Open source GIS & remote sensing for conservation (Beginner)

In Section 2, we will cover some of the basic principles of GIS which are essential to understand before fully diving in.

This session introduces the most common spatial data types you will come across when working in GIS. Spatial data types have different characteristics, specific use-cases and different processing methodologies, so it is important to have a good understanding of them at this point in the course. 

All GIS data must be associated with a coordinate reference system in order to visualise it spatially on a 2D surface or a computer screen. This session outlines some key concepts.

In this session, map projections are introduced. These transform the units of GIS data into metres, allowing the analyst to make geometric measurements, for example of distances and area. The Universal Transverse Mercator (UTM) system will be focused on here.

This session will explore practically the use of different coordinate reference systems/projections in QGIS and how this affects the display of data.

Spatial data may come from many different sources including primary data collected using a GPS device in the field, lists of coordinates, or secondary data accessed through online portals or received directly from outside agencies.

Let's review what we've covered in Section 2.

This section covers sourcing free geospatial data online including protected area shapefiles, rivers, species distributions and a digital elevation model. By the end of the section you will be able to add both raster and vector data into QGIS, change the colours and styles of these layers, add basic labels to features, develop a map layout and export that map as a PDF.

In the session, we will download a variety of open source global geospatial data relevant to conservation, including protected areas, rivers, species distribution data, and country boundaries. The attached resource lists some key links to useful geospatial datasets.

OpenStreetMap (OSM) is an essential tool in any GIS analyst's belt. A global vector map of roads, buildings, points of interest, infrastructure and more, OSM is managed and regularly updated by a global community of volunteers. HotOSM offers some useful tools for disaster response which will be briefly covered as well.     

Using data downloaded in the previous session, in this session we will explore adding spatial data to QGIS.

A quick session to demonstrate how to reproject vector data in QGIS. Don't forget to return to the sessions on coordinate reference systems in section 2 if you need a refresher!

This session outlines various techniques to extract and subset features from shapefiles, exploring tools such as Select by Location, Select by Attributes and Clip in QGIS.

In this session, we will explore how to symbolise vector layers / shapefiles, setting colours and styles effectively. 

This session will explore how to easily download the free SRTM digital elevation model (DEM) for anywhere in the world at 30 metres resolution.

In this session, we will look at editing the style and colour of raster layers, using the digital elevation model (DEM) downloaded in the previous session as an example. We will also explore some specific methods to effectively style topographical data, such as using hillshades.

This session provides an overview of basic map design and cartographic principles that should help you produce more informative and publication-ready maps.

In this session, we explore the creation of maps in QGIS using the Print Layout function. You will create and export a map using the datasets you have worked with throughout this section. 

Well done, you have completed section 3 and should now be able to find and download some spatial data, extract features of interest and create a map in QGIS!

In this section, we will cover some essential GIS skills to effectively work with vector data, including georeferencing, digitising, and working with attribute tables.

In many cases, you will only have an image or scan of a map, but you may need some of the data in a GIS format. This session outlines how to "georeference" a map in QGIS, whilst the next session outlines how to "digitise" elements of a map image. We will use a map of Boni and Dodori reserves in Kenya taken from a journal article. 

This session outlines how to create new vector data (points, lines, polygons), and digitise features from georeferenced imagery. We will use the map image georeferenced in the previous session to demonstrate this. 

In this session, we will explore how to conduct field calculations in the attribute table of a shapefile. We will use the reserve shapefile digitised in the previous session from the georeferenced Boni and Dodori image.  We will conduct basic geometry calculations including area and perimeter measurements, and indicate how to conduct further calculations such as converting measurement units. You can also see the cheat sheet linked for more field calculations which could come in handy. Please also refer to the Quick Reference document attached to get a grasp on field data types.   

