{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:41:47Z","timestamp":1770288107136,"version":"3.49.0"},"reference-count":93,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017437","name":"NASA Headquarters","doi-asserted-by":"publisher","award":["Future Investigators in Earth and Space Science Technology"],"award-info":[{"award-number":["Future Investigators in Earth and Space Science Technology"]}],"id":[{"id":"10.13039\/100017437","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005618","name":"North Carolina Space Grant","doi-asserted-by":"publisher","award":["Graduate Research Fellowship"],"award-info":[{"award-number":["Graduate Research Fellowship"]}],"id":[{"id":"10.13039\/100005618","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004318","name":"Microsoft","doi-asserted-by":"publisher","award":["AI for Earth Program"],"award-info":[{"award-number":["AI for Earth Program"]}],"id":[{"id":"10.13039\/100004318","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ability to accurately classify land cover in periods before appropriate training and validation data exist is a critical step towards understanding subtle long-term impacts of climate change. These trends cannot be properly understood and distinguished from individual disturbance events or decadal cycles using only a decade or less of data. Understanding these long-term changes in low lying coastal areas, home to a huge proportion of the global population, is of particular importance. Relatively simple deep learning models that extract representative spatiotemporal patterns can lead to major improvements in temporal generalizability. To provide insight into major changes in low lying coastal areas, our study (1) developed a recurrent convolutional neural network that incorporates spectral, spatial, and temporal contexts for predicting land cover class, (2) evaluated this model across time and space and compared this model to conventional Random Forest and Support Vector Machine methods as well as other deep learning approaches, and (3) applied this model to classify land cover across 20 years of Landsat 5 data in the low-lying coastal plain of North Carolina, USA. We observed striking changes related to sea level rise that support evidence on a smaller scale of agricultural land and forests transitioning into wetlands and \u201cghost forests\u201d. This work demonstrates that recurrent convolutional neural networks should be considered when a model is needed that can generalize across time and that they can help uncover important trends necessary for understanding and responding to climate change in vulnerable coastal regions.<\/jats:p>","DOI":"10.3390\/rs13193953","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Temporally Generalizable Land Cover Classification: A Recurrent Convolutional Neural Network Unveils Major Coastal Change through Time"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8997-5255","authenticated-orcid":false,"given":"Patrick Clifton","family":"Gray","sequence":"first","affiliation":[{"name":"Duke University Marine Laboratory, Nicholas School of the Environment, Duke University, Beaufort, NC 28516, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6675-6025","authenticated-orcid":false,"given":"Diego F.","family":"Chamorro","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9744-4588","authenticated-orcid":false,"given":"Justin T.","family":"Ridge","sequence":"additional","affiliation":[{"name":"Duke University Marine Laboratory, Nicholas School of the Environment, Duke University, Beaufort, NC 28516, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3259-7759","authenticated-orcid":false,"given":"Hannah Rae","family":"Kerner","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1459-655X","authenticated-orcid":false,"given":"Emily A.","family":"Ury","sequence":"additional","affiliation":[{"name":"Department of Biology, Duke University, Durham, NC 27708, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2424-036X","authenticated-orcid":false,"given":"David W.","family":"Johnston","sequence":"additional","affiliation":[{"name":"Duke University Marine Laboratory, Nicholas School of the Environment, Duke University, Beaufort, NC 28516, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111233","DOI":"10.1016\/j.rse.2019.111233","article-title":"Remote sensing of forest die-off in the Anthropocene: From plant ecophysiology to canopy structure","volume":"231","author":"Huang","year":"2019","journal-title":"Remote Sens. 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