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Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery

  • Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).

    Article 
    ADS 

    Google Scholar 

  • Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).

    Article 

    Google Scholar 

  • Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).

    Article 

    Google Scholar 

  • Assis, J., Serrão, E. A., Duarte, C. M., Fragkopoulou, E. & Krause-Jensen, D. Major expansion of marine forests in a warmer Arctic. Front. Mar. Sci. 9, 850368 (2022).

    Article 

    Google Scholar 

  • Assis, J. et al. Major shifts at the range edge of marine forests: The combined effects of climate changes and limited dispersal. Sci. Rep. 7(44348), 1–10 (2017).

    CAS 

    Google Scholar 

  • O’Leary, J. K. et al. The resilience of marine ecosystems to climatic disturbances. BioScience. https://doi.org/10.1093/biosci/biw161 (2017).

    Article 

    Google Scholar 

  • Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).

    Article 

    Google Scholar 

  • Filbee-Dexter, K. & Scheibling, R. E. Detrital kelp subsidy supports high reproductive condition of deep-living sea urchins in a sedimentary basin. Aquat. Biol. 23, 71–86 (2014).

    Article 

    Google Scholar 

  • Filbee-Dexter, K. Ocean forests hold unique solutions to our current environmental crisis. One Earth https://doi.org/10.1016/j.oneear.2020.05.004 (2020).

    Article 

    Google Scholar 

  • Krumhansl, K. A. & Scheibling, R. E. Production and fate of kelp detritus. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps09940 (2012).

    Article 

    Google Scholar 

  • Edwards, M. S. & Hernández-Carmona, G. Delayed recovery of giant kelp near its southern range limit in the North Pacific following El Niño. Mar. Biol. 147, 273–279 (2005).

    Article 

    Google Scholar 

  • Cavanaugh, K. C., Reed, D. C., Bell, T. W., Castorani, M. C. N. & Beas-Luna, R. Spatial variability in the resistance and resilience of giant kelp in southern and Baja California to a multiyear heatwave. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00413 (2019).

    Article 

    Google Scholar 

  • Butler, C. L., Lucieer, V. L., Wotherspoon, S. J. & Johnson, C. R. Multi-decadal decline in cover of giant kelp Macrocystis pyrifera at the southern limit of its Australian range. Mar. Ecol. Prog. Ser. 653, 1–18 (2020).

    Article 
    ADS 

    Google Scholar 

  • Martínez, B. et al. Distribution models predict large contractions of habitat-forming seaweeds in response to ocean warming. Divers. Distrib. 24, 1350–1366 (2018).

    Article 

    Google Scholar 

  • Bell, T. W., Allen, J. G., Cavanaugh, K. C. & Siegel, D. A. Three decades of variability in California’s giant kelp forests from the Landsat satellites. Remote Sens. Environ. 238, 110811 (2020).

    Article 
    ADS 

    Google Scholar 

  • Mann, M. E. & Emanuel, K. A. Atlantic Hurricane trends linked to climate change. Eos 87, 233–241 (2006).

    Article 
    ADS 

    Google Scholar 

  • Jensen, J. R., Estes, J. E. & Tinney, L. Remote sensing techniques for kelp surveys. Photogramm. Eng Remote Sens. 46, 743–755 (1980).

    Google Scholar 

  • Cavanaugh, K. C. et al. A review of the opportunities and challenges for using remote sensing for management of surface-canopy forming kelps. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.753531 (2021).

    Article 

    Google Scholar 

  • Cavanaugh, K. C., Siegel, D. A., Reed, D. C. & Dennison, P. E. Environmental controls of giant-kelp biomass in the Santa Barbara Channel, California. Mar. Ecol. Prog. Ser. 429, 1–17 (2011).

    Article 
    ADS 

    Google Scholar 

  • Kadhim, M. A. & Abed, M. H. Convolutional neural network for satellite image classification. Stud. Comput. Intell. 830, 165–178 (2020).

