Increased access to satellite imagery and new developments in remote sensing data analyses can support biodiversity conservation targets by stepping up monitoring processes at various spatial and temporal scales. More satellite imagery is becoming available as open data. Remote sensing based techniques to capitalise on the information contained in spatially-explicit species data, such as Global Biodiversity Information Facility (GBIF), are developing constantly. Current free and open data policy will have a dramatic impact on our ability to understand how biodiversity is being affected by anthropogenic pressures, while improving our ability to predict the consequences of changes at different scales.
In our latest Special Feature, ‘Improving Biodiversity Monitoring using Satellite Remote Sensing‘, Sandra Luque, Nathalie Pettorelli, Petteri Vihervaara and Martin Wegmann explain why tackling this challenge is worth doing. The articles demonstrate how combining satellite remote sensing data with ground observations and adequate modelling can help to give us a better understanding of natural systems, leading to improved management practices. They focus on three key conservation challenges:
- Monitoring of biodiversity
- Developing an improved understanding of biodiversity patterns
- Assessing biodiversity’s vulnerability to climate change
Many studies have shown how satellite remote sensing can be used to infer species richness. Few have addressed the measurement of species compositional turnover (or beta diversity) from satellite imagery though. This challenge is tackled in this special issue, by Duccio Rocchini et al. in ‘Measuring β‐diversity by remote sensing: A challenge for biodiversity monitoring‘. The authors provide an excellent example of how beta diversity can be estimated from satellite imagery.
In ‘Understanding and assessing vegetation health by in situ species and remote‐sensing approaches‘ Angela Lausch and colleagues compare different approaches to vegetation health monitoring – specifically in situ species approaches and remote sensing techniques. They provide an overview of a number of in situ species approaches, including the biological species concept, the phylogenetic species concept, and the morphological species concept. In addition to this, their paper includes an overview of the remote sensing spectral trait/spectral trait variation concept to monitor status, processes of stress, disturbances, and resource limitations affecting vegetation health.
Understanding Biodiversity Patterns
Satellite remote sensing has been extensively discussed in the context of biodiversity monitoring. But one of it’s obvious potential contributions – the ability to support the development and implementation of ecological models – is rarely mentioned. For this special issue, Damiano Pasetto et al. provide a review that illustrates how satellite remote sensing has been used in ecosystem models. One key message from ‘Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends‘ is that the use of satellite data and ecosystem models is far too uncommon. It is likely to increase in scope and intensity as a more satellite data become accessible though.
To develop a global understanding of the factors driving changes in biodiversity we need access to comparable information about elements of biodiversity in various places. Efforts to achieve this over the past decade have focused on the identification of essential biodiversity variables (EBVs). One of the aims of EBVs is to bring together the assessment of biodiversity monitoring at any scale, and to support the aims of the Convention on Biological Diversity and IPBES. One interesting aspect of this work which Samuel Alleaume et al. focus on in ‘A generic remote sensing approach to derive operational essential biodiversity variables for conservation planning‘ is that hybrid methods and data fusion using very high spatial resolution sensors are being explicitly considered and tested in.
Climate Change and Biodiversity
Satellite remote sensing technology is expected to represent one of the most cost‐effective ways to identify ecosystems put at risk from changes in climatic conditions. But this potential hasn’t been fully demonstrated yet. Focusing on a series of mangrove ecosystems around the world, Clare Duncan et al. illustrate how to capitalise on the current availability and diversity of satellite products in ‘Satellite remote sensing to monitor mangrove forest resilience and resistance to sea level rise‘. The authors assess coastal ecosystem resilience and resistance capacity to sea level rise, and identify landscape‐level and anthropogenic factors driving these capacities. Their approach provides a remarkable addition to the remote monitoring and assessment toolkit for adaptive coastal ecosystem management. It gives us a new opportunity to inform conservation and management priority assessments in data deficient regions.
The Special Feature ‘Improving biodiversity monitoring using satellite remote sensing‘ will be freely available to everyone for two months. No subscription required.