Biodiversity Net Gain (BNG)

Our success is built upon using multi-spectral ultra-high resolution (5cm) satellite imagery. With such detail, we can count individual trees in a residential garden or determine the green roof potential across urban environments. Naturally, property developers – especially large-scale asset managers – are concerned with much larger areas, and this is where we are unbeatable!

Our satellite data, some of which is derived from military satellites, is processed with computer algorithms to conduct biodiversity net gain assessments in an average time from instruction to report delivery of just seven days. This presents an innovative approach to understanding and managing the impacts of development projects on biodiversity. This method overcomes several difficulties traditionally associated with biodiversity assessments:

1. Scale and Accessibility: Traditional field surveys are labor-intensive, time-consuming, and often limited to specific locations or times. Satellite data, combined with AI, can monitor vast areas and inaccessible regions, providing a comprehensive view of biodiversity across different landscapes and ecosystems.
2. Temporal Analysis: Changes in biodiversity can be subtle and occur over long periods. Satellite imagery can offer historical data, enabling AI algorithms to analyse trends over time, identify patterns of change, and predict future biodiversity scenarios. This longitudinal analysis helps in understanding the impact of human activities and natural events on biodiversity.
3. Consistency and Standardisation: Manual surveys might vary significantly in methods and accuracy, depending on the expertise of the individuals conducting them. AI models, trained on satellite data, can apply consistent criteria across different areas and times, ensuring standardised assessments.
4. Cost-Effectiveness: Field surveys require significant human resources, time, and logistical arrangements, which can be costly. Using satellite data, particularly from public satellites like those from NASA or the European Space Agency, combined with AI, can significantly reduce the cost of biodiversity assessments.
5. Real-time Monitoring: Some AI techniques can process satellite data in near-real-time, allowing for the immediate detection of changes in land use, vegetation cover, or other indicators of biodiversity. This capability is crucial for timely interventions to mitigate negative impacts on biodiversity.
6. Integration of Diverse Data Types: AI can integrate satellite imagery with other data types, such as climate data, land use records, and human activity indicators, to provide a more holistic view of the factors affecting biodiversity. This multidimensional analysis can lead to more effective biodiversity conservation strategies.
7. Improved Prediction Models: AI can use satellite data to build predictive models of biodiversity change, helping in planning and implementing conservation efforts proactively rather than reactively. Predictive modelling is crucial for addressing potential threats before they lead to significant biodiversity loss.
8. Accessibility to Data: Satellite data, once processed and analyzed by AI, can be made accessible to a wide range of stakeholders, including conservationists, policy-makers, and the public. This transparency can improve collaboration and support for biodiversity conservation efforts.

We can detect small-scale biodiversity changes, and as we have already integrated these technologies into legal and regulatory frameworks for biodiversity net gain assessments, our clients are accessing proven technology which is 100% on a par with expensive on-the-ground consultants and all of the travel costs they incur.

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