    Article 

    Google Scholar 

  • Segal-Rozenhaimer, M., Li, A., Das, K. & Chirayath, V. Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN). Remote Sens. Environ. 237, 111446 (2020).

    Article 
    ADS 

    Google Scholar 

  • Canonico, G. et al. Global observational needs and resources for marine biodiversity. Front. Mar. Sci. 6, 367 (2019).

    Article 

    Google Scholar 

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Yu, L. & Gong, P. Google Earth as a virtual globe tool for Earth science applications at the global scale: Progress and perspectives. Int. J. Remote Sens. 33, 3966–3986 (2012).

    Article 

    Google Scholar 

  • Guirado, E., Tabik, S., Rivas, M. L., Alcaraz-Segura, D. & Herrera, F. Whale counting in satellite and aerial images with deep learning. Sci. Rep. 9, 14259 (2019).

    Article 
    ADS 

    Google Scholar 

  • Borowicz, A. et al. Aerial-trained deep learning networks for surveying cetaceans from satellite imagery. PLoS ONE 14, 1–15 (2019).

    Article 

    Google Scholar 

  • Lorencin, I., Anđelić, N., Mrzljak, V. & Car, Z. Marine objects recognition using convolutional neural networks. Nase More 66, 112–119 (2019).

    Article 

    Google Scholar 

  • Ridge, J. T., Gray, P. C., Windle, A. E. & Johnston, D. W. Deep learning for coastal resource conservation: Automating detection of shellfish reefs. Remote Sens. Ecol. Conserv. 6, 431–440 (2020).

    Article 

    Google Scholar 

  • Wang, Y. et al. Machine learning-based ship detection and tracking using satellite images for maritime surveillance. J. Ambient Intell. Smart Environ. 13, 361–371 (2021).

    Article 

    Google Scholar 

  • Han, Q., Yin, Q., Zheng, X. & Chen, Z. Remote sensing image building detection method based on Mask R-CNN. Complex Intell. Syst. https://doi.org/10.1007/s40747-021-00322-z (2021).

    Article 

    Google Scholar 

  • Girshick, R. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision (ICCV) 1440–1448. https://doi.org/10.1109/ICCV.2015.169 (2015).

  • Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal. Mach. Intell. 39, 28 (2017).

    Article 

    Google Scholar 

  • Shelhamer, E., Long, J. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 3431–3440 (2017).

    Article 

    Google Scholar 

  • He, K., Gkioxari, G., Dollár, P. & Girshick, R. Mask R-CNN. In Proceedings of the IEEE international Conference on Computer Vision (2017).

  • Arafeh-Dalmau, N. et al. Extreme Marine Heatwaves alter kelp forest community near its equatorward distribution limit. Front. Mar. Sci. 6, 1–18 (2019).

    Article 
    ADS 

    Google Scholar 

  • Nie, X., Duan, M., Ding, H., Hu, B. & Wong, E. K. Attention Mask R-CNN for ship detection and segmentation from remote sensing images. IEEE Access 8, 9325–9334 (2020).

    Article 

    Google Scholar 

  • Abdulla, W. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. GitHub Repository (2017).

  • Fragkopoulou, E. et al. Global biodiversity patterns of marine forests of brown macroalgae. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13450 (2022).

    Article 

    Google Scholar 

  • Markham, B. L., Storey, J. C., Williams, D. L. & Irons, J. R. Landsat sensor performance: History and current status. IEEE Trans. Geosci. Remote Sens. https://doi.org/10.1109/TGRS.2004.840720 (2004).

    Article 

    Google Scholar 

  • Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article 
    ADS 

    Google Scholar 

  • Aghamohamadnia, M. & Abedini, A. A morphology-stitching method to improve Landsat SLC-off images with stripes. Geodesy Geodyn. 5, 27–33 (2014).

    Article 

    Google Scholar 

  • Houskeeper, H. F. et al. Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas). PLoS ONE 17, e0257933 (2022).

