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PD at the HU Berlin
mail: angela.lausch@ufz.de http://www.ufz.de/index.php?de=4213
Scholar profile: http://scholar.google.de/citations?user=gWU0UO0AAAAJ&hl=de
Research gate: https://www.researchgate.net/profile/Angela_Lausch/
Landscape Ecology and Data Science
Time: Tuesday (9-13)
9-10:30 break of 15 min 10:45-12, 12-13 time for self-study
GIS-Pool: Space Online
Lectures period xxxxx
Project work: Project work (2500-3000 words), German, English possible,
(Literature administration with Mendeley, https://www.mendeley.com/? interaction_required=true, The project work can also be written by two people. But it must be obvious which part comes from which person.
Deadline/project work: 31. July 2022
Miscellaneous: The lectures and exercises are stored in Moodle
Learning and qualification goals:
Students gain basic knowledge of functional landscape ecology and trait ecology as a scientific discipline. Students will gain the ability to understand components of bio- and geodiversity and their interactions, to statistically analyse and evaluate them using different data science approaches. Students will gain basic knowledge about monitoring and modelling of Land-Use Intensity, Disturbances, Ecosystem Health, Hemeroby and Human well beeing.
Students have skills in systemic thinking and are able to approach scientific problems with the help of statistical/complex statistical models as well as conceptual models. Students gain insight into methods of data science, machine learning processes and the Semantic Web as a methodological basis for functional landscape ecology.
Organizational notes:
Contents: Lecture
- Organizational matters
- Scientific writing (short introduction)
- Introduction to the necessity of data science and digital geography/landscape ecology (
- Introduction and basics of functional landscape ecology, trait ecology
- Trait approach for the assessment of bio-geodiversity and its interactions, land-use intensity, distances, ecosystem health, hemeroby and human well beeing
- Landscape structure analysis (Landscape Metrics)
- Landscape modelling, construction of landscape models,
- Methods of data science (data mining procedures), recording of patterns in ecological data)
- Data preparation, use of databases in Landscape Ecology (GFBIO)
- Selection and application of machine learning algorithms (k-means clustering, principal component analysis, association analysis, social network analysis, regression analysis, SVM, decision tree, random forest, neural networks,
- Parameter selection, evaluation of the results
- GoFAIR
- Google Earth Engine deployment (BigData)
Contents: Seminar
- Exemplary examples of Landscape Ecology, Bio-Geodiversity, Land-Use Intensity, Disturbances, Ecosystem Health, Hemeroby as well as of Human well being, Land-Use-Intensity
- Data: Remote Sensing data products, vector data, trait data, landscape ecology databases
- Use of open access software (GIS, landscape metrics, data mining methods, geostatistics, databases - see below)
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Literatur |
Seminar: Working on the PC - using freely available software – mostly!
QGIS (Quantum GIS) - Home
http://www.qgis.org/de/site/about/index.html
Fragstats
(Analyses of landscape structures, landscape metrics)
http://www.umass.edu/landeco/research/fragstats/fragstats.html
GuidosToolbox
https://ec.europa.eu/jrc/en/scientific-tool/guidos-toolbox
VOSviewer
https://www.vosviewer.com/
Gephi
network analysis https://gephi.org/
InfraNodus
(commercial)
network analysis https://infranodus.com/
RapidMiner Studio -
(Tool for data mining and analysis of complex data)
Open Source in the test version for students (https://rapidminer.com/
KNIME
(Tool for data mining and analysis of complex data)
open source https://www.knime.com/
Textpad or others
(text editor for Big Data)
open source
https://www.textpad.com/
Mendeley
(Tool for literature administration)
open source
https://www.mendeley.com/?interaction_required=true
Recommendations for the following literature:
Books
- Reader, H.; Löffler, J. Landscape Ecology; Edition: 5th; UTB GmbH, 2017; ISBN 3825287181.
- Steinhardt, U.; Blumenstein, O.; Barsch, H. Textbook of Landscape Ecology; Spektrum Akademischer Verlag, 2012; ISBN 3827423961.
- Andreas Dengel Semantic Technologies: Fundamentals. Concepts. Applications; Spektrum Akademischer Verlag, 2011; ISBN 3827426634.
- Provost, F.; Fawcett, T. Data Science for Business: Practical application of data mining and data analytical thinking; mitp, 2017; ISBN 3958455468.
- Cavender-Bares, J., Gamon, J.A., Townsend, P.A., 2020. Remote Sensing of Plant Biodiversity, Remote Sensing of Plant Biodiversity. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-33157-3 (open access)
Publications (selection, will be made available as pdf, more will follow)
- Lausch, A.; Blaschke, T.; Haase, D.; Herzog, F.; Syrbe, R.-U.; Tischendorf, L.; Walz, U. Understanding and quantifying landscape structure – A review on relevant process characteristics, data models and landscape metrics. Modell. 2015, 295, 31–41.
- Lausch, A.; Bannehr, L.; Beckmann, M.; Boehm, C.; Feilhauer, H.; Hacker, J.M.; Heurich, M.; Jung, A.; Klenke, R.; Neumann, C.; et al. Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Indic. 2016, 70, 317–339.
- Lausch, A.; Erasmi, S.; King, D.; Magdon, P.; Heurich, M. Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics. Remote Sens. 2016, 8, 1029.
- Lausch, A.; Erasmi, S.; King, D.; Magdon, P.; Heurich, M. Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models. Remote Sens. 2017, 9, 129.
- Lausch, A.; Borg, E.; Bumberger, J.; Dietrich, P.; Heurich, M.; Huth, A.; Jung, A.; Klenke, R.; Knapp, S.; Mollenhauer, H.; et al. Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches. Remote Sens. 2018, 10, 1120.
- Lausch, A.; Olaf, B.; Stefan, K.; Leitao, P.; Jung, A.; Rocchini, D.; Schaepman, M., E.; Skidmore, A.K.; Tischendorf, L.; Knapp, S. Understanding and assessing vegetation health by in-situ species and remote sensing approaches. Methods Ecol. Evol. 2018, 00, 1–11.
- Lausch, A.; Baade, J.; Bannehr, L.; Borg, E.; Bumberger, J.; Chabrilliat, S.; Dietrich, P.; Gerighausen, H.; Glässer, C.; Hacker, J.M.; et al. Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics. Remote Sens. 2019, 11, 2356.
- Wellmann, T.; Haase, D.; Knapp, S.; Salbach, C.; Selsam, P.; Lausch, A. Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Indic. 2018, 85, 190–203.
- Kabisch, N.; Selsam, P.; Kirsten, T.; Lausch, A.; Bumberger, J. A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes. Indic. 2019, 99, 273–282.
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