An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme

Environmental Monitoring and Assessment

Volumn 190, Issue 1, 2018, Pages

  Valderrama Landeros, L ^a   Flores de Santiago, F ^b   Kovacs, J M ^c   Flores Verdugo, F ^b  

^a Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad ^*  (Mexico)

^b UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

^c NIPISSING UNIVERSITY   (Canada)

Author keywords

Forested wetland; Landsat 8; Sentinel 2; SPOT 5; WorldView 2

Indexed keywords

MAPPING; PIXELS; REMOTE SENSING; SATELLITES; WETLANDS;

FORESTED WETLANDS; LANDSAT; SENTINEL-2; SPOT-5; WORLDVIEW-2;

FORESTRY;

CLASSIFICATION; LANDSAT; MANGROVE; MAPPING; NDVI; REMOTE SENSING; SATELLITE DATA; SENTINEL; SPOT; WETLAND; WORLDVIEW;

ANALYTIC METHOD; ARTICLE; CLASSIFICATION ALGORITHM; DEGRADATION; ECOSYSTEM; ENVIRONMENTAL IMPACT ASSESSMENT; ENVIRONMENTAL MONITORING; FOREST MANAGEMENT; GEOGRAPHIC MAPPING; HYDROLOGY; LAGUNCULARIA RACEMOSA; LANDSAT 8; MANGROVE; MEXICO; REMOTE SENSING; RHIZOPHORA MANGLE; SATELLITE BASED REMOTE SENSOR; SENTINEL 2; SPECIES COMPOSITION; SPECIES IDENTIFICATION; SPOT 5; WORLDVIEW 2; COMBRETACEAE; FOREST; GROWTH, DEVELOPMENT AND AGING; PROCEDURES; RHIZOPHORACEAE; SENSITIVITY AND SPECIFICITY; WETLAND;

MEXICO [NORTH AMERICA]; PACIFIC COAST [MEXICO]; PACIFIC COAST [NORTH AMERICA];

LAGUNCULARIA RACEMOSA; RHIZOPHORA MANGLE; RHIZOPHORACEAE;

COMBRETACEAE; ENVIRONMENTAL MONITORING; FORESTS; MEXICO; REMOTE SENSING TECHNOLOGY; RHIZOPHORACEAE; SENSITIVITY AND SPECIFICITY; WETLANDS;

EID: 85038216358     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-017-6399-z     Document Type: Article

References (54)

