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Research Article

Enhancing the predictive performance of remote sensing for ecological variables of tidal flats using encoded features from a deep learning model

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Article: 2163048 | Received 15 Jun 2022, Accepted 21 Dec 2022, Published online: 03 Jan 2023

References

  • Adolph, W., H. Farke, S. Lehner, and M. Ehlers. 2018. “Remote Sensing Intertidal Flats with TerraSar-X. A SAR Perspective of the Structural Elements of a Tidal Basin for Monitoring the Wadden Sea.” Remote Sensing 10 (7): 1085. doi:10.3390/rs10071085.
  • Agarap, A. F. 2018. “Deep Learning Using Rectified Linear Units (ReLu).” ArXiv, no. 1: 2–21. http://arxiv.org/abs/1803.08375.
  • Alonso, A. C., D. S. V. M. van Maren, E. P. L. Elias, S. J. Holthuijsen, and Z. B. Wang. 2021. “The Contribution of Sand and Mud to Infilling of Tidal Basins in Response to a Closure Dam.” Marine geology 439 (February): 106544. doi:10.1016/j.margeo.2021.106544.
  • Bakker, W., B. J. Ens, A. Dokter, H. -J. van der Kolk, K. Rappoldt, M. van de Pol, K. Troost, et al. 2021. “Connecting Foraging and Roosting Areas Reveals How Food Stocks Explain Shorebird Numbers.” Estuarine, Coastal and Shelf Science 259 (June): 107458. doi:10.1016/j.ecss.2021.107458.
  • Baldi, P. 2012. “Autoencoders, Unsupervised Learning, and Deep Architectures.” ICML Unsupervised and Transfer Learning 37–50. doi:10.1561/2200000006.
  • Bartholdy, J., and S. Folving. 1986. “Sediment Classification and Surface Type Mapping in the Danish Wadden Sea by Remote Sensing.” Netherlands Journal of Sea Research 20 (4): 337–345. doi:10.1016/0077-7579(86)90001-3.
  • Beukema, J. J. 1976. “Biomass and Species Richness of the Macro-Benthic Animals Living on the Tidal Flats of the Dutch Wadden Sea.” Netherlands Journal of Sea Research 10 (2): 236–261. doi:10.1016/0077-7579(76)90017-X.
  • Bhandare, A., M. Bhide, P. Gokhale, and R. Chandavarkar. 2016. “Applications of Convolutional Neural Networks.” International Journal of Computer Science and Information Technologies 7 (5): 2206–2215. http://ijcsit.com/docs/Volume7/vol7issue5/ijcsit20160705014.pdf.
  • Bijleveld, A. I., R. B. MacCurdy, Y. -C. Chi Chan, E. Penning, R. M. Gabrielson, J. Cluderay, E. L. Spaulding, et al. 2016. “Understanding Spatial Distributions: Negative Density-Dependence in Prey Causes Predators to Trade-Off Prey Quantity with Quality.” Proceedings of the Royal Society B: Biological Sciences 283 (1828): 20151557. doi: 10.1098/rspb.2015.1557.
  • Bijleveld, A. I., J. A. van Gils, J. van der Meer, A. Dekinga, C. Kraan, H. W. van der Veer, and T. Piersma. 2012. “Designing a Benthic Monitoring Programme with Multiple Conflicting Objectives.” Methods in Ecology and Evolution 3 (3): 526–536. doi:10.1111/j.2041-210X.2012.00192.x.
  • Boere, G. C., and T. Piersma. 2012. “Flyway Protection and the Predicament of Our Migrant Birds: A Critical Look at International Conservation Policies and the Dutch Wadden Sea.” Ocean & coastal management 68: 157–168. doi:10.1016/j.ocecoaman.2012.05.019.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1007/978-3-030-62008-0_35.
  • Cao, Z., G. Liu, H. Zhan, L. Chaozheng, Y. You, C. Yang, and H. Jiang. 2016. ”Pore Structure Characterization of Chang-7 Tight Sandstone Using MICP Combined with N 2 GA Techniques and Its Geological Control Factors.” Nature, Vol. 6. Nature Publishing Group. 10.1038/srep36919
  • Chicco, D., M. J. Warrens, and G. Jurman. 2021. “The Coefficient of Determination R-Squared is More Informative Than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation.” PeerJ Computer Science 7: e623. doi:10.7717/PEERJ-CS.623.
  • Compton, T. J., S. Holthuijsen, A. Koolhaas, A. Dekinga, J. ten Horn, J. Smith, Y. Galama, et al. 2013. “Distinctly Variable Mudscapes: Distribution Gradients of Intertidal Macrofauna Across the Dutch Wadden Sea.” Journal of Sea Research 82: 103–116. doi:10.1016/j.seares.2013.02.002.
