6 EC
Semester 2, period 4
5264GRSE6Y
GIS and RS include a powerful set of computational techniques and methods for storing, retrieving and analysis of spatial and temporal distributed geographical data. Techniques will be introduced to analyze environmental problems. The course modules cover topics for new users of GIS and RS, but also for experienced users of GIS and RS.
The Earth Science master has three tracks, GED, EM and FPES. We stimulate flexibility in your choices to select GIS/RS techniques/skills and topics that closely fit to your personal study plan. Therefore you are, to a certain extent, free to select modules from an available set and you may formulate your own GIS/RS project, in close cooperation wth the coordinators.
In the self-tuition assignments we offer GIS visualization tools and techniques, for example 3D analysis and interpretation, statistical interpolation, weighting and ranking, suitability analyses, raster-based analysis, least-cost pathways, Web-services, model building, hydrological tools, python scripting and path-distance analysis. The remote sensing tools and techniques comprise amongst others supervised classification, change analysis, object-based image segentation and classification, band-ratio analysis, image preprocessing such as image enhancement and computing of vegetation indices.
The software uses is ArcGISdesktop (10.4/6), ArcGIS Pro, eCognition and ERDAS Imagine and the Google Earth Engine Environment.
Images and datasets used are highly diverse and range from Digital Elevation Models (DEMs), LiDAR data, Landsat imagery, SPOT imagery, Sentinel imagery, hyperspectral AVIRIS, Radar bands, orthophotos to thematic layers such as digital soil, geomorphological, Land Use Land Cover and other data.
The techniques and skills are applied in the assignments to a wide variety of landscapes and environmental and to diverse situations and/or topics such as flooding in Bangladesh, desertification in the Sahell zone, predictions of hummingbirds in the Andes, Land Use and Land Cover change in China, India and the Netherlands, urban areas, coastal areas of Liberia, food production in the Mekong delta and more.
After finishing a selection of assignments, the student continues an individual topic. A maximum 2 A4 large research outline is written, which has to be agreed on amongst the student and coordinator. The research project is documented in a technical GIS report with accompanying digital products . Students are encouraged to use their own (master) research data, although there are pre-fabricated topics available as well.
Published on Canvas
ArcGIS, ERDAS Imagine, eCognition, Google Earth Engine
Assignments, tutorials, datasets, literature, quizzes and other relevant information is published on Canvas.
After this course, the student is able to:
Detailed objectives per module are described in the module tutorials – see Canvas.
Lecture:
One lecture that will introduce the set-up of the course and provide a general overview of assignments.
Assignments/Modules:
Students without pre-knowledge: a collection of 17 self-tuition modules (assignments) is offered (total 6EC) which should be finished before the project starts. The content of the modules is described elsewhere in this document.
Students with pre-knowledge: a collection of 8 self-tuition modules (assignments) is offered (total 3EC) which should be finished before the project starts. The content of the modules and the project is described elsewhere in this document.
Self-study:
Dates according to the official course schedule, but students are free to work during other days in the GIS-studio, within the projected period (February - March). You can assign your name to a computer via the reservation system via: https://www.gis-studio.nl/index.php?page=reservations.
Project:
Students with GIS/RS experience decide on a two weeks project (3EC), depending on their interest and in agreement with the coordinators. After confirmation on the topic, the student prepares a short project plan (max 2 pages) in which the project is described, a work plan is made, the data is described (availability / metadata / format) and a workflow is presented in which the proposed techniques and processing steps in the software are visualized, and the foreseen deliverables are outlined.
|
Activity |
Number of hours |
remark |
|
Lecture |
2 |
presence recommended |
|
Module assignments |
83 |
self-tuition |
|
Project |
83 |
self-study |
|
Total |
168 |
|
The programme does not have requirements concerning attendance (OER-B).
