Distribution of wetlands in qomolangma national nature reserve

Wetland Science

Volumn 7, Issue 4, 2009, Pages 289-298

  Li, Guo Qing ^a   Kan, Ai Ke ^a   Wang, Xu Ben ^a   Gao, Zhi Yong ^b   Wang, Han ^b   Chen, Lu ^a   Yong, Zhong ^c  

^a CHENGDU UNIVERSITY OF TECHNOLOGY   (China)

^b Tibet Institute of Plateau Atmospheric and Environmental Science Research   (China)

^c Qomolangma National Nature Reserve   (China)

Author keywords

EVI; LSWI; MODIS image; MSAVI; Qomolangma national nature reserve; Wetland

Indexed keywords

BIODIVERSITY; ECOSYSTEM FUNCTION; ECOSYSTEM HEALTH; LANDSCAPE CHANGE; MODIS; NATURE RESERVE; RIFT ZONE; SEASONAL VARIATION; VEGETATION INDEX; WETLAND;

CHINA; QOMOLANGMA NATIONAL NATURE PRESERVE; XIZANG;

EID: 75149139837     PISSN: 16725948     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (59)

1. United Nations Educational, Scientific and Cultural Organization (UNESCO). Convention on wetlands of international importance especially as waterfowl habitat final text[C]//The International Conference on the Wetlands and Waterfowl at Ramsar, Iran, 2 February 1971.
2. Gulati R D, van Donk E. Lakes in the Netherlands, their origin, eutrophication and restoration: state - of - the - art review [J]. Hydrobiologia, 2002, 478:73-106.
3. Jeppesen E, Jensen J P, Sondergaard M, et al. Top-down control in freshwater lakes: the role of nutrient state, submerged macrophytes and water depth [J]. Hydrobiologia, 1997, 342/343:151-164.
4. Meerhoff M, Mazzeo N, Moss B, et al. The structuring role of free - floating versus submerged plants in a sub tropical shallow lake[J]. Aquatic Ecology, 2003, 37:377-391.
5. William F J, Barko J W, Buder M G. Shear stress and sediment resuspension in relation to submersed macrophyte biomass [J]. Hydrobiologia, 2004, 515:181-191.
6. Stuart R P, Douglas A S, David V M. Remotely sensed estimates of vegetation structural characteristics in restored wetland, Southern Califomia[J]. Photogrammetric Engineering & Remote Sensing, 1996, 65(4) :485-493.
7. Rundquist D C, Narumalani S, Narayanan R M. A review of wetlands remote sensing and defining new considerations [J]. Remote Sensing Reviews, 2001, 20:207-226.
8. Pavri F, Aber J S. Characterizing wetland landscapes; A spatio-temporal analysis of remotely sensed data at Cheyenne Bottoms, Kansas[J]. Physical Geography, 2004, 25:86-104.
9. Wolter P T, Johnston C A, Niemi G J. Mapping submergent aquatic vegetation in the US Great Lakes using Quickbird satellite data [J]. International Journal of Remote Sensing, 2005, 26 (23) :5255-5274.
10. Kumar P, Sanabada M K, Mohanty P R. Applications of remote sensing in spatial and temporal analysis of coastal wetlands vulnerability-A case study on Samung Lake of 0rissa[C]//Proceedings of the 15th Asian Conference on Remote Sensing, 17-23 November, Bangalore, India.
11. Wilen B O, Bates M K.The U.S. Fish and Wildlife Service's National Wetlands Inventory project[J]. Vegetatio, 1995, 118:153-169.
12. Macdnald H C, Waite W P, Demarcke J S. Use of seasat satellite radar imagery for the detection of standing water beneath forest vegetation[C]// Proceedings of the American Society of Photo-grammetry, Annual Technical Meeting, Niagara Falls, 1980.
13. Waite W P, Macdonald H C. Vegetation penetration with K-band imaging radars[J]. IEEE Transactions on Geoscience and Electronics, 1971, GE-9:147-155.
14. Butera M K. Remote sensing of wetlands[J]. IEEE Transaction-on Geoscience and Remote Sensing, 1983, NE-21:383-392.
15. Ozge K D, Zuhal A, Meryem B. Identification and mapping of submerged plants in a shallow lake using Quickbird satellite data [J]. Journal of Environmental Management, 2009, 90 (7) : 2 138-2 143.
16. Hollen B N. Characteristics of maximum - value composite images for temporal AVHRR data[J]. International Journal of Remote Sensing, 1986, 7:1 435-1 445.
17. Sawaya K E, Olmanson L G, Heinert N J, et al. Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery [J]. Remote Sensing of Environment, 2003, 88:144-156.
18. Martha S G, Emily H W, Nels B. Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh[J]. Remote Sensing of Environment, 2008, 112:4 048-4 060.
19. Rebelo L M, Finlayson C M, Nagabhatla N. Remote sensing and GIS for wetland inventory, mapping and change analysis[J]. Journal of Environmental Management, 2009, 90(7) :2 144-2 153.
20. Bustamante J, Pacios F, Diaz-Delgado R, et al. Predictive models of turbidity and water depth in the Doiiana marshes using Ijindsat TM and ETM + images [J]. Journal of Environmental Management, 2009, 90(7) :2 219-2 225.
21. Lunetta R S, Balogh M E. Application of multi-temporal Land-sat5 TM imagery for wetland identification [J]. Photogrammetric Engineering and Remote Sensing, 1999, 65(11) :1 303-1 310.
22. Zhao B, Yan Y, Guo H, et al. Monitoring rapid vegetation suecession in estuarine wetland using time series MODIS based indicators : An application in the Yangtze River Delta area [J]. Ecol. Indicat., 2009, 9(2) :346-356.
23. Melesse A M, Nangia V, Wang X X, et al. Wetland restoration response analysis using MODIS and groundwater data [J]. Sensors, 2007, 7(9) :1 916 -1 933.
24. Land processes distributed active archive center, NASA, USGS science for a changing world. Vegetation Indices 16 - Days L3 Global 250m (MOD13Q1). [2008-11-19]. https://Ipdaac.usgs.gov/Ipdaac/products/modis-products-table/ vegetation-indices/16-day-13-global-250m/v5/terra.
25. Land processes distributed active archive center, NASA, USGS science for a changing world. Surface Reflectance 8 - Day 13 Global 500m (MOD09A1). [2008-11-19]. https://Ipdaac.usgs.gov/Ipdaac/products/modis-products-table/ surface-reflectance/8-day-13-global-500m/v5/terra.
26. Qi J, Che H A, Huetea R, et al. A modified soil adjusted vegetation index [J]. Remote Sensing of the Environment, 1994, 48:119-126.
27. Sims D A, Gamon J A. Estimation of vegetation water content and photosynthetic tissue area from s pectral reflectance: A comparis on of indices based on liquid water and chlorophyll absorption features [J]. Remote Sensing of Environment, 2003, 84 (4);526-537.
28. Roberts D A, Green R O, Adams J B. Temporal and spatial patterns in vegetation and atmospheric properties from AVIIS [J]. Remote Sensing of Environment, 1997, 62(3): 223-240.
29. Xiao X, Boles S, Liu J, et al. Characterization of forest types in Northeastern China, using multi - temporal SPOT - 4 VEGETATION sensor data[J]. Remote Sensing of Environment, 2002, 82:335-348.
30. Tukey J W. Exploratory Data Analysis [M]. New York: Addison Wesley, 1977.