Why Good GIS Data is Critical for Credible Biodiversity Net Gain
When it comes to Biodiversity Net Gain assessments, we often focus on ecological surveys, habitat condition scores, or final biodiversity units. But what often goes unnoticed is the quality and standardisation of the GIS data that underpins everything. GIS data is the backbone of any BNG calculation. It defines where habitats start and end, shapes the area measurements that drive biodiversity units, and provides the spatial context for understanding site constraints and opportunities. When this data is inaccurate, incomplete, or inconsistent, the results are unreliable. The risks go beyond just numbers on a spreadsheet. They affect decision making, planning approvals, compensation calculations, and ultimately the credibility of the whole project.
Without consistent standards for spatial data, not a single biodiversity unit can be taken as valid at face value. That is not to say all BNG data is bad, but rather that without standardisation, we simply cannot tell which assessments are reliable just by looking at them. This is precisely why we created the FRIDAS checklist, a practical framework of good practice designed to ensure GIS data is robust, consistent, and trustworthy. Below we discuss the issues and impacts of a lack of GIS standardisation in BNG
Our first common issue we see is the use of inconsistent data formats across projects and teams. Different GIS software, varying coordinate reference systems, or incompatible file types create barriers to data integration and analysis. Which can cause errors in spatial calculations or even data loss when transferring between systems. This makes it difficult to share or compare BNG data meaningfully across stakeholders, which undermines transparency and efficiency.
Another major issue is the inaccuracy of red line boundaries, which define the project extent or development footprint. Red line boundaries are critical for setting the spatial context of BNG assessments, yet they are often poorly digitised or based on outdated plans. Misaligned or overly generalised boundaries can lead to habitat features being excluded or included incorrectly, distorting area calculations and impacting biodiversity unit totals. Furthermore, discrepancies between baseline and post-development red line boundaries will lead to unaccounted biodiversity units.
A further overlooked aspect is the non-reporting or poor handling of slope and terrain factors within GIS data. Slope can significantly affect habitat area calculations and ecological function, but it is rarely incorporated formally into BNG metrics. Ignoring slope leads to underestimation of habitat surface areas, especially on upland, hillside, or complex terrain. This introduces both mathematical and ecological inaccuracies. The FRIDAS checklist highlights slope as a key factor, encouraging practitioners to integrate terrain analysis for more realistic and defensible assessments.
Another issue is poorly digitised habitat boundaries. If polygons are drawn without care, with gaps or overlaps, or fail to align with reliable base mapping, the calculated habitat areas will be wrong. Since the metric depends heavily on area, even small errors can significantly skew biodiversity unit totals. This is particularly critical when dealing with high-value habitats or sites with complex, fragmented landscapes. In these cases, underestimating area can mean under-delivering on net gain, while overestimating can lead to false compliance and reputational risk.
Another frequent problem is the lack of metadata and documentation. Without clear records of data sources, collection dates, or processing steps, it becomes impossible to verify or audit the information. This lack of transparency undermines trust among stakeholders, including regulators, developers, and conservation organisations. It can also make it difficult to update or maintain datasets over time, which is essential for monitoring and adaptive management. Futhermore, there are no current requirements to disclose AI use within BNG assessments.
The hidden costs of poor GIS data are not just ecological or regulatory. They can have real financial consequences. Inaccurate assessments can lead to project delays, increased survey costs, or the need for costly remediation later on. For developers, this can mean unexpected expenses or legal challenges. For consultants, it can damage reputation and client relationships.
Addressing these issues requires adopting clear data standards and workflows. Consistency in digitising, topological checks, alignment with authoritative basemaps, and thorough metadata capture should be routine. The GIS data standard and the FRIDAS checklist we have developed provides a practical framework for achieving this, helping teams to produce spatial data that is robust, traceable, and fit for purpose.