1. Alongi, D. M. (2016). Mangroves. In M. J. Kennish (Ed.), Encyclopedia of estuaries (pp. 393–404). New York: Springer. https://doi.org/10.1007/978-94-017-8801-4_3.
2. Alsaaideh, B., Al-Hanbali, A., Tateishi, R., Kobayashi, T., & Hoan, N. T. (2013). Mangrove forests mapping in the southern part of Japan using Landsat ETM+ with DEM. Journal of Geographic Information Systems, 5(04), 369–377. https://doi.org/10.4236/jgis.2013.54035.
3. Blanco-Correa, M. (2011). Diagnóstico funcional de Marismas Nacionales. Tepic: Informe final de los convenios de coordinación entre la Universidad Autónoma de Nayarit y la Comisión Nacional Forestal con el patrocinio del Gobierno del Reino Unido.
4. Chadwick, J. (2011). Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties. International Journal of Remote Sensing, 32(21), 6765–6781. https://doi.org/10.1080/01431161.2010.512944.
5. Congalton, R., & Green, K. (1999). Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton: CRC/LEWIS Press.
6. Congedo, L. (2016). Semi-automatic classification plugin documentation. Technical report. https://doi.org/10.13140/RG.2.2.29474.02242/1. Accessed 14 Nov 2017.
7. De-León-Herrera, R., Flores-Verdugo, F., Flores-de-Santiago, F., & González-Farías, F. (2015). Nutrient removal in a closed silvofishery system using three mangrove species (Avicennia germinans, Laguncularia racemosa, and Rhizophora mangle). Marine Pollution Bulletin, 91(1), 243–248. https://doi.org/10.1016/j.marpolbul.2014.11.040.
8. Dronova, I. (2015). Object-based image analysis in wetland research: A review. Remote Sensing, 7(5), 6380–6413. https://doi.org/10.3390/rs70506380.
9. Duke, N. C. (2016). Oil spill impacts on mangroves: recommendations for operational planning and action based on a global review. Marine Pollution Bulletin, 109(2), 700–715. https://doi.org/10.1016/j.marpolbul.2016.06.082.
10. Duke, N. C., Meynecke, J. O., Dittmann, S., Ellison, A. M., Anger, K., Berger, U., Cannicci, S., Diele, K., Ewel, K. C., Field, C. D., Koedam, N., Lee, S. Y., Marchand, C., Nordhaus, I., & Dahdouh-Guebas, F. (2007). A world without mangroves? Science, 317(5834), 41–42. https://doi.org/10.1126/science.317.5834.41b.
11. Duke, N. C., Kovacs, J. M., Griffiths, A. D., Preece, L., Hill, D. J. E., van Oosterzee, P., Mackenzie, J., Morning, H. S., & Burrows, D. (2017). Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: a severe ecosystem response, coincidental with an unusually extreme weather event. Marine and Freshwater Research, 68(10), 1816–1829. https://doi.org/10.1071/MF16322.
12. Ferreira, A. C., Ganade, G., & Attayde, J. L. D. (2015). Restoration vs natural regeneration in neotropical mangrove: effects on plant biomass and crab communities. Ocean and Coastal Management, 110, 38–45. https://doi.org/10.1016/j.ocecoaman.2015.03.006.
13. Flores-de-Santiago, F., Kovacs, J. M., & Lafrance, P. (2013a). An object-oriented classification method for mapping mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data. International Journal of Remote Sensing, 34(2), 563–586. https://doi.org/10.1080/01431161.2012.715773.
14. Flores-de-Santiago, F., Kovacs, J. M., & Flores-Verdugo, F. (2013b). The influence of seasonality in estimating mangrove leaf chlorophyll-a content from hyperspectral data. Wetlands Ecology and Management, 21(3), 193–207. https://doi.org/10.1007/s11273-013-9290-x.
15. Flores-de-Santiago, F., Kovacs, J. M., Wang, J., Flores-Verdugo, F., Zhang, C., & González-Farías, F. (2016). Examining the influence of seasonality, condition, and species composition on mangrove leaf pigment contents and laboratory based spectroscopy data. Remote Sensing, 8(3), 226. https://doi.org/10.3390/rs8030226.
16. Flores-Verdugo, F. J., Day, J. W., & Briseño-Dueñas, R. (1987). Structure, litter fall, decomposition, and detritus dynamics of mangroves in a Mexican coastal lagoon with an ephemeral inlet. Marine Ecology Progress Series, 35, 83–90. https://doi.org/10.3354/meps035083.
17. Friess, D. A., & Webb, E. L. (2014). Variability in mangrove change estimates and implications for the assessment of ecosystem service provision. Global Ecology and Biogeogreography, 23(7), 715–725. https://doi.org/10.1111/geb.12140.
