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China’s wetland databases based on remote sensing technology

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Abstract

Wetland databases can provide the basic data that necessary for the protection and management of wetlands. A large number of wetland databases have been established in the world as well as in China. In this paper, we review China’s wetland databases based on remote sensing (RS) technology after introducing the background theory to the application of RS technology in wetland surveys. A key conclusion is that China’s wetland databases are far from sufficient in fulfilling protection and management needs. Our recommendations focus on the use of the hyper-spectral imagery, microwave data, multi-temporal images, and automatic classifications in order to improve the accuracy and efficiency of wetland inventory. Further, attention should also be paid to detect major biophysical features of wetlands and build wetland databases in years after the 1980s in China. Considering that great gap exists between RS experts and wetland experts, further cooperation between wetland scientists and RS scientists are needed to promote the application of RS in the foundation of wetland databases.

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References

  • Ausseil A, Dymond J R, Shepherd J D, 2007. Rapid mapping and prioritization of wetland sites in the Manawatu-Wanganui region, New Zealand. Environmental Management, 39: 316–325. doi: 10.1007/s00267-005-0223-1

    Article  Google Scholar 

  • Bao Y, Ren J, 2011. Wetland landscape classification based on the BP neural network in DaLinor Lake area. Procedia Environmental Sciences, 10: 2360–2366.

    Article  Google Scholar 

  • Bartsch A, Kidd R A, Pathe C et al., 2007. Satellite radar imagery for monitoring inland wetland in boreal and sub-arctic environments. Aquatic Conservation: Marine and Freshwater Ecosystems, 17: 305–317. doi: 10.1002/aqc.836

    Article  Google Scholar 

  • Belluco E, Camuffo M, Ferrari S et al., 2006. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105(1): 54–67. doi: 10.1016/j.rse.2006.06.006

    Article  Google Scholar 

  • Bourgeau-Chavez L L, Riordan K, Powell R B et al., 2009. Improving wetland characterization with multi-sensor, multi-temporal SAR and optical/infrared data fusion. Advances in Geoscience and Remote Sensing, 33: 679–708. doi: 10.5772/8327

    Google Scholar 

  • Breiman L, 2001. Random forests. Machine Learning, 45: 5–32. doi: 10.1023/A: 1010933404324

    Article  Google Scholar 

  • Brinson M M, 1993. A hydrogeomorphic classification for wetland. U.S. Army Engineers Waterways Experiment Station, Vicksburg, MS. Wetland Research Program Technical Report WRP-DE-4. http://el.erdc.usace.army.mil/elpubs/pdf/wrpde4.pdf. Accessed November 7, 2014.

    Google Scholar 

  • Bwangoy J R B, Hansen M C, Roy D P et al., 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. doi: 10.1016/j.rse.2009.08.004

    Article  Google Scholar 

  • Civco D, Hurd J, Prisloe S et al., 2006. Characterization of coastal wetland systems using multiple remote sensing data types and analytical techniques. IEEE International Conference of Geoscience Remote Sensing Symposium (IGARSS 2006), 3442–3446.

    Chapter  Google Scholar 

  • Congalton R G, Green K, 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. New York: CRC Press, 1–25.

    Google Scholar 

  • Congalton R G, Green K, Teply J, 1993. Mapping old growth forests on national forest and park lands in the Pacific-Northwest from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 59: 529–535.

    Google Scholar 

  • Costa M, 2004. Use of SAR satellites for mapping zonation of vegetation communities in the Amazon floodplain. International Journal of Remote Sensing, 25: 1817–1835. doi: 10.1080/0143116031000116985

    Article  Google Scholar 

  • Costa M, Telmer K H, 2007. Mapping and monitoring lakes in the Brazilian Pantanal wetland using synthetic aperture radar imagery. Marine and Freshwater Ecosystems, 17: 277–288. doi: 10.1002/aqc.849

    Article  Google Scholar 

  • Cowardin L M, Carter V, Golet F C et al., 1979. Classification of Wetland and Deepwater Habitats of the United States. U.S. Fish and Wildlife Service. FWS/OBS-79/31, 1–30.

    Google Scholar 

  • Dahl T E, Dick J, Swords J et al., 2009. Data Collection Requirements and Procedures for Mapping Wetland, Deepwater and Related Habitats of the United States. Division of Habitat and Resource Conservation, National Standards and Support Team. U.S. Fish and Wildlife Service, Madison.

    Google Scholar 

  • Davranche A, Poulin B, Lefebvre G et al., 2013. Mapping flooding regimes in Camargue wetland using seasonal multispectral data. Remote Sensing of Environment, 138: 165–171. doi: 10.1016/j.rse.2013.07.015

    Article  Google Scholar 

  • Dronova I, Gong P, Wang L, 2011. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China. Remote Sensing of Environment, 115(12): 3220–3236. doi: 10.1016/j.rse.2011.07.006

    Article  Google Scholar 

  • Dugan P, 1993. Wetland in Danger: A World Conservation Atlas. New York: Oxford University Press, 100–105.

