Biodiversity Monitoring through Remote Sensing Techniques

The field of biodiversity monitoring using remote sensing techniques has advanced rapidly in recent years, propelled by the need for timely, scalable and cost-effective tools to track species, communities and ecosystems. Satellite platforms now provide multi-spectral, hyperspectral and radar data that can be analysed to derive vegetation structure, land-cover change, habitat fragmentation and primary productivity at resolutions from sub-metre to kilometre scales. Airborne imaging spectroscopy and LiDAR have revolutionised the characterisation of canopy traits, biomass distribution and three-dimensional habitat complexity, while unmanned aerial vehicles and networked sensor arrays extend coverage to understorey vegetation and terrestrial fauna. A key conceptual framework driving this integration is the Essential Biodiversity Variable (EBV) schema, which translates heterogeneous remote sensing and ground-based observations into standardised metrics of species distributions, population abundances, functional traits and ecosystem structure. Coupled with machine learning algorithms and ecological models, these approaches enable continuous, global assessments of biodiversity change, informing conservation planning, policy reporting and ecosystem management. From tracking deforestation impacts on species richness to mapping invasive species spread and monitoring protected area effectiveness, remote sensing techniques offer unprecedented potential for early detection of biodiversity loss, evaluation of restoration efforts and support for international targets. Interdisciplinary collaboration among ecologists, remote sensing scientists and policymakers remains crucial to co-design workflows, validate products with field data and ensure that monitoring outputs directly inform conservation actions worldwide.

Research from Nature Portfolio

Recent studies have defined a comprehensive set of Essential Biodiversity Variables for species distribution and abundance, conceptualising a space–time–species framework that integrates sparse field observations with remotely sensed covariates to generate continuous, global-extent monitoring products. Building on this foundation, researchers have refined species traits EBVs—covering phenology, morphology, reproduction, physiology and movement—and outlined standardised, interoperable workflows for data collection, metadata management and reproducible modelling. These contributions have established the core EBV framework that underpins the integration of satellite and in situ data streams, enabling robust detection of biodiversity trends at scales relevant to policy and conservation decision-making.

Biodiversity Monitoring through Remote Sensing Techniques publication trend

The graph below shows the total number of articles in biodiversity monitoring through remote sensing techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Essential Biodiversity Variables (EBVs): Standardised metrics capturing core dimensions of biodiversity—such as species distributions, abundances and traits—to enable consistent, global-scale monitoring.

Imaging Spectroscopy: A remote sensing technique that acquires detailed spectral information per pixel, allowing inference of vegetation chemical and structural properties.

LiDAR (Light Detection and Ranging): A laser-based system that measures three-dimensional structural attributes of ecosystems, such as canopy height and biomass distribution.

In situ–satellite data fusion: The integration of ground-based observations with satellite imagery through computational models to enhance the accuracy and spatial resolution of biodiversity assessments.

References

1. Nature 4.0: A networked sensor system for integrated biodiversity monitoring. Global Change Biology (2023).
2. Advancing terrestrial biodiversity monitoring with satellite remote sensing in the context of the Kunming-Montreal global biodiversity framework. Ecological Indicators (2023).
3. The biodiversity survey of the Cape (BioSCape), integrating remote sensing with biodiversity science. npj Biodiversity (2025).
4. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biological Reviews (2017).
5. Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sensing in Ecology and Conservation (2016).
6. Towards global data products of Essential Biodiversity Variables on species traits. Nature Ecology & Evolution (2018).