{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T10:58:13Z","timestamp":1791457093435,"version":"4.3.4"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T00:00:00Z","timestamp":1788566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T00:00:00Z","timestamp":1788566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000040","name":"Agriculture and Agri-Food Canada","doi-asserted-by":"publisher","award":["J-002490"],"award-info":[{"award-number":["J-002490"]}],"id":[{"id":"10.13039\/501100000040","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004489","name":"Mitacs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004489","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Monit Assess"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Vegetated riparian buffers play a critical role in maintaining ecological health and water quality, yet efficient characterization and large-scale monitoring remain challenging due to the resource demands of traditional field campaigns. This study, conducted in an agricultural setting, introduces a straightforward, image-based methodology for riparian buffer characterization, exploiting advancements in deep convolutional neural networks (DCNN) and very high spatial resolution satellite imagery. Leveraging a large Riparian Strip Quality Index (RSQI) field dataset, the proposed approach adapts a Multi-View DCNN (MVDCNN) architecture, originally developed for 3D object recognition, to correlate satellite images of riparian strips with RSQI metrics. Of the seven spectral band combinations, multiple input views, and two training modes evaluated, the configuration using four views with RGB bands from a pretrained network achieved the best results. However, the alternative spectral band combinations produced similar levels of performance, suggesting that texture and shape information are key factors in the model\u2019s effectiveness. Comparisons with a conventional workflow involving object-based land cover classification followed by RSQI calculation indicate that the trained MVDCNN achieves stronger correlations between imagery and RSQI scores (average RMSE\u2009=\u20097.35, R\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.93 using RGB bands) compared to the object-based method (RMSE\u2009=\u200911.25, R\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.87). To our knowledge, this is the first direct application of DCNNs to riparian buffer quality assessment. Requiring minimal preprocessing and no photo-interpretation expertise, the proposed approach leverages existing field data to facilitate more accessible, scalable, and adaptable riparian buffer monitoring, with potential for application in diverse environmental contexts.\n                  <\/jats:p>","DOI":"10.1007\/s10661-026-15760-w","type":"journal-article","created":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T15:12:02Z","timestamp":1788621122000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A CNN-based approach to riparian buffer strip quality monitoring in agricultural areas using satellite imagery"],"prefix":"10.1007","volume":"198","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7244-5469","authenticated-orcid":false,"given":"Samuel","family":"de la Sablonni\u00e8re","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Foucher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yacine","family":"Bouroubi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Vigneault","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Etienne","family":"Lord","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,5]]},"reference":[{"issue":"1","key":"15760_CR1","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1111\/j.1745-7939.2010.01168.x","volume":"66","author":"S Ashraf","year":"2010","unstructured":"Ashraf, S., Brabyn, L., Hicks, B. J., & Collier, K. (2010). Satellite remote sensing for mapping vegetation in New Zealand freshwater environments: A review. New Zealand Geographer, 66(1), 33\u201343. https:\/\/doi.org\/10.1111\/j.1745-7939.2010.01168.x","journal-title":"New Zealand Geographer"},{"issue":"11","key":"15760_CR2","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1002\/esp.2166","volume":"36","author":"W Bertoldi","year":"2011","unstructured":"Bertoldi, W., Drake, N. A., & Gurnell, A. M. (2011). Interactions between river flows and colonizing vegetation on a braided river: Exploring spatial and temporal dynamics in riparian vegetation cover using satellite data. Earth Surface Processes and Landforms, 36(11), 1474\u20131486. https:\/\/doi.org\/10.1002\/esp.2166","journal-title":"Earth Surface Processes and Landforms"},{"issue":"3","key":"15760_CR3","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1890\/1051-0761(1998)008[0559:NPOSWW]2.0.CO;2","volume":"8","author":"SR Carpenter","year":"1998","unstructured":"Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., & Smith, V. H. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological Applications, 8(3), 559\u2013568. https:\/\/doi.org\/10.1890\/1051-0761(1998)008[0559:NPOSWW]2.0.CO;2","journal-title":"Ecological Applications"},{"key":"15760_CR4","doi-asserted-by":"publisher","unstructured":"Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014). Return of the devil in the details: Delving deep into convolutional nets. In BMVC 2014 - Proceedings of the British Machine Vision Conference 2014. https:\/\/doi.org\/10.5244\/c.28.6","DOI":"10.5244\/c.28.6"},{"issue":"1","key":"15760_CR5","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1111\/j.1526-100X.2011.00859.x","volume":"21","author":"KE Collins","year":"2013","unstructured":"Collins, K. E., Doscher, C., Rennie, H. G., & Ross, J. G. (2013). The effectiveness of Riparian \u2018Restoration\u2019 on water quality\u2014A case study of lowland streams in Canterbury, New Zealand. Restoration Ecology, 21(1), 40\u201348. https:\/\/doi.org\/10.1111\/j.1526-100X.2011.00859.x","journal-title":"Restoration Ecology"},{"key":"15760_CR6","unstructured":"Covabar. (2015). Rapport de Caract\u00e9risation Du Bassin Versant de La Rivi\u00e8re l\u2019Acadie\u202f: Secteur Napierville (Rapport de Caract\u00e9risation Du Bassin Versant de La Rivi\u00e8re Richelieu, Issue."},{"issue":"1","key":"15760_CR7","doi-asserted-by":"publisher","first-page":"4155","DOI":"10.1038\/s41467-026-70191-y","volume":"17","author":"RB Dala-Corte","year":"2026","unstructured":"Dala-Corte, R. B., Giam, X., & Wilcove, D. S. (2026). Assessing the effectiveness of riparian buffers in protecting biodiversity: A meta-analysis. Nature Communications, 17(1), 4155. https:\/\/doi.org\/10.1038\/s41467-026-70191-y","journal-title":"Nature Communications"},{"issue":"2","key":"15760_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/eco.1928","volume":"11","author":"LL de Sosa","year":"2018","unstructured":"de Sosa, L. L., Glanville, H. C., Marshall, M. R., Abood, S. A., Williams, A. P., & Jones, D. L. (2018a). Delineating and mapping riparian areas for ecosystem service assessment. Ecohydrology, 11(2), Article e1928. https:\/\/doi.org\/10.1002\/eco.1928","journal-title":"Ecohydrology"},{"key":"15760_CR9","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.scitotenv.2017.12.179","volume":"624","author":"LL de Sosa","year":"2018","unstructured":"de Sosa, L. L., Glanville, H. C., Marshall, M. R., Prysor Williams, A., & Jones, D. L. (2018b). Quantifying the contribution of riparian soils to the provision of ecosystem services. Science of the Total Environment, 624, 807\u2013819. https:\/\/doi.org\/10.1016\/j.scitotenv.2017.12.179","journal-title":"Science of the Total Environment"},{"issue":"2","key":"15760_CR10","doi-asserted-by":"publisher","first-page":"235","DOI":"10.23818\/limn.30.18","volume":"30","author":"MG Del T\u00e1nago","year":"2011","unstructured":"Del T\u00e1nago, M. G., & De Jal\u00f3n, D. G. (2011). Riparian quality index (RQI): A methodology for characterising and assessing the environmental conditions of riparian zones. Limnetica, 30(2), 235\u2013254. https:\/\/doi.org\/10.23818\/limn.30.18","journal-title":"Limnetica"},{"key":"15760_CR11","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 (pp. 248\u2013255). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"15760_CR12","unstructured":"Doyon, N. (2016). La bande riveraine. COBALI (Corporation du bassin versant de la rivi\u00e8re du Li\u00e8vre). Retrieved 2025\/01\/07 from https:\/\/www.cobali.org\/wp-content\/uploads\/2016\/11\/La-bande-riveraine.pdf"},{"key":"15760_CR13","doi-asserted-by":"publisher","unstructured":"Dufour, S., Bernez, I., Betbeder, J., Corgne, S., Hubert-Moy, L., Nabucet, J., Rapinel, S., Sawtschuk, J., & Troll\u00e9, C. (2013). Monitoring restored riparian vegetation: How can recent developments in remote sensing sciences help? Knowledge and Management of Aquatic Ecosystems, (410), Article 2. https:\/\/doi.org\/10.1051\/kmae\/2013068","DOI":"10.1051\/kmae\/2013068"},{"issue":"1","key":"15760_CR14","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1), 98\u2013136. https:\/\/doi.org\/10.1007\/s11263-014-0733-5","journal-title":"International Journal of Computer Vision"},{"key":"15760_CR15","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.ecolind.2014.02.008","volume":"41","author":"D Fern\u00e1ndez","year":"2014","unstructured":"Fern\u00e1ndez, D., Barqu\u00edn, J., \u00c1lvarez-Cabria, M., & Pe\u00f1as, F. J. (2014). Land-use coverage as an indicator of riparian quality. Ecological Indicators, 41, 165\u2013174. https:\/\/doi.org\/10.1016\/j.ecolind.2014.02.008","journal-title":"Ecological Indicators"},{"key":"15760_CR16","unstructured":"Gangbazo, G., Vall\u00e9e, P., \u00c9mond, C., Roy, J., Beaulieu, R., & Gagnon, E. (2006). Contr\u00f4le de la pollution diffuse d\u2019origine agricole : quelques r\u00e9flexions bas\u00e9es sur la mod\u00e9lisation de sc\u00e9narios de pratiques agricoles pour atteindre le crit\u00e8re du phosphore pour la pr\u00e9vention de l\u2019eutrophisation dans la rivi\u00e8re aux Brochets, Qu\u00e9bec [ISBN-10 : 2\u2013550\u201348209\u20133](978\u20132\u2013550\u201348209\u20133). https:\/\/www.environnement.gouv.qc.ca\/eau\/bassinversant\/fiches\/controle-rivbrochets.pdf"},{"key":"15760_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2025.124154","volume":"375","author":"SR Ghimire","year":"2025","unstructured":"Ghimire, S. R., Schumacher, B., Swanson, S., Hall, R., Hall, E. S., Zambrana, J., & Johnston, J. M. (2025). Assessing riparian functioning condition for improved ecosystem services: A case study of the Back Creek watershed (Virginia, USA). Journal of Environmental Management, 375, Article Article 124154. https:\/\/doi.org\/10.1016\/j.jenvman.2025.124154","journal-title":"Journal of Environmental Management"},{"issue":"8","key":"15760_CR18","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1080\/014311698215333","volume":"19","author":"G Gutman","year":"1998","unstructured":"Gutman, G., & Ignatov, A. (1998). The derivation of the green vegetation fraction from NOAA\/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing, 19(8), 1533\u20131543. https:\/\/doi.org\/10.1080\/014311698215333","journal-title":"International Journal of Remote Sensing"},{"key":"15760_CR19","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 770\u2013778). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"15760_CR20","doi-asserted-by":"publisher","first-page":"101604","DOI":"10.1016\/j.rsase.2025.101604","volume":"38","author":"M Hemati","year":"2025","unstructured":"Hemati, M., Mahdianpari, M., Shiri, H., Mohammadimanesh, F., Salehi, B., & Wu, J. (2025). Advancing wetland aboveground biomass mapping: Insights from high-resolution PlanetScope data in the Mississippi River Delta. Remote Sensing Applications: Society and Environment, 38, 101604. https:\/\/doi.org\/10.1016\/j.rsase.2025.101604","journal-title":"Remote Sensing Applications: Society and Environment"},{"key":"15760_CR21","doi-asserted-by":"publisher","first-page":"110652","DOI":"10.1016\/j.jenvman.2020.110652","volume":"267","author":"L Huylenbroeck","year":"2020","unstructured":"Huylenbroeck, L., Laslier, M., Dufour, S., Georges, B., Lejeune, P., & Michez, A. (2020). Using remote sensing to characterize riparian vegetation: A review of available tools and perspectives for managers. Journal of Environmental Management, 267, 110652. https:\/\/doi.org\/10.1016\/j.jenvman.2020.110652","journal-title":"Journal of Environmental Management"},{"issue":"3","key":"15760_CR22","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.ecolind.2008.05.013","volume":"9","author":"E Ivits","year":"2009","unstructured":"Ivits, E., Cherlet, M., Mehl, W., & Sommer, S. (2009). Estimating the ecological status and change of riparian zones in Andalusia assessed by multi-temporal AVHHR datasets. Ecological Indicators, 9(3), 422\u2013431. https:\/\/doi.org\/10.1016\/j.ecolind.2008.05.013","journal-title":"Ecological Indicators"},{"key":"15760_CR23","unstructured":"Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings."},{"key":"15760_CR24","doi-asserted-by":"publisher","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097\u20131105). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"15760_CR25","doi-asserted-by":"publisher","unstructured":"Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778\u2013782, Article 7891032. https:\/\/doi.org\/10.1109\/LGRS.2017.2681128","DOI":"10.1109\/LGRS.2017.2681128"},{"key":"15760_CR26","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.isprsjprs.2016.10.007","volume":"122","author":"M Li","year":"2016","unstructured":"Li, M., Stein, A., Bijker, W., & Zhan, Q. (2016). Urban land use extraction from Very High Resolution remote sensing imagery using a Bayesian network. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 192\u2013205. https:\/\/doi.org\/10.1016\/j.isprsjprs.2016.10.007","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"issue":"19","key":"15760_CR27","doi-asserted-by":"publisher","first-page":"3281","DOI":"10.3390\/rs17193281","volume":"17","author":"H Li","year":"2025","unstructured":"Li, H., Kurusu, H., Suzuki, Y., & Kuwahara, Y. (2025). Development of an optical-radar fusion method for riparian vegetation monitoring and its application to representative rivers in Japan. Remote Sensing, 17(19), 3281. https:\/\/doi.org\/10.3390\/rs17193281","journal-title":"Remote Sensing"},{"key":"15760_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2019.109391","volume":"249","author":"L Lind","year":"2019","unstructured":"Lind, L., Hasselquist, E. M., & Laudon, H. (2019). Towards ecologically functional riparian zones: A meta-analysis to develop guidelines for protecting ecosystem functions and biodiversity in agricultural landscapes. Journal of Environmental Management, 249, Article 109391. https:\/\/doi.org\/10.1016\/j.jenvman.2019.109391","journal-title":"Journal of Environmental Management"},{"key":"15760_CR29","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","volume":"152","author":"L Ma","year":"2019","unstructured":"Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166\u2013177. https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.04.015","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"issue":"3","key":"15760_CR30","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1111\/rec.12425","volume":"25","author":"WW Macfarlane","year":"2017","unstructured":"Macfarlane, W. W., McGinty, C. M., Laub, B. G., & Gifford, S. J. (2017). High-resolution riparian vegetation mapping to prioritize conservation and restoration in an impaired desert river. Restoration Ecology, 25(3), 333\u2013341. https:\/\/doi.org\/10.1111\/rec.12425","journal-title":"Restoration Ecology"},{"issue":"7","key":"15760_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/rs10071119","volume":"10","author":"M Mahdianpari","year":"2018","unstructured":"Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., & Zhang, Y. (2018). Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sensing, 10(7), Article 1119. https:\/\/doi.org\/10.3390\/rs10071119","journal-title":"Remote Sensing"},{"key":"15760_CR32","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.rse.2013.08.002","volume":"139","author":"I Mariotto","year":"2013","unstructured":"Mariotto, I., Thenkabail, P. S., Huete, A., Slonecker, E. T., & Platonov, A. (2013). Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sensing of Environment, 139, 291\u2013305. https:\/\/doi.org\/10.1016\/j.rse.2013.08.002","journal-title":"Remote Sensing of Environment"},{"key":"15760_CR33","doi-asserted-by":"publisher","unstructured":"Marmanis, D., Datcu, M., Esch, T., & Stilla, U. (2016). Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geoscience and Remote Sensing Letters, 13(1), 105\u2013109, Article 7342907. https:\/\/doi.org\/10.1109\/LGRS.2015.2499239","DOI":"10.1109\/LGRS.2015.2499239"},{"key":"15760_CR34","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.jenvman.2017.02.034","volume":"202","author":"A Michez","year":"2017","unstructured":"Michez, A., Pi\u00e9gay, H., Lejeune, P., & Claessens, H. (2017). Multi-temporal monitoring of a regional riparian buffer network (>12,000 km) with LiDAR and photogrammetric point clouds. Journal of Environmental Management, 202, 424\u2013436. https:\/\/doi.org\/10.1016\/j.jenvman.2017.02.034","journal-title":"Journal of Environmental Management"},{"issue":"2","key":"15760_CR35","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.ecolind.2005.03.011","volume":"6","author":"SJ Miller","year":"2006","unstructured":"Miller, S. J., Wardrop, D. H., Mahaney, W. M., & Brooks, R. P. (2006). A plant-based index of biological integrity (IBI) for headwater wetlands in central Pennsylvania. Ecological Indicators, 6(2), 290\u2013312. https:\/\/doi.org\/10.1016\/j.ecolind.2005.03.011","journal-title":"Ecological Indicators"},{"issue":"2","key":"15760_CR36","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1002\/aqc.529","volume":"13","author":"A Munn\u00e9","year":"2003","unstructured":"Munn\u00e9, A., Prat, N., Sol\u00e0, C., Bonada, N., & Rieradevall, M. (2003). A simple field method for assessing the ecological quality of riparian habitat in rivers and streams: QBR index. Aquatic Conservation: Marine and Freshwater Ecosystems, 13(2), 147\u2013163. https:\/\/doi.org\/10.1002\/aqc.529","journal-title":"Aquatic Conservation: Marine and Freshwater Ecosystems"},{"key":"15760_CR37","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1146\/annurev.ecolsys.28.1.621","volume":"28","author":"RJ Naiman","year":"1997","unstructured":"Naiman, R. J., & D\u00e9camps, H. (1997). The ecology of interfaces: Riparian zones. Annual Review of Ecology and Systematics, 28, 621\u2013658. https:\/\/doi.org\/10.1146\/annurev.ecolsys.28.1.621","journal-title":"Annual Review of Ecology and Systematics"},{"key":"15760_CR38","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","volume":"61","author":"K Nogueira","year":"2017","unstructured":"Nogueira, K., Penatti, O. A. B., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, 539\u2013556. https:\/\/doi.org\/10.1016\/j.patcog.2016.07.001","journal-title":"Pattern Recognition"},{"key":"15760_CR39","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1016\/j.scitotenv.2018.07.069","volume":"644","author":"J Novoa","year":"2018","unstructured":"Novoa, J., Chokmani, K., & Lhissou, R. (2018). A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery. Science of the Total Environment, 644, 1439\u20131451. https:\/\/doi.org\/10.1016\/j.scitotenv.2018.07.069","journal-title":"Science of the Total Environment"},{"key":"15760_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2022.109519","volume":"144","author":"G Pace","year":"2022","unstructured":"Pace, G., Guti\u00e9rrez-C\u00e1novas, C., Henriques, R., Carvalho-Santos, C., C\u00e1ssio, F., & Pascoal, C. (2022). Remote sensing indicators to assess riparian vegetation and river ecosystem health. Ecological Indicators, 144, Article 109519. https:\/\/doi.org\/10.1016\/j.ecolind.2022.109519","journal-title":"Ecological Indicators"},{"key":"15760_CR41","doi-asserted-by":"publisher","unstructured":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345\u20131359, Article 5288526. https:\/\/doi.org\/10.1109\/TKDE.2009.191","DOI":"10.1109\/TKDE.2009.191"},{"key":"15760_CR42","doi-asserted-by":"publisher","unstructured":"Penatti, O. A. B., Nogueira, K., & Dos Santos, J. A. (2015). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 44\u201351). https:\/\/doi.org\/10.1109\/CVPRW.2015.7301382","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"15760_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2025.114088","volume":"178","author":"J Petru\u017eelov\u00e1","year":"2025","unstructured":"Petru\u017eelov\u00e1, J., Konig, S., Heurich, M., Kop\u00e1\u010dek, J., Donnov\u00e1, S. D., Pol\u00e1\u0161kov\u00e1, V., Seifert, L., \u0160orfov\u00e1, V., Sroka, P., Straka, M., & Bojkov\u00e1, J. (2025). Assessing macroinvertebrate species composition in forest streams using LiDAR-based vegetation structure variables. Ecological Indicators, 178, Article Article 114088. https:\/\/doi.org\/10.1016\/j.ecolind.2025.114088","journal-title":"Ecological Indicators"},{"issue":"6","key":"15760_CR44","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1111\/j.1752-1688.2011.00571.x","volume":"47","author":"B Poff","year":"2011","unstructured":"Poff, B., Koestner, K. A., Neary, D. G., & Henderson, V. (2011). Threats to riparian ecosystems in Western North America: An analysis of existing literature. Journal of the American Water Resources Association, 47(6), 1241\u20131254. https:\/\/doi.org\/10.1111\/j.1752-1688.2011.00571.x","journal-title":"Journal of the American Water Resources Association"},{"issue":"6","key":"15760_CR45","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1093\/biosci\/biaa041","volume":"70","author":"T