Detecting Coastline Change with All Available Landsat Data over 1986–2015: A Case Study for the State of Texas, USA
<p>Location of the State of Texas and the distribution of Landsat scenes. Blue polygons represent the positions of Landsat imagery and red dot represents the location of Rockport station mentioned in <a href="#sec2dot5-atmosphere-09-00107" class="html-sec">Section 2.5</a>.</p> "> Figure 2
<p>(<b>a</b>) Annual distribution of Landsat images; (<b>b</b>) Temporal distribution of hurricane occurred in the study area. (TM: Thematic Mapper, ETM+: Enhanced Thematic Mapper Plus and OLI: Operational Land Imager).</p> "> Figure 3
<p>Observed annual sea level at Rockport station.</p> "> Figure 4
<p>Summarized methodology for estimating the temporal change of coastline and analyzing the potential influencing factors.</p> "> Figure 5
<p>An example of generating a land-water classification map around the Galveston, Texas on 13 July 2006. (<b>a</b>) Landsat false color composite image (band 432); (<b>b</b>) The modified Normalized Difference Water Index (MNDWI) map; (<b>c</b>) The frequency distribution of all data values of the MNDWI image; (<b>d</b>) Land-water classification map. In (<b>c</b>), the blue dashed line represents the zero-threshold for classifying land and water pixels.</p> "> Figure 6
<p>An example of generating an annual land-water map around the Galveston, Texas in 2006. (<b>a</b>) Heatmap showing number of clear observations per pixel around the Galveston, Texas during 2006; (<b>b</b>) Heatmap showing number of clear observations (i.e., without clouds and shadows) per pixel around the Galveston, Texas in 2006; (<b>c</b>) The WFI map in 2006; (<b>d</b>) The annual land-water classification map during 2006. A total of 38 Landsat images (21 TM images and 17 ETM+ images) were used for demonstrating the generation of the land-water map at an annual scale.</p> "> Figure 7
<p>Illustration of the estimation of coastline change rate. (<b>a</b>) portion 1 and portion 2 in the Texas State; (<b>b</b>) elements used for defining the coastline and estimating the rate of coastline change.</p> "> Figure 8
<p>Spatial distribution of coastline change rates in the State of Texas.</p> "> Figure 9
<p>Spatial distribution of uncertainties of coastline change rates in the State of Texas.</p> "> Figure 10
<p>Temporal variation in the position of the coastline for the State of Texas: (<b>a</b>) 5-year trend curve and linear regression during the period 1986–2015; (<b>b</b>) linear regression performed in three temporal intervals.</p> "> Figure 11
<p>(<b>a</b>) Relationship between coastline change rate and hurricane frequency; (<b>b</b>) Relationship between coastline change rate and hurricane concentration.</p> "> Figure 12
<p>Relationship between the change of coastline position and sea level: (<b>a</b>) annual coastline position and sea level; (<b>b</b>) change rate of coastline position and sea level during the periods 1986–1990, 1991–1995, 1996–2000, 2001–2005, 2006–2010 and 2011–2015, respectively.</p> "> Figure 13
<p>Comparison of coastline change rate with a higher (blue) coastline curvature and a lower (black) coastline curvature. The shadow area demonstrates corresponding confidence intervals. Number of coastline section on the horizontal axis indicate the central number of the moving spatial window used to derive the mean change rate of coastline.</p> "> Figure 14
<p>An example of generating an annual land-water map using Landsat images around the Galveston, Texas in 2006. (<b>a</b>) high-resolution image from google earth; (<b>b</b>) light detection and ranging (LIDAR)-based digital elevation model (DEM); (<b>c</b>) LIDAR-derived land-water map; (<b>d</b>) two land-water maps derived from LIDAR data and Landsat images.</p> "> Figure 15
<p>Comparison between our results and the results from previous studies. Grey points represent the coastline change during different years and the blue line represents the linear fitting line. The dashed grey line reflects the 1:1 line.</p> "> Figure 16
<p>Impact of satellite observation frequency on the estimation of coastline change rate. (<b>a</b>,<b>b</b>) represent the association of observation frequency with uncertainty of coastline position and uncertainty of coastline change rate, respectively.</p> "> Figure 17
