By Shashin Mishra,

VP of EMEA at AiDash

As we enter an era where environmental challenges intensify, cutting-edge solutions like artificial intelligence (AI) are reshaping how we address these issues. AI offers immense potential in driving sustainable development, particularly in achieving Biodiversity Net Gain (BNG) targets – where developments must leave the environment in a better state than before.

Given the scale of the biodiversity net gain that needs to be achieved, AI is going to be a crucial ally to policymakers. By harnessing vast amounts of remote sensing data, satellite imagery, and deploying sophisticated algorithms, AI can provide unprecedented insights into habitat health, biodiversity assessments, and ecosystem restoration strategies.

Understanding Biodiversity Net Gain (BNG)

As the UK continues to implement Biodiversity Net Gain (BNG) as a key policy under the Environment Act 2021, developers and landowners are facing the challenge of integrating environmental priorities into development projects. The goal of BNG is to ensure that new developments leave the natural environment in a measurably better state than it was before the project began. While BNG is critical for reversing biodiversity loss, the complexity of planning, assessing, and monitoring biodiversity outcomes often presents significant challenges to developers, ecologists, and local planning authorities (LPAs).

The Role of AI in Achieving BNG

AI has already demonstrated its value across various environmental domains, and its application to BNG is becoming increasingly apparent.  From data collection to monitoring and compliance, AI can revolutionise the key stages of the BNG process, making it more efficient and precise.

1.AI-Driven Data Collection

A fundamental aspect of achieving BNG is conducting accurate baseline biodiversity assessments of development sites. These assessments determine the existing quality and quantity of habitats, which form the basis for calculating biodiversity units under tools like the Defra Statutory Biodiversity Metric.

Traditional surveys often require significant time and effort from teams of ecologists who manually collect data across large or inaccessible areas. This process can be slow, labour-intensive, costly, and subject to human error. AI technologies, however, are transforming this space by improving the speed, accuracy, and scope of data collection.

• Harnessing Remote Sensing and Satellite Data for BNG through AI

Remote sensing and satellite data have become essential tools for environmental monitoring, but AI is the catalyst that elevates their potential for BNG initiatives. Through machine learning (ML) algorithms, AI can analyse massive datasets from satellites, drones, and ground-based sensors in ways that were previously impossible for human analysts.

One of the major benefits of integrating AI with remote sensing is the ability to perform continuous, large-scale habitat monitoring. This means that AI models can track changes in biodiversity and ecosystems over time, pinpointing specific areas that are experiencing degradation, habitat loss, or positive growth due to conservation efforts. For example, AI can process multi-spectral imagery to differentiate between various land cover types, detecting subtle changes in vegetation, water quality, or soil composition that are critical to BNG planning.

2.AI-Driven Habitat Mapping, Classification, and Condition Assessments

One of the biggest challenges in the field of biodiversity is the accurate mapping and assessment of habitats. Traditionally, habitat surveys have relied heavily on in-person fieldwork, which is time-consuming, resource-intensive, and often limited by human error. AI offers a rapid and scalable solution for habitat mapping, classification, and condition assessment—a cornerstone of achieving BNG.

• Habitat Mapping and Classification

AI can be used to analyse a variety of data sources—satellite imagery, LiDAR, hyperspectral imaging, and drone footage—to classify and map habitats with high precision. Machine learning models trained on large datasets can quickly identify different habitat types, such as wetlands, grasslands, or woodlands, down to fine levels of detail. These AI-driven classifications can vastly improve our understanding of the extent and condition of habitats in both urban and rural landscapes.

This capacity for detailed, large-scale habitat classification is critical when designing BNG strategies. To ensure the accuracy and reliability of habitat assessments, we must understand the key paraments for quality of imaging and why they’re important:

• Recent Dated Image – Utilizing imagery with a clear and recent date stamp is crucial. This ensures the data reflects the current state of the habitat, making it relevant for analysis and decision-making.
• High Resolution – High-resolution imagery is non-negotiable for detailed habitat mapping. This level of detail allows for precise delineation of habitat boundaries and identification of key features, minimizing errors and enhancing the overall quality of the assessment.
• Positionally accurate – Positionally accurate imagery is essential for precise habitat mapping and effective Biodiversity Net Gain (BNG) strategies. It ensures that habitat boundaries are mapped correctly, minimizing errors and overlaps.

Accurate data helps in identifying subtle differences and seasonal variations, ensuring reliable habitat assessments. This precision supports informed decision-making and effective tracking of ecological changes

• Condition Assessments: Rivers, Habitats, and Ecosystems

Beyond mapping, AI plays an essential role in habitat condition assessments, a critical requirement for BNG metrics. Whether assessing the health of a woodland, the biodiversity within a grassland, or the condition of a river, AI can process and analyse large datasets to monitor key ecological indicators such as vegetation health, species diversity, and water quality.

For instance, AI can be used to analyse imagery and sensor data to assess the physical and ecological condition of rivers. This continuous monitoring helps determine whether restoration efforts are effective and whether a river system is contributing positively to broader BNG goals.

3. AI for Biodiversity Net Gain Metric Calculations

A critical part of the BNG process is calculating biodiversity units using tools like the Defra Statutory Biodiversity Metric. These tools consider factors such as habitat size, condition, distinctiveness, and connectivity to quantify biodiversity losses and gains.

AI can assist in optimising these calculations by automating data input, improving accuracy, and ensuring compliance with BNG regulations.

4. AI in Monitoring and Compliance

Achieving BNG is not a one-time task but requires long-term commitment, with biodiversity enhancements needing to be maintained and monitored for at least 30 years. Ensuring that habitats created or restored as part of a BNG plan are thriving over time can be a complex and resource-intensive process. AI offers significant potential for automating and improving this monitoring process.

AI-enabled tools can continuously monitor biodiversity changes across a site, using data collected from remote sensors, satellites, or drones. AI algorithms can assess habitat health and detect early signs of degradation, such as invasive species encroachment, habitat fragmentation, or declining species populations. This allows for real-time responses to issues that could jeopardise a project’s BNG outcomes.

AI as a game-changer for sustainability

As we move towards a future where sustainable development and biodiversity conservation go hand in hand, AI has the potential to revolutionise how we achieve BNG goals, creating a win-win for both people and nature.