Biodiversity monitoring, earth observations and the ecology of scale.

Human activity and land-use change are dramatically altering the sizes, geographical distributions and functioning of biological populations worldwide, with tremendous consequences for human well-being. Yet our ability to measure, monitor and forecast biodiversity change - crucial to addressing it - remains limited. Biodiversity monitoring systems are being developed to improve this capacity by deriving metrics of change from an array of in situ data (e.g. field plots or species occurrence records) and Earth observations (EO; e.g. satellite or airborne imagery). However, there are few ecologically based frameworks for integrating these data into meaningful metrics of biodiversity change. Here, I describe how concepts of pattern and scale in ecology could be used to design such a framework. I review three core topics: the role of scale in measuring and modelling biodiversity patterns with EO, scale-dependent challenges linking in situ and EO data and opportunities to apply concepts of pattern and scale to EO to improve biodiversity mapping. From this analysis emerges an actionable approach for measuring, monitoring and forecasting biodiversity change, highlighting key opportunities to establish EO as the backbone of global-scale, science-driven conservation.

[1]  Cristina Milesi,et al.  Collaborative Supercomputing for Global Change Science , 2011 .

[2]  Sassan Saatchi,et al.  These are the days of lasers in the jungle , 2014, Carbon Balance and Management.

[3]  M. Araújo,et al.  BIOMOD – a platform for ensemble forecasting of species distributions , 2009 .

[4]  Patrick Wambacq,et al.  Speckle filtering of synthetic aperture radar images : a review , 1994 .

[5]  Joanne C. White,et al.  A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS , 2009 .

[6]  Erle C. Ellis,et al.  The spatial and temporal domains of modern ecology , 2018, Nature Ecology & Evolution.

[7]  P. Rodríguez,et al.  Geographic range, turnover rate and the scaling of species diversity , 2002 .

[8]  M. Rosenzweig,et al.  How are diversity and productivity related , 1993 .

[9]  Trevor H. Booth,et al.  bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies , 2014 .

[10]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[11]  Robert G. Wagner,et al.  Process versus empirical models: which approach for forest ecosystem management? , 1996 .

[12]  Andrea Sacchetti,et al.  The PRISMA Program , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[13]  L. Hannah,et al.  Scale effects in species distribution models: implications for conservation planning under climate change , 2009, Biology Letters.

[14]  Yadvinder Malhi,et al.  A comparison of plot‐based satellite and Earth system model estimates of tropical forest net primary production , 2015 .

[15]  John F. Mustard,et al.  A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data , 2007 .

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

[17]  J. A. Schaefer,et al.  Habitat Selection at Multiple Scales , 2009 .

[18]  J. Chambers,et al.  Lack of intermediate‐scale disturbance data prevents robust extrapolation of plot‐level tree mortality rates for old‐growth tropical forests , 2009 .

[19]  Jörg Müller,et al.  Modelling Forest α-Diversity and Floristic Composition - On the Added Value of LiDAR plus Hyperspectral Remote Sensing , 2012, Remote. Sens..

[20]  Francesco Camastra,et al.  Data dimensionality estimation methods: a survey , 2003, Pattern Recognit..

[21]  Roberta E. Martin,et al.  Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion , 2012 .

[22]  Supratik Mukhopadhyay,et al.  DeepSat: a learning framework for satellite imagery , 2015, SIGSPATIAL/GIS.

[23]  Gregory P. Asner,et al.  SCALE DEPENDENCE OF ABSORPTION OF PHOTOSYNTHETICALLY ACTIVE RADIATION IN TERRESTRIAL ECOSYSTEMS , 1998 .

[24]  R. Q. Thomas,et al.  Clustered disturbances lead to bias in large-scale estimates based on forest sample plots. , 2008, Ecology letters.

[25]  F. Baret,et al.  Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data , 2006 .

[26]  J. Oksanen Is the humped relationship between species richness and biomass an artefact due to plot size , 1996 .

[27]  Amy W. Ando,et al.  The economic value of grassland species for carbon storage , 2017, Science Advances.

[28]  Hooman Latifi,et al.  Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data , 2018 .

[29]  G. Daily,et al.  Predicting biodiversity change and averting collapse in agricultural landscapes , 2014, Nature.

[30]  J. V. Soares,et al.  Distribution of aboveground live biomass in the Amazon basin , 2007 .

[31]  D. Brito Overcoming the Linnean shortfall: Data deficiency and biological survey priorities , 2010 .

