Image source

Last year we were contacted by the Shire of Mundaring to design wildlife corridors in their local government area. The purpose was to maintain a healthy landscape, restoring connectivity and promoting local biodiversity through the expansion of available habitats. As a solution, we conducted a spatial analysis using QGIS to derive the optimum paths to establish connectivity between vegetation patches, considering a few datasets of a sufficiently high level resolution.

It is interesting to see how there are many toolboxes developed to design wildlife corridors using licensed GIS software, but not many of them are available to add in open source software. There are a few models that can be downloaded as standalone software, which aim to find optimum restorations paths or connect reserves. The problem with these is that they can be out of date (you can encounter many bugs while trying to get your results) or the parameters you can control are very limited, so there is little room for tailoring the corridors to your preference. 

Open-source software tools and plugins are often developed for a specific purpose and then put out to the public domain ‘as-is’ for others to adapt. For these niche applications, that might be as far as they get, and when you want to use them you are either limited to those application constraints or faced with a development cost to adjust the tool to your needs. There seems to be an opportunity here actually, to adapt open-source tools in QGIS and other software, and offer a solution that is tailored to defining wildlife corridor options. So I am looking into that as part of my professional development at Gaia Resources, and should have some news around this in the not-to-distant future!

The Least-Cost Path is one of the many plugins that can be added to QGIS. This kind of multi-criteria analysis is very popular for designing corridors for a range of applications like wildlife conservation and infrastructure planning.

The concept behind performing a Least Cost Path analysis is the following: given an origin and a destination point, the algorithm will search in a cost raster for the cells with the minimum value and create a corridor between them. 

If you are not familiar with GIS jargon, you might be wondering what a cost raster is. Long story short, the cost raster represents the potential resistance faced while transiting that path, and it combines a number of parameters that we’ll look into in a bit more detail below. Basically, each cell in your cost raster is the aggregation of scores that you apply to the input parameters. 

Image source

Let’s think of all the parameters that need to be considered while designing corridors, with the idea that each of these can be sourced as data inputs into the cost raster. First and foremost: vegetation, an essential component that provides habitat and shelter for biodiversity. Next, we want to provide some source of water for the wildlife, so this element should also be included. 

Knowing the land use types is also important while planning this kind of development, since some of them will be better to restore vegetation than others (I do not want to think of bandicoots establishing their home near a highway!). Talking about roads, that is another key dataset, since it would be ideal to avoid them (although crossing them is also possible, like in this example). Other datasets can be considered in this kind of study as well, it all depends on the requirements you want to cover with your corridors.

These parameters are given scores according to different criteria, considering how suitable they are for conservation purposes – and the scores are applied to the input datasets. For example, a criteria could be ‘main roads should be a severe impediment to wildlife corridor crossings, and wildlife corridors should not be present within 10m of a main road’. When the criteria are defined they will be assigned different scores according to a ranking, i.e. highways will have a higher cost than a minor road. Once the datasets are ready, they can be merged, and the scores will be added to each other; the final merged layer represents the cost raster that serves as input in the Least Cost Path plugin.

The model will create a corridor connecting one source point to a destination, or many corridors from a source to various points. A distinct advantage of using the Least Cost Path algorithm is that it is data-driven; it selects paths through analysis of inputs with defined rules that would otherwise be subjective and counter intuitive to choose while doing a visual analysis of the area. The idea is that the paths chosen by the tool will have a lower overall cost based on the data inputs. 

Data-driven models will often lead to some unexpected results, and a review of the results can highlight additional perspectives, missed criteria, opportunities for improvement or data inputs that can be better handled. This makes wildlife corridor mapping an iterative process, but the model itself is robust and, once established, can be repeated with ease. For example, you can incorporate particular strategic planning areas not represented in the land use dataset you used in the last run of the model, or introduce firebreaks and ideal spots for wildlife cross-overs to overcome barriers on main roads.

The corridors obtained from the model can be given different priorities, as they will have an assigned cost as a result of crossing through different areas. Since the corridors have diverse lengths, a good practice is to obtain the cost/length ratio. This helps organisations to decide – alongside environmental and planning objectives – where the best value is for their revegetation and investment.