    Article 
    CAS 

    Google Scholar 

  • Mantha, K. B. et al. From Fat Droplets to Floating Forests: Cross-Domain Transfer Learning Using a PatchGAN-Based Segmentation Model (2022).

  • Finger, D. J. I., McPherson, M. L., Houskeeper, H. F. & Kudela, R. M. Mapping bull kelp canopy in northern California using Landsat to enable long-term monitoring. Remote Sens. Environ. 254, 112243 (2021).

    Article 
    ADS 

    Google Scholar 

  • Siegel, D. A., Wang, M., Maritorena, S. & Robinson, W. Atmospheric correction of satellite ocean color imagery: The black pixel assumption. Appl. Opt. 39, 3582–3591 (2000).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Loisel, H., Nicolas, J. M., Sciandra, A., Stramski, D. & Poteau, A. Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean. J. Geophys. Res. Oceans https://doi.org/10.1029/2005JC003367 (2006).

    Article 

    Google Scholar 

  • Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Dutta, A. & Zisserman, A. The VIA annotation software for images, audio and video. In MM 2019: Proceedings of the 27th ACM International Conference on Multimedia. https://doi.org/10.1145/3343031.3350535 (2019).

  • Pfister, C. A., Berry, H. D. & Mumford, T. The dynamics of Kelp Forests in the Northeast Pacific Ocean and the relationship with environmental drivers. J. Ecol. 106, 1520–1533 (2018).

    Article 

    Google Scholar 

  • Cavanaugh, K. C., Cavanaugh, K. C., Bell, T. W. & Hockridge, E. G. An automated method for mapping giant kelp canopy dynamics from UAV. Front. Environ. Sci. 8, 587354 (2021).

    Article 

    Google Scholar 

  • Castorani, M. C. N. et al. Connectivity structures local population dynamics: A long-term empirical test in a large metapopulation system. Ecology 96, 3141–3152 (2015).

    Article 

    Google Scholar 

  • Irmak, E. Implementation of convolutional neural network approach for COVID-19 disease detection. Physiol. Genom. 52, 590–601 (2020).

    Article 
    CAS 

    Google Scholar 

  • Assis, J., Araújo, M. B. & Serrão, E. A. Projected climate changes threaten ancient refugia of kelp forests in the North Atlantic. Glob. Change Biol. 24, 1365–2486 (2017).

    Google Scholar 

  • Cao, C. et al. An improved faster R-CNN for small object detection. IEEE Access 7, 106838–106846 (2019).

    Article 

    Google Scholar 

  • Konar, J., Khandelwal, P. & Tripathi, R. Comparison of various learning rate scheduling techniques on convolutional neural network. In 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science, SCEECS 2020. https://doi.org/10.1109/SCEECS48394.2020.94 (2020).

  • LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).

    Article 

    Google Scholar 

  • Johnson, J. W. Automatic nucleus segmentation with mask-RCNN. Adv. Intell. Syst. Comput. 944, 399–407 (2020).

    Google Scholar 

  • Lin, T. Y. et al. Microsoft COCO: Common objects in context. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8693 LNCS (2014).

  • McKnight, P. E. & Najab, J. Mann-Whitney U Test. Corsini Encycl. Psychol. https://doi.org/10.1002/9780470479216.corpsy0524 (2010).

    Article 

    Google Scholar 

  • R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

    Google Scholar 

  • Haklay, M. & Weber, P. OpenStreet map: User-generated street maps. IEEE Pervasive Comput. 7, 12–18 (2008).

    Article 

    Google Scholar 

  • Wäldchen, J. & Mäder, P. Machine learning for image based species identification. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13075 (2018).

    Article 
    MATH 

    Google Scholar 

  • Weinstein, B. G. A computer vision for animal ecology. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.12780 (2018).

    Article 

    Google Scholar 

  • Chilson, C. et al. Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks. Remote Sens. Ecol. Conserv. 5, 20–32 (2019).

    Article 

    Google Scholar 

  • O’Gara, S. & McGuinness, K. Comparing data augmentation strategies for deep image classification. Ir. Mach. Vis. Image Process. Conf. https://doi.org/10.21427/148b-ar75 (2019).