    Google Scholar 

  • Dwivedi R, Rao B, Bhattacharya S, 1999. Mapping wetland of the Sundarban Delta and its environs using ERS-1 SAR data. International Journal of Remote Sensing, 20(11): 2235–2247. doi: 10.1080/014311699212227

    Article  Google Scholar 

  • Finlayson C M, Davidson N C, Spiers A G et al., 1999. Global wetland inventory-current status and future priorities. Marine and Freshwater Research, 50: 717–727. doi: 10.1071/MF99098

    Article  Google Scholar 

  • Finlayson C M, Valk A G, 1995. Wetland classification and inventory: a summary. Vegetatio, 118(1–2): 185–192. doi: 10.1007/BF00045199

    Article  Google Scholar 

  • Fluet-Chouinard E F, Lehner B, Rebelo L et al., 2015. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sensing of Environment, 158: 348–361. doi: 10.1016/j.rse.2014.10.015

    Article  Google Scholar 

  • Fournier R A, Grenier M, Lavoie A et al., 2007. Towards a strategy to implement the Canadian wetland inventory using satellite remote sensing. Canadian Journal of Remote Sensing, 33(S1): S1–S16. doi: 10.5589/m07-051

    Article  Google Scholar 

  • Giri C, Zhu Z, Reed B, 2005. A comparative analysis of the Global Land Cover 2000 and MODIS land cover datasets. Remote Sensing of Environment, 94: 123–132. doi: 10.1016/j.rse.2004.09.005

    Article  Google Scholar 

  • Gong P, Niu Z G, Cheng X et al., 2010. China’s wetland change (1990–2000) determined by remote sensing. Science in China Series D: Earth Sciences, 53: 1036–1042. doi: 10.1007/s11430-010-4002-3

    Article  Google Scholar 

  • Hall D K, 1996. Remote sensing applications to hydrology: Imaging radar. Hydrological Sciences Journal, 41(4): 609–624. doi: 10.1080/02626669609491528

    Article  Google Scholar 

  • Hansen M C, Reed B, 2000. A comparison of the IGBP DISCover and University of Maryland 1 KM global land cover products. International Journal of Remote Sensing, 21(6&7): 1365–1373. doi: 10.1080/014311600210218

    Article  Google Scholar 

  • Hess L L, Melack J M, Filoso S, 1995. Delineation of inundated area and vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 33(4): 896–904. doi: 10.1109/36.406675

    Article  Google Scholar 

  • Hewitt M J, 1990. Synoptic inventory of riparian ecosystems: the utility of Landsat Thematic Mapper data. Forest Ecology and Management, 33: 605–620. doi: 10.1016/0378-1127(90)90222-W

    Article  Google Scholar 

  • Hirano A, Madden M, Welch R, 2003. Hyperspectral image data for mapping wetland vegetation. Wetland, 23(2): 436–448. doi: 10.1672/18-20

    Article  Google Scholar 

  • Hladik C, Schalles J, Alber M, 2013. Salt marsh elevation and habitat mapping using hyperspectral and LIDAR data. Remote Sensing of Environment, 139: 318–330. doi: 10.1016/j.rse. 2013.08.003

    Article  Google Scholar 

  • Huang C, Peng Y, Lang M et al., 2014. Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data. Remote Sensing of Environment, 141: 231–242. doi: 10.1016/j.rse.2013.10.020

    Article  Google Scholar 

  • Judd C, Steinberg S, Shaughnessy F et al., 2007. Mapping salt marsh vegetation using aerial hyperspectral imagery and linear unmixing in Humboldt Bay, California. Wetland, 27(4): 1144–1152. doi: 10.1672/0277-5212(2007)27

    Article  Google Scholar 

  • Kasischke E S, Smith K B, Bourgeau-Chavez L L et al., 2003. Effects of seasonal hydrologic patterns in south Florida wetland on radar backscatter measured from ERS-2 SAR imagery. Remote Sensing of Environment, 88(4): 423–441. doi: 10.1016/j.rse.2003.08.016

    Article  Google Scholar 

  • Kellndorfer J M, Pierce L E, Dobson M C et al., 1998. Toward consistent regional-to-global-scale vegetation characterization using orbital SAR systems. IEEE Transactions on Geoscience and Remote Sensing, 36(5): 1396–1411. doi: 10.1109/36. 718844

    Article  Google Scholar 

  • Klemas V, 2011. Remote sensing techniques for studying coastal ecosystems: An overview. Journal of Coastal Research, 27(1): 2–17. doi: 10.2112/JCOASTRES-D-10-00103.1

    Article  Google Scholar 

  • Klemas V, 2013. Remote sensing of emergent and submerged wetland: an overview. International Journal of Remote Sensing, 34(18): 6286–6320. doi: 10.1080/01431161.2013. 800656

