[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge from remotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using high spatial resolution imagery and machine learning image classification algorithms for mapping heterogeneous wetland plant communities. This study addresses this void by analyzing whether machine learning classifiers such as decision trees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedge communities using high resolution aerial imagery and image texture data in the Everglades National Park, Florida. In addition to spectral bands, the normalized difference vegetation index, and first- and second-order texture features derived from the near-infrared band were analyzed. Classifier accuracies were assessed using confusion tables and the calculated kappa coefficients of the resulting maps. The results indicated that an ANN (multilayer perceptron based on back propagation) algorithm produced a statistically significantly higher accuracy (82.04 %) than the DT (QUEST) algorithm (80.48 %) or the maximum likelihood (80.56 %) classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-order texture features also provided computational advantages and results that were not significantly different from those using second-order texture features.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Armentano, T. V., Sah, J. P., Ross, M. S., Jones, D. T., Cooley, H. C., & Smith, C. S. (2006). Rapid responses of vegetation to hydrological changes in Taylor Slough, Everglades National Park, Florida, USA. Hydrobiologia, 569(1), 293–309. doi:10.1007/s10750-006-0138-8.

    Article  Google Scholar 

  • Ashish, D., McClendon, R. W., & Hoogenboom, G. (2009). Land-use classification of multispectral aerial images using artificial neural networks. International Journal of Remote Sensing, 30(8), 1989–2004. doi:10.1080/01431160802549187.

    Article  Google Scholar 

  • Baker, C., Lawrence, R., Montagne, C., & Patten, D. (2006). Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands, 26(2), 465–474.

    Article  Google Scholar 

  • Barbosa, I. (2010). Mapping wetland environments in the Brazilian Savannah from high resolution IKONOS image data. In W. Wagner & B. Székely (Eds.), ISPRS TC VII symposium (Vol. XXXVIII, Part 7B, pp. 62–67.).

  • Belluco, E., Camuffo, M., Ferrari, S., Modenese, L., Silvestri, S., Marani, A., & Marani, M. (2006). Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1), 54–67.

    Article  Google Scholar 

  • Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1993). Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data. International Journal of Remote Sensing, 14(15), 2883–2903. doi:10.1080/01431169308904316.

    Article  Google Scholar 

  • Berberoglu, S., Yilmaz, K. T., & Ozkan, C. (2004). Mapping and monitoring of coastal wetlands of Cukurova Delta in the Eastern Mediterranean region. Biodiversity and Conservation, 13, 615–633.

    Article  Google Scholar 

  • Berberoglu, S., Curran, P. J., Lloyd, C. D., & Atkinson, P. M. (2007). Texture classification of Mediterranean land cover. International Journal of Applied Earth Observation and Geoinformation, 9(3), 322–334.

    Article  Google Scholar 

  • Breiman, L., Friedman, J., Stone, C., & Olshen, R. (1984). Classification and regression trees. New York: Chapman & Hall.

    Google Scholar 

  • Bwangoy, J. R., Hansen, M. C., Roy, D. P., Grandi, G. D., & Justice, C. O. (2010). Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices. Remote Sensing of Environment, 114(1), 73–86.

    Article  Google Scholar 

  • Canty, M. J. (2010). Image analysis, classification, and change detection in remote sensing: With algorithms for ENVI/IDL, second edition (2nd ed.). Boca Raton: CRC Press.

    Google Scholar 

  • Childers, D. L., Jones, R. F., Noe, R., Rugge, G. B., Scinto, M., & Leonard, J. (2003). Decadal change in vegetation and soil phosphorus pattern across the Everglades landscape. Journal of Environmental Quality, 32(1), 344–362.

    Article  CAS  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46.

    Article  Google Scholar 

  • Davis, S. M., Gunderson, L. H., Park, W. A., Richardson, J. R., & Mattson, J. E. (1994). Landscape dimension, composition, and function in a changing Everglades ecosystem. In S. M. Davis & J. C. Ogden (Eds.), Everglades: The ecosystem and its restoration (pp. 419–444.).

  • Davis, S. M., Gaiser, E. E., Loftus, W. F., & Huffman, A. E. (2005). Southern marl prairies conceptual ecological model. Wetlands, 25(4), 821–831.

    Article  Google Scholar 

  • Donner, A., Shoukri, M. M., Klar, N., & Bartfay, E. (2000). Testing the equality of two dependent kappa statistics. Statistics in Medicine, 19(3), 373–387.

