Resource Type: Insight

Between 2020 and 2021 five of the 48 Local Nature Recovery Strategies (LNRS) areas conducted a pilot Local Nature Recovery Strategy scheme. These pilots highlighted the challenges of the LNRS process and provided valuable lessons on the best way forward for the responsible authorities to prepare their LNRS documents by the March 2025 deadline. The two components of the LNRS are the habitat map and the written report listing the chosen priorities and plans for nature recovery. The pilot showed how important it is for these to work well together and be developed concurrently. This ensures that the nature recovery opportunities can be correctly identified on the ground and that the measures proposed can be actioned in the future.

One issue identified by the pilot authorities was the difficulty in assessing habitat quality due to a lack of recent and relevant data. Collecting this information is important but a significant additional factor is assessing the large quantities of data. Clear and concise formatting allows the responsible authorities to quickly interpret and understand data to make the best decisions in terms of prioritisation. BiodiversityView data can create a colour coded map of the condition score across the LNRS area to highlight which habitats are performing poorly compared to the surrounding areas and could be considered as priorities for nature recovery.

As the natural world does not recognise the boundary lines of the LNRS areas, collaboration between the neighbouring responsible authorities will ensure that any planned measures in bordering habitat areas are consistent and supportive of each other. Because BiodiversityView is a nationwide dataset it can be a resource for multiple responsible authorities to work together to look at habitat and condition across boundaries. Using the same data and mapping will make it much easier for this LNRS collaboration between neighbouring responsible authorities.

Map Impact’s BiodiversityView provides a unique national dataset of assessed habitat conditions across the whole of England at 50m intervals. It is ideally suited to provide regional coverage of landscape areas, such as required by LNRS work, or early baseline site assessments required under BNG development planning requirements, where ‘triage, triage, triage’ is the best practice message coming from Natural England and the Planning Advisory Service.

In the final part of this blog series, we will discuss the challenges associated with developing and delivering LNRS. Local Authority teams are welcome to request a demonstration of BiodiversityView across their own specific area by e-mailing info@mapimpact.io.

It’s important to explain the approach that Map Impact are taking with satellite imagery, and where we’re fitting into the Biodiversity Net Gain workflow. We have created a spatial framework using the principles of the DEFRA metric calculator. Although this does not replace the calculator it can support rapid early stage screening and ongoing monitoring, bringing significant efficiency to the biodiversity net gain work flow.

Within the Defra metric calculator there are several things that are measured which inform biodiversity unit outputs; habitat size, habitat distinctness, strategic significance, and habitat condition. At Map Impact we have primarily focused on using satellite data to understand habitat condition. Condition is a critical part of the calculator as it’s used as a multiplier to inform the overall Biodiversity Net Gain and biodiversity unit score. If we take a Lowland Meadow as an example, this high distinctiveness habitat can have an overall score of 8 if the condition is 1 (Poor), but then if that’s in good condition it can be multiplied by 3 to become an overall score of 24.

But before we understand habitat condition we have to understand the underlying habitats themselves. To do this we have created a “Best-Available” Habitat data layer. This consists of open data which we have combined to create a nationwide Best-Available Habitat GIS layer. Map Impact is a UKHab partner so we have aligned this GIS layer with the UKHab classification system (the UKHab classification system underpins the DEFRA biodiversity metric calculator and is designed to provide a unified structure to habitat identification). We’re very keen to support UKHab and what it’s trying to achieve, and to have continued open conversations about where satellites and remote sensing can fit into that system and where it can’t.

When we come on to think about condition, we are conscious that this is highly complex and it’s incredibly difficult to truly understand the condition of a specific habitat type. The condition scoring criteria within the DEFRA biodiversity metric calculator requires a site visit to truly understand each of the requirements. At Map Impact we have taken an approach to condition scoring that provides a high-level category of good, moderate or poor. This uses satellite imagery to bench mark each habitat against the surrounding equivalent habitats in order to gauge performance. We’re using lots of characteristics within the spectral wavelength to understand how each habitat is performing. The spectral properties are applied differently depending on what the habitat type is. So, we know that a woodland performs very differently to a grassland for example, and that means that we can use different parts of that satellite light spectrum in order to understand those two habitats in different ways. Although not directly fulfilling the needs of the calculator, this provides a rapid wide-area assessment of condition that is consistent and objective.

Everything we’re producing is provided in hexagon grids and there’s a variety of different reasons why we’re using hexagons. Ecologically they’re a very good way of presenting this sort of data because they allow many more neighbours than a square would which means you can show much more natural shapes within the outputs that you create. They’re also highly efficient from a technology point of view. The hexagon system that we use covers the whole earth, it was created by Uber, and it’s a highly efficient data model that we’re applying this information to.

To finish, we are always at pains to point out that although Map Impact’s BiodiversityView provides a great tool for initial screening and assessment of a site, we would always recommend that an in-situ survey is performed by an ecologist to have the final say regarding the true assessment of biodiversity.