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Landscape Ecology and data science - Detailseite

  • Funktionen:
  • Online Belegung noch nicht möglich oder bereits abgeschlossen
Grunddaten
Veranstaltungsart Masterseminar Veranstaltungsnummer 3312132
Semester WiSe 2021/22 SWS 4
Rhythmus Moodle-Link  
Veranstaltungsstatus Freigegeben für Vorlesungsverzeichnis  Freigegeben  Sprache englisch
Belegungsfrist - Eine Belegung ist online erforderlich
Veranstaltungsformat Blended Course

Termine

Gruppe 1
Tag Zeit Rhythmus Dauer Raum Gebäude Raum-
plan
Lehrperson Status Bemerkung fällt aus am Max. Teilnehmer/-innen
Di. 09:00 bis 13:00 wöch 19.10.2021 bis 15.02.2022      findet statt    
Gruppe 1:
Zur Zeit keine Belegung möglich


Zugeordnete Person
Zugeordnete Person Zuständigkeit
Lausch, Angela , PD Dr rer nat. habil.
Studiengänge
Abschluss Studiengang LP Semester
Master of Science  Global Change Geography Hauptfach ( Vertiefung: kein LA; POVersion: 2016 )   10  -  
Master of Science  Global Change Geography Hauptfach ( Vertiefung: kein LA; POVersion: 2021 )     -  
Programmstud.-o.Abschl.MA  Global Change Geography Programm ( POVersion: 1999 )     -  
Zuordnung zu Einrichtungen
Einrichtung
Mathematisch-Naturwissenschaftliche Fakultät, Geographisches Institut
Inhalt
Kommentar

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)
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

  1. Reader, H.; Löffler, J. Landscape Ecology; Edition: 5th; UTB GmbH, 2017; ISBN 3825287181.
  2. Steinhardt, U.; Blumenstein, O.; Barsch, H. Textbook of Landscape Ecology; Spektrum Akademischer Verlag, 2012; ISBN 3827423961.
  3. Andreas Dengel Semantic Technologies: Fundamentals. Concepts. Applications; Spektrum Akademischer Verlag, 2011; ISBN 3827426634.
  4. Provost, F.; Fawcett, T. Data Science for Business: Practical application of data mining and data analytical thinking; mitp, 2017; ISBN 3958455468.
  5. 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)

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.

 

Prüfung

Testing:            

Lecture and 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.

 

Strukturbaum

Keine Einordnung ins Vorlesungsverzeichnis vorhanden. Veranstaltung ist aus dem Semester WiSe 2021/22. Aktuelles Semester: WiSe 2024/25.
Humboldt-Universität zu Berlin | Unter den Linden 6 | D-10099 Berlin