Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach
"> Figure 1
<p>Location of Selenga River Delta study area in the Kabansky District of the Baikalsky Nature Preserve, shown with bands 7, 4, and 3 from the WorldView-2 (WV2) imagery as background.</p> "> Figure 2
<p>Adjacent WorldView-2 multi-spectral images (near-infrared false-color composite of bands 7, 5, and 3) with ground control points and ground-truth sites.</p> "> Figure 3
<p>Normalized Difference Vegetation Index (NDVI) derived from the near-infrared 1 (NIR1) and red bands.</p> "> Figure 4
<p>Homogeneity texture measure based on the grey-level co-occurrence matrices (GLCM).</p> "> Figure 5
<p>The data processing and information flow of the hybrid classification approach.</p> "> Figure 6
<p>Level 3 classification at the Class level of the Cowardin scheme.</p> "> Figure 7
<p>Level 4 classification at the Subclass level of the Cowardin scheme.</p> "> Figure 8
<p>Genus and community aquatic and wetland habitat classification at Level 5.</p> "> Figure 9
<p>Coarse-scale binary classification of the Selenga River Delta.</p> ">
Abstract
:1. Introduction
2. Methods
2.1. Study Area
2.2. Satellite Data Acquisition and Processing
2.3. Vegetation Abundance and Habitat Structure Characterization
2.4. Hybrid Classification
2.4.1. Unsupervised Classification
2.4.2. Field Sampling
2.4.3. Development of Training and Validation Datasets
2.4.4. Indicator Species Analysis
2.4.5. Supervised Classification and Multi-Scale Aggregation
2.4.6. Testing the Efficacy of Additional Spectral Bands, NDVI, and Texture
3. Results
3.1. Vegetation and Site Data
3.2. Multi-Scale Hierarchical Habitat Classification
3.3. Accuracy Assessment
Class | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | PA | UA |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 92.5 | - | 0.1 | - | - | - | 26.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 92.5 | 92.7 |
2 | - | 60.7 | - | - | - | - | 0.5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 60.7 | 99.2 |
3 | 7.4 | 36.8 | 91.6 | 4.7 | - | 0.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 91.6 | 79.7 |
4 | - | - | 8.3 | 94.6 | 2.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 94.6 | 82.3 |
5 | - | - | - | 0.4 | 97.6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 97.6 | 99.5 |
6 | - | - | - | - | - | 93.5 | 5.3 | 2.1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 93.5 | 96.6 |
7 | 0.1 | 2.5 | - | - | - | 4.0 | 67.8 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 67.8 | 86.8 |
8 | - | - | 0.1 | - | - | 0.7 | - | 70.6 | 16.9 | - | - | - | - | - | - | - | - | - | - | - | - | - | 70.6 | 82.4 |
9 | - | - | - | - | - | 1.2 | - | 9.6 | 69.9 | 8.5 | 2.0 | 0.6 | - | - | - | - | - | - | - | - | - | 0.2 | 69.9 | 52.3 |
10 | - | 0.2 | - | - | - | 0.4 | - | 17.8 | 1.2 | 86.5 | 4.5 | 0.2 | - | - | - | - | - | - | - | - | - | - | 86.5 | 71.4 |
11 | - | - | - | - | - | - | - | - | 2.4 | 5.0 | 86.8 | 4.9 | - | - | - | - | - | - | - | - | - | - | 86.8 | 88.0 |
12 | - | - | - | - | - | - | - | - | - | - | 6.8 | 91.2 | 1.5 | - | - | - | - | - | - | - | - | - | 91.2 | 95.2 |
13 | - | - | - | - | - | - | - | - | - | - | - | 2.7 | 98.3 | - | - | - | - | - | - | - | - | - | 98.3 | 96.2 |
14 | - | - | - | 0.4 | 0.2 | - | - | - | 8.4 | - | - | - | - | 97.2 | 3.7 | - | - | - | 0.7 | - | - | 0.1 | 97.2 | 90.2 |
15 | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.8 | 79.3 | 4.8 | - | - | - | - | - | 0.1 | 79.3 | 84.3 |
