module 2 improvements (#39)

course/data_usecases/usecase_forests_tatras.md

@@ -23,7 +23,7 @@ Constant monitoring of the environment allows the detection of disturbances and
 
 In this course we will focus on forest disturbance detection in Tatras, which is the highest mountain range in the Carpathian Mountains. It is an important area for biodiversity conservation, protected by two national parks: the Slovak Tatranský Národný Park (TANAP) and the Polish Tatrzański Park Narodowy (TPN). Forests cover about 60% of the mountains, where 14% is dwarf pine shrubs ([Ochtyra et al., 2020](#references)). Spruce dominates the majority of the tree stands, while in the lower forest zone, spruce and fir together form tree stands.
 
-Strong winds and bark beetle (Ips typographus \[L.\]) outbreaks are the primary causes of vegetation disturbances in Polish-Slovak Tatra Mountains, in particular of spruce (Picea abies \[L.\] Karst.) ecosystems ([Mezei et al., 2014](#references)).
+Strong winds and bark beetle (*Ips typographus \[L.\]*) outbreaks are the primary causes of vegetation disturbances in Polish-Slovak Tatra Mountains, in particular of spruce (*Picea abies \[L.\] Karst.*) ecosystems ([Mezei et al., 2014](#references)).
 
 <center>
 
@@ -38,20 +38,20 @@ The use case is featured in the following parts of the course:
 
 - [Exercise: Principles of multispectral imaging (Module 2, Theme 1)](../module2/01_multispectral_principles/01_multispectral_principles_exercise.md)
 - [Exercise: Temporal information in satellite data (Module 2, Theme 2)](../module2/02_temporal_information/02_temporal_information_exercise.md)
-- [Exercise: Image processing workflow (Module 2, Theme 3)](../module2/03_image_processing/03_image_processing_exercise.md)
+- [Exercise: Image processing (Module 2, Theme 3)](../module2/03_image_processing/03_image_processing_exercise.md)
 - [Exercise: Vegetation change and disturbance detection (Module 2, Theme 5)](../module2/05_vegetation_monitoring/05_vegetation_monitoring_exercise.md)
-- [Case study: Forest disturbance detection (Tatras) (Module 2, Case Study 3)](../module2/08_cs_disturbance_detection/08_cs_disturbance_detection.md)
+- [Case study: Forest disturbance detection (Tatra Mountains) (Module 2, Case Study 3)](../module2/08_cs_disturbance_detection/08_cs_disturbance_detection.md)
 
 ## References
 
-Falťan, V., Katina, S., Minár, J., Polčák, N., Bánovský, M., Maretta, M., … & Petrovič, F. (2020). Evaluation of abiotic controls on windthrow disturbance using a generalized additive model: A case study of the Tatra National Park, Slovakia. Forests, 11(12), 1259. <https://doi.org/10.3390/f11121259>
+Falťan, V., Katina, S., Minár, J., Polčák, N., Bánovský, M., Maretta, M., … & Petrovič, F. (2020). *Evaluation of abiotic controls on windthrow disturbance using a generalized additive model: A case study of the Tatra National Park, Slovakia.* Forests, 11(12), 1259. <https://doi.org/10.3390/f11121259>
 
-Kennedy, R. E., Cohen, W. B., & Schroeder, T. A. (2007). Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110(3), 370-386. <https://doi.org/10.1016/j.rse.2010.07.008>
+Kennedy, R. E., Cohen, W. B., & Schroeder, T. A. (2007). T*rajectory-based change detection for automated characterization of forest disturbance dynamics*. Remote Sensing of Environment, 110(3), 370-386. <https://doi.org/10.1016/j.rse.2010.07.008>
 
-Mezei, P., Grodzki, W., Blaženec, M., & Jakuš, R. (2014). Factors influencing the wind–bark beetles’ disturbance system in the course of an Ips typographus outbreak in the Tatra Mountains. Forest Ecology and Management, 312, 67-77. <https://doi.org/10.1016/j.foreco.2013.10.020>
+Mezei, P., Grodzki, W., Blaženec, M., & Jakuš, R. (2014). F*actors influencing the wind–bark beetles’ disturbance system in the course of an Ips typographus outbreak in the Tatra Mountains*. Forest Ecology and Management, 312, 67-77. <https://doi.org/10.1016/j.foreco.2013.10.020>
 
