Understanding and assessing vegetation health by in situ species and remote‐sensing approaches

Human activities exert stress on and create disturbances to ecosystems, decreasing their diversity, resilience and ultimately the health of ecosystems and their vegetation. In environments with rapid changes in vegetation health (VH), progress is needed when it comes to monitoring these changes and underlying causes. There are different approaches to monitoring VH such as in situ species approaches and the remote-sensing approach. Here we provide an overview of in situ species approaches, that is, the biological, the phylogenetic, and the morphological species concept, as well as an overview of the remote-sensing spectral trait/spectral trait variations concept to monitor the status of VH as well as processes of stress, disturbances, and resource limitations affecting VH. The approaches are compared with regard to their suitability for monitoring VH, and their advantages, disadvantages, potential, and requirements for being linked are discussed. No single approach is sufficient to monitor the complexity and multidimensionality of VH over the short to long term and on local to global scales. Rather, every approach has its pros and cons, making it all the more necessary to link approaches. In this paper, we present a framework and list crucial requirements for coupling approaches and integrating additional monitoring elements to form a multisource vegetation health monitoring network (MUSO-VH-MN). When it comes to linking the different approaches, data, information, models or platforms in a MUSO-VH-MN, big data with its complexity and syntactic and semantic heterogeneity and the lack of standardized approaches and VH protocols pose the greatest challenge. Therefore, Data Science with the elements of (a) digitalization, (b) semantification, (c) ontologization, (d) standardization, (e) Open Science, as well as (f) open and easy analyzing tools for assessing VH are important requirements for monitoring, linking, analyzing, and forecasting complex and multidimensional changes in VH.

[1]  P. Cheptou,et al.  Rapid evolution of seed dispersal in an urban environment in the weed Crepis sancta , 2008, Proceedings of the National Academy of Sciences.

[2]  W. McDowell,et al.  The next generation of site-based long-term ecological monitoring: Linking essential biodiversity variables and ecosystem integrity. , 2018, The Science of the total environment.

[3]  Shahid Naeem,et al.  Functional and phylogenetic diversity as predictors of biodiversity--ecosystem-function relationships. , 2011, Ecology.

[4]  Susan L Ustin,et al.  Remote sensing of plant functional types. , 2010, The New phytologist.

[5]  C. Wirth,et al.  Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China , 2014 .

[6]  Eric Garnier,et al.  Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: a standardized methodology and lessons from an application to 11 European sites. , 2007, Annals of botany.

[7]  E. Mayr,et al.  The biological meaning of species , 1969 .

[8]  T. Reitalu,et al.  Changes in trait divergence and convergence along a productivity gradient in wet meadows , 2014 .

[9]  Ellen I. Damschen,et al.  Integrative modelling reveals mechanisms linking productivity and plant species richness , 2016, Nature.

[10]  D. Faith.  Conservation evaluation and phylogenetic diversity , 1992 .

[11]  Rogier de Jong,et al.  Variability and evolution of global land surface phenology over the past three decades (1982–2012) , 2016, Global change biology.

[12]  Shawn Bowers,et al.  Advancing ecological research with ontologies. , 2008, Trends in ecology & evolution.

[13]  M. Cadotte.  Experimental evidence that evolutionarily diverse assemblages result in higher productivity , 2013, Proceedings of the National Academy of Sciences.

[14]  Rebekka R. E. Artz,et al.  Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review. , 2018, The Science of the total environment.

[15]  Markus Neteler,et al.  Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges , 2010, Ecol. Informatics.

[16]  J. Lawton,et al.  The Ecotron: A Controlled Environmental Facility for the Investigation of Population and Ecosystem Processes , 1993 .

[17]  S. Schmidtlein,et al.  Mapping plant species in mixed grassland communities using close range imaging spectroscopy , 2017 .

[18]  Daniel P Faith,et al.  Phylodiversity to inform conservation policy: An Australian example. , 2015, The Science of the total environment.