16 | - | - | - | - | - | - | - | - | - | - | - | - | - | 1.2 | 16.6 | 94.2 | - | 0.4 | 2.2 | - | - | 0.8 | 94.2 | 88.2 |
17 | - | - | - | - | - | - | - | - | 1.2 | - | - | - | 0.2 | 0.4 | 0.5 | - | 83.9 | 19.8 | 7.9 | - | - | 0.1 | 83.9 | 78.9 |
18 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.7 | 12.2 | 74.4 | 27.7 | 1.3 | - | 1.0 | 74.4 | 53.4 |
19 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 2.5 | 3.0 | 60.9 | - | - | - | 60.9 | 93.0 |
20 | - | - | - | - | - | - | - | - | - | - | - | 0.2 | - | - | - | - | 1.0 | 2.2 | 0.1 | 94.4 | 8.1 | 14.2 | 94.6 | 74.5 |
21 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 89.1 | 2.1 | 89.1 | 94.7 |
22 | - | - | - | - | - | - | - | - | - | - | - | 0.3 | - | 0.4 | - | 0.3 | 0.4 | 0.2 | 0.4 | 4.3 | 2.8 | 81.4 | 81.4 | 95.0 |
3.4. ISA/Vegetation Analyses
Class # | Class Name (Indicator and/or Hab. Descriptor) | Indicator Taxa/Habitat | Indicator Value Mean | Indicator p-Value | Dominant Species/Substrate |
---|---|---|---|---|---|
1 | Deep Water with Sand Bottom | Not detected | Open water | ||
2 | Shallow Water with Sediment | Not detected | Open water | ||
3 | Shallow Water with Mud Bottom | Not detected | Open water | ||
4 | Very Shallow Water with Sand Bottom | Not detected | Open water | ||
5 | Shallow Water with Sand Bottom | Not detected | Open water | ||
6 | Submerged Aquatic Vascular (Potamogeton) | Potamogeton | 21.1 | 0.0464 | Potamogeton |
7 | Submerged Aquatic Vascular (Sparganium) | Sparganium | 20.9 | 0.0158 | Sparganium |
8 | Submerged Aquatic Vascular (Utricularia) | Utricularia | 31.1 | 0.0661 | Utricularia |
9 | Submerged and Floating Vascular (Agrostis/Eleocharis) | Not detected | Agrostis and Eleocharis | ||
10 | Very Sparse Floating Vascular (Nymphoides) | Not detected | Nymphoides | ||
11 | Sparse Floating Vascular (Nymphoides) | Not detected | Nymphoides | ||
12 | Dense Floating Vascular (Nymphoides) | Not detected | Nymphoides | ||
13 | Very Dense Floating Vascular (Nymphoides) | Nymphoides | 14.3 | 0.0001 | Nymphoides |
14 | Persistent Emergent (Phragmites) | Phragmites | 25.6 | 0.0044 | Phragmites |
15 | Persistent Emergent (Bare Ground and Carex) | Bare Ground | 22.3 | 0.0276 | Bare Ground and Carex |
16 | Persistent Emergent (Equisetum) | Equisetum | 16.3 | 0.0262 | Equisetum |
17 | Persistent Emergent (Thatch) | Thatch | 15.7 | 0.0006 | Thatch |
18 | Persistent Emergent (Carex) | Carex | 17.0 | 0.0028 | Carex |
19 | Persistent Emergent (Calamagrostis) | Calamagrostis | 16.6 | 0.0365 | Calamagrostis |
20 | Persistent Emergent (Scolochloa) | Scolochloa | 22.3 | 0.0097 | Scolochloa |
21 | Persistent Emergent (Amoria/Galeopsis/Trifolium) | Not detected | Amoria, Galeopsis, and Trifolium | ||
22 | Scrub-shrub (Salix with Calamagrostis) | Not detected | Salix and Calamagrostis |
Class # | Class Name (Indicator and/or hab. Descriptor) | Indicator Taxa/Habitat | Indicator Value Mean | Indicator p-Value | Dominant Species/Substrate |
---|---|---|---|---|---|
1 | Deep Water with Sand Bottom | Open water | 15.4 | 0.0127 | Open Water |
2 | Shallow Water with Mud Bottom | Not detected | Open Water | ||
3 | Shallow Water with Sand bottom | Not detected | Open Water | ||
4 | Submerged Aquatic Vascular (Myriophyllum) | Myriophyllum | 17.9 | 0.0119 | Myriophyllum |
5 | Sparse Floating Vascular (Nuphar) | Nuphar | 19.7 | 0.0411 | Nuphar |
6 | Dense Floating Vascular (Nymphoides) | Nymphoides | 15.9 | 0.0001 | Nymphoides |
