How Can QGIS Improve Great Crested Newt Surveys & Pond Detection?
Introduction
For many ecologists, the mention of a great crested newt survey brings to mind early starts, late finishes, coke bottles, and during the desk study stage, trying to work out whether that suspiciously round dark object on an aerial photo is a pond… or a trampoline!
I remember this well from my time at GiGL, where I conceived and supervised a London garden research project, based on aerial imagery. One of the biggest frustrations my colleague Chloe faced was identifying ponds, something a client reiterated to me only recently.
So, in this article, I want to explore how QGIS and spatial datasets can improve the great crested newt survey process by helping ecologists identify and map ponds more quickly and accurately before setting foot on site. I will also look at how QField and Mergin Maps can make field surveys more efficient once work begins.
Why Is Great Crested Newt Survey Planning So Challenging?
Great crested newts remain one of the UK’s most heavily protected species, and local planning authorities must consider their presence when determining planning applications.
Putting aside District Level Licensing, if suitable habitat is present within 250m of a development site, surveys are often required, and insufficient ecological appraisal can delay; or even derail; development proposals.
Historically, identifying ponds near a development site has been a highly manual process. Ecologists often begin by reviewing aerial imagery and Local Environmental Record Centre (LERC) data. This frequently leads to site inspections and, in some cases, knocking on doors to gain access to private gardens.
Once ponds are identified, initial eDNA sampling can establish probable absence or presence. If eDNA confirms presence, further surveys may be required, often involving bottle trapping across six visits, with at least three undertaken between mid-April and mid-May.
The workload escalates rapidly. Given the time-sensitive nature of the survey season, anything that streamlines the identification and planning stage is invaluable.
How Can QGIS Help Identify Ponds for Great Crested Newt Surveys?
Rather than relying solely on visual interpretation of aerial imagery, ecologists can combine multiple spatial datasets, and use AI to classify images within QGIS, to build a far more accurate understanding of pond distribution and habitat suitability before heading into the field.
If you are looking to improve your wider ecology mapping processes, see also How to Make Your Ecology Mapping Workflows Accurate, Efficient and Standardised – Part 4: Data.
Supplement traditional desk study with a wider range of data
OS MasterMap Topography Layer
This authoritative dataset identifies water features (although not always very small garden ponds), canopy cover and shading, as well as potential great crested newt barriers such as roads and urban development.
Natural England Priority Ponds Dataset
Derived from OS MasterMap, this identifies ponds that have associated pond survey records, including Clean Water for Wildlife and Priority Pond classifications.
Natural England Great Crested Newt eDNA Pond Survey Data
This dataset contains Habitat Suitability Index pond surveys undertaken for District Level Licensing. While the distribution is skewed towards licensing areas, it provides a useful record of previously surveyed ponds.
National Soil Map Data
NATMAP Vector, held by Cranfield University, includes polygons classified as “lake or water body”. And it’s recently been made openly available!
Local Environmental Record Centre Data
Though commonly used, it’s worth saying that LERC data should always be an essential component of an ecological desk study. While coverage, currency, and spatial accuracy can vary, these records remain highly valuable.
Speed Up Pond Mapping with GeoAI and Aerial Imagery
GeoAI Plugin
Recent developments in QGIS plugins and machine learning workflows allow users to automatically digitise waterbodies from aerial imagery. The QGIS GeoAI plugin can automatically detect water and create vector polygons.
Instead of manually tracing every suspected pond, ecologists can now use AI-assisted classification to detect likely water features at scale. I recommend you use with an orthorectified, high resolution aerial like Bluesky 12.5cm or 5cm Imagery.
You can watch a GeoAI plugin Tutorial here
Use LiDAR to Detect Hidden or Seasonal Ponds
LiDAR can take pond detection even further. By using DEM models and terrain analysis, ecologists can identify:
subtle depressions
ephemeral ponds
historic pond basins
wetland scrapes
These may otherwise be obscured by tree cover or invisible on aerial imagery. This is particularly valuable because many great crested newt ponds are seasonal, muddy or heavily vegetated.
By layering LiDAR with the other datasets listed above in QGIS, ecologists can cross-reference multiple evidence sources rather than relying on a single aerial photograph.
How Can Digital Tools Improve Great Crested Newt Field Surveys?
Great crested newt survey programmes often involve multiple ponds, repeated visits and tight timing windows around dusk and dawn.
Mobile tools such as QField and Mergin Maps streamline fieldwork by allowing surveyors to:
collect pond data digitally
record bottle trap locations
relocate traps easily on future visits
geotag photographs
input survey notes directly into GIS-enabled devices on site
If you are still using paper-based workflows, you may also find useful: Why Ecologists Should Stop Using Paper Maps and Try One of These Tools for Field Surveys with QGIS.
The result is:
reduced transcription errors
faster data processing
improved consistency across survey teams
quicker turnaround for reporting
Why Smarter Great Crested Newt Survey Workflows Matter
Despite the arrival of District Level Licensing in some areas, great crested newt surveys remain a regular part of many ecologists’ workloads. By using QGIS as a hub to integrate and analyse spatial data, ecologists can streamline pond detection and target surveys more effectively. Combined with mobile survey forms synced through QField or Mergin Maps, this can significantly improve the efficiency of fieldwork.
In a sector where survey windows are short, workloads are growing and planning pressures continue to increase, improved workflows are essential. And if we can stop mistaking trampolines for ponds, so much the better.
Want Help Improving Your Great Crested Newt Survey Workflow?
If you would like support integrating QGIS, spatial data, QField or Mergin Maps into your ecological workflows, feel free to get in touch.
Or explore Spatialsesh’s QGIS Training Courses and support resources to help your team work faster and more confidently with GIS.