Remote sensing enabled essential biodiversity variables for biodiversity assessment and monitoring: technological advancement and potentials

Abstract

The strong contribution of remote sensing has led to the development of the concept of the Remote Sensing enabled Essential Biodiversity Variables which represents a set of variables that can be monitored from space. This work synthesizes current state of research and technological development in use of remote sensing enabled essential biodiversity variables. The issue of scale, satellite observation requirements and status of remote sensing have been discussed in the context of monitoring of community composition, plant functional types, vegetation structure, canopy diversity, targeted animal groups, fragmentation, disturbances and as an input for biodiversity modelling, and Earth Observations based variables. This work highlighted existing approaches for addressing community level biodiversity and discusses in the context of Earth Observations as which are key components for biodiversity monitoring strategy. Biodiversity monitoring could be improved by using new satellite sensors and the synergy of remotely sensed data from multiple sensors which are providing hyperspatial, hyperspectral and hypertemporal observations. The use of remote sensing for operational monitoring of biodiversity is still under development as existing approaches and techniques have not holistically addressed the metrics of essential biodiversity variables.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. Adamo M et al (2014) Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC). Landscape Ecol 29(6):1045–1067

    Google Scholar 

  2. Araya S, Ostendorf B, Lyle G, Lewis M (2018) CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery. Ecological Informatics 46:45–56

    Google Scholar 

  3. Bergen K, Goetz S, Dubayah R, Henebry G, Hunsaker C, Imhoff M, Nelson R, Parker G, Radeloff V (2009) Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for Lidar and Radar spaceborne missions. J Geophysical Research. https://doi.org/10.1029/2008JG000883

    Article  Google Scholar 

  4. Boyd DS, Danson FM (2005) Satellite remote sensing of forest resources: three decades of research development. Prog Phys Geogr 29(1):1–26

    Google Scholar 

  5. Clark ML, Roberts DA (2012) Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier. Remote Sensing 4:1820–1855

    Google Scholar 

  6. Couteron P, Barbier N, Gautier D (2006) Textural ordination based on Fourier spectral decomposition: A method to analyze and compare landscape patterns. Landscape Ecol 21:555–567

    Google Scholar 

  7. Duro DC, Coops NC, Wulder MA, Han T (2007) Development of a large area biodiversity monitoring system driven by remote sensing. Progress in Phyical Geography 31:235–260

    Google Scholar 

  8. Feng X, Fu B, Yang X, Lü Y (2010) Remote sensing of ecosystem services: An opportunity for spatially explicit assessment. Chin Geogra Sci 20(6):522–535

    Google Scholar 

  9. Feret J-B, Asner GP (2014) Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol Appl 24:1289–1296

    PubMed  Google Scholar 

  10. Fernández N (2013) Earth observation for species diversity assessment and monitoring. In: Alcaraz-Segura DB, Straschnoy (eds) Earth observation of ecosystem services. CRC Press Taylor & Francis Group, Boca Raton, pp 151–177

    Google Scholar 

  11. Gitay H, Noble I (1997) What are functional types and how should we seek them. In: Smith TM, Shugart HH, Woodward FI (eds) Plant functional types: their relevance to ecosystem properties and global change vol 1. Cambridge University Press, Cambridge., pp 3–19

    Google Scholar 

  12. GOFC-GOLD (2017). A Sourcebook of Methods and Procedures for Monitoring Essential Biodiversity Variables in Tropical Forests with Remote Sensing. Eds: GOFC-GOLD & GEO BON. Report version UNCBD COP-13, GOFC-GOLD Land Cover Project Office, Wageningen University, The Netherlands.

  13. Goodwin N, Turner R, Merton R (2005) Classifying eucalyptus forests with high spatial and spectral resolution imagery: an investigation of individual species and vegetation communities. Aust J Bot 53:337–345

    Google Scholar 

  14. Groom G, Stjernholm M, Nielsen RD, Fleetwood A, Petersen IK (2013) Remote Sensing Image Data and Automated Analysis to Describe Marine Bird Distributions and Abundances. Ecological Informatics 14:2–8. https://doi.org/10.1016/j.ecoinf.2012.12.001

    Article  Google Scholar 

  15. Hazen H (2009) Biodiversity Mapping International Encyclopedia of Human Geography. Elsevier, Amsterdam The Netherlands, pp 314–319