7 | Persistent Emergent (Phragmites) | Phragmites | 17.8 | 0.0017 | Phragmites |
8 | Persistent Emergent (Equisetum/Bare Ground) | Equisetum Bare Ground | 16.7, 18.3 | 0.0508, 0.0217 | Equisetum and Bare Ground |
9 | Persistent Emergent (Thatch) | Thatch | 16.4 | 0.0014 | Thatch |
10 | Persistent Emergent (Carex) | Carex | 17.6 | 0.0274 | Carex |
11 | Persistent Emergent (Scolochloa) | Scolochloa | 17.8 | 0.0069 | Scolochloa |
12 | Persistent Emergent (Amoria) | Not detected | Amoria | ||
13 | Scrub-Shrub (Salix with Calamagrostis) | Calamagrostis | 16.9 | 0.0873 | Salix and Calamagrostis |
3.5. Improved Classification with Additional Spectral Bands and Metrics
Input | Overall Accuracy |
---|---|
4 traditional bands only (red, blue, green, NIR) | 79.0% |
4 traditional bands plus coastal band | 80.4% |
4 traditional bands plus yellow, red-edge, NIR2 bands | 82.0% |
8 bands (4 traditional bands plus 4 new bands) | 82.9% |
8 bands plus NDVI | 83.9% |
8 bands plus Texture | 84.8% |
8 bands plus NDVI and Texture | 86.5% |
Class # | Class Name | NDVI | Homogeneity | ||
---|---|---|---|---|---|
Mean | Stdv | Mean | Stdv | ||
1 | Deep Water with Sand Bottom | −0.33 | 0.04 | 0.72 | 0.09 |
2 | Shallow Water with Sediment | −0.19 | 0.05 | 0.86 | 0.16 |
3 | Shallow Water with Mud Bottom | −0.27 | 0.11 | 0.87 | 0.16 |
4 | Very Shallow Water with Sand Bottom | −0.18 | 0.09 | 0.87 | 0.13 |
5 | Shallow Water with Sand Bottom | 0.00 | 0.04 | 0.66 | 0.16 |
6 | Submerged Aquatic Vascular (Potamogeton) | −0.05 | 0.08 | 0.90 | 0.16 |
7 | Submerged Aquatic Vascular (Sparganium) | −0.23 | 0.04 | 0.97 | 0.05 |
8 | Submerged Aquatic Vascular (Utricularia) | 0.19 | 0.06 | 0.80 | 0.15 |
9 | Submerged and Floating Vascular (Agrostis/Eleocharis) | 0.36 | 0.08 | 0.45 | 0.19 |
10 | Very Sparse Floating Vascular (Nymphoides) | 0.25 | 0.08 | 0.61 | 0.15 |
11 | Sparse Floating Vascular (Nymphoides) | 0.32 | 0.06 | 0.64 | 0.14 |
12 | Dense Floating Vascular (Nymphoides) | 0.41 | 0.05 | 0.60 | 0.14 |
13 | Very Dense Floating Vascular (Nymphoides) | 0.57 | 0.04 | 0.52 | 0.16 |
14 | Persistent Emergent (Phragmites) | 0.32 | 0.07 | 0.64 | 0.12 |
15 | Persistent Emergent (Bare Ground and Carex) | 0.41 | 0.06 | 0.72 | 0.12 |
16 | Persistent Emergent (Equisetum) | 0.54 | 0.04 | 0.71 | 0.14 |
17 | Persistent Emergent (Thatch) | 0.37 | 0.06 | 0.67 | 0.16 |
18 | Persistent Emergent (Carex) | 0.51 | 0.04 | 0.59 | 0.11 |
19 | Persistent Emergent (Calamagrostis) | 0.39 | 0.04 | 0.75 | 0.11 |
20 | Persistent Emergent (Scolochloa) | 0.67 | 0.05 | 0.50 | 0.17 |
21 | Persistent Emergent (Amoria/Galeopsis/Trifolium) | 0.78 | 0.03 | 0.63 | 0.15 |
22 | Scrub-shrub (Salix with Calamagrostis) | 0.66 | 0.06 | 0.24 | 0.11 |
4. Discussion
4.1. Classification of the Selenga River Delta
4.1.1. Classification Overview
4.1.2. Classification Error Assessment
4.2. Indicator Species Analysis
4.3. Additional Bands, NDVI, and Texture Metrics
5. Summary
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Daily, G.C.; Alexander, S.; Ehrlich, P.R.; Goulder, L.; Lubchenco, J.; Matson, P.A.; Mooney, H.A.; Postel, S.; Schneider, S.H.; Tilman, D. Ecosystem Services: Benefits Supplied to Human Societies by Natural Ecosystems; Ecological Society of America: Washington, DC, USA, 1997. [Google Scholar]
- Millennium Ecosystem Assessment. In Millennium Ecosystem Assessment Synthesis Report; Millennium Ecosystem Assessment: Washington, DC, USA, 2005.