  • Drent, J., R. Bijkerk, M. Herlyn, M. Grotjahn, J. Voß, M. -C. Carausu, and D. W. Thieltges. 2017. “Wadden Sea Quality Status Report - Alien Species.” Wadden Sea Quality Status Report. qsr.waddensea-worldheritage.org/reports/alien-species.
  • Etter, R. J., and J. Frederick Grassle. 1992. “Patterns of Species Diversity in the Deep Sea as a Function of Sediment Particle Size Diversity.” Nature 360 (6404): 576–578. doi:10.1038/360576a0.
  • Gao, H., Z. Liu, L. Van Der Maaten, and K. Q. Weinberger. 2017. “Densely Connected Convolutional Networks.” In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2261–2269. doi: 10.1109/CVPR.2017.243.
  • Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep Learning. MIT Press. www.deeplearning.org.
  • Guo, W., W. Yang, H. Zhang, and G. Hua. 2018. “Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network.” Remote Sensing 10 (1): 131. doi:10.3390/rs10010131.
  • Hedley, J. D., C. Roelfsema, V. Brando, C. Giardino, T. Kutser, S. Phinn, P. J. Mumby, O. Barrilero, J. Laporte, and B. Koetz. 2018. “Coral Reef Applications of Sentinel-2: Coverage, Characteristics, Bathymetry and Benthic Mapping with Comparison to Landsat 8.” Remote Sensing of Environment 216 (April): 598–614. doi:10.1016/j.rse.2018.07.014.
  • Hollebrandse, F. A. P. 2005. “Temporal Development of the Tidal Range in the Southern North Sea.” Delft University of Technology. https://www.semanticscholar.org/paper/Temporal-development-of-the-tidal-range-in-the-Sea-Hollebrandse/dfb2742f3009b87125d8e9df7d421a9162e9bcfe.
  • Howard, A. G., M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. 2017. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.” Arxiv. http://arxiv.org/abs/1704.04861.
  • Joyce, J. M. 2011. “Kullback-Leibler Divergence.” In International Encyclopedia of Statistical Science, edited by M. Lovric, 720–722. Berlin: Springer. doi:10.1007/978-3-642-04898-2_327.
  • Jung, Y., and J. Jianhua. 2015. “A K -Fold Averaging Cross-Validation Procedure.” Journal of Nonparametric Statistics 27 (2): 167–179. doi:10.1080/10485252.2015.1010532.
  • Junjie, H., Y. Zhang, and T. Okatani. 2019. Visualization of Convolutional Neural Networks for Monocular Depth Estimation, 3869–3878. http://arxiv.org/abs/1904.03380.
  • Kahng, M., N. Thorat, D. Horng Polo Chau, F. B. Viégas, and M. Wattenberg. 2019. “GAN Lab: Understanding Complex Deep Generative Models Using Interactive Visual Experimentation.” IEEE Transactions on Visualization and Computer Graphics 25 (1): 310–320. doi:10.1109/TVCG.2018.2864500.
  • Kaiming, H., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. 10.1109/CVPR.2016.90.
  • Kingma, D. P., and M. Welling. 2014. “Auto-Encoding Variational Bayes.” 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, Banff, AB, Canada, April 14-16.
  • Kloepper, S., R. Strempel, A. Bostelmann et al. 2017. CWSS(2017) Introduction In: Wadden Sea Quality Status Report 2017. Common Wadden Sea Secretariat, Wilhelmshaven, Germany: Wadden Sea Ecosystem. https://qsr.waddensea-worldheritage.org/reports/introduction
  • Lee, S., I. Park, B. J. Joo Koo, J. -H. Hyung Ryu, J. -K. Kuk Choi, and H. J. Jun Woo. 2013. “Macrobenthos Habitat Potential Mapping Using GIS-Based Artificial Neural Network Models.” Marine pollution bulletin 67 (1–2): 177–186. doi:10.1016/j.marpolbul.2012.10.023.
  • Levin, L. A., D. F. Boesch, A. Covich, C. Dahm, C. Erséus, K. C. Ewel, R. T. Kneib, et al. 2001. “The Function of Marine Critical Transition Zones and the Importance of Sediment Biodiversity.” Ecosystems 4 (5): 430–451. doi:10.1007/s10021-001-0021-4.
  • Madhuanand, L., F. Nex, and M. Ying Yang. 2021. “Self-supervised monocular depth estimation from oblique UAV videos.“ ISPRS Journal of Photogrammetry and Remote Sensing 176: 1–14. doi:10.1016/j.isprsjprs.2021.03.024.
