Journal of Resources and Ecology >
Remote Sensing Classification of Marsh Wetland with Different Resolution Images
Received date: 2015-12-28
Revised date: 2016-02-15
Online published: 2016-04-12
Supported by
This study was jointly supported by the National Science and Technology Support Program (No; 2013BAC03B05), Ecological environment evaluation of disaster area(No; O7M73120AM)
Successful biological monitoring depends on judicious classification. An attempt has been made to provide an overview of important characteristics of marsh wetland. Classification was used to describe ecosystems and land cover patterns. Different spatial resolution images show different landscape characteristics. Several classification images were used to map and monitor wetland ecosystems of Honghe National Nature Reserve (HNNR) at a plant community scale. HNNR is a typical inland wetland and fresh water ecosystem in the North Temperate Zone. SPOT-5 10 m × 10 m, 20 m × 20 m, and 30 m × 30 m images and Landsat -5 Thematic Mapper (TM) images were used to classify based on maximum likelihood classification (MLC) algorithms. In order to validate the precision of the classifications, this study used aerial photography classification maps as training samples because of their high accuracy. The accuracy of the derived classes was assessed with the discrete multivariate technique called KAPPA accuracy. The results indicate: (1) training samples are important to classification results. (2) Image classification accuracy is always affected by areal fraction and aggregation degree as well as by diversities and patch shape. (3) The core zone area is protected better than buffer zone and experimental zone wetland. The experimental zone degrades fast because of irrational development by humans.
LI Na, XIE Gaodi, ZHOU Demin, ZHANG Changshun, JIAO Cuicui . Remote Sensing Classification of Marsh Wetland with Different Resolution Images[J]. Journal of Resources and Ecology, 2016 , 7(2) : 107 -114 . DOI: 10.5814/j.issn.1674-764x.2016.02.005
1 Ahmad, W., et al. 1992. Land cover mapping in a rugged terrain area using Landsat MSS data. International Journal of Remote Sensing, 13(4): 673-683
2 Atkinson, P.M., & Aplin, P. 2004. Spatial variation in land cover and choice of spatial resolution for remote sensing. International Journal of Remote Sensing, 25(18): 3687-3702
3 Atkinson, P.M., & Tate, N.J. 2004. Spatial Scale Problems and Geostatistical Solutions: A Review. Professional Geographer, 52(4): 607-623
4 Bian, L. (1997). Multiscale nature of spatial data in scaling up environmental models. In, In Scale in RCII/otc Sensing and GIS, eds, D.A. Quattrochi and Bian, L.
5 Dicks, S.E., & Lo, T.H.C. 1990. Evaluation of thematic map accuracy in a land-use and land-cover mapping program. Photogrammetric Engineering & Remote Sensing, 56(9): 1247-1252
6 Gupta, V.K., et al. 2009. Scale problems in hydrology. Contributions of the Robertson Workshop. Hydrological Processes, 9(3/4):243-250.
7 Hay, G.J., et al. 1997. Spatial thresholds, image-objects, and upscaling: A multiscale evaluation. Remote Sensing of Environment, 62(1): 1-19
8 Jensen, J.R. 2004. Introductory Digital Image Processing. Series in Geographic Information Science, 2nd edn
9 Lilesand, T., & Kiefer, R. 1994. Remote Sensing and Image Interpretation. Remote Sensing & Image Interpretation
10 Li Na, Zhou Demin, Zhao Kuiyi. 2011. Marsh classification mapping at a community scale using high-resolution imagery. Acta Ecologica Sinica, 31(22):6717-6726. (in Chinese)
11 Liu Xingtu, Ma Xuehui. Environmental Changes and Ecological Conservation in the Sanjiang Plain. Beijing: Science Press, 2002. (in Chinese)
12 Nature, I.U.f.C.o., & Bureau, N.R.R.C. (1984). Convention on Wetlands of International Importance Especially as Waterfowl Habitat : proceedings of the second conference of the parties: Groningen, Netherlands, 7 to 12 May, 1984. Ramsar Convention Bureau, International Union for Conservation of Nature and Natural Resources
13 Ozesmi, S.L., & Bauer, M.E. 2002. Satellite Remote Sensing of Wetlands. Wetlands Ecology & Management, 10(5): 381-402
14 Quattrochi, D.A., & Goodchild, M.F. (1997). Scale in remote sensing and GIS. Lewis Publishers
15 Shanmugam, P., et al. 2006. 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. Ecological Modelling, 194(4): 379-394
16 Wu, H., & Li, Z.L. 2009. Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors, 9(3): 1768-1793
17 Zhou, D.M., et al. 2009. Driving Forces for the Marsh Wetland Degradation in the Honghe National Nature Reserve in Sanjiang Plain, Northeast China. Environmental Modeling & Assessment, 14(1): 101-111
/
| 〈 |
|
〉 |