- Dahl, T.E. Status and Trends of Wetlands in the Conterminous United States 1986 to 1997; US Fish and Wildlife Service: Washington, DC, USA, 2000. [Google Scholar]
- Dahl, T.E.; Watmough, M.D. Current approaches to wetland status and trends monitoring in prairie Canada and the continental United States of America. Can. J. Remote Sens. 2007, 33, S17–S27. [Google Scholar] [CrossRef]
- Finlayson, C.; Davidson, N.; Spiers, A.; Stevenson, N. Global wetland inventory—Current status and future priorities. Mar. Freshw. Res. 1999, 50, 717–727. [Google Scholar] [CrossRef]
- Mitsch, W.J. Wetlands; Van Nostrand Rheinhold: New York, NY, USA, 1993; p. 722. [Google Scholar]
- Robarts, R.D.; Zhulidov, A.V.; Pavlov, D.F. The state of knowledge about wetlands and their future under aspects of global climate change: The situation in Russia. Aquat. Sci. 2013, 75, 27–38. [Google Scholar] [CrossRef]
- Titus, J.; Hudgens, D.; Trescott, D.; Craghan, M.; Nuckols, W.; Hershner, C.; Kassakian, J.; Linn, C.; Merritt, P.; McCue, T. State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast. Environ. Res. Lett. 2009, 4, 044008. [Google Scholar] [CrossRef]
- McKinney, R.A.; Charpentier, M.A. Extent, properties, and landscape setting of geographically isolated wetlands in urban southern New England watersheds. Wetl. Ecol. Manag. 2009, 17, 331–344. [Google Scholar] [CrossRef]
- Ozesmi, S.L.; Bauer, M.E. Satellite remote sensing of wetlands. Wetl. Ecol. Manag. 2002, 10, 381–402. [Google Scholar] [CrossRef]
- Bourgeau-Chavez, L.L.; Riordan, K.; Powell, R.B.; Miller, N.; Nowels, M. Improving wetland characterization with multi-sensor, multi-temporal SAR and optical/infrared data fusion. In Advances in Geoscience and Remote Sensing; INTECH: Rijeka, Croatia, 2009. [Google Scholar]
- Wickham, J.; Stehman, S.; Smith, J.; Yang, L. Thematic accuracy of the 1992 National Land-Cover Data for the western United States. Remote Sens. Environ. 2004, 91, 452–468. [Google Scholar] [CrossRef]
- Wright, C.; Gallant, A. Improved wetland remote sensing in Yellowstone national park using classification trees to combine TM imagery and ancillary environmental data. Remote Sens. Environ. 2007, 107, 582–605. [Google Scholar] [CrossRef]
- Klemas, V. Remote sensing of wetlands: Case studies comparing practical techniques. J. Coast. Res. 2011, 27, 418–427. [Google Scholar] [CrossRef]
- Adam, E.; Mutanga, O.; Rugege, D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review. Wetl. Ecol. Manag. 2010, 18, 281–296. [Google Scholar]
- Silva, T.S.; Costa, M.P.; Melack, J.M.; Novo, E.M. Remote sensing of aquatic vegetation: Theory and applications. Environ. Monitor. Assess. 2008, 140, 131–145. [Google Scholar] [CrossRef]
- Hess, L.L.; Melack, J.M.; Filoso, S.; Wang, Y. Delineation of inundated area and vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Trans. Geosci. Remote Sens. 1995, 33, 896–904. [Google Scholar] [CrossRef]
- Kasischke, E.S.; Bourgeau-Chavez, L.L. Monitoring South Florida wetlands using ERS-1 SAR imagery. Photogram. Eng. Remote Sens. 1997, 63, 281–291. [Google Scholar]
- Kushwaha, S.; Dwivedi, R.; Rao, B. Evaluation of various digital image processing techniques for detection of coastal wetlands using ERS-1 SAR data. Int. J. Remote Sens. 2000, 21, 565–579. [Google Scholar] [CrossRef]
- Townsend, P.A.; Walsh, S.J. Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing. Geomorphology 1998, 21, 295–312. [Google Scholar] [CrossRef]
- DigitalGlobe. Whitepaper: The Benefits of the 8 Spectral Bands of Worldview-2. DigitalGlobe. 2010. Available online: https://www.digitalglobe.com/sites/default/files/DG-8SPECTRAL-WP_0.pdf (accessed on 22 June 2014).
