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Cover Picture and Issue Information
Author(s) -
Sandra Luque,
N. Pettorelli,
Petteri Vihervaara,
Martin Wegmann
Publication year - 2018
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12882
Subject(s) - computer science , cover (algebra) , land cover , vegetation (pathology) , remote sensing , information retrieval , metric (unit) , geography , ecology , cartography , land use , engineering , operations management , economics , mechanical engineering , medicine , pathology , biology
This month’s cover image shows a physiognomic map of natural vegetation in open landscape from Isère region (Alpes, France). The image was produced using a combination of optical remote‐sensing data. It is based on the fusion of a mono‐date image with SPOT 7, image at a very high spatial resolution (THRS, at metric resolution) and an annual time series of high spatial resolution images from Sentinel‐2 (HRS, at decametric resolution). The processing consists in segmenting the THRS image into objects which are characterized according to an object oriented hierarchical approach. Then, natural habitats are characterized based on the annual time series of the HRS images. Time series are used to characterize herbaceous areas (lawns or grasslands) at the level of plant biomass productivity. The associated article – A generic remote sensing approach to derive Essential Biodiversity Variables (EBVs) – provides a generic approach in response to national conservation targets based on the concept of EBV’s accessed by remote‐sensing (RS‐enabled EBVs). Alleaume et al. present an operational approach to derive five RS‐enabled EBVs from natural and semi‐natural open ecosystems to support policy‐making in order to fulfil the EU conservation strategy. Photo credit: © Samuel Alleaume, TETIS, Irstea