M1 dev (#30)

* revision of theme 1 raster time series processing tutorial (xarray)

* revision of theme 2sentinel2 stac tutorial

* revise theme 2 gee python api tutorial

* revise point cloud exploration tutorial: metadata and package management recommendations

* revise theme 3 landcover classification tutorial

* edit environment setup instructions for theme 2 tutorials (simplified, less specific, refer to software page for easier course maintenance)

* revise module1.md practical parts overview

* simplify environment setup instructions in theme 1 xarray tutorial

* revise theme 3 snow cover time series tutorial

* revise theme 3 snow cover time series tutorial with excercise

* revise theme 4 tutorial on forest disturbance assessment

* theme 3 landcover classification tutorial: add import of folium.plugins

* revise theme 6 accuracy assessment tutorial

* repair link to t6 excercise section in m1 overview

* revise classification accuracy metrics

* add links to module 2

* add links to #algorithms in m2t5

* add section about feature selection and links to m2t4

* add ml references

* add mlxtend package to list of ml tools

* add short explanation of CV and bootstrapping with links to literature and to M2T4

* repair internal links

* add links to m2 and m3

* add links to m2 and m3

* repair internal links

* correct typos

* add link to Module 2 Excercise 2 (tools for reference data acquisition)

* snow cover maps: add discrete ticks and colormap to colorscale

* minor corrections in comments

* Added self-eval quizzes

* Adapting question 1

* fix typos

* add T5 tutorial: Sentinel-1/-2 fusion for surface water monitoring

* add link to theme 5 tutorial (S1/S2 fusion)

* add link to theme 5 tutorial (S1/S2 fusion)

* update theme 5 theory (spatio-temporal data fusion

* add lineplot with s1 s2 water extent

* Added exercise video and adapted the instructions.

* update t5 water surface tutorial

* add figures

* revision of t5, add figures, references, excercise

* remove comments

* add new files

* replace fusion levels figure

* fix typo in t5 quiz checker

---------

Co-authored-by: unknown <andreas.mayr@uibk.ac.at>
Co-authored-by: jdvorak <jakub.dvorak@natur.cuni.cz>

course/module1/05_spatio_temporal_data_fusion/05_spatio-temporal_data_fusion.md

@@ -178,7 +178,7 @@ In addition to the fusion approaches outlined above, there are plenty of studies
 <input type="checkbox" name="q_03">may be used to improve the spatial and temporal resolution of an analysis.<br>
 <input type="checkbox" name="q_03">is sometimes needed for long-term monitoring tasks where the observation period must be extended beyond the operating time of a single system.<br>
 <input type="checkbox" name="q_03">is a typical strategy for streamlining the preprocessing steps.<br>
-<div hidden id="correct_q_03">may be used to improve the spatial and temporal resolution of an analysis.&is sometimes needed for long-term monitoring tasks where the observation period must be extended beyond the operating time of a single system</div>
+<div hidden id="correct_q_03">may be used to improve the spatial and temporal resolution of an analysis.&is sometimes needed for long-term monitoring tasks where the observation period must be extended beyond the operating time of a single system.</div>
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