A generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning

The open access availability of satellite images from new sensors characterized by various spatial and temporal resolutions provides new challenges and possibilities for biodiversity conservation. Methodologies aiming at characterizing vegetation type, phenology, and function can now benefit from metric spatial resolution imagery combined with an improved revisit capability. Here, we test hybrid methods and data fusion, using very high spatial resolution (VHSR) sensors in different complex landscapes encompassing three French biogeographical regions. The methodological approach presented herein has a generic value in response to national conservation targets based on the concept of essential biodiversity variables accessed by remote sensing (RS-enabled EBVs). We focused on deriving five RS-enabled EBVs from natural and seminatural open ecosystems: (1) ecosystem distribution, (2) land cover, (3) heterogeneity, (4) primary productivity and (5) vegetation phenology. The challenge was to develop a method that would be technically feasible, economically viable, and sustainable in time. We demonstrated that it is possible to derive key parameters required to develop a set of EBVs from remote sensing (RS) data. The combined use of remote sensing data sources with various spatial, temporal, and spectral resolutions is essential to obtain different indicators of natural habitats. One major current challenge for an improved contribution of RS to conservation is to strengthen multiple collaborative frameworks among remote sensing scientists, conservation biologists, and ecologists in order to increase the efficiency of methodological exchange and draw benefits for successful conservation planning strategies.

[1]  C. A. Mücher,et al.  Environmental science: Agree on biodiversity metrics to track from space , 2015, Nature.

[2]  S. Franklin.  Using spatial Co-occurrence texture to increase forest structure and species composition classification accuracy , 2001 .

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  Clement Atzberger,et al.  Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data , 2012, Remote. Sens..

[5]  Walter Jetz,et al.  Global priorities for an effective information basis of biodiversity distributions , 2015, Nature Communications.

[6]  Pierre-Yves Henry,et al.  Habitat monitoring in Europe: a description of current practices , 2008, Biodiversity and Conservation.

[7]  Frédéric Baret,et al.  Estimating Biophysical Variables at 250 M with Reconstructed EOS/MODIS Time Series to Monitor Fragmented Landscapes , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[8]  K. Meissner,et al.  How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring , 2017 .

[9]  N. Pettorelli,et al.  Essential Biodiversity Variables , 2013, Science.

[10]  N. Pettorelli,et al.  Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions , 2016 .

[11]  Anne Bowser,et al.  Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale , 2018, Biological reviews of the Cambridge Philosophical Society.

[12]  Anne Bowser,et al.  Building essential biodiversity variables(EBVs) of species distribution and abundanceat a global scale , 2017 .

[13]  Emilio Padoa-Schioppa,et al.  Earth observation for habitat mapping and biodiversity monitoring , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[14]  Qing Xiao,et al.  Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Gary N. Geller,et al.  The role of space agencies in remotely sensed essential biodiversity variables , 2016 .

[16]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[17]  Jarrett E. K. Byrnes,et al.  A global synthesis reveals biodiversity loss as a major driver of ecosystem change , 2012, Nature.

[18]  Kurt C. Lawrence,et al.  Discriminant analysis of dual-wavelength spectral images for classifying poultry carcasses , 2002 .

[19]  Hervé Le Men,et al.  Scale-Sets Image Analysis , 2005, International Journal of Computer Vision.

[20]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[21]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[22]  Laurence Hubert-Moy,et al.  Structural and functional mapping of geosigmeta in Atlantic coastal marshes (France) using a satellite time series , 2018 .

[23]  Caspar A. Mücher,et al.  Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward , 2011 .

[24]  Gérard Dedieu,et al.  A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images , 2015, Remote. Sens..

[25]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[26]  Emmanuel Christophe,et al.  The Orfeo Toolbox remote sensing image processing software , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[27]  Clare Duncan,et al.  How do we want Satellite Remote Sensing to support biodiversity conservation globally? , 2016 .

[28]  S. Díaz,et al.  Biodiversity Loss Threatens Human Well-Being , 2006, PLoS biology.

[29]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[30]  Liangpei Zhang,et al.  Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.

[31]  Forest Isbell,et al.  Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory , 2012, Proceedings of the National Academy of Sciences.

[32]  A. Skidmore,et al.  Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets , 2015 .

[33]  N. Pettorelli,et al.  Satellite remote sensing for applied ecologists: opportunities and challenges , 2014 .

[34]  Walter Jetz,et al.  A global, remote sensing‐based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling , 2015 .

[35]  Michael Förster,et al.  Remote sensing for mapping natural habitats and their conservation status - New opportunities and challenges , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[36]  V. Radeloff,et al.  Image texture as a remotely sensed measure of vegetation structure , 2012 .

[37]  T. Warner,et al.  Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects , 2011 .

[38]  Daniel Clewley,et al.  Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data , 2011 .

[39]  Matthew B. Jones,et al.  Challenges and Opportunities of Open Data in Ecology , 2011, Science.

[40]  Tatsuya Amano,et al.  Spatial Gaps in Global Biodiversity Information and the Role of Citizen Science , 2016 .

[41]  Duccio Rocchini,et al.  Advancing species diversity estimate by remotely sensed proxies: A conceptual review , 2015, Ecol. Informatics.

[42]  Duccio Rocchini,et al.  Will remote sensing shape the next generation of species distribution models? , 2015 .

[43]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.