O03: Earth Observation Data Analysis []

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====== O03: Earth Observation Data Analysis ======




===== Lectures ======

  - Introduction into the Case Study [[https://www.youtube.com/watch?v=u9n3hkuwO2g]]
  - Predictive Modelling [[https://www.youtube.com/watch?v=X1H8g_LaNQ0]]


===== Lecture Questions ======

As machine learning and spatial modelling is probably a new field for you, try to make sense of all the important terms first:

  * What are predictors and what is a response variable?
  * What problems can occur if we say a model is "overfitted"?
  * How can one detect if a model is overfitted?
  * What is the difference between a classification and a regression model?

===== Reading Task =====

Read the Article [[https://www.mdpi.com/2072-4292/10/4/580|"Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory"]] by Tedros M. Berhane et al. (2018). You can download the article [[https://www.mdpi.com/2072-4292/10/4/580|here]] as it is published as open access.

While the topic of this article is about wetland mapping (i.e. called a categorial map in the [[en:courses:training:element-01:online-week:ow02|article yesterday]]), it is also a very nice introduction to the machine learning workflow in a practical example without being too technical. If you do not understand the text word for word, do not worry - we will discuss the main outcomes and messages in person next week. For now focus on the main ideas and try to answer the following questions:

  * What is a decision tree?
  * What is the difference between a decision tree and a rule-based classification?
  * What was the field data used for in this study?
  * How was the remote sensing data processed prior to the classification?
  * What positive and negative aspects do the authors identify for the usage of a random forest model for classifications?


===== R Practice =====

To put the modelling concepts into practice, [[https://drive.google.com/file/d/1saVoBkGtW0SQ6jKzV4-CCidgHSd3yiqY/view?usp=sharing|please download these resources]] and find the Rscript ''TASK-modelling.R'' and data ''fogo_plots.csv''. Open the .R file in Rstudio. IIt will guide you through to calculate a linear model. If this was easy for you, you can continue with the R practice in [[en:courses:training:element-01:online-week:ow04|O04: Current Applications of Remote Sensing]] where you will learn to train a random forest model.

You can find an [[https://drive.google.com/file/d/1X80RF2xsBmM6zskf7BKoTSfv8tD1QBiO/view?usp=sharing|example solution here]].


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==== Lecture References ====

Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016). https://doi.org/10.1038/533452a

Blanco, C. M. G.; Gomez, V. M. B.; Crespo, P. & Ließ, M.Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest Geoderma, Elsevier BV, 2018, 316, 100-114

Börner K., Boyack K.W., Milojević S., Morris S. (2012) An Introduction to Modeling Science: Basic Model Types, Key Definitions, and a General Framework for the Comparison of Process Models. In: Scharnhorst A., Börner K., van den Besselaar P. (eds) Models of Science Dynamics. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23068-4_1

Breiman, L.Random Forests Machine Learning, Springer Science and Business Media LLC, 2001, 45, 5-32

Hansen, M. C.; Potapov, P. V.; Moore, R.; Hancher, M.; Turubanova, S. A.; Tyukavina, A.; Thau, D.; Stehman, S. V.; Goetz, S. J.; Loveland, T. R.; Kommareddy, A.; Egorov, A.; Chini, L.; Justice, C. O. & Townshend, J. R. G.High-Resolution Global Maps of 21st-Century Forest Cover Change Science, American Association for the Advancement of Science (AAAS), 2013, 342, 850-853

Maxwell, A. E.; Warner, T. A. & Fang, F.Implementation of machine-learning classification in remote sensing: an applied review International Journal of Remote Sensing, Informa UK Limited, 2018, 39, 2784-2817

Meyer, H.; Katurji, M.; Appelhans, T.; Müller, M.; Nauss, T.; Roudier, P. & Zawar-Reza, P.Mapping Daily Air Temperature for Antarctica Based on MODIS LST Remote Sensing, MDPI AG, 2016, 8, 732

Yaseen, Z. M.; Al-Juboori, A. M.; Beyaztas, U.; Al-Ansari, N.; Chau, K.-W.; Qi, C.; Ali, M.; Salih, S. Q. & Shahid, S.Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models Engineering Applications of Computational Fluid Mechanics, Informa UK Limited, 2019, 14, 70-89