Remote Sensing Applications in Biodiversity Conservation

Remote sensing has become an indispensable tool for biodiversity conservation by enabling the systematic observation of ecosystems at multiple spatial and temporal scales. Optical satellite sensors, radar, LiDAR and hyperspectral instruments capture data on habitat structure, vegetation health, disturbance events and landscape configuration across vast areas. These observations support the mapping of species distributions, the detection of land‐cover change and the evaluation of ecosystem function, often through indices such as the Normalized Difference Vegetation Index (NDVI) or ecosystem functional attributes derived from time‐series data. Uncrewed aerial vehicles (UAVs) and high‐resolution commercial satellites complement broad‐scale monitoring by providing centimetric detail on canopy structure and microhabitat conditions. Advances in machine learning, convolutional neural networks and cloud computing platforms have accelerated the processing of petabyte‐scale imagery, allowing near‐real‐time assessments of deforestation, wetland loss and coastal degradation. Integrating spectral, structural and topographic variables enhances species distribution models and enables the identification of biodiversity hotspots and ecological corridors. Globally, remote sensing informs the design of protected-area networks, guides restoration efforts and underpins early warning systems for illegal land use. By linking remotely sensed abiotic heterogeneity with field observations of species richness and functional traits, practitioners can prioritise actions that bolster ecosystem resilience under climate change and land-use pressures.

Research from Nature Portfolio

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Research from all publishers

A recent study demonstrated that unclassified spectral heterogeneity metrics derived from Landsat 8 outperformed traditional classified land-cover products in predicting bird species richness across two spatial grains. By computing multiple measures of reflectance variability and combining them with landscape type information, researchers showed that interactions between spectral heterogeneity and habitat context explain avian diversity more effectively than single‐source land-cover maps. This work highlights the potential of raw multispectral data to streamline biodiversity mapping without the need for prior image classification.

In a pan-European analysis of forest plots, geodiversity components—including topographic heterogeneity, landform diversity and soil physical properties—were weakly but significantly associated with plant trait richness and evenness. Structural equation modelling revealed that elevation variance and temperature gradients exert primary controls on trait diversity, while other aspects of geodiversity contribute additional explanatory power at larger plot sizes. The study underscores the value of combining remotely sensed terrain metrics with in situ trait databases to unravel the abiotic drivers of functional biodiversity.

Work on linking biodiversity and geodiversity at continental scales employed satellite-derived elevation data from the Shuttle Radar Topography Mission to quantify relationships with alpha, beta and gamma diversity of forest trees. Generalised linear and beta regression models across multiple spatial grains revealed distinct scaling behaviours: variation in elevation correlated most strongly with beta and gamma diversity, emphasising the role of topographic complexity in shaping community composition. This research advocates for interdisciplinary integration of remote sensing, geosciences and ecological inventories to guide conservation planning across heterogeneous landscapes.

Remote Sensing Applications in Biodiversity Conservation publication trend

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

Technical terms

Remote sensing: The acquisition of information about Earth’s surface and atmosphere via satellite or airborne sensors without direct contact.

Spectral heterogeneity: Variability in reflectance values across multiple wavelengths within a landscape used to infer habitat complexity.

Geodiversity: The variety of abiotic elements and processes in a landscape, including landforms, soils, hydrology and substrate materials.

Species distribution model (SDM): A predictive framework linking species occurrences with environmental variables, often used to map habitat suitability.

Normalized Difference Vegetation Index (NDVI): A spectral index calculated from red and near-infrared reflectance to quantify green vegetation biomass and vigour.

References

1. The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type. International Journal of Applied Earth Observation and Geoinformation (2024).
2. What is the Relationship Between Plant Trait Diversity and Geodiversity? A Plot‐Based, Pan‐European Analysis. Global Ecology and Biogeography (2024).
3. Towards connecting biodiversity and geodiversity across scales with satellite remote sensing. Global Ecology and Biogeography (2019).
4. Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling. PLOS ONE (2018).