{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:47:25Z","timestamp":1765486045631,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2016,8,17]],"date-time":"2016-08-17T00:00:00Z","timestamp":1471392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Special Issue (SI) on \u201cRemote Sensing in Coastal Environments\u201d presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp habitats. The SI is comprised of twenty-one papers, covering a broad range of research topics that employ remote sensing imagery, models, and techniques to monitor water quality, vegetation, habitat suitability, and geomorphology in the coastal zone. This preface provides a brief summary of each article published in the SI.<\/jats:p>","DOI":"10.3390\/rs8080665","type":"journal-article","created":{"date-parts":[[2016,8,17]],"date-time":"2016-08-17T10:23:21Z","timestamp":1471429401000},"page":"665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Preface: Remote Sensing in Coastal Environments"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8192-7681","authenticated-orcid":false,"given":"Deepak","family":"Mishra","sequence":"first","affiliation":[{"name":"Center for Geospatial Research, Department of Geography, University of Georgia, 210 Field Street, Room 204, Athens, GA 30602, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5149-048X","authenticated-orcid":false,"given":"Richard","family":"Gould","sequence":"additional","affiliation":[{"name":"Bio-Optical\/Physical Processes and Remote Sensing Section, Naval Research Laboratory Code 7331, Building 1009, Stennis Space Center, MS 39529, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"El-habashi, A., Ioannou, I., Tomlinson, M., Stumpf, R., and Ahmed, S. (2016). Satellite retrievals of Karenia brevis harmful algal blooms in the West Florida Shelf using neural networks and comparisons with other techniques. Remote Sens., 8.","DOI":"10.3390\/rs8050377"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Long, N., Millescamps, B., Guillot, B., Pouget, F., and Bertin, X. (2016). Monitoring the topography of a dynamic tidal inlet using UAV imagery. Remote Sens., 8.","DOI":"10.3390\/rs8050387"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ko, D., Gould, R., Penta, B., and Lehrter, J. (2016). Impact of satellite remote sensing data on simulations of coastal circulation and hypoxia on the Louisiana Continental Shelf. Remote Sens., 8.","DOI":"10.3390\/rs8050435"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ximenes, A., Maeda, E., Arcoverde, G., and Dahdouh-Guebas, F. (2016). Spatial assessment of the bioclimatic and environmental factors driving mangrove tree species\u2019 distribution along the Brazilian Coastline. Remote Sens., 8.","DOI":"10.3390\/rs8060451"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Botha, E., Brando, V., and Dekker, A. (2016). Effects of per-pixel variability on uncertainties in bathymetric retrievals from high-resolution satellite images. Remote Sens., 8.","DOI":"10.3390\/rs8060459"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"O\u2019Donnell, J., and Schalles, J. (2016). Examination of abiotic drivers and their influence on spartina alterniflora biomass over a twenty-eight year period using Landsat 5 TM satellite imagery of the Central Georgia Coast. Remote Sens.","DOI":"10.3390\/rs8060477"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Uhl, F., Bartsch, I., and Oppelt, N. (2016). Submerged kelp detection with hyperspectral data. Remote Sens.","DOI":"10.3390\/rs8060487"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Otero, V., Quisthoudt, K., Koedam, N., and Dahdouh-Guebas, F. (2016). Mangroves at their limits: Detection and area estimation of mangroves along the Sahara Desert Coast. Remote Sens., 8.","DOI":"10.3390\/rs8060512"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dunkin, L., Reif, M., Altman, S., and Swannack, T. (2016). A spatially explicit, multi-criteria decision support model for loggerhead sea turtle nesting habitat suitability: A remote sensing-based approach. Remote Sens.","DOI":"10.3390\/rs8070573"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Anderson, C., Carter, G., and Funderburk, W. (2016). The use of aerial RGB imagery and LIDAR in comparing ecological habitats and geomorphic features on a natural versus man-made barrier island. Remote Sens., 8.","DOI":"10.3390\/rs8070602"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ye, H., Li, J., Li, T., Shen, Q., Zhu, J., Wang, X., Zhang, F., Zhang, J., and Zhang, B. (2016). Spectral classification of the Yellow Sea and implications for coastal ocean color remote sensing. Remote Sens.","DOI":"10.3390\/rs8040321"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tosi, L., Da Lio, C., Strozzi, T., and Teatini, P. (2016). Combining L- and X-band SAR interferometry to assess ground displacements in heterogeneous coastal environments: The Po River Delta and Venice Lagoon, Italy. Remote Sens.","DOI":"10.3390\/rs8040308"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ody, A., Doxaran, D., Vanhellemont, Q., Nechad, B., Novoa, S., Many, G., Bourrin, F., Verney, R., Pairaud, I., and Gentili, B. (2016). Potential of high spatial and temporal ocean color satellite data to study the dynamics of suspended particles in a Micro-Tidal River Plume. Remote Sens., 8.","DOI":"10.3390\/rs8030245"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Wang, X., Paull, D., and Gao, J. (2016). Application of the geostationary ocean color imager to mapping the diurnal and seasonal variability of surface suspended matter in a macro-tidal estuary. Remote Sens., 244.","DOI":"10.3390\/rs8030244"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Flores-de-Santiago, F., Kovacs, J., Wang, J., Flores-Verdugo, F., Zhang, C., and Gonz\u00e1lez-Far\u00edas, F. (2016). Examining the influence of seasonality, condition, and species composition on mangrove leaf pigment contents and laboratory based spectroscopy data. Remote Sens.","DOI":"10.3390\/rs8030226"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Han, B., Loisel, H., Vantrepotte, V., M\u00e9riaux, X., Bry\u00e8re, P., Ouillon, S., Dessailly, D., Xing, Q., and Zhu, J. (2016). Development of a semi-analytical algorithm for the retrieval of suspended particulate matter from remote sensing over clear to very turbid waters. Remote Sens.","DOI":"10.3390\/rs8030211"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Misbari, S., and Hashim, M. (2016). Change detection of submerged seagrass biomass in shallow coastal water. Remote Sens.","DOI":"10.3390\/rs8030200"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dewi, R., Bijker, W., Stein, A., and Marfai, M. (2016). Fuzzy classification for shoreline change monitoring in a part of the Northern Coastal Area of Java, Indonesia. Remote Sens.","DOI":"10.3390\/rs8030190"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Martin, J., Eugenio, F., Marcello, J., and Medina, A. (2016). Automatic sun glint removal of multispectral high-resolution Worldview-2 imagery for retrieving coastal shallow water parameters. Remote Sens.","DOI":"10.3390\/rs8010037"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Reichstetter, M., Fearns, P., Weeks, S., McKinna, L., Roelfsema, C., and Furnas, M. (2015). Bottom reflectance in ocean color satellite remote sensing for coral reef environments. Remote Sens.","DOI":"10.3390\/rs71215852"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"O\u2019Connell, J., Byrd, K., and Kelly, M. (2015). A Hybrid model for mapping relative differences in belowground biomass and root: Shoot ratios using spectral reflectance, foliar N and plant biophysical data within coastal marsh. 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