    Article  CAS  Google Scholar 

  • Foody, G. M., McCulloch, M. B., & Yates, W. B. (1995). The effect of training set size and composition on artificial neural network classification. International Journal of Remote Sensing, 16(9), 1707–1723. doi:10.1080/01431169508954507.

    Article  Google Scholar 

  • Friedl, M. A., & Brodley, C. E. (1997). Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3), 399–409.

    Article  Google Scholar 

  • Fuller, D. O. (2005). Remote detection of invasive Melaleuca trees (Melaleuca quinquenervia) in South Florida with multispectral IKONOS imagery. International Journal of Remote Sensing, 26(5), 1057–1063. doi:10.1080/01430060512331314119.

    Article  Google Scholar 

  • Ghedira, H., Bernier, M., & Ouarda, T. B. M. J. (2000). Application of neural networks for wetland classification in RADARSAT SAR imagery. In Geoscience and Remote Sensing Symposium Proceedings. (Vol. 2, pp. 675–677.). Presented at the Geoscience and Remote Sensing Symposium. IGARSS 2000. IEEE 2000 International.

  • Gilmore, M. S., Wilson, E. H., Barrett, N., Civco, D. L., Prisloe, S., Hurd, J. D., & Chadwick, C. (2008). Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh. Remote Sensing of Environment, 112(11), 4048–4060.

    Article  Google Scholar 

  • Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnell, R. B., & Viau, A. A. (2003). Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture, 39(2), 67–93.

    Article  Google Scholar 

  • Gunderson, L. H. (1997). Vegetation of the Everglades: Determinants of community composition. In S. M. Davis & J. C. Ogden (Eds.), Everglades: The ecosystem and its restoration (pp. 323–340). Baca Raton: CRC Press.

    Google Scholar 

  • Haralick, R. M., Dinstein, I., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621.

    Article  Google Scholar 

  • Harvey, K. R., & Hill, G. J. E. (2001). Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: a comparison of aerial photography, Landsat TM and SPOT satellite imagery. International Journal of Remote Sensing, 22(15), 2911–2925. doi:10.1080/01431160119174.

    Article  Google Scholar 

  • Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River N.J.: Prentice Hall.

  • Hepner, G., Logan, T., Ritter, N., & Bryant, N. (1990). Artificial neural network classification using a minimal training set: comparison to conventional supervised classification. Photogrammetric Engineering and Remote Sensing, 56(4), 469–473.

    Google Scholar 

  • Jenkins, C. N., Powell, R. D., Bass, O. L., & Pimm, S. L. (2003). Demonstrating the destruction of the habitat of the Cape Sable seaside sparrow (Ammodramus maritimus mirabilis). Animal Conservation, 6(1), 29–38.

  • Johansen, K., Coops, N. C., Gergel, S. E., & Stange, Y. (2007). Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sensing of Environment, 110(1), 29–44.

    Article  Google Scholar 

  • Junk, W. J., Brown, M., Campbell, I. C., Finlayson, M., Gopal, B., Ramberg, L., & Warner, B. G. (2006). The comparative biodiversity of seven globally important wetlands: a synthesis. Aquatic Sciences, 68(3), 400–414. doi:10.1007/s00027-006-0856-z.

    Article  Google Scholar 

  • Kim, H., & Loh, W. Y. (2001). Classification trees with unbiased multiway splits. Journal of the American Statistical Association, 96(454), 589–604.

    Article  Google Scholar 

  • Kindscher, K., Fraser, A., Jakubauskas, M. E., & Debinski, D. M. (1997). Identifying wetland meadows in Grand Teton National Park using remote sensing and average wetland values. Wetlands Ecology and Management, 5(4), 265–273.

    Article  Google Scholar 

  • Larose, D. T. (2004). Discovering knowledge in data: An introduction to data mining (1st ed.). Hoboken, New Jersey: Wiley, John & Sons.

  • Larsen, L., Aumen, N., Bernhardt, C., Engel, V., Givnish, T., Hagerthey, S., et al. (2011). Recent and historic drivers of landscape change in the Everglades ridge, slough, and tree island mosaic. Critical Reviews in Environmental Science and Technology, 41, 344–381. doi:10.1080/10643389.2010.531219.

    Article  CAS  Google Scholar 

  • Lloyd, C. D., Berberoglu, S., Curran, P. J., & Atkinson, P. M. (2004). A comparison of texture measures for the per-field classification of Mediterranean land cover. International Journal of Remote Sensing, 25(19), 3943–3965. doi:10.1080/0143116042000192321.

    Article  Google Scholar 

  • Lockwood, J. L., Fenn, K. H., Caudill, J. M., Okines, D., Bass, O. L., Duncan, J. R., & Pimm, S. L. (2001). The implications of Cape Sable seaside sparrow demography for Everglades restoration. Animal Conservation, 4(3), 275–281.