[19]  G. Daily,et al.  Loss of avian phylogenetic diversity in neotropical agricultural systems , 2014, Science.

[20]  Jorge Soberón.  Grinnellian and Eltonian niches and geographic distributions of species. , 2007, Ecology letters.

[21]  Philip Lewis,et al.  Hyperspectral remote sensing of foliar nitrogen content , 2012, Proceedings of the National Academy of Sciences.

[22]  D. Tilman,et al.  Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function , 2018, Nature Ecology & Evolution.

[23]  Y. J. Kang,et al.  Correction: Corrigendum: MicroRNA122 is a key regulator of α-fetoprotein expression and influences the aggressiveness of hepatocellular carcinoma , 2012 .

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

[25]  Heiko Balzter,et al.  Connecting Earth observation to high-throughput biodiversity data , 2017, Nature Ecology &Evolution.

[26]  I. Kühn,et al.  Challenging urban species diversity: contrasting phylogenetic patterns across plant functional groups in Germany. , 2008, Ecology letters.

[27]  B. Bohannan,et al.  Microbial Biogeography: From Taxonomy to Traits , 2008, Science.

[28]  Angela Lausch,et al.  Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing , 2018 .

[29]  Thomas Blaschke,et al.  Understanding and quantifying landscape structure – A review on relevant process characteristics, data models and landscape metrics , 2015 .

[30]  Arief Wijaya,et al.  An integrated pan‐tropical biomass map using multiple reference datasets , 2016, Global change biology.

[31]  M. Schaepman,et al.  Review of optical-based remote sensing for plant trait mapping , 2013 .

[32]  Jens Kattge,et al.  The emergence and promise of functional biogeography , 2014, Proceedings of the National Academy of Sciences.

[33]  Gregory Asner,et al.  An Approach for Foliar Trait Retrieval from Airborne Imaging Spectroscopy of Tropical Forests , 2018, Remote. Sens..

[34]  Laura M. Jackson,et al.  Finding Our Way through Phenotypes , 2015, PLoS biology.

[35]  Andy Purvis,et al.  Hotspots and the conservation of evolutionary history , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Roberta E. Martin,et al.  Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy , 2015 .

[37]  M. Fischer,et al.  A meta-analysis of trait differences between invasive and non-invasive plant species. , 2010, Ecology letters.

[38]  R. Valentini,et al.  Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data , 2016 .

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

[40]  Gregory Asner,et al.  Estimating Vegetation Beta Diversity from Airborne Imaging Spectroscopy and Unsupervised Clustering , 2013, Remote. Sens..

[41]  M. Chytrý,et al.  Is phylogenetic diversity a good proxy for functional diversity of plant communities? A case study from urban habitats , 2016 .

[42]  A. McMichael,et al.  Assessing ecosystem health. , 1998, Trends in ecology & evolution.

[43]  Marco Heurich,et al.  Understanding Forest Health with Remote Sensing-Part II - A Review of Approaches and Data Models , 2017, Remote. Sens..

[44]  Richard B. Primack,et al.  Phylogenetic patterns of species loss in Thoreau's woods are driven by climate change , 2008, Proceedings of the National Academy of Sciences.

[45]  M. Keller,et al.  Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity , 2016, Global change biology.

[46]  M. Schaepman,et al.  Mapping functional diversity from remotely sensed morphological and physiological forest traits , 2017, Nature Communications.

[47]  Michael E. Schaepman,et al.  Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators , 2017 .

[48]  Yufeng Ge,et al.  High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging , 2017, Front. Plant Sci..

[49]  Heiko Paulheim,et al.  Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches , 2018, Remote. Sens..

[50]  Giles M. Foody,et al.  Remotely sensed spatial heterogeneity as an exploratory tool for taxonomic and functional diversity study , 2018 .

[51]  Ingolf Kühn,et al.  A comparative test of phylogenetic diversity indices , 2008, Oecologia.