[32]  Liana Anderson,et al.  Biome-Scale Forest Properties in Amazonia Based on Field and Satellite Observations , 2012, Remote. Sens..

[33]  Geoffrey J. Hay,et al.  Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline , 2008 .

[34]  S. Levin The problem of pattern and scale in ecology , 1992 .

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

[36]  Gretchen C. Daily,et al.  Quantifying and sustaining biodiversity in tropical agricultural landscapes , 2016, Proceedings of the National Academy of Sciences.

[37]  Julia A. Barsi,et al.  The next Landsat satellite: The Landsat Data Continuity Mission , 2012 .

[38]  Lander Baeten,et al.  Global meta-analysis reveals no net change in local-scale plant biodiversity over time , 2013, Proceedings of the National Academy of Sciences.

[39]  J. Lamarque,et al.  Global Biodiversity: Indicators of Recent Declines , 2010, Science.

[40]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[41]  S. Ollinger,et al.  A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems , 2008 .

[42]  U. Benz,et al.  The EnMAP hyperspectral imager—An advanced optical payload for future applications in Earth observation programmes , 2006 .

[43]  Chengquan Huang,et al.  Forest disturbance across the conterminous United States from 1985-2012: The emerging dominance of forest decline , 2016 .

[44]  P. Fisher The pixel: A snare and a delusion , 1997 .

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

[46]  Henrique M. Pereira,et al.  Global Biodiversity Change: The Bad, the Good, and the Unknown , 2012 .

[47]  G. Foody,et al.  Measuring and modelling biodiversity from space , 2008 .

[48]  D. Rocchini Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery , 2007 .

[49]  Clayton C. Kingdon,et al.  Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. , 2014, Ecological applications : a publication of the Ecological Society of America.

[50]  Duccio Rocchini,et al.  Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images , 2010, Remote. Sens..

[51]  R. Lucas,et al.  New global forest/non-forest maps from ALOS PALSAR data (2007–2010) , 2014 .

[52]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

[53]  A. Magurran,et al.  Fifteen forms of biodiversity trend in the Anthropocene. , 2015, Trends in ecology & evolution.

[54]  R. Dirzo,et al.  Defaunation in the Anthropocene , 2014, Science.

[55]  Floyd M. Henderson,et al.  Radar detection of wetland ecosystems: a review , 2008 .

[56]  D. Butler Earth observation enters next phase , 2014, Nature.

[57]  Walter Jetz,et al.  Integrating biodiversity distribution knowledge: toward a global map of life. , 2012, Trends in ecology & evolution.

[58]  Peter B Adler,et al.  Productivity Is a Poor Predictor of Plant Species Richness , 2011, Science.

[59]  William W. Hargrove,et al.  A continental strategy for the National Ecological Observatory Network , 2008 .

[60]  Russell Main,et al.  L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[61]  Michael E. Schaepman,et al.  Sentinels for science: potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land , 2012 .

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

[63]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[64]  Wolfgang Schwanghart,et al.  Spatial bias in the GBIF database and its effect on modeling species' geographic distributions , 2014, Ecol. Informatics.

[65]  A. Peterson,et al.  INTERPRETATION OF MODELS OF FUNDAMENTAL ECOLOGICAL NICHES AND SPECIES' DISTRIBUTIONAL AREAS , 2005 .

[66]  T. Rangel,et al.  Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot , 2006 .

[67]  Martin van Leeuwen,et al.  Assessing the impact of sub-pixel vegetation structure on imaging spectroscopy via simulation , 2015, Defense + Security Symposium.

[68]  J. Randerson,et al.  Global net primary production: Combining ecology and remote sensing , 1995 .

[69]  W. Jetz,et al.  On the decline of biodiversity due to area loss , 2015, Nature Communications.

[70]  C. Woodcock,et al.  Continuous monitoring of forest disturbance using all available Landsat imagery , 2012 .

[71]  Walter Jetz,et al.  Species richness, hotspots, and the scale dependence of range maps in ecology and conservation , 2007, Proceedings of the National Academy of Sciences.

[72]  Peter Haase,et al.  Bridging the gap between biodiversity data and policy reporting needs: An Essential Biodiversity Variables perspective , 2016 .

[73]  David E. Knapp,et al.  Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy , 2015, PloS one.

[74]  David A. Clausi,et al.  An Evaluation of Radarsat-1 and ASTER Data for Mapping Veredas (Palm Swamps) , 2008, Sensors.