    Article 

    Google Scholar 

  • Li, W., Chen, C., Zhang, M., Li, H. & Du, Q. Data augmentation for hyperspectral image classification with deep CNN. IEEE Geosci. Remote Sens. Lett. 16, 593–597 (2019).

    Article 
    ADS 

    Google Scholar 

  • Bharati, P. & Pramanik, A. Deep learning techniques—R-CNN to Mask R-CNN: A survey. In Computational Intelligence in Pattern Recognition (eds Das, A. K. et al.) 657–668 (Springer, 2020).

    Chapter 

    Google Scholar 

  • Li, A. S., Chirayath, V., Segal-Rozenhaimer, M., Torres-Perez, J. L. & van den Bergh, J. NASA NeMO-Net’s convolutional neural network: Mapping marine habitats with spectrally heterogeneous remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 5115–5133 (2020).

    Article 
    ADS 

    Google Scholar 

  • Hamilton, S. L., Bell, T. W., Watson, J. R., Grorud-Colvert, K. A. & Menge, B. A. Remote sensing: generation of long-term kelp bed data sets for evaluation of impacts of climatic variation. Ecology 101, e03031 (2020).

    Article 

    Google Scholar 

  • Bell, T. W., Cavanaugh, K. C. & Siegel, D. A. Remote monitoring of giant kelp biomass and physiological condition: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission. Remote Sens. Environ. 167, 218–228 (2015).

    Article 
    ADS 

    Google Scholar 

  • Schroeder, S. B., Dupont, C., Boyer, L., Juanes, F. & Costa, M. Passive remote sensing technology for mapping bull kelp (Nereocystis luetkeana): A review of techniques and regional case study. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2019.e00683 (2019).

    Article 

    Google Scholar 

  • Kristollari, V. & Karathanassi, V. Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery. Remote Sens. Geoinf. Environ. https://doi.org/10.1117/12.2571111 (2020).

    Article 

    Google Scholar 

  • Wilson, M. J. & Oreopoulos, L. Enhancing a simple MODIS cloud mask algorithm for the landsat data continuity mission. IEEE Trans. Geosci. Remote Sens. 51, 723–731 (2013).

    Article 
    ADS 

    Google Scholar 

  • Zhuge, X. Y., Zou, X. & Wang, Y. A fast cloud detection algorithm applicable to monitoring and nowcasting of daytime cloud systems. IEEE Trans. Geosci. Remote Sens. 55, 6111–6119 (2017).

    Article 
    ADS 

    Google Scholar 

  • Lin, T. Y. et al. Feature pyramid networks for object detection. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (2017).

  • Jacox, M. G. et al. Impacts of the 2015–2016 El Niño on the California Current System: Early assessment and comparison to past events. Geophys. Res. Lett. https://doi.org/10.1002/2016GL069716 (2016).

    Article 

    Google Scholar 

  • Chavez, F. P. et al. Biological and chemical consequences of the 1997–1998 El Niño in central California waters. Prog. Oceanogr. https://doi.org/10.1016/S0079-6611(02)00050-2 (2002).

    Article 

    Google Scholar 

  • Tegner, M. J. & El Dayton, P. K. Niño effects on Southern California kelp forest communities. Adv. Ecol. Res. 17, 243–279 (1987).

    Article 

    Google Scholar 

  • Bartsch, I. et al. Changes in kelp forest biomass and depth distribution in Kongsfjorden, Svalbard, between 1996–1998 and 2012–2014 reflect Arctic warming. Polar Biol. 39, 2021–2036 (2016).

    Article 

    Google Scholar 

  • Simonson, E. J., Scheibling, R. E. & Metaxas, A. Kelp in hot water: I. Warming seawater temperature induces weakening and loss of kelp tissue. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps11438 (2015).

    Article 

    Google Scholar 

  • Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00734 (2019).

    Article 

    Google Scholar 


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