  • Mahendran, A., and A. Vedaldi. 2015. “Understanding Deep Image Representations by Inverting Them.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition June 07-12: 5188–5196. doi: 10.1109/CVPR.2015.7299155.
  • Marencic, H. 2009. “Quality Status Report 2009. Wadden Sea Ecosystem No. 25.” http://www.waddensea-secretariat.org/sites/default/files/downloads/qsr-2009.pdf.
  • Menze, B. H., B. M. Kelm, R. Masuch, U. Himmelreich, P. Bachert, W. Petrich, and F. A. Hamprecht. 2009. “A Comparison of Random Forest and Its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data.” BMC Bioinformatics 10 (1): 1–16. doi:10.1186/1471-2105-10-213.
  • Miloslavich, P., N. J. Bax, S. E. Simmons, E. Klein, W. Appeltans, O. Aburto-Oropeza, M. Andersen Garcia, et al. 2018. “Essential Ocean Variables for Global Sustained Observations of Biodiversity and Ecosystem Changes.” Global Change Biology 24 (6): 2416–2433. doi:10.1111/gcb.14108.
  • Murray, N. J., S. R. Phinn, M. DeWitt, R. Ferrari, R. Johnston, M. B. Lyons, N. Clinton, D. Thau, and R. A. Fuller. 2019. ”The Global Distribution and Trajectory of Tidal Flats.” Nature, Vol. 565. Springer US. doi: 10.1038/s41586-018-0805-8.
  • Nguyen, A., J. Yosinski, and J. Clune. 2016. “Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned by Each Neuron in Deep Neural Networks.” http://arxiv.org/abs/1602.03616.
  • Nugroho, H., M. Susanty, A. Irawan, M. Koyimatu, and A. Yunita. 2020. “Fully Convolutional Variational Autoencoder for Feature Extraction of Fire Detection System.” Journal of Computer Science and Information 13 (1): 9. doi:10.21609/jiki.v13i1.761.
  • Olden, J. D., J. J. Lawler, and N. Leroy Poff. 2008. “Machine Learning Methods Without Tears: A Primer for Ecologists.” The Quarterly Review of Biology 83 (2): 171–193. doi:10.1086/587826.
  • Park, W., Y. Kyung Lee, D. Jae Kwon, and J. Sun Won. 2014. “Optical Remote Sensing for Long-Term Changes of Surface Sediments on Tidal Flats: Preliminary Results in the West Coast of Korea.” International Geoscience and Remote Sensing Symposium (IGARSS), 4335–4338. doi:10.1109/IGARSS.2014.6947449.
  • Paszke, A., G. Chanan, Z. Lin, S. Gross, E. Yang, L. Antiga, and Z. Devito. 2017. ”Automatic Differentiation in PyTorch. NIPS 2017 Workshop Autodiff Submission. Dec 4-9, Long Beach Convention Center, Long Beach.” Nips 1–4.
  • Philippart, C. J. M., J. J. Beukema, G. C. Cadée, R. Dekker, P. W. Goedhart, J. M. Van Iperen, M. F. Leopold, and P. M. J. Herman. 2007. “Impacts of Nutrient Reduction on Coastal Communities.” Ecosystems 10 (1): 95–118. doi:10.1007/s10021-006-9006-7.
  • Piersma, T., R. Hoekstra, A. Dekinga, A. Koolhaas, P. Wolf, P. Battley, and P. Wiersma. 1993. “Scale and Intensity of Intertidal Habitat Use by Knots Calidris Canutus in the Western Wadden Sea in Relation to Food, Friends and Foes.” Netherlands Journal of Sea Research 31 (4): 331–357. doi:10.1016/0077-7579(93)90052-T.
  • Puls, W., K. H. van Bernem, D. Eppel, H. Kapitza, A. Pleskachevsky, R. Riethmüller, and B. Vaessen. 2012. “Prediction of Benthic Community Structure from Environmental Variables in a Soft-Sediment Tidal Basin (North Sea).” Helgoland Marine Research 66 (3): 345–361. doi:10.1007/s10152-011-0275-y.
  • Rainey, M. P., A. N. Tyler, R. G. Bryant, D. J. Gilvear, and P. McDonald. 2000. “The Influence of Surface and Interstitial Moisture on the Spectral Characteristics of Intertidal Sediments: Implications for Airborne Image Acquisition and Processing.” International Journal of Remote Sensing 21 (16): 3025–3038. doi:10.1080/01431160050144938.