[75]  Kai Wang,et al.  Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists , 2010, Sensors.

[76]  D. Butler Many eyes on Earth , 2014, Nature.

[77]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[78]  Yoan Fourcade,et al.  Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics , 2018 .

[79]  Jonathan M. Chase,et al.  Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. , 2013, Ecology letters.

[80]  W. Wagner,et al.  Global monitoring of wetlands--the value of ENVISAT ASAR Global mode. , 2009, Journal of environmental management.

[81]  K. McGarigal,et al.  Multi-scale habitat selection modeling: a review and outlook , 2016, Landscape Ecology.

[82]  Georgina M. Mace,et al.  Distorted Views of Biodiversity: Spatial and Temporal Bias in Species Occurrence Data , 2010, PLoS biology.

[83]  Sihui Wang,et al.  An Improved Algorithm for Forest Fire Detection Using HJ Data , 2012 .

[84]  Gretchen C Daily,et al.  Predictive model for sustaining biodiversity in tropical countryside , 2011, Proceedings of the National Academy of Sciences.

[85]  Karl Segl,et al.  Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products - Examples for Ground Reflectance Retrieval and Vegetation Indices , 2015, Remote. Sens..

[86]  Duccio Rocchini,et al.  International Journal of Remote Sensing High-resolution Satellite Remote Sensing: a New Frontier for Biodiversity Exploration in Indian Himalayan Forests High-resolution Satellite Remote Sensing: a New Frontier for Biodiversity Exploration in Indian Himalayan Forests , 2022 .

[87]  G. Asner,et al.  Spatially explicit analysis of field inventories for national forest carbon monitoring , 2016, Carbon Balance and Management.

[88]  P. White,et al.  The distance decay of similarity in biogeography and ecology , 1999 .

[89]  Michel Loreau,et al.  Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. , 2016, Ecology.

[90]  R. Houghton,et al.  Tropical forests are a net carbon source based on aboveground measurements of gain and loss , 2017, Science.

[91]  Peter Gege,et al.  Characterisation methods for the hyperspectral sensor HySpex at DLR's calibration home base , 2012, Remote Sensing.

[92]  Roberta E. Martin,et al.  Large-scale climatic and geophysical controls on the leaf economics spectrum , 2016, Proceedings of the National Academy of Sciences.

[93]  M. Fladeland,et al.  Remote sensing for biodiversity science and conservation , 2003 .

[94]  R. Whittaker,et al.  Scale and species richness: towards a general, hierarchical theory of species diversity , 2001 .

[95]  W. Cohen,et al.  Estimates of forest canopy height and aboveground biomass using ICESat , 2005 .

[96]  A. P. Williams,et al.  Empirical and process-based approaches to climate-induced forest mortality models , 2013, Front. Plant Sci..

[97]  V. Meentemeyer,et al.  Concepts of Scale in Landscape Ecology , 1999 .

[98]  Kristine N. Stewart,et al.  Averting biodiversity collapse in tropical forest protected areas , 2012, Nature.

[99]  P. Legendre,et al.  ANALYZING BETA DIVERSITY: PARTITIONING THE SPATIAL VARIATION OF COMMUNITY COMPOSITION DATA , 2005 .

[100]  Zheng Zhang,et al.  Phenology-Based Vegetation Index Differencing for Mapping of Rubber Plantations Using Landsat OLI Data , 2015, Remote. Sens..

[101]  P. Gong,et al.  Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .

[102]  J. Funk,et al.  Revisiting the Holy Grail: using plant functional traits to understand ecological processes , 2017, Biological reviews of the Cambridge Philosophical Society.

[103]  S. Running,et al.  What does Remote Sensing Do for Ecology , 1991 .

[104]  J. Calabrese,et al.  Stacking species distribution models and adjusting bias by linking them to macroecological models , 2014 .

[105]  Ingolf Kühn,et al.  Patterns of beta diversity in Europe: the role of climate, land cover and distance across scales , 2012 .

[106]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[107]  Josef Kellndorfer,et al.  Large-Area Classification and Mapping of Forest and Land Cover in the Brazilian Amazon: A Comparative Analysis of ALOS/PALSAR and Landsat Data Sources , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[108]  Wilfried Thuiller,et al.  From species distributions to meta-communities. , 2015, Ecology letters.

[109]  Wei Guo,et al.  Suomi NPP/VIIRS: improving drought watch, crop loss prediction, and food security , 2015 .

