Methods and Tools for Landscape Ecology

To better understand and communicate the patterns and processes studied by landscape ecologists, numerous methods and tools have been developed to collect, visualise, analyse and/or model data over space and time.

These methods and tools, for example satellite remote sensing and GIS, have evolved quickly over recent decades along with technological advances such as increasing computational power. Selecting the best approach for a particular piece of work depends on the objectives and focus of the study as well as the time, skills and budget available. The more general drive to improve the availability of free and open source (FOSS) tools and data across science has been echoed in landscape ecology and is reflected in this overview.

Existing landscape ecology data

There are many existing datasets available for landscape ecology research and practise. When deciding which datasets are most appropriate for a particular study or project, users should consider attributes such as how the data has been collected or generated, as well as the type (quantitative or qualitative), spatial and temporal scale (resolution and extent/timescale), spatial comprehensiveness and accuracy. Remote sensing and other technological advances have improved the availability of increasingly accurate and fine spatial and temporal resolution data. Often the challenge is navigating the plethora of online data portals available to find the most appropriate source! A few examples (with a focus on open access UK data) are given below:

Collecting new landscape ecology data

Some landscape ecology studies require the collection of new data, tailored to answer a particular research, policy or practice objective. Fieldwork design and planning can be a difficult challenge; selecting sites, and organising access to these sites, can often involve considerable planning and resources.


As comprehensive surveys of the entire population or landscape under study are usually impossible, landscape ecologists typically collect data from a sample that can be selected using random, systematic or stratified approaches. Data can be collected using transects (continuous survey routes) or at static points. When deciding on a sampling strategy, careful consideration around issues such as sample size, independence, context dependency and representativeness of the variables under consideration is required to enable statistically robust and meaningful analysis and interpretation, whilst also accounting for the practicality and expense of the field work.

Field Methods

The field methods employed by landscape ecologists vary hugely depending on the focus of the study. Many approaches are observational, whereby the natural variation in a variable of interest across space or time is studied (also referred to as “natural experiments”). In some cases, this spatial variation is used to infer temporal processes and to make projections through time, particularly when data aren’t available, or monitoring is unachievable over the time period of interest (this is known as “space for time substitution”). Some landscape ecology field studies employ experimental manipulation of the components of the landscapes under study, such as the use of prescribed burning or species exclusion zones.

In terms of the equipment used, recent technological advances have improved the cost effectiveness of using remote monitoring via sensors such as camera traps, acoustic monitors, geotags, people- counters or meteorological sensors. Social data can also be collected remotely via participatory mapping and survey tools. Remote data collection can greatly enhance the volume and temporal resolution of observations recorded. However, for many purposes, skilled field workers are essential to ensure data completeness, accuracy, and validity.

Once collected, whether remotely or in person, further laboratory or computer-based work is often needed to sense check and prepare the raw data for analysis. Artificial intelligence is sometimes used at this point, such as the application of algorithms to predict the identity of taxonomic groups recorded via images or sounds.

Visualising landscape ecology data

Once the data required for a landscape ecology project has been collected, spatial elements can be mapped and visualised using either proprietary Geographic Information Systems (GIS; e.g. ArcGIS), or software that is fully FOSS (e.g. QGIS) or that has free subscription options (e.g., Google Earth Engine). These tools allow the user to overlay, manipulate and interrogate spatio-temporal data, and to produce output maps and graphs to communicate patterns and results to others. Coding software such as R and Python are also increasingly being used for these purposes, the advantages of which are set out in the next section on data analysis.

Analysing and modelling landscape ecology data

There are a plethora of modelling approaches, tools and coding packages available to support the common landscape ecology tasks listed below. Coding languages such as R and Python are increasingly used over stand-alone, purpose-built data analysis and visualisation tools because they provide a powerful, adaptable and free option for easily automating, collaborating on and publishing workflows. Although these FOSS options require users to learn the base coding language, there is an ever-growing catalogue of sophisticated and well-maintained packages available for landscape ecology analysis, and associated online coding communities for support and guidance.

Specific methods and tools include:

  • Connectivity and fragmentation analyses to understand the structural and functional connectivity of habitats and landscape components can be carried out using various methods such as graph theory, habitat networks, least cost connectivity and circuit connectivity. Tools include Condatis, Circuitscape and Conefor.
  • Remote sensing tools (e.g. Google Earth Engine, ERDAS IMAGINE, ENVI and various R packages) can be used to process imagery and extract information from remote sensing data such as LiDAR and Synthetic Aperture Radar (SAR).
  • Scenarios and storylines can be developed to explore possible impacts of policies or management actions, and to understand their desirability with stakeholders. They can also be used to provide the ‘boundary conditions’ for models and simulations (see below). Scenarios of future land cover change can be developed using software such as the Land Change Modeller (LCM) in Terrset.
  • Landscape pattern analysis is useful to explore the role of the spatial configuration of landscape elements on ecological processes (e.g. habitat configuration on population dynamics). To quantify landscape pattern numerous metrics and indices have been developed and tools like Fragstats and the ‘landscapemetrics’ R package make calculating them relatively straight-forward.
  • Modelling and simulation can be useful to understand the importance of different processes on producing patterns and system states, and when combined with scenarios to explore possible alternative futures. Examples include multi-model inference from statistical models, species distribution models (e.g. using MaxEnt), landscape simulation models (e.g. LANDIS), among others.
  • Valuation tools to holistically evaluate human and natural elements of landscape are commonly associated with ecosystem services, including the InVest suite of free, open-source software models for mapping and valuing goods and services from nature, and the CoSting Nature policy support tools for mapping supply and demand for 18 ecosystem services at local to national scales.
  • Conservation planning and opportunity mapping tools like Marxan and Zonation are valuable for spatial planning and identification of priority areas, incorporating measures of habitat quality and connectivity.

Communicating landscape ecology

Alongside traditional output types such as static maps and reports, landscape ecologists can make use of increasingly sophisticated options for communicating results in more interactive and visual online formats. These include the production of online maps and tools via approaches such as Shiny R, which provides a flexible approach to designing interactive tools that allow users to visualise, explore and interrogate data online. Alternatively, ArcGIS StoryMaps provides the facility to integrate maps and visual results with text narrative and images to provide an online ‘book’ designed to guide the reader through a project storyline.

These examples are just a few of the wide range of methods and tools required for a holistic and interdisciplinary discipline like landscape ecology. Watch and join Landscape Connections webinars to see how some of these methods and tools have been used in practice.


Text: Chloe Bellamy, James Millington, and ialeUK (2024) CC BY-NC


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