<p>Demonstration of the variation of coastline around Galveston, Texas in 2006. (<b>a</b>) Heatmap showing number of clear observations per pixel around the Galveston, Texas during 2006; (<b>b</b>) The area of coastline during one year. In (<b>b</b>), orange represents the area always observed as land and blue represents the area always observed as water. Yellow indicates the variation extent of dynamics coastline during one year.</p> ">
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Remotely Sensed Data
2.3. Validation Data
2.4. Hurricane Data
2.5. Sea Level Data
3. Methodology
3.1. Annual Land—Water Maps Generation
3.2. Coastline Change Rate Estimation
3.3. Uncertainty Assessment
3.4. Impact of Hurricane on Coastline Change
3.5. Impact of Sea Level Variation on Coastline Change
3.6. Coastline Morphology Anlysis
3.7. Effect of Observation Frequency on Estimation of Coastline Change Rate
4. Results
4.1. Spatial Pattern Analysis of Coastline Change
4.2. Temporal Variation Analysis of Coastline Change
4.3. Influencing Factor Analysis
4.4. Validation of Land-Water Map Using LIDAR Data
4.5. Validation of Coastline Change in the Texas State
5. Discussions
5.1. Influencing Factors of Coastline Change
5.2. Effect of Observation Frequency on Estimation of Coastline Change Rate
5.3. Further Considerations
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Yang, J.; Gong, P.; Fu, R.; Zhang, M.; Chen, J.; Liang, S.; Xu, B.; Shi, J.; Dickinson, R. The role of satellite remote sensing in climate change studies. Nat. Clim. Chang. 2013, 3, 875–883. [Google Scholar] [CrossRef]
- Ranasinghe, R.; Callaghan, D.; Stive, M.J.F. Estimating coastal recession due to sea level rise: Beyond the Bruun rule. Clim. Chang. 2011, 110, 561–574. [Google Scholar] [CrossRef]
- Zhang, K.; Douglas, B.C.; Leatherman, S.P. Global warming and coastal erosion. Clim. Chang. 2004, 64, 41–58. [Google Scholar] [CrossRef]
- Fearnley, S.M.; Miner, M.D.; Kulp, M.; Bohling, C.; Penland, S. Hurricane impact and recovery shoreline change analysis of the Chandeleur Islands, Louisiana, USA: 1855 to 2005. Geo-Mar. Lett. 2009, 29, 455–466. [Google Scholar] [CrossRef]
- Syvitski, J.P.M.; Kettner, A.J.; Overeem, I.; Hutton, E.W.H.; Hannon, M.T.; Brakenridge, G.R.; Day, J.; Vörösmarty, C.; Saito, Y.; Giosan, L.; et al. Sinking deltas due to human activities. Nat. Geosci. 2009, 2, 681–686. [Google Scholar] [CrossRef]
- Jennings, S. Coastal tourism and shoreline management. Ann. Tour. Res. 2004, 31, 899–922. [Google Scholar] [CrossRef]
- Tanaka, N.; Sato, S. Topographic change resulting from construction of a harbor on a beach: Kashima Port. Coast. Eng. 1976, 1977, 1824–1843. [Google Scholar]
- Peterson, C.H.; Bishop, M.J. Assessing the environmental impacts of beach nourishment. Bioscience 2005, 55, 887–896. [Google Scholar] [CrossRef]
- Muttitanon, W.; Tripathi, N.K. Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. Int. J. Remote Sens. 2005, 26, 2311–2323. [Google Scholar] [CrossRef]
- Shalaby, A.; Tateishi, R. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the northwestern coastal zone of Egypt. Appl. Geogr. 2007, 27, 28–41. [Google Scholar] [CrossRef]
- Defeo, O.; McLachlan, A.; Schoeman, D.S.; Schlacher, T.A.; Dugan, J.; Jones, A.; Lastra, M.; Scapini, F. Threats to beach ecosystems: A review. Estuar. Coast. Shelf Sci. 2009, 81, 1–12. [Google Scholar] [CrossRef]
- Cazenave, A.; Cozannet, G.L. Sea level rise and its coastal impacts. Earth’s Future 2014, 2, 15–34. [Google Scholar] [CrossRef]
- Le Cozannet, G.; Garcin, M.; Yates, M.; Idier, D.; Meyssignac, B. Approaches to evaluate the recent impacts of sea-level rise on shoreline changes. Earth-Sci. Rev. 2014, 138, 47–60. [Google Scholar] [CrossRef] [Green Version]
- Boak, E.H.; Turner, I.L. Shoreline definition and detection: A review. J. Coast. Res. 2005, 214, 688–703. [Google Scholar] [CrossRef]
- Gens, R. Remote sensing of coastlines: Detection, extraction and monitoring. Int. J. Remote Sens. 2010, 31, 1819–1836. [Google Scholar] [CrossRef]
- Thieler, E.R.; Himmelstoss, E.A.; Zichichi, J.L.; Ergul, A. The Digital Shoreline Analysis System (DSAS) Version 4.0-An ArcGIS Extension for Calculating Shoreline Change (No. 2008-1278); US Geological Survey: Reston, VA, USA, 2009.