[110]  Roberta E. Martin,et al.  Landscape-Scale Controls on Aboveground Forest Carbon Stocks on the Osa Peninsula, Costa Rica , 2015, PloS one.

[111]  E. Fleishman,et al.  Comparative influence of spatial scale on beta diversity within regional assemblages of birds and butterflies , 2004 .

[112]  Gretchen C. Daily,et al.  Developing a Scientific Basis for Managing Earth's Life Support Systems , 1999 .

[113]  P. White,et al.  Scale Dependence and the Species-Area Relationship , 1994, The American Naturalist.

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

[115]  Fred A. Kruse,et al.  Effect of Reduced Spatial Resolution on Mineral Mapping Using Imaging Spectrometry - Examples Using Hyperspectral Infrared Imager (HyspIRI)-Simulated Data , 2011, Remote. Sens..

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

[117]  Kenta Ogawa,et al.  USage of cloud climate data in operation misson plan simulation for Japanese future hyperspectral and multispectral senor: HISUI , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[118]  R. Loyola,et al.  Defining spatial conservation priorities in the face of land-use and climate change , 2013 .

[119]  Mevin B Hooten,et al.  Iterative near-term ecological forecasting: Needs, opportunities, and challenges , 2018, Proceedings of the National Academy of Sciences.

[120]  Molly E. Brown,et al.  Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[121]  Simon D. Jones,et al.  Are landscape ecologists addressing uncertainty in their remote sensing data? , 2012, Landscape Ecology.

[122]  Paolo Manghi,et al.  Navigating the unfolding open data landscape in ecology and evolution , 2018, Nature Ecology & Evolution.

[123]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[124]  Bradford A. Hawkins,et al.  Eight (and a half) deadly sins of spatial analysis , 2012 .

[125]  Jianzhong Feng,et al.  New Fast Detection Method of Forest Fire Monitoring and Application Based on FY-1D/MVISR Data , 2007, CCTA.

[126]  Filippo Bussotti,et al.  Positive biodiversity-productivity relationship predominant in global forests , 2016, Science.

[127]  Eren Turak,et al.  Building a global observing system for biodiversity , 2012 .

[128]  S. Higgins,et al.  TRY – a global database of plant traits , 2011, Global Change Biology.

[129]  Roberta E. Martin,et al.  Amazonian landscapes and the bias in field studies of forest structure and biomass , 2014, Proceedings of the National Academy of Sciences.

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

[131]  K. Gaston,et al.  Scaling Biodiversity: The scaling of spatial turnover: pruning the thicket , 2007 .

[132]  Christopher B. Field,et al.  2 – Ecological Scaling of Carbon Gain to Stress and Resource Availability , 1991 .

[133]  H. Mooney,et al.  Toward a Global Biodiversity Observing System , 2008, Science.

[134]  J. Wiens Spatial Scaling in Ecology , 1989 .

[135]  Steven J. Cooke,et al.  Taxonomic bias and international biodiversity conservation research , 2017 .

[136]  Rob H. G. Jongman,et al.  A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring , 2013 .

[137]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[138]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[139]  Eric Armijo,et al.  Challenges and opportunities for the Bolivian Biodiversity Observation Network , 2015 .

[140]  Laura A. Brandt,et al.  Comparison of climate envelope models developed using expert-selected variables versus statistical selection , 2017 .

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

[142]  J. Elith,et al.  Sensitivity of predictive species distribution models to change in grain size , 2007 .

[143]  Sassan Saatchi,et al.  Modeling distribution of Amazonian tree species and diversity using remote sensing measurements , 2008 .

[144]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

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

[146]  Chengquan Huang,et al.  Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error , 2013, Int. J. Digit. Earth.

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

[148]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[149]  Nathalie Pettorelli,et al.  Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science , 2017 .

[150]  R. Dirzo,et al.  Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines , 2017, Proceedings of the National Academy of Sciences.

[151]  H. Nagendra,et al.  Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats , 2013 .

[152]  G. Asner Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[153]  D. Richardson,et al.  Delayed biodiversity change: no time to waste. , 2015, Trends in ecology & evolution.

[154]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[155]  Eric Garnier,et al.  From Plant Traits to Plant Communities: A Statistical Mechanistic Approach to Biodiversity , 2006, Science.

[156]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[157]  Nathalie Pettorelli,et al.  Satellite remote sensing, biodiversity research and conservation of the future , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.