Riis","year":"2020","unstructured":"Riis, T., Kelly-Quinn, M., Aguiar, F. C., Manolaki, P., Bruno, D., Bejarano, M. D., Clerici, N., Fernandes, M. R., Franco, J. C., Pettit, N., Portela, A. P., Tammeorg, O., Tammeorg, P., Rodr\u00edguez-Gonz\u00e1lez, P. M., & Dufour, S. (2020). Global overview of ecosystem services provided by riparian vegetation. BioScience, 70(6), 501\u2013514. https:\/\/doi.org\/10.1093\/biosci\/biaa041","journal-title":"BioScience"},{"key":"15760_CR46","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.jenvman.2016.11.036","volume":"202","author":"M Rinaldi","year":"2017","unstructured":"Rinaldi, M., Belletti, B., Bussettini, M., Comiti, F., Golfieri, B., Lastoria, B., Marchese, E., Nardi, L., & Surian, N. (2017). New tools for the hydromorphological assessment and monitoring of European streams. Journal of Environmental Management, 202, 363\u2013378. https:\/\/doi.org\/10.1016\/j.jenvman.2016.11.036","journal-title":"Journal of Environmental Management"},{"issue":"1","key":"15760_CR47","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s10661-022-10667-8","volume":"195","author":"P Rivas-Fandino","year":"2022","unstructured":"Rivas-Fandino, P., Acuna-Alonso, C., Novo, A., Pacheco, F. A. L., & Alvarez, X. (2022). Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: Riverine management in the smallholding. Environmental Monitoring and Assessment, 195(1), 81. https:\/\/doi.org\/10.1007\/s10661-022-10667-8","journal-title":"Environmental Monitoring and Assessment"},{"issue":"3","key":"15760_CR48","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1109\/TGRS.2015.2478379","volume":"54","author":"A Romero","year":"2016","unstructured":"Romero, A., Gatta, C., & Camps-Valls, G. (2016). Unsupervised deep feature extraction for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 54(3), 1349\u20131362. https:\/\/doi.org\/10.1109\/TGRS.2015.2478379","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"2","key":"15760_CR49","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1111\/1365-2664.12201","volume":"51","author":"V Rosset","year":"2014","unstructured":"Rosset, V., Ang\u00e9libert, S., Arthaud, F., Bornette, G., Robin, J., Wezel, A., Vallod, D., & Oertli, B. (2014). Is eutrophication really a major impairment for small waterbody biodiversity? Journal of Applied Ecology, 51(2), 415\u2013425. https:\/\/doi.org\/10.1111\/1365-2664.12201","journal-title":"Journal of Applied Ecology"},{"key":"15760_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2020.106627","volume":"117","author":"D Saha","year":"2020","unstructured":"Saha, D., Das, D., Dasgupta, R., & Patel, P. P. (2020). Application of ecological and aesthetic parameters for riparian quality assessment of a small tropical river in eastern India. Ecological Indicators, 117, Article Article 106627. https:\/\/doi.org\/10.1016\/j.ecolind.2020.106627","journal-title":"Ecological Indicators"},{"key":"15760_CR51","unstructured":"Saint-Jacques, N., & Richard, Y. (1998). D\u00e9veloppement d'un indice de qualit\u00e9 de la bande riveraine: application \u00e0 la rivi\u00e8re Chaudi\u00e8re et mise en relation avec l'int\u00e9grit\u00e9 biotique du milieu aquatique. In minist\u00e8re de l'Environnement et de la Faune (Ed.), Le bassin de la rivi\u00e8re Chaudi\u00e8re: l'\u00e9tat de l'\u00e9cosyst\u00e8me aquatique - 1996 (pp. 6.1\u20136.41). Direction des \u00e9cosyst\u00e8mes aquatiques. https:\/\/www.environnement.gouv.qc.ca\/eau\/eco_aqua\/IQBR\/rapport.pdf"},{"key":"15760_CR52","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L., Qiu, C., & Zhu, X. X. (2019). SEN12MS -- A curated dataset of georeferenced multi-spectral sentinel-1\/2 imagery for deep learning and data fusion. arXiv preprint arXiv:1906.07789. https:\/\/arxiv.org\/abs\/1906.07789","DOI":"10.5194\/isprs-annals-IV-2-W7-153-2019"},{"issue":"10","key":"15760_CR53","first-page":"1157","volume":"65","author":"MJ Schuft","year":"1999","unstructured":"Schuft, M. J., Moser, T. J., Wigington, P. J., Jr., Stevens, D. L., Jr., McAllister, L. S., Chapman, S. S., & Ernst, T. L. (1999). Development of landscape metrics for characterizing riparian-stream networks. Photogrammetric Engineering and Remote Sensing, 65(10), 1157\u20131167.","journal-title":"Photogrammetric Engineering and Remote