  • Loh, W. Y., & Shih, Y. S. (1997). Split selection methods for classification trees. Statistica Sinica, 7, 815–840.

    Google Scholar 

  • Madden, M., Jones, D., & Vilchek, L. (1999). Photointerpretation key for the Everglades vegetation classification system. Photogrammetric Engineering & Remote Sensing, 65(2), 171–177.

    Google Scholar 

  • Marella, R. L. (2009). Water withdrawals, use, and trends in Florida, 2005 (Scientific Investigations Report No. 2009–5125). Reston, Virginia: Florida Department of Environmental Protection, U.S. Department of the Interior, U.S. Geological Survey.

  • Mas, J. F. (2004). Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks. Estuarine, Coastal and Shelf Science, 59(2), 219–230.

    Article  Google Scholar 

  • Mas, J. F., & Flores, J. J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3), 617–663. doi:10.1080/01431160701352154.

    Article  Google Scholar 

  • Mather, P. (2004). Computer processing of remotely sensed images: An introduction (3rd ed.). Chichester: Wiley.

    Google Scholar 

  • Maxa, M., & Bolstad, P. (2009). Mapping northern wetlands with high resolution satellite images and LiDAR. Wetlands, 29(1), 248–260.

    Article  Google Scholar 

  • Michishita, R., Xu, B., & Gong, P. (2008). A decision tree classifier for the monitoring of wetland vegetation using Aster data in the Poyang lake region, China. In The international archives of the photogrammetry, remote sensing and spatial information sciences. (Vol. XXXVII. Part B8., p. 8). Beijing, China.

  • Mitsch, W., & Gosselink, J. (2007). Wetlands (4th ed.). Hoboken: Wiley.

    Google Scholar 

  • Nielsen, E. M., Prince, S. D., & Koeln, G. T. (2008). Wetland change mapping for the US mid-Atlantic region using an outlier detection technique. Remote Sensing of Environment, 112(11), 4061–4074.

    Article  Google Scholar 

  • Nott, M. P., Bass, O. L., Fleming, D. M., Killeffer, S. E., Fraley, N., Manne, L., et al. (1998). Water levels, rapid vegetational changes, and the endangered Cape Sable seaside-sparrow. Animal Conservation, 1(1), 23–32.

  • Olmsted, I. C., & Armentano, T. V. (1997). Vegetation of Shark Slough, Everglades National Park (Technical Report No. 97-001) (p. 43). Everglades National Park: South Florida Natural Resources Center.

    Google Scholar 

  • Ozesmi, S. L., & Bauer, M. E. (2002). Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10(5), 381–402.

    Article  Google Scholar 

  • Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86(4), 554–565.

    Article  Google Scholar 

  • Paola, J. D., & Schowengerdt, R. A. (1995). A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and Remote Sensing, 33(4), 981–997.

    Article  Google Scholar 

  • Pearlstine, L. G., Smith, S. E., Brandt, L. A., Allen, C. R., & Kitchens, W. M. (2002). Assessing state-wide biodiversity in the Florida Gap analysis project. Journal of Environmental Management, 66, 127–144.

    Article  CAS  Google Scholar 

  • Pimm, S. L., Lockwood, J. L., Jenkins, C., Curnutt, J. L., Nott, M. P., Powell, R., & Bass Jr., O. L. (2002). A sparrow in the grass. A report on the first ten years of research on the Cape Sable Seaside Sparrow (p. 182). Homestead, Florida: National Park Service, Everglades National Park.

  • Ranawana, R., & Palade, V. (2005). A neural network based multi-classifier system for gene identification in DNA sequences. Neural Computing and Applications, 14(2), 122–131.

  • RECOVER (Restoration Coordination & Verification). (2004). CERP monitoring and assessment plan: Part 1 monitoring and supporting research. West Palm Beach: United States Army Corps of Engineers, FL, USA and South Florida Water Management District.

    Google Scholar 

  • Richards, J., & Jia, X. (2006). Remote sensing digital image analysis an introduction. Berlin: Springer.

    Google Scholar 

  • Richardson, C. (2010). The Everglades: North America’s subtropical wetland. Wetlands Ecology and Management, 18(5), 517–542.

    Article  Google Scholar 

  • Ross, M. S., Reed, D. L., Sah, J. P., Ruiz, P. L., & Lewin, M. T. (2003). Vegetation: environment relationships and water management in Shark Slough, Everglades National Park. Wetlands Ecology and Management, 11(5), 291–303.

