TY  - JOUR
A1  - Koehler, Jonas
A1  - Kuenzer, Claudia
T1  - Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review
JF  - Remote Sensing
N2  - Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.
KW  - forecast
KW  - Earth Observation
KW  - land surface
KW  - land use
KW  - land cover
KW  - time series
KW  - machine learning
KW  - Markov chains
KW  - modeling
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216285
SN  - 2072-4292
VL  - 12
IS  - 21
ER  - 
TY  - JOUR
A1  - Kaltdorf, Martin
A1  - Srivastava, Mugdha
A1  - Gupta, Shishir K.
A1  - Liang, Chunguang
A1  - Binder, Jasmin
A1  - Dietl, Anna-Maria
A1  - Meir, Zohar
A1  - Haas, Hubertus
A1  - Osherov, Nir
A1  - Krappmann, Sven
A1  - Dandekar, Thomas
T1  - Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach
JF  - Frontiers in Molecular Bioscience
N2  - New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.
KW  - metabolism
KW  - targets
KW  - antimycotics
KW  - modeling
KW  - structure
KW  - interaction
KW  - fungicide
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-147396
VL  - 3
ER  -