Match to species/home-range and policy needs.

Field plots and citizen science with QA.

Use open archives and cloud processing platforms.

Stratified sampling and domain adaptation.

Align with GBF and national indicators.

Open data lowers costs; compute and annotation dominate.

Respect data licenses, especially commercial sensors.

Publish code, parameters, and versions.

Ecology Topic 8: Biodiversity Monitoring with Remote…

Q: Which sensor for forests?

Combine optical for species proxies and LiDAR for structure.

Q: How to handle clouds?

Use compositing and radar; apply gap filling.

Sensors and Data

Remote sensing for biodiversity monitoring utilizes a range of sensors, including optical systems that capture multispectral and hyperspectral data to identify species proxies, radar which penetrates cloud cover, and LiDAR, providing detailed 3D structural information about ecosystems.

The temporal resolution of the data is crucial; high-frequency observations allow for tracking seasonal changes and dynamic processes, while effective cloud mitigation strategies are essential to ensure data availability throughout the year.

Indicators

Remote sensing techniques enable the assessment of key biodiversity indicators such as habitat extent and condition, fragmentation patterns within landscapes, and proxies for functional diversity – reflecting the range of ecological roles present in a given area.

By analyzing remotely sensed data, scientists can quantify changes in these crucial metrics, providing valuable insights into the health and resilience of ecosystems.

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Workflows

A typical workflow for biodiversity monitoring using remote sensing involves several stages: initial data preprocessing to correct atmospheric distortions and geometric inaccuracies, followed by feature engineering to extract relevant spectral and spatial characteristics.

Machine learning classification or regression models are then trained on this processed data, often incorporating ground truth observations, alongside rigorous uncertainty accounting to quantify the reliability of the derived maps and estimates.

Examples

One example is the Habitat Condition Index (HCI), calculated from remotely sensed vegetation indices like NDVI, which provides a quantitative measure of habitat quality across large areas.

To create an HCI map, multi-year composites are assembled to smooth out temporal variability, and models are trained using ground plots with detailed ecological measurements, allowing for the mapping of trends with associated uncertainty bounds.

Frequently asked questions

Which sensor for forests?

For forest monitoring, a combination of optical sensors is particularly effective in identifying species proxies through spectral signatures, while LiDAR provides detailed structural information about the forest canopy and biomass.

How to handle clouds?

Cloud cover can significantly impact remote sensing data; therefore, compositing multiple observations over time is a common strategy, alongside the application of gap-filling techniques to interpolate missing values based on surrounding areas.

What spatial scale?

The appropriate spatial scale for biodiversity monitoring using remote sensing depends on the specific research question and needs to be aligned with the scale at which species inhabit their territories and relevant policy requirements.

Ground truth?

Collecting ground truth data is essential for validating remotely sensed results; field plots provide detailed ecological measurements, while citizen science initiatives can supplement this effort with valuable observations, all supported by rigorous quality assurance procedures.

Data platforms?

Utilizing open archives like Copernicus and Google Earth Engine allows researchers to access a wealth of remote sensing data and leverage cloud processing platforms for efficient data analysis and model training.

Bias mitigation?

To minimize bias, stratified sampling techniques are employed to ensure representative coverage across diverse habitat types, alongside domain adaptation methods to address potential discrepancies between remotely sensed data and ground-based measurements.

Policy links?

Remote sensing outputs should be aligned with established biodiversity frameworks like the Global Biodiversity Framework (GBF) and national indicators to facilitate effective conservation planning and policy implementation.

Costs?

While initial investment in hardware and software can be significant, open data initiatives are increasingly lowering costs; however, compute resources for processing large datasets and annotation efforts remain the dominant cost drivers.

Licensing?

It is crucial to respect the licenses associated with remote sensing data, particularly commercial sensors, ensuring compliance with usage restrictions and attribution requirements.

Reproducibility?

To ensure scientific rigor and facilitate collaboration, all code, parameters used in models, and version numbers of datasets should be publicly documented and readily available for replication.

Try it live

Everything above runs in your browser — open Biodiversity Remote-Sensing Monitor and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Biodiversity Remote-Sensing Monitor simulation

Biodiversity Monitoring with Remote Sensing