  • Rammer, W., and R. Seidl. 2019. “Harnessing Deep Learning in Ecology: An Example Predicting Bark Beetle Outbreaks.” Frontiers in plant science 10. doi:10.3389/fpls.2019.01327.
  • Rijkswaterstaat. n.d. “Getij | Rijkswaterstaat.”|“Getij | Rijkswaterstaat.” Accessed November 1, 2021. https://www.rijkswaterstaat.nl/water/waterdata-en-waterberichtgeving/waterdata/getij.
  • Ryu, J. -H., Y. -H. Ho Na, J. -S. Sun Won, and R. Doerffer. 2004. “A Critical Grain Size for Landsat ETM+ Investigations into Intertidal Sediments: A Case Study of the Gomso Tidal Flats, Korea.” Estuarine, Coastal and Shelf Science 60 (3): 491–502. doi:10.1016/j.ecss.2004.02.009.
  • Ryu, J. -H., J. -K. Kuk Choi, and Y. -K. Kyung Lee. 2014. “Potential of Remote Sensing in Management of Tidal Flats: A Case Study of Thematic Mapping in the Korean Tidal Flats.” Ocean & coastal management 102: 458–470. doi:10.1016/j.ocecoaman.2014.03.003.
  • Sehgal, S. 2012. “Remotely Sensed LANDSAT Image Classification Using Neural Network Approaches.” Computer Science 2 (5): 43–46. https://www.semanticscholar.org/paper/Remotely-Sensed-LANDSAT-Image-Classification-Using-Sehgal/ff66711f110389327c7aa3b34b81c664866d8d25.
  • Simonyan, K., A. Vedaldi, and A. Zisserman. 2014. “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.” 2nd International Conference on Learning Representations, ICLR 2014 - Workshop Track Proceedings, April 14-16, Banff, Canada, 1–8.
  • “User Guides - Sentinel-2 MSI.” 2015. ESA-Sentinel 2. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi.
  • Van der Wal, D., and P. M. J. Herman. 2007. “Regression-Based Synergy of Optical, Shortwave Infrared and Microwave Remote Sensing for Monitoring the Grain-Size of Intertidal Sediments.” Remote Sensing of Environment 111 (1): 89–106. doi:10.1016/j.rse.2007.03.019.
  • Van der Wal, D., P. M. J. Herman, R. M. Forster, T. Ysebaert, F. Rossi, E. Knaeps, Y. M. G. Plancke, and S. J. Ides. 2008. “Distribution and Dynamics of Intertidal Macrobenthos Predicted from Remote Sensing: Response to Microphytobenthos and Environment.” Marine Ecology Progress Series 367 (June 2014): 57–72. doi:10.3354/meps07535.
  • Van der Wal, D., P. Herman, and T. Ysebaert. 2004. “Space-Borne Synthetic Aperture Radar of Intertidal Flat Surfaces as a Basis for Predicting Benthic Macrofauna Distribution.” EARSeL EProceedings 3 (1): 69–80.
  • Willcock, S., J. Martínez-López, D. A. P. Hooftman, K. J. Bagstad, S. Balbi, A. Marzo, C. Prato, et al. 2018. “Machine Learning for Ecosystem Services.” Ecosystem Services 33: 165–174. doi:10.1016/j.ecoser.2018.04.004.
  • Yates, M. G., A. R. Jones, S. McGrorty, and J. D. Goss-Custard. 1993. “The Use of Satellite Imagery to Determine the Distribution of Intertidal Surface Sediments of the Wash, England.” Estuarine, Coastal and Shelf Science 36 (4): 333–344. doi: 10.1006/ecss.1993.1020.
  • Yosinski, J., J. Clune, Y. Bengio, and H. Lipson. 2014. “How Transferable are Features in Deep Neural Networks?” Advances in Neural Information Processing Systems 4 (Jan): 3320–3328.
  • Zeiler, M. D., and R. Fergus. 2014. “Visualizing and Understanding Convolutional Networks.” In ECCV, 8689 LNCS:818–833. doi:10.1007/978-3-319-10590-1_53.
  • Zhang, G., M. Wang, and K. Liu. 2019. “Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China.” International Journal of Disaster Risk Science 10 (3): 386–403. doi:10.1007/s13753-019-00233-1.
  • Zwarts, L., and J. H. Wanink. 1993. “How the Food Supply Harvestable by Waders in the Wadden Sea Depends on the Variation in Energy Density, Body Weight, Biomass, Burying Depth and Behaviour of Tidal-Flat Invertebrates.” Netherlands Journal of Sea Research 31 (4): 441–476. doi:10.1016/0077-7579(93)90059-2.