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Novel approach to enhance coastal habitat and biotope mapping with drone aerial imagery analysis

  • 1.

    Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 2.

    Lefcheck, J. S., Wilcox, D. J., Murphy, R. R., Marion, S. R. & Orth, R. J. Multiple stressors threaten the imperiled coastal foundation species eelgrass (Zostera marina) in Chesapeake Bay, USA. Glob. Change Biol. 32, 202–3483 (2017).

    Google Scholar 

  • 3.

    Duarte, C. M. et al. Rebuilding marine life. Nature 580, 39–51 (2020).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 4.

    Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).

    PubMed  Article  Google Scholar 

  • 5.

    Liquete, C. et al. Current status and future prospects for the assessment of marine and coastal ecosystem services: A systematic review. PLoS ONE 8, e67737 (2013).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 6.

    Bayley, D. T. I. & Mogg, A. O. M. Chapter 6—New Advances in Benthic Monitoring Technology and Methodology. World Seas: An Environmental Evaluation 121–132 (Elsevier, Amsterdam, 2018). https://doi.org/10.1016/B978-0-12-805052-1.00006-1.

    Google Scholar 

  • 7.

    González-Rivero, M. et al. The Catlin Seaview Survey—Kilometre-scale seascape assessment, and monitoring of coral reef ecosystems. Aquat. Conserv. Mar. Freshw. Ecosyst. 24, 184–198 (2014).

    Article  Google Scholar 

  • 8.

    Ventura, D., Bruno, M., Jona Lasinio, G., Belluscio, A. & Ardizzone, G. A low-cost drone based application for identifying and mapping of coastal fish nursery grounds. Estuar. Coast. Shelf Sci. 171, 85–98 (2016).

    ADS  Article  Google Scholar 

  • 9.

    Pyle, R. L. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) 12, 959–972 (Springe, Berlin, 2019).

  • 10.

    Lam, K. et al. A comparison of video and point intercept transect methods for monitoring subtropical coral communities. J. Exp. Mar. Biol. Ecol. 333, 115–128 (2006).

    Article  Google Scholar 

  • 11.

    Dumas, P., Bertaud, A., Peignon, C., Léopold, M. & Pelletier, D. A ‘quick and clean’ photographic method for the description of coral reef habitats. J. Exp. Mar. Biol. Ecol. 368, 161–168 (2009).

    Article  Google Scholar 

  • 12.

    Monteiro, J. G., Almeida, C., Freitas, R., Delgado, A. & Porteiro, F. Coral assemblages of Cabo Verde: preliminary assessment and description. Proceedings of the 11th ICRS (2009).

  • 13.

    Beijbom, O. et al. Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation. PLoS ONE 10, e0130312 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 14.

    Chennu, A., Färber, P., De’ath, G., de Beer, D. & Fabricius, K. E. A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats. Sci. Rep. 7, 1–12 (2017).

    CAS  Article  Google Scholar 

  • 15.

    Purkis, S. J. Remote sensing tropical coral reefs: The view from above. Annu. Rev. Mar. Sci. 10, 149–168 (2018).

    ADS  Article  Google Scholar 

  • 16.

    Kao, H.-M. et al. Determination of shallow water depth using optical satellite images. Int. J. Remote Sens. 30, 6241–6260 (2009).

    ADS  Article  Google Scholar 

  • 17.

    Saul, S. & Purkis, S. Semi-automated object-based classification of coral reef habitat using discrete choice models. Remote Sens. 7, 15894–15916 (2015).

    ADS  Article  Google Scholar 

  • 18.

    Marcello, J., Eugenio, F. & Marques, F. Benthic mapping using high resolution multispectral and hyperspectral imagery. In IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium 1535–1538 (2018). https://doi.org/10.1109/IGARSS.2018.8519166

  • 19.

    Chénier, R., Faucher, M.-A. & Ahola, R. Satellite-derived bathymetry for improving canadian hydrographic service charts. ISPRS Int. J. Geo-Inf. 7, 306–315 (2018).

    Article  Google Scholar 

  • 20.

    Casella, E. et al. Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques. Coral Reefs 36, 269–275 (2016).

    ADS  Article  Google Scholar 

  • 21.

    Chust, G., Galparsoro, I., Borja, Á., Franco, J. & Uriarte, A. Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery. Estuar. Coast. Shelf Sci. 78, 633–643 (2008).

    ADS  Article  Google Scholar 

  • 22.

    Garcia, R., Hedley, J., Tin, H. & Fearns, P. A method to analyze the potential of optical remote sensing for benthic habitat mapping. Remote Sens. 7, 13157–13189 (2015).