Additional requirements for this course:
You have the opportunity during this course to use the computers in the GIS-studio. We made collective reservations in the GIS-studio for you already. If you need additional reservations, you can do so via http://www.gis-studio.nl, see separate paragraph on GIS-studio regulations, near the end of this course manual. Thijs de Boer (w.m.deboer@uva.nl) is manager of the GIS-studio, and can provide you with necessary information. Within the 8 weeks period you should be able to finish the course, perhaps earlier than the deadline. The course is self-tuition, which means minimum contact with the coordinator/teacher, because the modules are self-explanatory. In summary, you do not need to be present in the GIS-studio, if you use the eCognition software (only available in GIS-studio) then a computer there is necessary. For ArcGIS and ERDAS Imagine student licenses are available. Contact Thijs de Boer for further information.
| Item and weight | Details |
|
Final grade | |
|
0.5 (50%) GIS/RS-Project | Must be ≥ 5.5 |
|
0.5 (50%) Module assignments | Must be ≥ 5.5 |
Depending on your pre-knowledge in GIS/RS you will select 9 or 17 assignments (modules) from the full set of assignments. For each of these assignments you will receive a partial grade, that adds up to either 50% (students with pre-knowledge) or 100% of your final grade (students without pre-knowledge). Each assignment has its own 'quiz' in Canvas, in which questions about the assignment are answered, and/or delivefrables such as a map is handed in.
Students with pre-knowledge continue on a project which will be graded, based on two deliverables 1) the digital deliverables and 2) a technical GIS/RS report. Details on these criteria will be published on the Canvas digital learning environment.
Contact the course coordinator to make an appointment for inspection.
The course is self-tuition; students are encouraged to work on their own. First consult help from he help-functions, google about any problem you are confronted with, the teachers are available 1 hour during the scheduled periods as published on DataNose. You may make separate appointments per email.
Earthquakes are destructive tectonic hazards that occur all over our world. But why do earthquakes occur more often in some places than in others and what does "magnitude 6.8" really mean? You will be working and visualizing and explain how earthquakes relate to plate boundaries in a GIS through: a. Query earthquake attributes to reveal how depth and magnitude relate to spatial location, b. Create a report of countries affected by tsunamis, a secondary earthquake hazard, c. Map earthquake faults to determine plate interaction, d. Visualize earthquake data in 3D, e. Map earthquake data and local geology to determine seismic hazard.
This module introduces you to preparing spatial data in ArcGIS, so that you can efficiently organize and carry out a GIS project. The term ‘spatial (or geospatial) data’ is also known as geographic information and describes features or boundaries on Earth (or any other planetary body). Spatial data are frequently stored as coordinates and are visualized through maps, which can be accessed, manipulated and analyzed in a Geographic Information System, such as ArcGIS. You will use images made by André Kuipers made from the International Space Station (ISS) in 2012.
This assignment consists of two chapters:
1) Introduction: a text about the theory on geodatabases, based on the Help of ArcGIS.
2) A case study exercise, written by the staff of the GIS-studio. You will try to find the most ideal areas for growing coffee beans on the ‘Big Island’ of Hawaii.
This assignment is a follow-up of the previous module and consists of three chapters:
1) Introduction: a text about the theory on Spatial Analyst, an extension to ArcGIS, based on the Help of ArcGIS. Read this text carefully and study the illustrations.
2) A tutorial, based on the ESRI module ‘The ArcGIS Building a Geodatabase Tutorial’. This tutorial supports a practical introduction to geodatabases.
3) A case study, written by the staff of the GIS-studio. You will try to find the most ideal areas for growing coffee beans on the ‘Big Island’ of Hawaii with the help of Model Builder in ArcGIS.