We can all help protect biodiversity by managing the environment in local areas. Organisations can use wildlife corridors to make meaningful engagement with landowners and feed into the planning process – giving everyone a chance to contribute to the local biodiversity in their area. If this project sounded interesting to you and would like to do something similar, reach out and start a conversation with us via email, or through our social media platforms –  Twitter, LinkedIn or Facebook. 

Rocio

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A GIS Health Check involves a process of investigation and discovery of an organisation’s Geographic Information System.  It includes a set of stakeholder interviews coupled with a hands-on exploration of the GIS data and software. It also considers other aspects of the broader system environment; but, at the end of the day, what are the benefits of a GIS Health Check? 

Well, let me explain by giving you an example. 

Think of the uses your company makes of spatial information. Can you identify any pain points, or areas where you think things could be done more effectively? Throughout the process, these are the key business problems we want to come back to, to ensure that whatever is recommended, is aimed at delivering real benefit and value. These may be things that jump out straight away in interviewing stakeholders, or they may be more stealthily embedded issues that need more analysis and thinking. Before we think about the solution, it is critical that the GIS Health Check identifies the problems that need to be solved. 

During a GIS Health Check, we consider five elements: People, Processes, Data, Software and Hardware. When these elements are appropriately resourced and working together effectively, we can say we have a healthy GIS system. However, if one (or more) element is not operating efficiently, or is lacking focus in the organisation, there are problems in the organisations’ workflow.

Each organisation has a different aim when requesting a GIS Health Check. For some, a major issue is about historical data management and a resulting lack of structure in the database system (e.g. layer naming, authoritative sources, accessibility). Others might be more interested in improving their processes, writing documentation on them and making sure all the GIS stakeholders are aware and brought into a consistent framework. There are companies that think the software they are using might not be the best fit for their work, or that alternative products may be available that will deliver better value and efficiency. Most of the time, it is a combination of issues from the different elements. 

So when it comes down to an individual GIS Health Check – what do we at Gaia Resources actually do? Even though the objectives are different for each organisation, the approach is pretty much the same. We start by checking the current state of the system: we interview all the GIS users in the company, in order to identify what is working well and where are the ongoing issues. We also get inside your system to ‘lift the hood’ and evaluate the organisations’ spatial data and how it is organised, checking for duplicate files, naming conventions and degree of adoption, folder structures, software used, key business processes supported by spatial information, and many other aspects. 

We also tap into the knowledge of the organisation’s stakeholders, to understand the business context, related strategy objectives and to gain an understanding of how staff think their Geographic Information System should evolve. Basically, what does the future state look like?

Based on our review of the current and desired future state, we put our thinking caps on to brainstorm and provide recommendations. These are meant to improve the future state of the company’s GIS environment, and provide tangible strategies and actions for getting there. The recommendations are classified in terms of  priority, estimated effort and category (e.g. the 5 GIS environment elements covered above). 

Gaia Resources has conducted a significant number of these GIS health checks over the years, as mentioned in this blog. Some of our clients are IGO, Redbank Copper, MBS Environmental, OEPA and recently, Carbon Neutral. We keep in touch with these organisations, and with some we continue to support them with QGIS training and software development. 

Having been through the process, and really benefiting from the previous GIS Health Checks deliverables, I took a page out of our own book so to speak and developed a guideline on “How to deliver a GIS Health Check.” We reviewed previous projects and identified all the common points for a successful Health Check. That guideline is now a resource for our Data Science team and project managers who will no doubt be helping more organisations in the future. 

To summarise (and answer my first question), having your GIS checked can bring many long-lasting benefits, from improving workflow efficiency and consistency, to enhancing decision making and building capabilities in your team. We help organisations achieve this by working with them to focus on the key business challenges where spatial information can play a role. 

If you think the GIS environment in your organisation could do with a review, reach out and start a conversation with us via email, or through our social media platforms –  Twitter, LinkedIn or Facebook. We are here to help!

Rocio

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To recap, we chose a challenge which involved identifying landslide risk using science and community inputs.  The World Health Organisation reports that between 1998-2017, landslides have affected an estimated 4.8 million people with over 18,000 fatalities. 

We processed remotely sensed imagery and developed a method and FastAPI prototype app to detect the early stages of a landslide through canopy movement on slopes, providing a solution that could be used by government agencies to warn the population living in risk areas. Click here (https://youtu.be/gvxFAl7jtqc) to view a short video that shows how our prototype application works.

