177-798X},
  doi       = {10.1007/s00704-020-03178-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-232717},
  pages     = {1467-1481},
  year      = {2020},
  abstract  = {Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases arecommonly considered some of the most obvious effects of climate change. However, current climate models lack a representationof vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modelingapproach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data foranalyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of severalmeteorological variables were used as predictors and phenological anomalies of the flowering date of the test plantForsythiasuspensaas predictand. Several cross-validated prediction models using various numbers and differently constructed predictorswere developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observationsper phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust andsuccessful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its averagecoefficient of determination and root mean square error (RMSE) per station are 60\% and ± 8.6 days, respectively. However, theprediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparablelevel of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants andregions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century comparedto the phenology of the reference period (1961-1990).},
  language  = {en}
}
@article{VikukFuchsKrischkeetal.2020,
  author    = {Vikuk, Veronika and Fuchs, Benjamin and Krischke, Markus and Mueller, Martin J. and Rueb, Selina and Krauss, Jochen},
  title     = {Alkaloid Concentrations of Lolium perenne Infected with Epichlo{\"e} festucae var. lolii with Different Detection Methods—A Re-Evaluation of Intoxication Risk in Germany?},
  series = {Journal of Fungi},
  volume    = {6},
  journal   = {Journal of Fungi},
  number    = {3},
  issn      = {2309-608X},
  doi       = {10.3390/jof6030177},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213171},
  year      = {2020},
  abstract  = {Mycotoxins in agriculturally used plants can cause intoxication in animals and can lead to severe financial losses for farmers. The endophytic fungus Epichlo{\"e} festucae var. lolii living symbiotically within the cool season grass species Lolium perenne can produce vertebrate and invertebrate toxic alkaloids. Hence, an exact quantitation of alkaloid concentrations is essential to determine intoxication risk for animals. Many studies use different methods to detect alkaloid concentrations, which complicates the comparability. In this study, we showed that alkaloid concentrations of individual plants exceeded toxicity thresholds on real world grasslands in Germany, but not on the population level. Alkaloid concentrations on five German grasslands with high alkaloid levels peaked in summer but were also below toxicity thresholds on population level. Furthermore, we showed that alkaloid concentrations follow the same seasonal trend, regardless of whether plant fresh or dry weight was used, in the field and in a common garden study. However, alkaloid concentrations were around three times higher when detected with dry weight. Finally, we showed that alkaloid concentrations can additionally be biased to different alkaloid detection methods. We highlight that toxicity risks should be analyzed using plant dry weight, but concentration trends of fresh weight are reliable.},
  language  = {en}
}
@article{KnauerGessnerFensholtetal.2016,
  author    = {Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Kuenzer, Claudia},
  title     = {An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes},
  series = {Remote Sensing},
  volume    = {8},
  journal   = {Remote Sensing},
  number    = {5},
  doi       = {10.3390/rs8050425},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180712},
  pages     = {425},
  year      = {2016},
  abstract  = {Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km\(^{2}\) in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture},
  language  = {en}
}