A variety of methods can be used to model the surface of a feature or a phenomenon. Surfaces are for example Digital Terrain Models or a rainfall map. This module introduces you to two surface-creation techniques: interpolation and density. You can use interpolation to model the surface – the Digital Terrain Model (DTM) of a feature or a phenomenon—all you need are sample points, an interpolation method, and an understanding of the feature or the phenomenon being modeled. The idea behind interpolation is simple: estimating unknown values using a sample of known values. Although certain methods of interpolation (such as inverse distance weighted – IDW - and Spline) solve this problem differently, they each work with the same underlying principle, called spatial autocorrelation. Density functions, on the other hand, allow you to see the highest and the lowest concentrations of features in your data. In density surfaces, the cell values always indicate the number of features per unit of area, such as the number of people per square mile. Density surfaces are great for visualizing patterns in the data that otherwise might not be apparent.
In the 1990 Natuurbeleidsplan (Nature Policy Plan), the problems of many very shattered nature areas in the Netherlands (in Dutch: ‘versnippering’) were recognized, and the Ecologische Hoofdstructuur van Nederland (EHS) (National Ecological Network) was introduced. Its aim is to connect the main wildlife areas in the Netherlands by the year 2018. As a result, an overpass for animals, the ‘Natuurbrug’, has been built near Hilversum (www.natuurbrug.nl). This was one of the first major nature development projects for the new EHS. This overpass - the longest in the country - creates an ecological corridor between the habitats of the ‘Utrechtse Heuvelrug’, the central wildlife areas in ‘Het Gooi’, and the ‘Vechtstreek’. You will simulate the location of the Ecoduct in ArcGIS.
In this module you will learn how to automatically extract a hydrological stream network from a digital elevation model and compare this to a traditional stream network digitized from a paper topographical map. ArcGIS is a powerful platform for the modeling of hydrological processes and extracting hydrological features. An important dataset for hydrological modeling is a digital elevation model (DEM). Delineation of watersheds and sub-catchments and extraction of the hydrological stream network can be calculated from a DEM. Based on this stream network, various hydrological parameters can be calculated and flow tracing can be carried out. In this module you will explore some of the techniques and tools available in ArcGIS.
This module introduces you to preparing spatial data in ArcGIS, in order to efficiently make use of the possibilities to attain spatial data on the internet. The term ‘spatial (or geospatial) data’ is also known as geographic information and describes features or boundaries on Earth (or any other planetary body). Spatial data is frequently stored as coordinates and is visualized through maps, which can be accessed, manipulated and analyzed in a Geographic Information Systems environment, such as ArcGIS. Spatial data is more and more present on the internet and is often presented in the form of Webservices. Webservices we will focus on are Web Mapping Services (WMS) and Web Feature Services (WFS).
This module is a refresher into ArcGIS. Students attending this course have GIS pre-knowledge, but that could have been a while ago… In this module you will work with a series of datasets from the Netherlands - the TOP10vector, The Algemeen Hoogte Model (Digital Elevation Model, AHN2 5m), the Geomorfologische Kaart van Nederland (Geomorphological Map, GKN50), the Bodemkaart van Nederland (Soil map, BKN50) and the Geologische Kaart van Nederland (Geological Map, GK50). You will use ArcCatalog, ArcMap, and some of the ArcToolbox programs in order to efficiently store, manage, analyze and display geospatial data in a correct manner. In addition, there is a raw point measurement map of electrical conductivity that you will use to interpolate subsurface conductivity in relation to sand thickness.
The goal of this exercise is to understand natural processes in a coastal ecosystem from panchromatic
stereo aerial photographs using anaglyph imagery in ERDAS, and:
- To apply Air Photo Interpretation elements (API’s) in coastal areas.
- To interpret and analyze API’s for the reconstruction of the local landscape development.
- To evaluate the area in qualitative terms for its potential for generating drinking water.