- Yuzugullu, O.; Aksoy, A. Generation of the bathymetry of a eutrophic shallow lake using Worldview-2 imagery. J. Hydroinform. 2014, 16, 50–59. [Google Scholar] [CrossRef]
- Lee, K.R.; Kim, A.M.; Olsen, R.; Kruse, F.A. Using Worldview-2 to determine bottom-type and bathymetry. Proc. SPIE 2011. [Google Scholar] [CrossRef]
- Asmaryan, S.; Warner, T.A.; Muradyan, V.; Nersisyan, G. Mapping tree stress associated with urban pollution using the Worldview-2 red edge band. Remote Sens. Lett. 2013, 4, 200–209. [Google Scholar] [CrossRef]
- Cronk, J.K.; Fennessy, M.S. Wetland Plants: Biology and Ecology; CRC Press: New York, NY, USA, 2001. [Google Scholar]
- Collin, A.; Hench, J.L. Towards deeper measurements of tropical reefscape structure using the Worldview-2 spaceborne sensor. Remote Sens 2012, 4, 1425–1447. [Google Scholar] [CrossRef]
- Reshitnyk, L.; Costa, M.; Robinson, C.; Dearden, P. Evaluation of Worldview-2 and acoustic remote sensing for mapping benthic habitats in temperate coastal Pacific waters. Remote Sens. Environ. 2014, 153, 7–23. [Google Scholar]
- Lantz, N.J.; Wang, J. Object-based classification of Worldview-2 imagery for mapping invasive common reed, Phragmites australis. Can. J. Remote Sens. 2013, 39, 328–340. [Google Scholar] [CrossRef]
- Ramsar Convention on Wetlands Factsheet. Information Sheet on Ramsar Wetlands. Available online: http://sites.wetlands.org/reports/ris/2RU018en.pdf (accessed on 13 November 2012).
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef]
- Haralick, R.M.; Shanmugam, K.; Dinstein, I.H. Textural features for image classification. IEEE Trans. Syst. Man. Cybern. 1973, 3, 610–621. [Google Scholar] [CrossRef]
- Gyninova, A.; Korsunov, V. The soil cover of the Selenga Delta area in the Baikal region. Eurasian Soil Sci. 2006, 39, 243–250. [Google Scholar] [CrossRef]
- Konovalova, T. Mapping geosystems in the Selenga River delta. Mapp. Sci. Remote Sens. 2002, 39, 295–302. [Google Scholar]
- Ilyicheva, E. Dynamics of the Selenga River network and delta structure. Geogr. Nat. Resour. 2008, 29, 343–347. [Google Scholar] [CrossRef]
- Pavelsky, T.M.; Smith, L.C. Remote sensing of hydrologic recharge in the Peace–Athabasca Delta, Canada. Geophys. Res. Lett. 2008, 35, L08403. [Google Scholar] [CrossRef]
- Hui, Y.; Rongqun, Z.; Xianwen, L. Classification of wetland from tm imageries based on decision tree. WSEAS Trans. Infor. Sci. Appl. 2009, 6, 1790–0832. [Google Scholar]
- Melesse, A.M.; Jordan, J.D.; Graham, W.D. Enhancing land cover mapping using Landsat derived surface temperature and NDVI. Bridges 2001, 10, 439. [Google Scholar]
- Narumalani, S.; Jensen, J.R.; Burkhalter, S.; Althausen, J.D.; Mackey, H.E., Jr. Aquatic macrophyte modeling using GIS and logistic multiple regression. Photogram. Eng. Remote Sens. 1997, 63, 41–49. [Google Scholar]
- Hodgson, M.; Jensen, J.; Mackey H., Jr.; Coulter, M. Remote sensing of wetland habitat: A wood stork example. Photogram. Eng. Remote Sens. 1987, 53, 1075–1080. [Google Scholar]
- Dufrêne, M.; Legendre, P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol. Monogr. 1997, 67, 345–366. [Google Scholar]
- Ball, G.H.; Hall, D.J. Isodata, A Novel Method of Data Analysis and Pattern Classification; DTIC Document; Stanford Research Institute: Menlo Park, CA, USA, 1965. [Google Scholar]