Sensing"},{"key":"15760_CR54","doi-asserted-by":"publisher","unstructured":"Scott, G. J., England, M. R., Starms, W. A., Marcum, R. A., & Davis, C. H. (2017). Training deep convolutional neural networks for land-cover classification of high-resolution imagery. IEEE Geoscience and Remote Sensing Letters, 14(4), 549\u2013553, Article 7858676. https:\/\/doi.org\/10.1109\/LGRS.2017.2657778","DOI":"10.1109\/LGRS.2017.2657778"},{"issue":"2","key":"15760_CR55","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2134\/jeq2011.0439","volume":"41","author":"MI Stutter","year":"2012","unstructured":"Stutter, M. I., Chardon, W. J., & Kronvang, B. (2012). Riparian buffer strips as a multifunctional management tool in agricultural landscapes: Introduction. Journal of Environmental Quality, 41(2), 297\u2013303. https:\/\/doi.org\/10.2134\/jeq2011.0439","journal-title":"Journal of Environmental Quality"},{"key":"15760_CR56","doi-asserted-by":"publisher","unstructured":"Su, H., Maji, S., Kalogerakis, E., & Learned-Miller, E. (2015). Multi-view convolutional neural networks for 3D shape recognition. In Proceedings of the IEEE International Conference on Computer Vision (pp. 945\u2013953). https:\/\/doi.org\/10.1109\/ICCV.2015.114","DOI":"10.1109\/ICCV.2015.114"},{"issue":"3","key":"15760_CR57","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/MGRS.2021.3089174","volume":"9","author":"G Sumbul","year":"2021","unstructured":"Sumbul, G., De Wall, A., Kreuziger, T., Marcelino, F., Costa, H., Benevides, P., Caetano, M., Demir, B., & Markl, V. (2021). BigEarthNet-MM: A large-scale, multimodal, multilabel benchmark archive for remote sensing image classification and retrieval [software and data sets]. IEEE Geoscience and Remote Sensing Magazine, 9(3), 174\u2013180. https:\/\/doi.org\/10.1109\/MGRS.2021.3089174","journal-title":"IEEE Geoscience and Remote Sensing Magazine"},{"key":"15760_CR58","doi-asserted-by":"publisher","unstructured":"Tan, M., Pang, R., & Le, Q. V. (2020). EfficientDet: Scalable and efficient object detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 10778\u201310787). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"15760_CR59","doi-asserted-by":"publisher","unstructured":"Trang, T. N. T., Policht-Latawiec, A., D\u0105browska, J., & Micha\u0142owska, K. (2025). Current challenges, methods, and strategies for reducing the transfer of nonpoint source pollution from agricultural areas to surface water. Journal of Water and Land Development, 2025-July-September, (66), 187\u2013205. https:\/\/doi.org\/10.24425\/jwld.2025.155314","DOI":"10.24425\/jwld.2025.155314"},{"key":"15760_CR60","unstructured":"Xia, G. S., Yang, W., Delon, J., Gousseau, Y., Sun, H., & Ma\u00eetre, H. (2010). Structural high-resolution satellite image indexing. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (pp. 298\u2013303)."},{"key":"15760_CR61","doi-asserted-by":"publisher","unstructured":"Yang, Y., & Newsam, S. (2010). Bag-of-visual-words and spatial extensions for land-use classification. In GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (pp. 270\u2013279). https:\/\/doi.org\/10.1145\/1869790.1869829","DOI":"10.1145\/1869790.1869829"}],"container-title":["Environmental Monitoring and Assessment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10661-026-15760-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10661-026-15760-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10661-026-15760-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T10:45:57Z","timestamp":1791456357000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10661-026-15760-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,5]]},"references-count":61,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["15760"],"URL":"https:\/\/doi.org\/10.1007\/s10661-026-15760-w","relation":{},"ISSN":["1573-2959"],"issn-type":[{"value":"1573-2959","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,5]]},"assertion":[{"value":"16 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All authors have read, understood, and have complied as applicable with the statement on \u201cEthical responsibilities of Authors\u201d as found in the Instructions for Authors.","order":1,"name":"Ethics","label":"Declarations","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Clinical trial number","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"1042"}}