In this exercise you will use multi-temporal SPOT XS satellite imagery using band ratio analysis to evaluate flood hazard in the confluence area of the Rivers Ganga and Jamuna in Bangladesh, Southwest of the capital city Dhaka. In this area river dynamics are enormous. River channels may change their position over several kilometers within only a few years. You will evaluate the potential flooded areas covered during three periods:
1. During a dry season, based on a SPOT image from 9-1-1987.
2. During a moderately severe flood, based on a SPOT image from 7-11-1987.
3. During a severe flood, based on a SPOT image from 10-10-1988.
The Normalized Differential vegetation Index (NDVI) can be used to compare remote sensing images of different dates to study vegetation change. Vegetation indices (VI’s) are usually calculated and used as a quantitative measure to assess vegetation conditions from remote sensing images. Many studies have shown that VI’s are related to biophysical parameters, such as Leaf Area Index (LAI), canopy cover, biomass, and more. Hence, we can use VI’s to detect spatial and temporal vegetation change. We will use the NDVI to illustrate the use of a VI in Burkina Faso. Burkina Faso – to the south of the Sahara – shows particular gradients.
Landsat satellite images will be processed from which land use and land cover (LULC) will be classified, with a focus on urban sprawl in relation to agriculture. Satellite images from two dates (1979 and 2007) are classified and compared to quantify and visualize urban sprawl / LULC change. The area selected is in northeastern China, near Shenyang city, the capital city of the province Liaoning. See also https://en.wikipedia.org/wiki/Shenyang
In this exercise you will create distribution maps for two species of hummingbird that occur along the Ecuadorian Andes: Metallura tyrianthina (Tyrian Metaltail), and Metallura baroni (Violet-throated Metaltail). We will use two different approaches to determine where along the Andean ridge of Ecuador these species are likely to occur. This module takes more time and insight from the student and will be weighted twice as strong as the other modules. Time average spent on this module: 2-2.5 days. You may regard this as an own project and we will be happy to receive alternative and challenging, innovative solutions.
The concept behind eCognition is that important semantic information necessary to interpret an image is not represented in single pixels, but in meaningful image objects and their mutual relationships. The basic difference, especially when compared to pixel-based procedures, is that eCognition does not classify single pixels, but rather image objects which are extracted in a previous image segmentation step. You will be introduced into this new method by classifying impervious surfaces – however, keep in mind that you can, and will, apply this technique to other remotely sensed imagery. For this module you do not need to hand in any answers, but you are expected to finalize this module. Reserve a computer in the GIS-studio!
Should be done after module 15! Based on that module you will classify a WorldView image of Flevoland using object-based image segmentation and classification techniques. Remember you may have finished a pixel-based ERDAS classification module earlier using the ERDAS or ArcGIS Pro software, from the area in China.
In this module you will learn how to implement height data from airborn laser scans (LiDAR) in a GIS. LIDAR stands for Light Detection and Ranging of Laser Imaging Detection and Ranging and is an optical remote-sensing technique that measures distances with a pulsed laser light to sample the surface of the earth. You will work in ArcGIS Desktop.
This module is in progress: it illustrated how to calculate features from point-cloud data and to process the features for use in analysis pipelines. Will be (hopefully) released during the course.
a. In this module you will learn fundamental Python concepts and be introduced to the Python scripting environment in ArcGIS. Tips and techniques to help you master proper Python syntax, script flow, and error handling are presented. You will follow the "Python for everyone" e-learning module on ESRI's Virtual Campus (https://www.esri.com/training/). To start the e-learning module, you should log in with your ESRI account and search the Training Catalog for this module. The questions of the e-learning module are substituted with the questions that you can find in the Canvas Quiz below. You will work in ArcGIS Desktop.
b. GIS is often used to model or predict the likely locations of rare or endangered populations of plants and animals (Engler et al., 2004). By processing spatial data such as potential habitats and the physical/biological needs of species, knowledge is gained about where populations may occur. Management needs like critical habitat can then be adjusted accordingly. The goal of this module is to create a critical habitat map of the Sierra Nevada yellow-legged frog (Rana sierrae) based on open source datasets and species’ knowledge. You will be using Python in ArcGIS Pro.