    ADS  Article  Google Scholar 

  • 23.

    Hernandez, W. & Armstrong, R. Deriving bathymetry from multispectral remote sensing data. JMSE 4, 8 (2016).

    Article  Google Scholar 

  • 24.

    Gonzalez, L. et al. Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors 16, 97 (2016).

    Article  Google Scholar 

  • 25.

    Jiménez López, J. & Mulero-Pázmány, M. Drones for conservation in protected areas: Present and future. Drones 3, 10 (2019).

    Article  Google Scholar 

  • 26.

    Chirayath, V. & Earle, S. A. Drones that see through waves—Preliminary results from airborne fluid lensing for centimetrescale aquatic conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 237–250 (2016).

    Article  Google Scholar 

  • 27.

    Giordano, F., Mattei, G., Parente, C., Peluso, F. & Santamaria, R. Integrating sensors into a marine drone for bathymetric 3D surveys in shallow waters. Sensors 16, 41–17 (2016).

    Article  Google Scholar 

  • 28.

    Collin, A. et al. Very high resolution mapping of coral reef state using airborne bathymetric LiDAR surface-intensity and drone imagery. Int. J. Remote Sens. 00, 1–13 (2018).

    Google Scholar 

  • 29.

    Konar, B. & Iken, K. The use of unmanned aerial vehicle imagery in intertidal monitoring. Deep-Sea Res. Part II(147), 79–86 (2018).

    Article  Google Scholar 

  • 30.

    Parsons, M., Bratanov, D., Gaston, K. J. & Gonzalez, F. UAVs, hyperspectral remote sensing, and machine learning revolutionizing reef monitoring. Sensors 18, 2026 (2018).

    Article  Google Scholar 

  • 31.

    Rossiter, T., Furey, T., McCarthy, T. & Stengel, D. B. UAV-mounted hyperspectral mapping of intertidal macroalgae. Estuar. Coast. Shelf Sci. https://doi.org/10.1016/j.ecss.2020.106789 (2020).

    Article  Google Scholar 

  • 32.

    United Nations Environment Programme. Out of the Blue. 1–96 (UNEP, 2020).

  • 33.

    Monteiro, J. G. & Lopez, J. J. Map of Quinta do Lorde Bay—Madeira Island. 1–3 (2020). doi:https://doi.org/10.22541/au.158939921.14824633

  • 34.

    Stumpf, R. P., Holderied, K. & Sinclair, M. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr. 48, 547–556 (2003).

    ADS  Article  Google Scholar 

  • 35.

    Conger, C. L., Hochberg, E. J., Fletcher, C. H. & Atkinson, M. J. Decorrelating remote sensing color bands from bathymetry in optically shallow waters. IEEE Trans. Geosci. Remote Sens. 44, 1655–1660 (2006).

    ADS  Article  Google Scholar 

  • 36.

    Clarke, K. & Warwick, R. Change in Marine Communities: An Approach to Statistical Analysis (Primer-e Ltd, London, 2014).

    Google Scholar 

  • 37.

    Baldwin, C. C., Tornabene, L. & Robertson, D. R. Below the mesophotic. Sci. Rep. https://doi.org/10.1038/s41598-018-23067-1 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  • 38.

    Olenin, S. & Ducrotoy, J.-P. The concept of biotope in marine ecology and coastal management. J. Exp. Mar. Biol. Ecol. 53, 20–29 (2006).

    CAS  Google Scholar 

  • 39.

    Frazão Santos, C. et al. in World Seas: An Environmental Evaluation 571–592 (Elsevier, Amsterdam, 2019). https://doi.org/10.1016/B978-0-12-805052-1.00033-4

  • 40.

    Mumby, P. J. et al. Remote sensing of coral reefs and their physical environment. Mar Polut Bull 48, 219–228 (2004).

    CAS  Article  Google Scholar 

  • 41.

    Hayes, R. & Goreau, T. Satellite-derived sea surface temperature from Caribbean and Atlantic coral reef sites, 1984–2003. Rev. Biol. Trop. 56, 97–118 (2008).

    Google Scholar 

  • 42.

    Sugara, A. A., Siregar, V. P. V. & Agus, S. B. S. Classification of benthic habitat of shallow water using worldview-2 image with in-situ and drone data. Jurnal Ilmu dan Teknologi Kelautan Tropis 12, 135–150 (2020).

    Article  Google Scholar 

  • 43.

    Murfitt, S. L. et al. Applications of unmanned aerial vehicles in intertidal reef monitoring. Sci. Rep. https://doi.org/10.1038/s41598-017-10818-9 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  • 44.