Trees tilting due to the effect of rock/soil sliding. Image credit

As part of the award provided by the Space Apps Perth Organising team, I went with my teammates Khan Rahman, John Duncan and Jared Rolt to visit the BINAR labs, at Curtin University. This is the place where WA’s first homegrown satellite was created, a cubesat that is currently flying in low earth orbit. As a big space fan I really enjoyed learning more about the engineering process and the challenges that were faced during the design. 

A replica of the cubesat in the BINAR labs

Later in November the Global Finalists for the hackathon were released, and Landslide Detection Squad was one of the 37 projects that would compete for a place between the 10 best in the world. That was such good news! Finally, in December I woke up to an email saying that our team was one of the 10 Global Winners. We won in the Local impact category, because our solution demonstrates the greatest potential for local impact. All the global finalists projects were very solid and interesting, so we are very proud of being selected as global winners. It would be great to see our project work, and be of use in situations of emergency.

Now we are waiting to receive more details on the global award, which consists of an invitation to the Winners Trip, which could potentially include viewing a spacecraft launch at a NASA facility. This is dependent on the pandemic situation, but visiting NASA’s headquarters is a very exciting opportunity.

If you have a project involving similar issues, we’d love to hear from you. As with many hackathon events, a lot of good concepts and prototypes come to life, but the next steps can be a bit hazy for turning that into a usable product or putting it to use in the real world. Developers give up their weekends and brain cells to get to that point, and often need further grant or project funding to take a product forward. Our team thinks we really have something here, and NASA certainly thinks so!

Contact us via email or start a conversation with us on one of our social media platforms –  Twitter, LinkedIn or Facebook.

Rocio

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From the remote sensing related challenges, one of them particularly caught my attention:  Identifying risk with science + communities, focusing on landslides. The challenge consisted of developing a tool using data from NASA satellites and ground-based sources to determine the risk of landslides in rural communities, and share the results with local communities and governments. I spoke to some friends about this, we decided to participate and registered as a team: the Landslide Detection Squad.

Trees tilting due to the effect of rock/soil sliding. Image credit mdpi.com

Before the event there was an information night held by Space Hub Perth and Core Innovation Hub, where speakers from BINAR, ICRAR and previous winners of the hackathon from Perth answered questions and gave us an insight of the current spatial situation in WA and Australia. It was a good chance to get motivation for working on spatial topics.

On the day of the hackathon, the team gathered at UWA Venture, excited to start the challenge. We kicked off by pitching some ideas and brainstorming on a white board, until we came up with what would be our project. Our objective was to detect the early stages of a landslide, with the purpose of warning the population living in risk areas. We needed to identify a parameter to measure that would indicate some kind of movement on the ground, so we chose trees. On forested slopes, tilting of trees and change in canopy structure can indicate soil and rock movement is occurring, which is a precursor to landslide events. Due to the use of remote sensing data, our tool could be applied in forested areas prone to landslide occurrence at a low cost.

Remote sensing data provides a low cost option to detect changes in canopy structure. Photo Credit

Photo Credit

Taking advantage of new sensors (e.g. cubesats and UAVs) providing high-resolution images and deep learning technologies for tree detection, we proposed and prototyped a method that detects a canopy movement on slopes that is indicative of landslide risk. This information could be used to enhance landslide risk models and is embedded within a system that generates dynamic risk maps, publishes alerts when situations are critical, and supports government agencies to plan ahead for emergency situations.

Spending the weekend doing something for science might not sound attractive to everyone, but it has its rewards! Chris from Gaia Resources, who was very supportive of my participation, suggested making this hackathon part of my professional development plan with the company. Taking part in this event allowed me to network with people from different backgrounds who are interested in the space field. As a team, we learned about landslides and prevention systems, and got the chance to build a new model which could help prevent disasters. 

However, that is not it: our team came first in Perth and our project will be competing in the global round! Now it is a matter of waiting for the global results. Even if we do not win, taking part in a hackathon was a great experience and I encourage everyone to try it!

Have you ever participated in a hackathon? How did you find the experience? Please share in the comments, or start a conversation with us on one of our social media platforms –  Twitter, LinkedIn or Facebook.

Rocío

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