    Google Scholar 

  16. Hernández-Stefanoni JL, Dupuy JM, Johnson KD, Birdsey R, Tun-Dzul F, Peduzzi A, Caamal-Sosa JP, Sánchez-Santos G, López-Merl’ D (2014) Improving species diversity and biomass estimates of tropical dry forests using airborne LiDAR. Remote Sensing 6:4741–4763

    Google Scholar 

  17. Higgins MA, Asner GP, Perez E, Elespuru N, Tuomisto H, Ruokolainen K, Alonso A (2012) Use of Landsat and SRTM data to detect broad-scale biodiversity patterns in Northwestern Amazonia. Remote Sensing 4:2401–2418

    Google Scholar 

  18. Hirschmugl M, Ofner M, Raggam J, Schardt M (2007) Single tree detection in very high resolution remote sensing data. Remote Sens Environ 110(4):533–544

    Google Scholar 

  19. https://www.cbd.int/sp

  20. https://www.scopus.com/

  21. Immitzer M, Atzberger C, Koukal T (2012) Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sensing 4:2661–2693

    Google Scholar 

  22. Immitzer M, Vuolo F, Atzberger C (2016) First experience with sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sensing 8:166

    Google Scholar 

  23. Jha CS, Rakesh SJ, Reddy CS, Rajashekar G, Maity S, Patnaik C, Das A, Misra A, Singh CP, Mohapatra J, Krishnayya NSR, Kiran S, Townsend P, Martinez M (2019) Characterization of species diversity and forest health using AVIRIS-NG hyperspectral remote sensing data. Curr Sci 116(7):1124–1135

  24. Kim M, Madden M, Warner TA (2009) Forest type mapping using object-specific texture measures from multispectral Ikonos imagery. Photogrammetric Engineering & Remote Sensing 75:819–829

    Google Scholar 

  25. Kuenzer C, Ottinger M, Wegmann M, Guo H, Wang C, Zhang J, Dech S, Wikelski M (2014) Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks. Int J Remote Sens 35:6599–6647

    Google Scholar 

  26. Lang S, Mairota P, Pernkopf L, Schioppa EP (2015) Earth observation for habitat mapping and biodiversity monitoring. Int J Appl Earth Obs Geoinf 37:1–6

    Google Scholar 

  27. Laurin GV, Puletti N, Hawthorne W, Liesenberg V, Corona P, Papale D, Chen Q, Valentini R (2016) Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sens Environ 176:163–176

    Google Scholar 

  28. Lausch A et al (2016) Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecol Ind 70:317–339

    Google Scholar 

  29. Leutner BF, Reineking B, Müller J, Bachmann M, Beierkuhnlein C, Dech S, Wegmann M (2012) Modelling forest alpha-diversity and floristic composition - On the added value of LiDAR plus hyperspectral remote sensing. Remote Sensing 4:2818–2845

    Google Scholar 

  30. Leyequien E, Verrelst J, Slot M, Schaepman-Strub G, Heitkönig IMA, Skidmore A (2007) Capturing the Fugitive: Applying Remote Sensing to Terrestrial Animal Distribution and Diversity. Int J Appl Earth Obs Geoinf 9(1):1–20. https://doi.org/10.1016/j.jag.2006.08.002

    Article  Google Scholar 

  31. Levin SA (1992) The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture Ecology 73:1943–1967

    Google Scholar 

  32. Li X, He HS, Bu R, Wen Q, Chang YHu, Y & Li, Y (2005) The adequacy of different landscape metrics for various landscape patterns. Pattern Recogn 38:2626–2638

    Google Scholar 

  33. Liu H, Dong P (2014) A new method for generating canopy height models from discrete-return LiDAR point clouds. Remote Sensing Letters 5(6):575–582

    Google Scholar 

  34. Loozen Y, Rebel KT, de Jong SM, Lu M, Ollinger SV, Wassen MJ, Karssenberg D (2020) Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method. Remote Sens Environ 247:111933

    Google Scholar 

  35. Mairota P, Cafarelli B, Labadessa R, Lovergine F, Tarantino C, Lucas RM, Didham RK (2015) Very high resolution Earth observation features for monitoring plant and animal community structure across multiple spatial scales in protected areas. Int J Appl Earth Obs Geoinf 37:100–105

    Google Scholar 

  36. McGarigal K, Cushman S, Regan C (2005) Quantifying terrestrial habitat loss and fragmentation: a protocol. University of Massachusetts, Department of Natural Resources Conservation, Amherst, MA, p 113