Another important skill in GIS is to analyse data within the attribute table of a vector dataset. You will often want to summarise data within columns (fields), similar to how you might analyse data within Excel. In this session we will use a protected area dataset and country boundary dataset for Latin America to calculate the percentage area of each country that is protected. We will use the Statistics by Category tool to get statistics of a particular field (area in this case) summarised by a specific attribute (countries in this case). We will also use the Dissolve tool to combine overlapping features and calculate total area this way.

In this session we will demonstrate the development of a choropleth map. Choropleth maps are used to map quantities, whereby features are shaded based on specified value ranges. We will use the data generated in the previous session to map the countries of Latin America based on the percentage land area that is protected.

Whilst we have covered basic labelling of features in QGIS already, there is much more that can be done! This session will cover manually moving labels, labelling of linear features, and rule-based labelling.

Congratulations on completing Section 4 - hopefully you now feel more comfortable working with vector data in QGIS and feel equipped with some of the knowledge required to start working effectively with this data.

Welcome to Section 5, where we will cover survey design and data collection to get you prepared to undertake ecological (or social) surveys utilising GIS, GPS devices, and a range of mobile-based tools.

This session outlines some of the key concepts of survey design, including survey techniques and sampling methods. Also see the attached lecture on sampling for more details on survey design. However, note that this section in general is geared towards the use of GIS for surveys and this session is not intended to be a comprehensive overview of survey design.

Also attached is a paper from Arabuko Sokoke National Reserve in Kenya, where a survey grid was created in order to position camera traps. A paper from Dja Faunal Reserve in Southeastern Cameroon is also attached, where transects were used to monitor primate populations using a distance sampling technique. 

This session outlines how to create survey grids and transect lines in QGIS using different sampling methods.

The purpose of this session is to outline some of the most common data collection tools that can be used for ecological and social surveys for conservation-related activities. The following tools are covered:

(1) Tools to locate survey points: GPS devices, Maps.Me;

(2) Tools to collect and record survey-related data: ODK, CyberTracker, SMART, notepads/data sheets;

(3) Tools to support in recording spatial information: ODK, GPS Tools, GPS devices.

Whilst there are many data collection tools available, some of which were introduced in the previous session; here, we will focus in on ODK as it is such an effective and versatile tool. We will see how to develop a survey to use in ODK by creating and correctly formatting an XLS form. We will also load the completed survey onto the KOBO server so that it can be deployed and accessed through a mobile device (or multiple mobile devices) and used offline.

We looked at how to prepare sampling points and transects in session 5.3. This session outlines how to load that data onto your mobile device (using the Maps.Me app), or a GPS device so that you can locate your sampling locations when in the field.

This session demonstrates the collection of data in the field, including locating sampling locations using Maps.Me; recording survey responses offline using the ODK form we prepared earlier; and collecting spatial data including GPS coordinates and tracks, mapping areas, and taking compass bearings.

In the session, we will download and clean data collected offline using the ODK form in the previous session. Whilst this focusses on ODK, the process would be similar for other data collection apps, where you would also need to download and clean any data collected before using it for further analysis.

Well done for completing section 5! You should now be aware of how to design and prepare surveys, collect data in the field, and download and clean that data ready for further analysis.

Welcome to Section 6! In this section we will cover management and visualisation of survey data.

In this session, we will explore some basic features of Google Earth (find a link to download the software in the attached resources). We will use some real data indicating the locations of camera traps in the Sokole Reserve in Kenya (find it attached in the resources section). This data also indicates details of the habitat type which was appended from a land cover map. This data is in shapefile format, but other data formats can also be added in a similar way. For more details on importing GPS data (.GPX) directly from a GPS device see the attached resource.

At the end of the session, you should be able to add and visualise spatial data to QGIS, create new features, view historic imagery, and even create a basic exportable map!

Often data will be in tabular format, usually in a file type called CSV (comma separated value). In this session, we will use a CSV file indicating camera trap locations and one indicating trap rate for Aders Duiker for the same camera traps - please download these from the resources section. We will see how to format these CSV files correctly so they can be read by QGIS, then import the datasets and visualise them based on the coordinate fields. Finally, we will join the trap rate data to the camera trap locations.