- Shaw, J.B.; Wolinsky, M.A.; Paola, C.; Voller, V.R. An image-based method for shoreline mapping on complex coasts. Geophys. Res. Lett. 2008, 35, L12405. [Google Scholar] [CrossRef]
- Li, W.; Gong, P. Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery. Remote Sens. Environ. 2016, 179, 196–209. [Google Scholar] [CrossRef]
- Rahman, A.F.; Dragoni, D.; El-Masri, B. Response of the Sundarbans coastline to sea level rise and decreased sediment flow: A remote sensing assessment. Remote Sens. Environ. 2011, 115, 3121–3128. [Google Scholar] [CrossRef]
- Honeycutt, M.G.; Crowell, M.; Douglas, B.C. Shoreline-position forecasting: Impact of storms, rate-calculation methodologies, and temporal scales. J. Coast. Res. 2001, 17, 721–730. [Google Scholar]
- Karunarathna, H.; Reeve, D.E. A hybrid approach to model shoreline change at multiple timescales. Cont. Shelf Res. 2013, 66, 29–35. [Google Scholar] [CrossRef]
- Ghoneim, E.; Mashaly, J.; Gamble, D.; Halls, J.; AbuBakr, M. Nile delta exhibited a spatial reversal in the rates of shoreline retreat on the Rosetta promontory comparing pre- and post-beach protection. Geomorphology 2015, 228, 1–14. [Google Scholar] [CrossRef]
- Yu, K.; Hu, C.; Muller-Karger, F.E.; Lu, D.; Soto, I. Shoreline changes in west-central Florida between 1987 and 2008 from Landsat observations. Int. J. Remote Sens. 2011, 32, 8299–8313. [Google Scholar] [CrossRef]
- Chen, W.-W.; Chang, H.-K. Estimation of shoreline position and change from satellite images considering tidal variation. Estuar. Coast. Shelf Sci. 2009, 84, 54–60. [Google Scholar] [CrossRef]
- Ford, M. Shoreline changes interpreted from multi-temporal aerial photographs and high resolution satellite images: Wotje atoll, Marshall Islands. Remote Sens. Environ. 2013, 135, 130–140. [Google Scholar] [CrossRef]
- Frihy, O.E.; Dewidar, K.M.; Nasr, S.M.; El Raey, M.M. Change detection of the northeastern Nile Delta of Egypt: Shoreline changes, spit evolution, margin changes of Manzala lagoon and its islands. Int. J. Remote Sens. 1998, 19, 1901–1912. [Google Scholar] [CrossRef]
- Fletcher, C.H.; Romine, B.M.; Genz, A.S.; Barbee, M.M.; Dyer, M.; Anderson, T.R.; Lim, S.C.; Vitousek, S.; Bochicchio, C.; Richmond, B.M. National Assessment of Shoreline Change: Historical Shoreline Change in the Hawaiian Islands; U.S. Department of the Interior: Washington, DC, USA, 2011.