    Kaplanis, N. J., Edwards, C. B., Eynaud, Y. & Smith, J. E. Future sea-level rise drives rocky intertidal habitat loss and benthic community change. PeerJ 8, e9186–e9221 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  • 45.

    Chatzinikolaou, E. Use and limitations of ecological models. Transit. Waters Bull. 6, 34–41 (2012).

    Google Scholar 

  • 46.

    de Carneiro, L. R. A., Lima, A. P., Machado, R. B. & Magnusson, W. E. Limitations to the use of species-distribution models for environmental-impact assessments in the Amazon. PLoS ONE 11, e0146543 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 47.

    van der Wal, D., van Dalen, J., Dool, den, A. W.-V., Dijkstra, J. T. & Ysebaert, T. Biophysical control of intertidal benthic macroalgae revealed by high-frequency multispectral camera images. J. Sea Res. 90, 111–120 (2014).

  • 48.

    Goldberg, J. & Wilkinson, C. in Status of coral reefs of the World (ed. Wilkinson, C.) 1, 67–92 (Status of coral reefs of the World, 2004).

  • 49.

    Fabry, V. J., Seibel, B. A. & Feely, R. A. Impacts of ocean acidification on marine fauna and ecosystem processes. ICES J. Mar. Sci. 65, 414–432 (2008).

    CAS  Article  Google Scholar 

  • 50.

    Radeta, M. et al. in Human-Computer Interaction—INTERACT 2019, vol. 11748, 237–248 (Springer, Cham, 2019).

  • 51.

    Rusu, E. & Guedes Soares, C. Wave energy pattern around the Madeira Islands. Energy 45, 771–785 (2012).

    Article  Google Scholar 

  • 52.

    Pullen, J., Caldeira, R., Doyle, J. D., May, P. & Tomé, R. Modeling the airsea feedback system of Madeira Island. J. Adv. Model. Earth Syst. 9, 1641–1664 (2017).

    ADS  Article  Google Scholar 

  • 53.

    Kahng, S. E. et al. Community ecology of mesophotic coral reef ecosystems. Coral Reefs 29, 255–275 (2010).

    Article  Google Scholar 

  • 54.

    Earth Systems Research Institute (ESRI). ArcGIS Desktop: Release 10 (2011).

  • 55.

    Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogram. Remote Sens. 65, 2–16 (2010).

    ADS  Article  Google Scholar 

  • 56.

    Darwish, A., Leukert, K. & Reinhardt, W. Image segmentation for the purpose of object-based classification. in 3, 2039–2041 (IEEE, 2003).

  • 57.

    Qian, Y., Zhou, W., Yan, J., Li, W. & Han, L. Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sens. 7, 153–168 (2015).

    ADS  Article  Google Scholar 

  • 58.

    Masi, B., Macedo, I. & Zalmon, I. Benthic community zonation in a breakwater on the North Coast of the State of Rio de Janeiro, Brazil. Braz. Arch. Biol. Technol. 52, 637–646 (2009).

    Article  Google Scholar 

  • 59.

    Sangil, C. et al. Shallow subtidal macroalgae in the North-eastern Atlantic archipelagos (Macaronesian region): A spatial approach to community structure. Eur. J. Phycol. 00, 1–16 (2018).

    Google Scholar 

  • 60.

    Su, T.-C. & Chou, H.-T. Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: A case study of Tain-Pu Reservoir in Kinmen, Taiwan. Remote Sens. 7, 10078–10097 (2015).

    ADS  Article  Google Scholar 

  • 61.

    Kohler, K. & Gill, S. Coral Point Count with Excel Extensions (CPCe): A Visual Basic Program for the determination of coral and substrate coverage using random point count methodology. Comput. Geosci. 32, 1259–1269 (2006).

    ADS  Article  Google Scholar 

  • 62.

    Clarke, K. R. & Gorley, R. N. Getting started with PRIMER V7 (PRIMER-E, Plymouth, 2015).

    Google Scholar 

  • 63.

    Berman, J. & Bell, J. J. Spatial Variability of Sponge Assemblages on the Wellington South Coast, New Zealand. Open Mar. Biol. J. 4, 12–25 (2010). https://doi.org/10.2174/1874450801004010012.

  • 64.

    Rawson, C. A. et al. Benthic macroinvertebrate assemblages in remediated wetlands around Sydney, Australia. Ecotoxicology 19, 1589–1600 (2010).

    CAS  PubMed  Article  Google Scholar 

  • 65.

    Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA for PRIMER: a guide to software and statistical methods. (PRIMER-E Ltd, 2008).


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