- Tou, J.T.; Gonzalez, R.C. Pattern Recognition Principles; Addison-Wesley: London, UK, 1974. [Google Scholar]
- Jain, A.K.; Dubes, R.C. Algorithms for Clustering Data; Prentice-Hall, Inc.: Englewood Cliffs, NJ, USA, 1988. [Google Scholar]
- Jensen, J.R. Remote Sensing of the Environment: An Earth Resource Perspective; Prentice-Hall, Inc.: Upper Saddle River, NJ, USA, 2007. [Google Scholar]
- Richards, J.; Jia, X. Remote Sensing Digital Image Analysis—An Introduction, 3rd ed.; Springer-Verlag New York, Inc.: New York, NY, USA, 2006. [Google Scholar]
- McCune, B.; Grace, J.B.; Urban, D.L. Analysis of Ecological Communities; MjM Software Design: Gleneden Beach, OR, USA, 2002. [Google Scholar]
- Brinson, M.M. A Hydrogeomorphic Classification for Wetlands; Technical Report WRP-DE-4; US Army Corps of Engineers, Waterways Experiment Station: Washington, DC, USA, 1993. [Google Scholar]
- Foody, G.M.; Campbell, N.; Trodd, N.; Wood, T. Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogram. Eng. Remote Sens. 1992, 58, 1335–1341. [Google Scholar]
- Cowardin, L.M.; Carter, V.; Golet, F.C.; LaRoe, E.T. Classification of Wetlands and Deepwater Habitats of the United States; Fish and Wildlife Service, US Department of the Interior: Washington, DC, USA, 1979. [Google Scholar]
- Ramsar Convention of Wetlands. The Ramsar Convention Definition of “Wetland” and Classification System for Wetland Type. Available online: http://www.ramsar.org/cda/en/ramsar-activities-cepa-classification-system/main/ramsar/1-63-69%5E21235_4000_0 (accessed on 2 Ocotober 2012).
- Cowardin, L.M.; Golet, F.C. US Fish and Wildlife Service 1979 wetland classification: A review. Vegetatio 1995, 118, 139–152. [Google Scholar] [CrossRef]
- Lillesand, T.M.; Kiefer, R.W.; Chipman, J.W. Remote Sensing and Image Interpretation; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2004. [Google Scholar]
- Stankiewicz, K.; Dabrowska-Zielinska, K.; Gruszczynska, M.; Hoscilo, A. Mapping vegetation of a wetland ecosystem by fuzzy classification of optical and microwave satellite images supported by various ancillary data. In Proceedinsg of International Symposium on Remote Sensing, Crete, Greece, 23 September 2002; International Society for Optics and Photonics: Bellingham, WA, USA, 2003; pp. 352–361. [Google Scholar]
- Oki, K.; Oguma, H.; Sugita, M. Subpixel classification of alder trees using multitemporal Landsat Thematic Mapper imagery. Photogram. Eng. Remote Sens. 2002, 68, 77–82. [Google Scholar]
- Shanmugam, P.; Ahn, Y.-H.; Sanjeevi, S. A comparison of the classification of wetland characteristics by linear spectral mixture modelling and traditional hard classifiers on multispectral remotely sensed imagery in southern India. Ecol. Model. 2006, 194, 379–394. [Google Scholar] [CrossRef]
- Wang, J.; Lang, P.A. Detection of cypress canopies in the Florida Panhandle using subpixel analysis and GIS. Remote Sens. 2009, 1, 1028–1042. [Google Scholar] [CrossRef]
- Huguenin, R.L.; Karaska, M.A.; van Blaricom, D.; Jensen, J.R. Subpixel classification of bald cypress and tupelo gum trees in Thematic Mapper imagery. Photogram. Eng. Remote Sens. 1997, 63, 717–724. [Google Scholar]