18. Gao, J. (1998). A hybrid method toward accurate mapping of mangroves in a marginal habitat from SPOT multispectral data. International Journal of Remote Sensing, 19(10), 1887–1899. https://doi.org/10.1080/014311698215045.
19. Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x.
20. Green, E. P., Clark, C. D., Mumby, P. J., Edwards, A. J., & Ellis, A. C. (1998). Remote sensing techniques for mangrove mapping. International Journal of Remote Sensing, 19(5), 935–956. https://doi.org/10.1080/014311698215801.
21. Guo, M., Li, J., Sheng, C., Xu, J., & Wu, L. (2017). A review of wetland remote sensing. Sensors, 17(4), 777. https://doi.org/10.3390/s17040777.
22. Heenkenda, M. K., Joyce, K. E., Maier, S. W., & Bartolo, R. (2014). Mangrove species identification: Comparing WorldView-2 with aerial photographs. Remote Sensing, 6(7), 6064–6088. https://doi.org/10.3390/rs6076064.
23. Heenkenda, M. K., Joyce, K. E., Maier, S. W., & Bruin, S. D. (2015). Quantifying mangrove chlorophyll from high spatial resolution imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 234–244. https://doi.org/10.1016/j.isprsjprs.2015.08.003.
24. Horstman, E. M., Dohmen-Janssen, C. M., Bouma, T. J., & Hulscher, S. J. M. H. (2015). Tidal-scale flow routing and sedimentation in mangrove forests: Combining field data and numerical modeling. Geomorphology, 228, 244–262. https://doi.org/10.1016/j.geomorph.2014.08.011.
25. Ibharim, N. A., Mustapha, M. A., Lihan, T., & Mazlan, A. G. (2015). Mapping mangrove changes in the Matang mangrove forest using multi temporal satellite imageries. Ocean and Coastal Management, 114, 64–76. https://doi.org/10.1016/j.ocecoaman.2015.06.005.
26. Jensen, R. J. (2016). Introductory digital image processing: a remote sensing perspective. Upper Saddle River: Prentice Hall 544 pp.
27. Koedsin, W., & Vaiphasa, C. (2013). Discrimination of tropical mangroves at the species level with EO-1 Hyperion data. Remote Sensing, 5(7), 3562–3582. https://doi.org/10.3390/rs5073562.
28. Kovacs, J. M. (2000). Perceptions of environmental change in a tropical coastal wetland. Land Degradation and Development, 11(3), 209–220. https://doi.org/10.1002/1099-145X(200005/06)11:3<209::AID-LDR378>3.0.CO;2-Y.
29. Kovacs, J. M., Wang, J., & Blanco-Correa, M. (2001). Mapping disturbances in a mangrove forest using multi-date Landsat TM imagery. Environmental Management, 27(5), 763–776. https://doi.org/10.1007/s002670010186.
30. Kovacs, J. M., Flores-Verdugo, F., Wang, J., & Aspden, L. P. (2004). Estimating leaf area index of a degraded mangrove forest using high spatial resolution satellite data. Aquatic Botany, 80(1), 13–22. https://doi.org/10.1016/j.aquabot.2004.06.001.
31. Kovacs, J. M., Wang, J., & Flores-Verdugo, F. (2005). Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific. Estuarine, Coastal and Shelf Science, 62(1-2), 377–384. https://doi.org/10.1016/j.ecss.2004.09.027.
32. Kovacs, J. M., King, J. M. L., Flores-de-Santiago, F., & Flores-Verdugo, F. (2009). Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach. Environmental Monitoring and Assessment, 157(1-4), 137–149. https://doi.org/10.1007/s10661-008-0523-z.
33. Kovacs, J. M., Flores-de-Santiago, F., Bastien, J., & Lafrance, P. (2010). An assessment of mangroves in Guinea, West Africa, using a field and remote sensing based approach. Wetlands, 30(4), 773–782. https://doi.org/10.1007/s13157-010-0065-3.
34. Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V., & Dech, S. (2011). Remote sensing of mangrove ecosystems: a review. Remote Sensing, 3(12), 878–928. https://doi.org/10.3390/rs3050878.
35. Lee, T. M., & Yeh, H. C. (2009). Applying remote sensing techniques to monitor shifting wetland vegetation: a case of study of Danshui river estuary mangrove communities, Taiwan. Ecological Engineering, 35(4), 487–496. https://doi.org/10.1016/j.ecoleng.2008.01.007.
36. Li, X., Yeh, A. G. O., Wang, S., Liu, K., Liu, X., Qian, J., & Chen, X. (2007). Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images. International Journal of Remote Sensing, 28(24), 5567–5582. https://doi.org/10.1080/01431160701227638.