    Article  Google Scholar 

  • Ross, M. S., Mitchell-Bruker, S., Sah, J. P., Stothoff, S., Ruiz, P. L., Reed, D. L., et al. (2006). Interaction of hydrology and nutrient limitation in the Ridge and Slough landscape of the southern Everglades. Hydrobiologia, 569(1), 37–59. doi:10.1007/s10750-006-0121-4.

    Article  CAS  Google Scholar 

  • Rutchey, K., Schall, T., Doren, R., Atkinson, A., Ross, M., Jones, D., et al. (2006). Vegetation classification for South Florida natural areas (Open-File Report No. 2006-1240) (p. 142). Saint Petersburg: United States Geological Survey.

    Google Scholar 

  • Rutchey, K., Schall, T., & Sklar, F. (2008). Development of vegetation maps for assessing Everglades restoration progress. Wetlands, 28(3), 806–816.

    Article  Google Scholar 

  • Schmidt, K. S., & Skidmore, A. K. (2003). Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment, 85(1), 92–108.

    Article  Google Scholar 

  • Sesnie, S. E., Gessler, P. E., Finegan, B., & Thessler, S. (2008). Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments. Remote Sensing of Environment, 112(5), 2145–2159.

    Article  Google Scholar 

  • Szantoi, Z., Escobedo, F., Abd-Elrahman, A., Smith, S., & Pearlstine, L. (2013). Analyzing fine-scale wetland composition using high resolution imagery and texture features. International Journal of Applied Earth Observation and Geoinformation, 23, 204–212. doi:10.1016/j.jag.2013.01.003.

    Article  Google Scholar 

  • Wang, L., Sousa, W. P., Gong, P., & Biging, G. S. (2004). Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment, 91(3–4), 432–440.

    Article  Google Scholar 

  • Wolf, P., & Dewitt, B. (2000). Elements of photogrammetry: With applications in GIS (3rd ed.). Boston: McGraw-Hill.

    Google Scholar 

  • Wright, C., & Gallant, A. (2007). Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment, 107(4), 582–605.

    Article  Google Scholar 

  • Yang, C. C., Prasher, S. O., Enright, P., Madramootoo, C., Burgess, M., Goel, P. K., & Callum, I. (2003). Application of decision tree technology for image classification using remote sensing data. Agricultural Systems, 76(3), 1101–1117.

    Article  Google Scholar 

  • Zhang, S., Liu, H. X., Gao, D. T., & Wang, W. (2003). Surveying the methods of improving ANN generalization capability. In Proceedings of the second international conference on machine learning and cybernetics (Vol. 2, pp. 1259–1263.). Presented at the Second International Conference on Machine Learning and Cybernetics, Xian, China.

  • Zweig, C. L., & Kitchens, W. M. (2008). Effects of landscape gradients on wetland vegetation communities: information for large-scale restoration. Wetlands, 28(4), 1086–1096.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Land Resource Management Unit, Joint Research Centre, European Commission, Ispra, 21027, VA, Italy

    Zoltan Szantoi

  2. School of Forest Resources and Conservation, University of Florida, 361 Newins-Ziegler Hall, PO Box 110410, Gainesville, FL, 32611-0410, USA

    Francisco J. Escobedo

  3. Gulf Coast REC / School of Forest Resources and Conservation – Geomatics Program, University of Florida, 1200 N. Park Road, Plant City, FL, 33563, USA

    Amr Abd-Elrahman

  4. South Florida Natural Resources Center, Everglades National Park, National Park Service, 950 N. Krome Avenue, Homestead, FL, 33030, USA

    Leonard Pearlstine

  5. School of Forest Resources and Conservation – Geomatics Program, University of Florida, 305 Reed Lab, P.O. Box 110565, Gainesville, FL, 32611-0565, USA

    Bon Dewitt

  6. School of Forest Resources and Conservation – Geomatics Program, University of Florida, 301 Reed Lab, PO Box 110565, Gainesville, FL, 32611-0565, USA

    Scot Smith

Authors
  1. Zoltan Szantoi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco J. Escobedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amr Abd-Elrahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Leonard Pearlstine
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bon Dewitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Scot Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zoltan Szantoi.

Additional information

Research was conducted in the Geomatics program at the University of Florida's School of Forest Resources & Conservation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Szantoi, Z., Escobedo, F.J., Abd-Elrahman, A. et al. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features. Environ Monit Assess 187, 262 (2015). https://doi.org/10.1007/s10661-015-4426-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-015-4426-5

Keywords

Search

Navigation