-Nikolov, C., Konôpka, B., Kajba, M., Galko, J., Kunca, A., & Janský, L. (2014). Post-disaster forest management and bark beetle outbreak in Tatra National Park, Slovakia. Mountain Research and Development, 34(4), 326-335. <https://doi.org/10.1659/MRD-JOURNAL-D-13-00017.1>
+Nikolov, C., Konôpka, B., Kajba, M., Galko, J., Kunca, A., & Janský, L. (2014). *Post-disaster forest management and bark beetle outbreak in Tatra National Park, Slovakia*. Mountain Research and Development, 34(4), 326-335. <https://doi.org/10.1659/MRD-JOURNAL-D-13-00017.1>
 
-Ochtyra, A., Marcinkowska-Ochtyra, A., & Raczko, E. (2020). Threshold-and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains. Remote Sensing of Environment, 249, 112026. <https://doi.org/10.1016/j.rse.2020.112026>
+Ochtyra, A., Marcinkowska-Ochtyra, A., & Raczko, E. (2020). *Threshold-and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains*. Remote Sensing of Environment, 249, 112026. <https://doi.org/10.1016/j.rse.2020.112026>
 
-Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment, 144, 152-171. <https://doi.org/10.1016/j.rse.2014.01.011>
+Zhu, Z., & Woodcock, C. E. (2014). *Continuous change detection and classification of land cover using all available Landsat data*. Remote sensing of Environment, 144, 152-171. <https://doi.org/10.1016/j.rse.2014.01.011>

course/data_usecases/usecase_tundra_karkonosze.md

@@ -40,12 +40,12 @@ In this course, we will use **multitemporal Sentinel-2 data** and **reference da
 
 The use case is featured in the following parts of the course:
 
-- [Exercise: Image processing workflow (Module 2, Theme 3)](../module2/03_image_processing/03_image_processing_exercise.md)
-- [Exercise: Multitemporal classification (Module 2, Theme 4)](../module2/04_multitemporal_classification/04_multitemporal_classification.md)
-- [Case study: Monitoring tundra grasslands (Karkonosze) (Module 2, Case study 1)](../module2/06_cs_tundra_grasslands/06_cs_tundra_grasslands.md)
+- [Exercise: Image processing (Module 2, Theme 3)](../module2/03_image_processing/03_image_processing_exercise.md)
+- [Exercise: Multitemporal classification (Module 2, Theme 4)](../module2/04_multitemporal_classification/04_multitemporal_classification_exercise.md)
+- [Case study: Monitoring tundra grasslands (Karkonosze/Krkonoše Mountains (Module 2, Case Study 1)](../module2/06_cs_tundra_grasslands/06_cs_tundra_grasslands.md)
 
 ## References
 
-Beniston, M. (2016). Environmental change in mountains and uplands. Routledge.
+Beniston, M. (2016). *Environmental change in mountains and uplands*. Routledge.
 
-Wakulińska, M., & Marcinkowska-Ochtyra, A. (2020). Multi-temporal sentinel-2 data in classification of mountain vegetation. Remote Sensing, 12(17), 2696. <https://doi.org/10.3390/rs12172696>
+Wakulińska, M., & Marcinkowska-Ochtyra, A. (2020). *Multi-temporal sentinel-2 data in classification of mountain vegetation*. Remote Sensing, 12(17), 2696. <https://doi.org/10.3390/rs12172696>

course/module2/01_multispectral_principles/01_multispectral_principles_exercise.md

@@ -47,6 +47,8 @@ By clicking on the **image below** you will be taken to the video on data collec
 
 [![Data collection video on YouTube](media_exercise/E-TRAINEE_Video_M2_part1.jpg)](https://www.youtube.com/watch?v=l7yvqFoo8rE&list=PLyrFi-gvJfnt5qeGNkKxJGCWqWyYItOE6&index=3)
 
+All data for parts 2 and 3 are provided through [Zenodo](https://zenodo.org/records/10003575) (module2_theme_1_exercise.zip).
+
 ## Part 2: Project preparation
 