- Baker, C.; Lawrence, R.; Montagne, C.; Patten, D. Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands 2006, 26, 465–474. [Google Scholar] [CrossRef]
- Bolstad, P.V.; Lillesand, T. Improved classification of forest vegetation in northern Wisconsin through a rule-based combination of soils, terrain, and Landsat Thematic Mapper data. For. Sci. 1992, 38, 5–20. [Google Scholar]
- Frick, A.; Steffenhagen, P.; Zerbe, S.; Timmermann, T.; Schulz, K. Monitoring of the vegetation composition in rewetted peatland with iterative decision tree classification of satellite imagery. Photogram. Fernerkund. Geoinfor. 2011, 2011, 109–122. [Google Scholar] [CrossRef]
- Lunetta, R.S.; Balogh, M.E. Application of multi-temporal Landsat 5 TM imagery for wetland identification. Photogram. Eng. Remote Sens. 1999, 65, 1303–1310. [Google Scholar]
- Na, X.; Zhang, S.; Zhang, H.; Li, X.; Yu, H.; Liu, C. Integrating TM and ancillary geographical data with classification trees for land cover classification of marsh area. Chin. Geogr. Sci. 2009, 19, 177–185. [Google Scholar]
- Fournier, R.A.; Grenier, M.; Lavoie, A.; Hélie, R. Towards a strategy to implement the Canadian wetland inventory using satellite remote sensing. Can. J. Remote Sens. 2007, 33, S1–S16. [Google Scholar] [CrossRef]
- Frohn, R.; Autrey, B.; Lane, C.; Reif, M. Segmentation and object-oriented classification of wetlands in a Karst Florida landscape using multi-season Landsat-7 ETM+ imagery. Int. J. Remote Sens. 2011, 32, 1471–1489. [Google Scholar] [CrossRef]
- Grenier, M.; Labrecque, S.; Garneau, M.; Tremblay, A. Object-based classification of a SPOT-4 image for mapping wetlands in the context of greenhouse gases emissions: The case of the Eastmain region, Québec, Canada. Can. J. Remote Sens. 2008, 34, S398–S413. [Google Scholar] [CrossRef]
- Powers, R.P.; Hay, G.J.; Chen, G. How wetland type and area differ through scale: A GEOBIA case study in Albertaʼs boreal plains. Remote Sens. Environ. 2012, 117, 135–145. [Google Scholar] [CrossRef]
- Chen, Q. Comparison of Worldview-2 and IKONOS-2 Imagery for Identifying Tree Species in the Habitat of An Endangered Bird Species in Hawaii 8-Band Research Challenge; DigitalGlobe: Longmont, CO, USA, 2011. [Google Scholar]
- Immitzer, M.; Atzberger, C.; Koukal, T. Tree species classification with random forest using very high spatial resolution 8-band Worldview-2 satellite data. Remote Sens. 2012, 4, 2661–2693. [Google Scholar]
- Souza, U.D.; Kux, H.J. Use of Worldview-2 images and the knowledge-based software interimage for the classification of land cover in an urban area. In Proceedings of the 4th GEOBIA Conference, Rio de Janeiro, Brazil, 7–9 May 2012.
Appendix
Wetland Class | 4 Traditional Bands only (Red, Blue, Green, NIR) | 4 Traditional Bands plus Coastal Band | 4 Traditional Bands plus Yellow, Red-Edge, NIR2 Bands | 8 Bands (4 Traditional Bands plus 4 New Bands) | 8 Bands plus NDVI | 8 Bands plus Texture | 8 Bands plus NDVI and Texture | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PA (%) | UA (%) | PA (%) | UA (%) | PA (%) | UA (%) | PA (%) | UA (%) | PA (%) | UA (%) | PA (%) | UA (%) | PA (%) | UA (%) | |
