Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. The journal provides a forum for the rapid publication of peer-reviewed, multidisciplinary research from the interface between remote sensing science and ecology and conservation. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The journal’s intended audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students.
GOOD NEWS! Remote Sensing in Ecology and Conservation has been accepted for indexing in Scopus!
The RSEC blog provides a platform for authors to promote their research through written posts, podcasts, images and videos. We welcome guest posts on all aspects of remote sensing science relevant to ecology and conservation. See here for details.
Biodiversity monitoring makes a central scientific contribution to conservation management and environmental policy. Without it, we have an impaired evidence-base for decision-making in areas such as species management, and forest and agriculture policy. Yet for the world’s terrestrial mammals, traditional monitoring systems based on direct observations have been relatively limited in spatial, temporal and taxonomic coverage, and in the quality and depth of information they provide. This is in large part because terrestrial mammals are typically difficult to reliably observe in a way that generates robust data on distribution and abundance.
An integrative modeling approach to mapping wetlands and riparian areas in a heterogeneous Rocky Mountain watershed
We used machine learning, Landsat 8 imagery and geomorphometric indices to map the distribution of wetlands and riparian areas in a highly variable Rocky Mountain watershed. We used a presence‐background approach to develop and compare predictions from three popular algorithms: boosted regression trees, MaxEnt and random forests. We demonstrate how integrating ecological interpretation into the modeling workflow can inform conventional accuracy statistics and help bridge field‐based and remote sensing perspectives. Our approach requires only public data that are widely available, and can be easily adapted to other heterogeneous mountain settings.