37. Lillesand, T. M., Kiefer, R. W., & Chapman, J. W. (2008). Remote sensing and image interpretation. New York: John Wiley & Sons.
38. Martin, T. S. H., Olds, A. D., Pitt, K. A., Johnston, A. B., Butler, I. R., Maxwell, P. S., & Connolly, R. M. (2015). Effective protection of fish on inshore coral reefs depends on the scale of mangrove-reef connectivity. Marine Ecology Progress Series, 527, 157–165. https://doi.org/10.3354/meps11295.
39. McCarthy, M. J., Merton, E. J., & Muller-Karger, F. E. (2015). Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery. International Journal of Applied Earth Observation and Geoinformation, 40, 11–18. https://doi.org/10.1016/j.jag.2015.03.011.
40. Mukherjee, N., Sutherland, W. J., Dicks, L., Hugé, J., Koedam, N., & Dahdouh-Guebas, F. (2014). Ecosystem service valuations of mangrove ecosystems to inform decision making and future valuation exercises. PLoS One, 9(9), e107706. https://doi.org/10.1371/journal.pone.0107706.
41. Ose, K. (2015). Geosud Toa Reflectance. QGIS Python Plugins Repository. https://plugins.qgis.org/plugins/geosudRefToa/. Accessed 14 Nov 2017.
42. Otero, V., Quisthoudt, K., Koedam, N., & Dahdouh-Guebas, F. (2016). Mangroves at their limits: Detection and area estimation of mangroves along the Sahara desert coast. Remote Sensing, 8(6), 512. https://doi.org/10.3390/rs8060512.
43. Pettorelli, N. (2013). The normalized differential vegetation index. Oxford: Oxford University Press. https://doi.org/10.1093/acprof:osobl/9780199693160.001.0001.
44. Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., Turner, W., & Milner-Gulland, E. J. (2014). Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology, 51(4), 839-848. https://doi.org/10.1111/1365-2664.12261
45. Richter, R., & Schläpfer, D. (2016). Atmospheric / topographic correction for satellite imagery. ATCOR-2/3 User Guide, Version 9.0.2. http://www.rese.ch/pdf/atcor3_manual.pdf. Accessed 14 Nov 2017.
46. Saintilan, N., Wilson, N. C., Rogers, K., Rajkaran, A., & Krauss, K. W. (2014). Mangrove expansion and salt marsh decline at mangrove poleward limits. Global Change Biology, 20(1), 147–157. https://doi.org/10.1111/gcb.12341.
47. Son, N. T., Thanh, B. X., & Da, C. T. (2016). Monitoring mangrove forest changes from multi-temporal Landsat data in Can Gio biosphere reserve, Vietnam. Wetlands, 36(3), 565–576. https://doi.org/10.1007/s13157-016-0767-2.
48. Troche-Souza, C., Rodriguez-Zuñiga, M. T., Velázquez-Salazar, S., Valderrama-Landeros, L., Villeda-Chávez, E., Alcántara-Maya, A., Vázquez-Balderas, B., Cruz-López, M. I., & Ressl, R. (2016). Manglares de México: extensión, distribución y monitoreo (1970/1980–2015). Mexico City: CONABIO.
49. Van Bochove, J. W., Sullivan, E., & Nakamura, T. (2014). The importance of mangroves to people: A call to action. Cambridge: United Nations Environment Programme World Conservation Monitoring Centre.
50. Vo, T. Q., Kuenzer, C., & Oppelt, N. (2015). How remote sensing supports mangrove ecosystem service valuation: a case study in Ca Mau province, Vietnam. Ecosystem Services, 14, 67–75.
51. Walters, B. B., Rönnbäck, P., Kovacs, J. M., Crona, B., Hussain, S. A., Badola, R., Primavera, J. H., Barbier, E., & Dahdouh-Guebas, F. (2008). Ethnobiology, socio-economics and management of mangrove forests: a review. Aquatic Botany, 89(2), 220–236. https://doi.org/10.1016/j.aquabot.2008.02.009.
52. Wang, T., Zhang, H., Lin, H., & Fang, C. (2016). Textural-spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 imagery. Remote Sensing, 8, 24.
53. Ximenes, A. C., Maeda, E. E., Arcoverde, G. F. B., & Dahdouh-Guebas, F. (2016). Spatial assessment of the bioclimatic and environmental factors driving mangrove tree species’ distribution along the Brazilian coastline. Remote Sensing, 8(6), 451. https://doi.org/10.3390/rs8060451.
54. Zhang, C., Kovacs, J. M., Liu, Y., Flores-Verdugo, F., & Flores-de-Santiago, F. (2014). Separating mangrove species and conditions using laboratory hyperspectral data: A case study of a degraded mangrove forest of the Mexican Pacific. Remote Sensing, 6(12), 11673–11688. https://doi.org/10.3390/rs61211673.