Conventional multi-spectral remote sensors, such as the Landsat Thematic Mapper and SPOT multispectral data, record data within only a few relatively broad wavelength bands. These broadband systems can only discriminate general material types and land cover units at the earth surface. As more experience is obtained with the use of multi-spectral images, more detailed information can be extracted from remotely sensed imagery. Hyperspectral sensing meets this demand exactly.
Most optical remote sensing methods make use of the visible EM spectrum. This works quite well in cloudless areas, but within equatorial areas cloud cover could result in bad quality images with low visibility. In this module radar remote sensing is introduced, radar waves have a much longer wavelength (1mm-1m) and can therefore penetrate clouds. This technology is therefore interesting, especially for areas with high cloud cover throughout the year such as tropical rain forests. An example is presented from Vietnam. You will work in ArcGIS Pro.
LISS-III satellite images will be processed from which land use and land cover (LULC) will be classified. The satellite images are mosaicked, classified and compared to support quantification and visualization of LULC. You will select a unique area and timespan (somewhere within India) after discussion with fellow students, and compare your results. You will work in ArcGIS Pro.
a) Introduction to working with Google Earth Engine
b) Image analysis using Google Earth Engine for forestry
In progress: will become available during the course
Selection of assignments
The assignments are mostly designed by IBED staff members and (former) master students. The assignments should be finished, preferably in the order as listed below: each assignment will take you (mean) half to a full day to finish, with some exceptions, that could take a bit longer. Within this course, the student will be able to progress along 2 possible ‘tracks’:
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
| Students | Activity | Start | End | Hand in |
| Without pre-knowledge | 17 modules |
February 4 |
March 29 | Quizzes in Canvas |
| With pre-knowledge | 9 modules |
February 4 |
March 1 | Quizzes in Canvas |
|
With pre-knowledge |
Project |
March 1 | March 29 |
Assignment in Canvas: - datasets - technical report |
The schedule for this course is published on DataNose.
Getting started in the GIS-studio of IBED
Computers
The computers should be turned off after your work is done. Also the monitors can be turned off.
GIS accounts
To use the computers you need an account and a password. Staff, postgraduates and PhD students of IBED can use their own (UvA-) account. Students enrolled in a course can use their student login name or number. If you do not have a login name or number, you will receive a guest account from the staff of the GIS-studio.
The account information looks as follows:
This account information is computer-generated and is personal. However, the password is also known to the staff of the GIS-studio. Do not change this password!! Otherwise we will not be able to help you when you forget or lose your password !
Network drives
After you have logged in and have opened My Computer on your desktop you might see:
The network drives fnwi-public and g-gis10-fnwi are your shares on the FNWI-server. From these shares a back-up is made every night. This is not the case for the local drives (C:), (D:) and (E:).
g-gis10-fnwi (for example) is your personal share and is used to store your profiles (preferences, short cuts on your desktop, personal documents, etc.) so you are not dependent on a specific computer. Any GIS computer in the GIS-studio that you log in to with your account will connect to your personal share and open your profiles. This personal share has a default capacity of 10 Gb which is enough for most GIS and RS purposes.
On Canvas you can find the exercises, examples, tutorials and manuals that you will need. Of course you have read/write access to your personal share on the network so you can copy the files that you have worked on and want to keep for later use to this share (and be assured of a back-up). You MUST first copy the module files that you need to a self-named folder (usually with your name in it) to the D drive.
A normal working scheme is as follows:
Locate the files you need from Canvas and copy them to a new self-named folder (usually with your name in it) on the local D-drive (NOT on the C-drive !).
Printing
There is an printer in the IBED pantry (red block with also a kitchen and toilets). See the whiteboard in the GIS-Studio for information on how to connect to this printer.
Tutorials
Tutorials for ArcGIS can be found on the ArcGIS Resource Center: http://resources.arcgis.com/en/help/
Another fine reference is the online Help system in ArcGIS. It is located under the Help menu. There are two options:
Software