    Google Scholar 

  37. McMahon CR, Howe H, van den Hoff J, Alderman R, Brolsma H et al (2014) Satellites, the All-Seeing Eyes in the Sky: Counting Elephant Seals from Space. PLoS ONE 9(3):e92613. https://doi.org/10.1371/journal.pone.0092613

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Mendenhall CD, Shields-Estrada A, Krishnaswami AJ, Daily GC (2016) Quantifying and sustaining biodiversity in tropical agricultural landscapes. Proc Natl Acad Sci USA 113:14544–14551

    CAS  PubMed  Google Scholar 

  39. Nagendra H (2001) Using remote sensing to assess biodiversity. Int J Remote Sens 22:2377–2400

    Google Scholar 

  40. Noss RF (1990) Indicators for monitoring biodiversity: a hierarchical approach. Conserv Biol 4:355–364

    Google Scholar 

  41. O’Neill RV, King AW (1998) Homage to St.Michael: Or why are there so many books on scale?" in Ecological Scale, Theory and Applications. In: Peterson DL, Parker VT (eds) Ecological scale: theory and applications. Columbia University Press, New York, pp 3–15

    Google Scholar 

  42. Paganini M, Leidner AK, Geller G, Turner W, Wegmann M (2016) The role of space agencies in remotely sensed essential biodiversity variables. Remote Sensing in Ecology and Conservation 2(3):132–140

    Google Scholar 

  43. Pereira HM et al (2013) Essential biodiversity variables. Science 339(6117):277–278

    CAS  PubMed  Google Scholar 

  44. Pettorelli N et al (2016) Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sensing in Ecology and Conservation 2(3):122–131

    Google Scholar 

  45. Pontius J, Schaberg P, Hanavan R (2020) Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance and Decline for Protection of Biodiversity. In: Cavender-Bares J, Gamon J, Townsend P (eds) Remote Sensing of Plant Biodiversity. Springer, Cham, pp 121–154

    Google Scholar 

  46. Proisy C, Barbier N, Guéroult M, Pélissier R, Gastellu-Etchegorry JP, Grau E, Couteron P (2012) Biomass prediction in tropical forests: the canopy grain approach. In: Fatoyinbo L (ed) Remote Sensing of Biomass-Principles and Applications. IntechOpen, London, pp 59–76

    Google Scholar 

  47. Radeloff VC, Dubinin M, Coops NC, Allen AM, Brooks TM, Clayton MK, Costa GC, Graham CH, Helmers DP, Ives AR, Kolesov D et al (2019) The dynamic habitat indices (DHIs) from MODIS and global biodiversity. Remote Sens Environ 222:204–214

    Google Scholar 

  48. Reddy CS, Khuroo AA, Harikrishna P, Saranya KRL, Jha CS, Dadhwal VK (2014) Threat evaluation for biodiversity conservation of forest ecosystems using geospatial techniques: A case study of Odisha, India. Ecol Eng 69:287–303

    Google Scholar 

  49. Reddy CS, Saranya KRL, Pasha SV, Satish KV, Jha CS, Diwakar PG, Dadhwal VK, Rao PVN, Krishna Murthy YVN (2018) Assessment and monitoring of deforestation and forest fragmentation in South Asia since the 1930s. Global Planet Change 161:132–148

    Google Scholar 

  50. Rocchini D, Boyd DS, Féret JB, Foody GM, He KS, Lausch A, Pettorelli N (2016) Satellite remote sensing to monitor species diversity: Potential and pitfalls. Remote Sensing in Ecology and Conservation 2(1):25–36

    Google Scholar 

  51. Rodriguez JP et al (2011) Establishing IUCN red list criteria for threatened ecosystems. Conserv Biol 25:21–29

    PubMed  Google Scholar 

  52. Roy PS, Kushwaha SPS, Murthy MSR, Roy A, Kushwaha D, Reddy CS, Behera MD, Padalia H, Mathur VB, Singh S, Jha CS, Porwal MC (2012) Biodiversity Characterisation at Landscape Level: National Assessment. Indian Institute of Remote Sensing, Dehra Dun

    Google Scholar 

  53. Secades, C., O'Connor, B., Brown, C., Walpole, M. (2014). Earth Observation for Biodiversity Monitoring: A review of current approaches and future opportunities for tracking progress towards the Aichi Biodiversity Targets. Secretariat of the Convention on Biological Diversity, Montréal, Canada. Technical Series No. 72, 183 pages.