 This part is devoted to project preparation in QGIS software. In the beginning coordinate reference system will be set up. Universal Transverse Mercator zone 34 is chosen because of it is adequate for Tatra Mountains region and PlanetScope, Sentinel-2 and Landsat data are distributed in it (the aerial orhtoimagery will be reprojected on the fly properly, MODIS data needs to be reprojected). Each type of data will be visualised in two composites. We want to compare the possibility to recognize various objects using different types of visualisations.

course/module2/02_temporal_information/02_temporal_information.md

@@ -260,7 +260,7 @@ D. New AstroSat Optical Modular Instrument
 
 <div id="correct_q_01" hidden="">
 
-B D C A
+C D B A
 
 </div>
 

course/module2/02_temporal_information/02_temporal_information_exercise.md

@@ -25,7 +25,7 @@ The main objective of this exercise is not only to illustrate various examples o
 For this exercise you will need the following software, data and tools:
 
 - **Software** - R and RStudio. You can access environment setup tutorial for the whole Module 2 here: [R environment setup tutorial](../../software/software_r_language.md). After following the setup guide you should have all the necessary packages installed.
-- **Data** - downloaded data provided through [Zenodo](https://zenodo.org/record/8402925)
+- **Data** - downloaded data provided through [Zenodo](https://zenodo.org/records/10003575)
 
 Follow the suggested working environment setup in order for the provided relative paths to work properly.
 

course/module2/04_multitemporal_classification/04_multitemporal_classification_exercise.md

@@ -26,7 +26,7 @@ The primary aim of this exercise is to demonstrate one of the many approaches yo
 For this exercise you will need the following software, data and tools:
 
 - **Software** - R and RStudio. You can access environment setup tutorial for the whole Module 2 here: [R environment setup tutorial](../../software/software_r_language.md). After following the setup guide you should have all the necessary packages installed.
-- **Data** - downloaded data provided through [Zenodo](https://zenodo.org/record/8402925). If you went through **[Module 2 Theme 3 exercise Pipeline 1](../03_image_processing/03_image_processing_exercise.md#processing-pipeline-1)** you can download image the data from your Google Drive.
+- **Data** - downloaded data provided through [Zenodo](https://zenodo.org/records/10003575). If you went through **[Module 2 Theme 3 exercise Pipeline 1](../03_image_processing/03_image_processing_exercise.md#processing-pipeline-1)** you can download image the data from your Google Drive.
 
 Follow the suggested working environment setup in order for the provided relative paths to work properly.
 

course/module2/05_vegetation_monitoring/05_vegetation_monitoring_exercise.md

@@ -1,6 +1,6 @@
 ---
-title: "Vegetation change and disturbance detection"
-description: "This is the fifth theme within the Satellite Multispectral Images Time Series Analysis module."
+title: "Vegetation change and disturbance detection - Exercise"
+description: This is the exercise in the fifth theme within the Satellite Multispectral Images Time Series Analysis module.
 dateCreated: 2023-08-31
 authors: Adrian Ochtyra, Krzysztof Gryguc
 contributors: Aram Takmadżan
@@ -10,7 +10,7 @@ output:
     pandoc_args: "--wrap=none"
 ---
 
-Vegetation change and disturbance detection
+Vegetation change and disturbance detection - Exercise
 ================
 
 ## Exercise - Forest disturbance mapping in Tatras using LandTrendr
@@ -29,7 +29,7 @@ The second part will use R and RStudio. You can access environment setup tutoria
 
 ### Data
 
-Download data provided through [Zenodo](https://zenodo.org/record/8402925).
+Download data provided through [Zenodo](https://zenodo.org/records/10003575).
 
 #### Imagery data
 

course/module2/06_cs_tundra_grasslands/06_cs_tundra_grasslands.md

@@ -25,7 +25,7 @@ Before you continue, get familiar with the use case: **[Land cover monitoring in
 
 Wakulińska, M., & Marcinkowska-Ochtyra, A. (2020). *Multi-temporal sentinel-2 data in classification of mountain vegetation*. Remote Sensing, 12(17), 2696. <https://doi.org/10.3390/rs12172696>
 
-Download exercise data provided through [Zenodo](https://zenodo.org/record/8402925).
+Download exercise data provided through [Zenodo](https://zenodo.org/records/10003575).
 