1 | 84.91 | 86.20 | 87.50 | 97.77 | 85.98 | 99.31 | 87.82 | 99.59 | 91.98 | 92.75 | 89.69 | 97.87 | 92.47 | 92.74 |
2 | 64.66 | 87.36 | 67.30 | 87.64 | 60.86 | 88.37 | 61.51 | 88.59 | 61.79 | 98.19 | 61.42 | 90.39 | 60.65 | 99.24 |
3 | 66.98 | 72.20 | 68.38 | 72.05 | 73.72 | 72.63 | 74.89 | 74.40 | 88.54 | 79.40 | 80.44 | 76.23 | 91.56 | 79.66 |
4 | 95.47 | 55.54 | 93.29 | 55.66 | 95.65 | 61.98 | 94.80 | 62.90 | 95.69 | 77.42 | 93.48 | 68.45 | 94.59 | 82.26 |
5 | 96.32 | 99.73 | 95.90 | 99.56 | 98.32 | 99.68 | 98.79 | 99.68 | 97.06 | 99.62 | 98.79 | 98.17 | 97.55 | 99.46 |
6 | 95.49 | 97.69 | 95.25 | 97.36 | 91.43 | 99.12 | 91.58 | 98.99 | 86.65 | 97.96 | 93.62 | 98.02 | 93.49 | 96.58 |
7 | 76.84 | 86.13 | 94.62 | 96.36 | 95.52 | 90.22 | 95.94 | 90.58 | 70.70 | 76.37 | 83.78 | 93.62 | 67.79 | 86.79 |
8 | 72.63 | 76.57 | 72.43 | 75.54 | 68.93 | 78.27 | 69.34 | 78.01 | 70.58 | 79.40 | 66.67 | 79.61 | 70.55 | 82.40 |
9 | 68.23 | 54.78 | 66.43 | 52.72 | 70.04 | 53.30 | 68.95 | 51.07 | 69.68 | 54.21 | 70.04 | 50.26 | 69.88 | 52.25 |
10 | 79.96 | 56.22 | 78.68 | 60.99 | 82.30 | 55.70 | 79.74 | 58.07 | 84.84 | 71.87 | 81.24 | 54.04 | 86.52 | 71.35 |
11 | 77.94 | 81.67 | 80.81 | 81.99 | 75.66 | 81.07 | 78.02 | 80.82 | 88.22 | 90.37 | 78.36 | 80.19 | 86.76 | 88.00 |
12 | 88.31 | 94.58 | 88.89 | 94.43 | 88.27 | 94.44 | 88.45 | 94.23 | 91.89 | 96.20 | 87.33 | 93.21 | 91.19 | 95.17 |
13 | 97.29 | 92.92 | 97.36 | 93.62 | 97.36 | 90.26 | 97.42 | 90.65 | 99.03 | 95.17 | 96.91 | 90.60 | 98.28 | 96.22 |
14 | 94.26 | 88.53 | 96.95 | 88.81 | 95.60 | 91.37 | 97.92 | 92.82 | 97.07 | 91.17 | 97.56 | 92.48 | 97.15 | 90.19 |
15 | 81.99 | 73.72 | 84.76 | 76.88 | 86.57 | 76.31 | 87.67 | 78.83 | 82.83 | 83.64 | 87.40 | 78.78 | 79.26 | 84.31 |
16 | 91.60 | 75.33 | 90.83 | 78.84 | 90.22 | 86.63 | 90.17 | 88.05 | 94.27 | 85.59 | 89.45 | 93.52 | 94.20 | 88.18 |
17 | 82.30 | 81.49 | 83.26 | 78.74 | 80.47 | 74.56 | 81.76 | 74.01 | 82.30 | 81.66 | 86.17 | 70.47 | 83.93 | 78.91 |
18 | 78.56 | 48.30 | 76.65 | 48.06 | 81.24 | 48.80 | 79.93 | 48.73 | 77.55 | 49.51 | 75.88 | 52.26 | 74.40 | 53.42 |
19 | 59.93 | 88.59 | 59.14 | 90.08 | 47.12 | 92.01 | 47.04 | 92.45 | 57.94 | 92.72 | 49.50 | 93.17 | 68.92 | 92.99 |
20 | 84.34 | 62.93 | 85.29 | 65.34 | 92.77 | 67.37 | 93.09 | 68.72 | 95.86 | 63.79 | 88.93 | 76.77 | 94.43 | 74.54 |
21 | 80.22 | 89.17 | 80.89 | 91.53 | 82.95 | 94.44 | 83.99 | 95.85 | 86.55 | 93.04 | 86.42 | 97.79 | 89.10 | 94.74 |
22 | 59.64 | 82.04 | 64.76 | 84.17 | 72.95 | 92.16 | 75.70 | 93.44 | 65.86 | 94.37 | 87.68 | 93.55 | 81.43 | 95.02 |
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Lane, C.R.; Liu, H.; Autrey, B.C.; Anenkhonov, O.A.; Chepinoga, V.V.; Wu, Q. Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach. Remote Sens. 2014, 6, 12187-12216. https://doi.org/10.3390/rs61212187
Lane CR, Liu H, Autrey BC, Anenkhonov OA, Chepinoga VV, Wu Q. Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach. Remote Sensing. 2014; 6(12):12187-12216. https://doi.org/10.3390/rs61212187
Chicago/Turabian StyleLane, Charles R., Hongxing Liu, Bradley C. Autrey, Oleg A. Anenkhonov, Victor V. Chepinoga, and Qiusheng Wu. 2014. "Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach" Remote Sensing 6, no. 12: 12187-12216. https://doi.org/10.3390/rs61212187