- Stockdonf, H.F.; Holman, R.A. Estimation of shoreline position and change using airborne topographic lidar data. J. Coast. Res. 2002, 18, 502–513. [Google Scholar]
- Pianca, C.; Holman, R.; Siegle, E. Shoreline variability from days to decades: Results of long-term video imaging. J. Geophys. Res. Oceans 2015, 120, 2159–2178. [Google Scholar] [CrossRef]
- Harley, M.D.; Turner, I.L.; Short, A.D.; Ranasinghe, R. Assessment and integration of conventional, RTK-GPS and image-derived beach survey methods for daily to decadal coastal monitoring. Coast. Eng. 2011, 58, 194–205. [Google Scholar] [CrossRef]
- Gong, P. Remote sensing of environmental change over China: A review. Chin. Sci. Bull. 2012, 57, 2793–2801. [Google Scholar] [CrossRef]
- Hui, F.; Xu, B.; Huang, H.; Yu, Q.; Gong, P. Modelling spatial-temporal change of Poyang Lake using multitemporal Landsat imagery. Int. J. Remote Sens. 2008, 29, 5767–5784. [Google Scholar] [CrossRef]
- Kovalskyy, V.; Roy, D.P. The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30 m Landsat data product generation. Remote Sens. Environ. 2013, 130, 280–293. [Google Scholar] [CrossRef]
- Almonacid-Caballer, J.; Sánchez-García, E.; Pardo-Pascual, J.E.; Balaguer-Beser, A.A.; Palomar-Vázquez, J. Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator. Mar. Geol. 2016, 372, 79–88. [Google Scholar] [CrossRef]
- Leatherman, S.P. Coastal geomorphic responses to sea-level Rise, Galveston Bay, Texas. In Greenhouse Effect and Sea-Level Rise: A Challenge for this Generation; Barth, M.C., Titus, J.G., Eds.; Van Nostrand Reinhold: New York, NY, USA, 1984; pp. 151–178. [Google Scholar]
- Paine, J.G.; Caudle, T.L.; Andrews, J.R. Shoreline and sand storage dynamics from annual airborne Lidar surveys, Texas Gulf coast. J. Coast. Res. 2017, 33, 487–506. [Google Scholar] [CrossRef]
- Sea Level Trends. Available online: https://tidesandcurrents.noaa.gov/ (accessed on 1 July 2015).
- Roth, D. Texas Hurricane History; Long Island Business News: New York, NY, USA, 2006. [Google Scholar]
- Effect of Hurricane Ike in Texas. Available online: https://en.wikipedia.org/wiki/Effects_of_Hurricane_Ike_in_Texas#cite_note-YNaft-2 (accessed on 1 July 2015).
- Zane, D.F.; Bayleyegn, T.M.; Hellsten, J.; Beal, R.; Beasley, C.; Haywood, T.; Wiltz-beckham, D.; Wolkin, A.F. Tracking deaths related to hurricane Ike, Texas, 2008. Disaster Med. Public Health Prep. 2011, 5, 23–28. [Google Scholar] [CrossRef] [PubMed]
- Center, N.H. Tropical Cyclone Report Hurricane Ike (al092008) 1–14 September 2008; National Hurricane Center: Gainesville, FL, USA, 2008. [Google Scholar]
- Song, C.; Woodcock, C.E.; Seto, K.C.; Lenney, M.P.; Macomber, S.A. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens. Environ. 2001, 75, 230–244. [Google Scholar] [CrossRef]
- Lin, C.; Wu, C.C.; Tsogt, K.; Ouyang, Y.C.; Chang, C.I. Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery. Inf. Process. Agric. 2015, 2, 25–36. [Google Scholar] [CrossRef]
- Unisys Weather Information System. Available online: http://weather.unisys.com/hurricane/ (accessed on 1 July 2015).
- Fitzgerald, D.M.; Fenster, M.S.; Argow, B.A.; Buynevich, I.V. Coastal impacts due to sea-level rise. Annu. Rev. Earth Planet. Sci. 2007, 36, 601–647. [Google Scholar] [CrossRef]
- Real-time Water Level Information in the USA. Available online: https://tidesandcurrents.noaa.gov/stations.html?type=Water+Levels (accessed on 1 July 2015).