 ### Imagery data
 

course/module2/08_cs_disturbance_detection/08_cs_disturbance_detection.md

@@ -39,7 +39,7 @@ Before you continue, get familiar with the use case (if you did it before in The
 
 Ochtyra, A., Marcinkowska-Ochtyra, A., & Raczko, E. (2020). *Threshold and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains*. Remote Sensing of Environment, 249, 112026. <https://doi.org/10.1016/j.rse.2020.112026>
 
-Download data provided through [Zenodo](https://zenodo.org/record/8402925).
+Download data provided through [Zenodo](https://zenodo.org/records/10003575).
 
 ### Imagery data
 

course/module2/module2.md

@@ -32,9 +32,9 @@ This module is structured into the following themes:
 - **[Image processing](03_image_processing/03_image_processing.md)**  
 - **[Multitemporal classification](04_multitemporal_classification/04_multitemporal_classification.md)**  
 - **[Vegetation change and disturbance detection](05_vegetation_monitoring/05_vegetation_monitoring.md)**  
-- **Case study: Monitoring tundra grasslands (Karkonosze/Krkonoše Mountains)\](06_cs_tundra_grasslands/06_cs_tundra_grasslands.md)**
-- **Case study: Effects of pollution in Ore Mountains\](07_cs_forest_changes/07_cs_forest_changes.md)**
-- **Case study: Forest disturbance detection (Tatra Mountains)\](08_cs_disturbance_detection/08_cs_disturbance_detection.md)**
+- **[Case study: Monitoring tundra grasslands (Karkonosze/Krkonoše Mountains)](06_cs_tundra_grasslands/06_cs_tundra_grasslands.md)**
+- **[Case study: Effects of pollution in Ore Mountains](07_cs_forest_changes/07_cs_forest_changes.md)**
+- **[Case study: Forest disturbance detection (Tatra Mountains)](08_cs_disturbance_detection/08_cs_disturbance_detection.md)**
 
 ## Prerequisites to perform this module
 
@@ -59,11 +59,11 @@ For this module, you will need the software listed below. If you did not install
 
 ### Use Cases
 
-Research-oriented case studies in this module are introduced in **[Monitoring mountain vegetation in Karkonosze/Krkonoše Mountains (Poland/Czechia)](../data_usecases/usecase_tundra_karkonosze.md)** and **[Vegetation disturbance detection in Polish-Slovak Tatra Mountains](../data_usecases/usecase_forests_tatras.md)** use case documents. Familiarize yourself with them to have a better understanding of the analyses performed in Case Studies.
+Research-oriented case studies in this module are introduced in **[Monitoring mountain vegetation in Karkonosze/Krkonoše Mountains (Poland/Czechia)](../data_usecases/usecase_tundra_karkonosze.md)**, **[Forest disturbances in Ore Mountains (Czechia)](../data_usecases/usecase_ore_mts_disturbance.md)** and **[Vegetation disturbance detection in Polish-Slovak Tatra Mountains](../data_usecases/usecase_forests_tatras.md)** use case documents. Familiarize yourself with them to have a better understanding of the analyses performed in Case Studies.
 
 ### Data
 
-Data for the exercises is provided through [Zenodo](https://zenodo.org/record/8402925). Some input imagery is produced throughout the course. The Zenodo package is structured in a way enabling relative paths in R scripts.
+Data for the exercises is provided through [Zenodo](https://zenodo.org/records/10003575). Some input imagery is produced throughout the course. Below you can see the folder tree of data from Module 2.
 
     module2/
     ├───case_study_1
@@ -91,7 +91,7 @@ Data for the exercises is provided through [Zenodo](https://zenodo.org/record/84
         ├───data_exercise/
         └───results/
 
-Each folder in the main catalog contains short description of the data inside in `README.txt` file. Input data is provided in `data_exercise` folders. Empty (except Theme 1) `results` folders are provided to store the outputs. After downloading the package you should follow the **[R language tutorial](../software/software_r_language.md)** to create an environment and start R project in main `module2` catalog.
+Each folder in the main catalog contains short description of the data inside in `README.txt` file. Input data is provided in `data_exercise` folders. Empty (except Theme 1) `results` folders are provided to store the outputs. After downloading the packages you should follow the **[R language tutorial](../software/software_r_language.md)** to create an environment and start R project in the main `module2` catalog.
 
 ## Start the module