In case you want to combine two shapefiles or add additional features to the same shapefile, this is where the Merge and Append tools (respectively) come in. In this session, we will use the resultant camera trap shapefile dataset from the previous session and merge/append the attached files, which contain data relating to additional camera traps/surveys. 

This session covers visualisation of survey data, based on (1) graduated symbology / colours, and (2) as charts.

Firstly, we will demonstrate the use of graduated symbology and graduated colours to represent camera trap data from Boni Reserve in Kenya based on trap rate of one specific species.

Secondly, we will use data from a bird of prey point count survey in Georgia to demonstrate how to visualise data as pie charts indicating the number of birds of different species counted at each vantage point. The attached Field Key file indicates which bird species the field name relates to in the survey shapefile. The use of chart symbology is useful when you are monitoring multiple species at a single sample point and you want to show the most common species identified.

This session covers two additional ways of visualising survey data - interpolation, and hotspot mapping.

After introducing some theory, we will demonstrate the use of Inverse Distance Weighted (IDW) interpolation on species presence and absence data to give a rough estimate of species distribution. We will then outline the use of hotspot mapping using the Kernel Density function to identify wildfire hotspots. 

Congratulations on completing Section 6! You should now be aware of some of the essential ways of managing and visualising survey data.

Welcome to the final section of the beginner course on open source GIS and remote sensing for conservation. Here, we will cover some common vector spatial analysis tools which can help you answer key questions about spatial relationships and interactions. 

This session introduces an essential GIS skill, providing an example of buffering the boundary of a vast national park in South Sudan to delineate a buffer zone and identify roads within this area.

This lecture introduces three key vector overlay tools, demonstrating how to use them using real environmental data. These are (1) Erase: tool used to erase part of a shapefile based on where it spatially overlays with another shapefile; (2) Intersect: used to retain only areas of a shapefile that spatially overlay with another shapefile (including the attributes of both features, unlike the Clip tool); (3) Union: produces a union between two adjoining shapefiles, retaining both. 

We will use a range of datasets and examples relevant to conservation to demonstrate the application of these tools.

We have covered ordinary table joins, but what if you want to join data from other features based on a spatial relationship? That's where Spatial Join comes in. Relevant examples of this in practice include if you wanted to join the underlying land cover type to camera trap locations, or if you wanted to analyse the total number of wildlife crime incidents within protected areas.

Nearest Neighbour is another essential spatial analysis tool. This tool takes the attributes from the nearest feature in another shapefile and also provides the distance. This could be useful for example when analysing the impact of distance to protected area edge on species distribution. In this case you could use the Nearest Neighbour tool to determine the distance from e.g. camera traps to the protected area boundary and also get the name and other details of that protected area boundary.   

Boolean overlay is the process of classifying areas based on three "Boolean", i.e. binary operators. These are AND, OR and NOT, which relate to the Union, Intersect and Erase tools in GIS respectively. Combinations of these operators can be used to undertake basic suitability analysis. We will use the example of identifying suitable habitat for lynx based on a number of rigid requirements. Note that suitability analysis can also be conducted using weighted overlay, based on "fuzzy logic", thereby accounting for the level of suitability. This will be covered in the advanced section of the course.

In this session, we will explore another use of Boolean overlay. We will use a range of spatial analysis tools to refine a survey study area based on a set of rigid requirements. This is useful if you have predefined environmental parameters within which you want to conduct your survey based on known species presence or absence, accessibility, etc. 

Congrats on completing the final section of the Beginner part of the course! You are now ready to proceed to the final assignment.

You have now completed the beginner part of the course "Open Source GIS and Remote Sensing for Conservation." Well done! In this session, review what you have learned and take a peek at what is coming next on the Advanced course.