- Zhu, Z.; Woodcock, C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 2012, 118, 83–94. [Google Scholar] [CrossRef]
- Cui, B.L.; Li, X.Y. Coastline change of the Yellow River estuary and its response to the sediment and runoff (1976–2005). Geomorphology 2011, 127, 32–40. [Google Scholar] [CrossRef]
- Li, Y.; Gong, X.; Guo, Z.; Xu, K.; Hu, D.; Zhou, H. An index and approach for water extraction using Landsat-OLI data. Int. J. Remote Sens. 2016, 37, 3611–3635. [Google Scholar] [CrossRef]
- Kelly, J.T.; Gontz, A.M. Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices. Int. J. Appl. Earth Obs. Geoinf. 2018, 65, 92–104. [Google Scholar] [CrossRef]
- Xu, H. Modification of Normalised Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [Google Scholar] [CrossRef]
- Sagin, J.; Sizo, A.; Wheater, H.; Jardine, T.D.; Lindenschmidt, K.E. A water coverage extraction approach to track inundation in the Saskatchewan River Delta, Canada. Int. J. Remote Sens. 2015, 36, 764–781. [Google Scholar] [CrossRef]
- Acharya, T.; Yang, I.T.; Subedi, A.; Lee, D.H. Change detection of Lakes in Pokhara, Nepal using Landsat Data. In Proceedings of the 3rd International Electronic Conference on Sensors and Applications, online, 15–30 November 2016. [Google Scholar]
- Fisher, A.; Flood, N.; Danaher, T. Comparing Landsat water index methods for automated water classification in Eastern Australia. Remote Sens. Environ. 2016, 175, 167–182. [Google Scholar] [CrossRef]
- Sagar, S.; Roberts, D.; Bala, B.; Lymburner, L. Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations. Remote Sens. Environ. 2017, 195, 153–169. [Google Scholar] [CrossRef]
- Romine, B.M.; Fletcher, C.H.; Frazer, L.N.; Genz, A.S.; Barbee, M.M.; Lim, S.C. Historical shoreline change, southeast Oahu, Hawaii; applying polynomial models to calculate shoreline change rates. J. Coast. Res. 2009, 25, 1236–1253. [Google Scholar] [CrossRef]
- Houser, C.; Hapke, C.; Hamilton, S. Controls on coastal dune morphology, shoreline erosion and barrier island response to extreme storms. Geomorphology 2008, 100, 223–240. [Google Scholar] [CrossRef]
- Ozturk, D.; Beyazit, I.; Kilic, F. Spatiotemporal analysis of shoreline changes of the Kizilirmak Delta. J. Coast. Res. 2014, 31, 1389–1402. [Google Scholar] [CrossRef]
- Filho, P.W.M.S.; Martins, E.D.S.F.; Costa, F.R.D. Using mangroves as a geological indicator of coastal changes in the Bragança macrotidal flat, Brazilian Amazon: A remote sensing data approach. Ocean Coast. Manag. 2006, 49, 462–475. [Google Scholar] [CrossRef]
- Chu, Z.X.; Sun, X.G.; Zhai, S.K.; Xu, K.H. Changing pattern of accretion/erosion of the modern Yellow River (Huanghe) subaerial delta, China: Based on remote sensing images. Mar. Geol. 2006, 227, 13–30. [Google Scholar] [CrossRef]
- El-Raey, M.; Sharaf El-Din, S.H.; Khafagy, A.A.; Abo Zed, A.I. Remote sensing of beach erosion / accretion patterns along Damietta-port said shoreline, Egypt. Int. J. Remote Sens. 1999, 20, 1087–1106. [Google Scholar] [CrossRef]
- Liao, E.; Lu, W.; Yan, X.H.; Jiang, Y.; Kidwell, A. The coastal ocean response to the global warming acceleration and hiatus. Sci. Rep. 2015, 5, 16630. [Google Scholar] [CrossRef] [PubMed]
- Puig, M.; del Rio, L.; Plomaritis, T.A.; Benavente, J. Influence of storms on coastal retreat in SW Spain. J. Coast. Res. 2014, 70, 193–198. [Google Scholar] [CrossRef]
- Anderson, T.R.; Frazer, L.N.; Fletcher, C.H. Transient and persistent shoreline change from a storm. Geophys. Res. Lett. 2010, 37, 162–169. [Google Scholar] [CrossRef]
- Nebel, S.H.; Trembanis, A.C.; Barber, D.C. Tropical cyclone frequency and barrier island erosion rates, Cedar Island, Virginia. J. Coast. Res. 2013, 286, 133–144. [Google Scholar] [CrossRef]
- Bruun, P. The Bruun rule of erosion by sea-level rise: A discussion on large-scale two-and three-dimensional usages. J. Coast. Res. 1988, 4, 627–648. [Google Scholar]
- Splinter, K.D.; Turner, I.L.; Davidson, M.A. How much data is enough? The importance of morphological sampling interval and duration for calibration of empirical shoreline models. Coast. Eng. 2013, 77, 14–27. [Google Scholar] [CrossRef]
- Reif, M.K.; Macon, C.L.; Wozencraft, J.M. Post-Katrina land-cover, elevation, and volume change assessment along the south shore of lake Pontchartrain, Louisiana, U.S.A. J. Coast. Res. 2011, 62, 30–39. [Google Scholar] [CrossRef]
- Paine, J.G.; Caudle, T.L.; Andrews, J.L. Shoreline Movement along the Texas Gulf Coast, 1930’s to 2012; Final Report to the Texas General Land Office, Bureau of Economic Geology; the University of Texas: Austin, TX, USA, 2014. [Google Scholar]
- Paine, J.G.; Mathew, S.; Caudle, T. Historical shoreline change through 2007, Texas Gulf Coast: Rates, contributing causes, and Holocene context. Gcags J. 2012, 1, 13–26. [Google Scholar]
- Morton, R.A.; Miller, T.L.; Moore, L.J. National Assessment of Shoreline Change: Part 1 Historical Shoreline Changes and Associated Coastal Land Loss along the U.S. Gulf of Mexico; USGS—U.S. Geological Survey, Center for Coastal & Regional Marine Studies: Reston, VA, USA, 2004.