  54. Singh H, Garg RD, Karnataka HC, Roy A (2018) Spatial landscape model to characterize biological diversity using R statistical computing environment. J Environ Manag 206:1211–1223

    Google Scholar 

  55. Skidmore A et al (2015) Environmental science: Agree on biodiversity metrics to track from space. Nature 523:403–405

    CAS  PubMed  Google Scholar 

  56. Stoms DM, Estes JE (1993) A remote sensing research agenda for mapping and monitoring biodiversity. Int J Remote Sens 14(10):1839–1860

    Google Scholar 

  57. Tarr NM (2019) Demonstrating a conceptual model for multispecies landscape pattern indices in landscape conservation. Landscape Ecol 34(9):2133–2147

    Google Scholar 

  58. Turner MG, Dale VH, Gardner RH (1989) Predicting across scales: theory development and testing. Landscape Ecol 3:245–252

    Google Scholar 

  59. Turner MG, Gardner RH, O’Neill RV (2001) Landscape Ecology in Theory and Practice. Springer, New York

    Google Scholar 

  60. Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing for biodiversity science and conservation. Trends Ecol Evol 18:306–314

    Google Scholar 

  61. Underwood EC, Ustin SL, Ramirez CM (2007) A comparison of spatial and spectral image resolution for mapping invasive plants in coastal California. Environ Manage 39:63–83

    PubMed  Google Scholar 

  62. USNVC. (2017). United States National Vegetation Classification Database, V2.01. Federal Geographic Data Committee, Vegetation Subcommittee, Washington DC. (usnvc.org).

  63. Ustin SL, Gamon JA (2010) Remote sensing of plant functional types. New Phytol 186:795–816

    PubMed  Google Scholar 

  64. Van der Maarel, E. & Franklin, J. (2012). Vegetation ecology. John Wiley & Sons. Indicative definitions taken from the Report of the ad hoc technical expert group on forest biological diversity. ps://www.cbd.int/forest/definitions.shtml.

  65. Vierling KT, Vierling LA, Gould WA, Martinuzzi S, Clawges RM (2008) Lidar: shedding new light on habitat characterization and modeling. Front Ecol Environ 6:90–98

    Google Scholar 

  66. Wang R, Gamon JA (2019) Remote sensing of terrestrial plant biodiversity. Remote Sens Environ 231:111218

    Google Scholar 

  67. Wang K, Franklin SE, Guo X, Cattet M (2010) Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists. Sensors 10(11):9647–9667

    PubMed  Google Scholar 

  68. Wilson EO (ed) (1988) Biodiversity. National Academy Press, Washington DC

    Google Scholar 

  69. Wessel M, Brandmeier M, Tiede D (2018) Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data. Remote Sensing 10(9):1419

    Google Scholar 

  70. Westman WE, Strong LL, Wilcox BA (1989) Tropical deforestation and species endangerment: the role of remote sensing. Landscape Ecol 3(2):97–109

    Google Scholar 

  71. Wu J, Li H (2006) Concepts of scale and scaling. In: WU J, JONES KB, LI H, LOUCKS OL (Eds), Scaling and Uncertainty Analysis in Ecology. Springer, Dordrecht

  72. Wulder MA, Hall RJ, Coops NC, Franklin SE (2004) High spatial resolution remotely sensed data for ecosystem characterization. Bioscience 54(6):511–521

    Google Scholar 

  73. Zhang Z, Cao L, She G (2017) Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests. Remote Sensing 9(9):940

    Google Scholar 

Download references

Acknowledgements

This work has been carried out as part of a project on Biodiversity Characterisation at Community level in India using Earth Observation Data. We gratefully acknowledge the Department of Biotechnology and the Department of Space, Government of India for supporting this research. We are grateful to Director, NRSC, Hyderabad, Director, IIRS, Dehradun and Director, French Institute of Pondicherry for providing all necessary support to carry out the study.

Author information

Affiliations

Authors

Corresponding author

Correspondence to C. Sudhakar Reddy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Communicated by Neil Brummitt.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Reddy, C.S., Kurian, A., Srivastava, G. et al. Remote sensing enabled essential biodiversity variables for biodiversity assessment and monitoring: technological advancement and potentials. Biodivers Conserv 30, 1–14 (2021). https://doi.org/10.1007/s10531-020-02073-8

Download citation

Keywords

  • Earth observation
  • Scale
  • Structure
  • Composition
  • Function