[52]  S. Goetz,et al.  Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment , 2014, Global change biology.

[53]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[54]  Anna F. Cord,et al.  Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives , 2016 .

[55]  A. Skidmore,et al.  Spectral discrimination of vegetation types in a coastal wetland , 2003 .

[56]  Christian Wirth,et al.  The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[57]  Roberta E. Martin,et al.  Global variability in leaf respiration in relation to climate, plant functional types and leaf traits. , 2015, The New phytologist.

[58]  Marco Heurich,et al.  Understanding Forest Health with Remote Sensing -Part I - A Review of Spectral Traits, Processes and Remote-Sensing Characteristics , 2016, Remote. Sens..

[59]  P. Poschlod,et al.  Eutrophication and fragmentation are related to species’ rate of decline but not to species rarity: results from a functional approach , 2008, Biodiversity and Conservation.

[60]  Andreas Schmidt,et al.  Data mining and linked open data – New perspectives for data analysis in environmental research , 2015 .

[61]  J. Greenberg,et al.  Measuring landscape-scale spread and persistence of an invaded submerged plant community from airborne remote sensing. , 2016, Ecological applications : a publication of the Ecological Society of America.

[62]  Daniel G. Milchunas,et al.  Plant trait responses to grazing – a global synthesis , 2007 .

[63]  D. Tilman,et al.  Diversity of plant evolutionary lineages promotes arthropod diversity. , 2012, Ecology letters.

[64]  M. Grube,et al.  Molecular approaches and the concept of species and species complexes in lichenized fungi , 2000 .

[65]  Susan L Ustin,et al.  Remote sensing of canopy chemistry , 2013, Proceedings of the National Academy of Sciences.

[66]  Gregory P. Asner,et al.  Observing Changing Ecological Diversity in the Anthropocene , 2013 .

[67]  P. Cox,et al.  Observing terrestrial ecosystems and the carbon cycle from space , 2015, Global change biology.

[68]  Roberta E. Martin,et al.  Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing , 2016 .

[69]  Patrick Hostert,et al.  Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP , 2015, Remote. Sens..

[70]  N. Coops,et al.  Satellites: Make Earth observations open access , 2014, Nature.

[71]  Felix Müller,et al.  Long-Term Ecological Research , 2010 .

[72]  M. Schildhauer,et al.  Monitoring plant functional diversity from space , 2016, Nature Plants.

[73]  Roberta E. Martin,et al.  Environmental controls on canopy foliar nitrogen distributions in a Neotropical lowland forest. , 2016, Ecological applications : a publication of the Ecological Society of America.

[74]  M. Winter,et al.  Increasing species richness but decreasing phylogenetic richness and divergence over a 320-year period of urbanization , 2017 .

[75]  Chang-Hoi Ho,et al.  Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008 , 2011 .

[76]  T. M. Bezemer,et al.  Biodiversity increases the resistance of ecosystem productivity to climate extremes , 2015, Nature.

[77]  W. Turner.  Sensing biodiversity , 2014, Science.

[78]  L. Voesenek,et al.  Plants are less negatively affected by flooding when growing in species-rich plant communities. , 2017, The New phytologist.

[79]  G. Daily,et al.  Biodiversity loss and its impact on humanity , 2012, Nature.

[80]  Wolfgang Nentwig,et al.  Disentangling the role of environmental and human pressures on biological invasions across Europe , 2010, Proceedings of the National Academy of Sciences.

[81]  H. Mooney,et al.  Shifting plant phenology in response to global change. , 2007, Trends in ecology & evolution.

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

[83]  S. Wright,et al.  The global spectrum of plant form and function , 2015, Nature.

[84]  Roberta E. Martin,et al.  Targeted carbon conservation at national scales with high-resolution monitoring , 2014, Proceedings of the National Academy of Sciences.

[85]  F. Müller.  Indicating ecosystem and landscape organisation , 2005 .