- Morton, R.A. Stages and durations of post-storm beach recovery, Southeastern Texas Coast, U.S.A. J. Coast. Res. 1994, 10, 884–908. [Google Scholar]
- Frazer, L.N.; Anderson, T.R.; Fletcher, C.H. Modeling storms improves estimates of long-term shoreline change. Geophys. Res. Lett. 2009, 37, 1437–1454. [Google Scholar] [CrossRef]
- Coco, G.; Senechal, N.; Rejas, A.; Bryan, K.R.; Capo, S.; Parisot, J.P.; Brown, J.A.; MacMahan, J.H.M. Beach response to a sequence of extreme storms. Geomorphology 2014, 204, 493–501. [Google Scholar] [CrossRef] [Green Version]
- Óscar, F. Storm groups versus extreme single storms: Predicted erosion and management consequences. J. Coast. Res. 2005, 21, 221–227. [Google Scholar]
- Mulcahy, N.; Kennedy, D.M.; Blanchon, P. Hurricane-induced shoreline change and post-storm recovery: Northeastern Yucatan Peninsula, Mexico. J. Coast. Res. 2016, 2, 1192–1196. [Google Scholar] [CrossRef]
- Leatherman, S.P.; Zhang, K.; Douglas, B.C. Sea level rise shown to drive coastal erosion. Eos Trans. Am. Geophys. Union 2000, 81, 55–57. [Google Scholar] [CrossRef]
- Bullard, F.M. Source of beach and river sands on gulf coast of Texas. Geol. Soc. Am. Bull. 1942, 53, 1021–1043. [Google Scholar] [CrossRef]
- Ells, K.D. Convexity, Concavity, and Human Agency in Large-Scale Coastline Evolution. Ph.D. Thesis, Duke University, Durham, NC, USA, 2014. [Google Scholar]
- Phillips, J.D. Erosion and Planform Irregularity of an Estuarine Shoreline. Zeitschrift fiir Geomorphologie; Gerbruder Borntr: Berlin, Germany, 1989; pp. 59–71. [Google Scholar]
- Letetrel, C.; Karpytchev, M.; Bouin, M.N.; Marcos, M.; Santamaríagómez, A.; Wöppelmann, G. Estimation of vertical land movement rates along the coasts of the Gulf of Mexico over the past decades. Cont. Shelf Res. 2015, 111, 42–51. [Google Scholar] [CrossRef]
Path/Row | Level | Scene Amount per Sensor | Total Amount per Scene | ||
---|---|---|---|---|---|
TM | ETM+ | OLI | |||
024/039 | L1T | 372 | 284 | 48 | 704 |
025/039 | L1T | 396 | 279 | 53 | 730 |
025/040 | L1T | 387 | 286 | 47 | 722 |
026/040 | L1T | 379 | 282 | 50 | 719 |
026/041 | L1T | 369 | 288 | 52 | 719 |
026/042 | L1T | 401 | 299 | 52 | 763 |
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Xu, N. Detecting Coastline Change with All Available Landsat Data over 1986–2015: A Case Study for the State of Texas, USA. Atmosphere 2018, 9, 107. https://doi.org/10.3390/atmos9030107
Xu N. Detecting Coastline Change with All Available Landsat Data over 1986–2015: A Case Study for the State of Texas, USA. Atmosphere. 2018; 9(3):107. https://doi.org/10.3390/atmos9030107
Chicago/Turabian StyleXu, Nan. 2018. "Detecting Coastline Change with All Available Landsat Data over 1986–2015: A Case Study for the State of Texas, USA" Atmosphere 9, no. 3: 107. https://doi.org/10.3390/atmos9030107