    Article  Google Scholar 

  • Kloiber S M, Macleod R D, Smith A J et al., 2014. A semi-automated, multi-source data fusion update of a wetland inventory for east-central Minnesota, USA. Wetland, 35: 335–348. doi: 10.1007/s13157-014-0621-3

    Article  Google Scholar 

  • Kokaly R F, Couvillion B R, Holloway J M et al., 2013. Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes. Remote Sensing of Environment, 129: 210–230. doi: 10.1016/j.rse.2012.10.028

    Article  Google Scholar 

  • Lang M W, Townsend P A, Kasischke E S, 2008. Influence of incidence angle on detecting flooded forests using C-HH synthetic aperture radar data. Remote Sensing of Environment, 112(10): 3898–3907. doi: 10.1016/j.rse.2008.06.013

    Article  Google Scholar 

  • Le Toan T, Beaudoin A, Riom J et al., 1992. Relating forest biomass to SAR data. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 403–411. doi: 10.1109/36.134089

    Article  Google Scholar 

  • Lehnera B, Döll P, 2004. Development and validation of a global database of lakes, reservoirs and wetland. Journal of Hydrology, 296: 1–22. doi: 10.1016/j.jhydrol.2004.03.028

    Article  Google Scholar 

  • Leonard P B, Baldwin R F, Homyack J A et al., 2012. Remote detection of small wetland in the Atlantic Coastal Plain of North America: local relief models, ground validation, and high-throughput computing. Forest Ecology and Management, 284: 107–115. doi: 10.1016/j.foreco.2012.07.034

    Article  Google Scholar 

  • Li J, Chen W, 2005. A rule-based method for mapping Canada’s wetland using optical, radar and DEM data. International Journal of Remote Sensing, 26(22): 5051–5069. doi: 10.1080/01431160500166516

    Article  Google Scholar 

  • Liaw A, Wiener M, 2002. Classification and regression by random forest. R News, 2(3): 18–22.

    Google Scholar 

  • Liu J, Liu M, Deng X et al., 2002. The land use and land cover change database and its relative studies in China. Journal of Geographical Sciences, 12: 275–282.

    Article  Google Scholar 

  • Liu J, Liu M, Zhuang D et al., 2003. Study on spatial pattern of land-use change in China during 1995–2000. Science in China Series D: Earth Sciences, 46: 373–384.

    Article  Google Scholar 

  • Liu J, Tian H, Liu M et al., 2005. China’s changing landscape during the 1990s: large-scale land transformations estimated with satellite data. Geophysical Research Letters, 32: L02405. doi: 10.1029/2004GL021649

    Google Scholar 

  • Liu J, Zhang Z, Xu X et al., 2010. Spatial patterns and driving forces of land use change in China during the early 21st century. Journal of Geographical Sciences, 20: 483–494.

    Article  Google Scholar 

  • Liu Ping, Guan Lei, Lyu Cai et al., 2011. Technical characteristics and application prospects of achievements of the second national wetland investigation. Wetland Science, 9: 284–289. (in Chinese)

    Google Scholar 

  • Loveland T R, Reed B C, Brown J F et al., 2000. Development of a global land cover characteristics database and IGBP DISCover from 1-km AVHRR data. International Journal of Remote Sensing, 21(6–7): 1303–1330. doi: 10.1080/014311 600210191

    Article  Google Scholar 

  • Lunetta R S, Balogh M E, 1999. Application of multi-temporal Landsat 5 TM imagery for wetland identification. Photogrammetric Engineering and Remote Sensing, 65(11): 1303–1310.

    Google Scholar 

  • Maria G P, Haydee K, Patricia K, 2002. Mapping wetland using multi-temporal RADARSAT-1 data and a decision-based classifier. Canadian Journal of Remote Sensing, 28(2): 175–186. doi: 10.5589/m02-014

    Article  Google Scholar 

  • Martinez J M, Le Toan T, 2007. Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sensing of Environment, 108(3): 209–223. doi: 10.1016/j.rse.2006.11.012

    Article  Google Scholar 

  • Matthews E, Fung I, 1987. Methane emission from natural wetland: global distribution, area, and environmental characteristics of sources. Global Biogeochemical Cycles, 1: 61–86. doi: 10.1029/GB001i001p00061

    Article  Google Scholar 

  • Melton J R, Wania R, Hodson E L et al., 2013. Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP). Biogeosciences, 10: 753–788. doi: 10.5194/bg-10-753-2013

    Article  Google Scholar 

  • Miller J B, Gatti L V, D’Amelio M T S et al., 2007. Airborne measurements indicate large methane emissions from the Eastern Amazon basin. Geophysical Research Letters, 34: 1–5. doi: 10.1029/2006GL029213

    Google Scholar 

  • Mitra S, Wassmann R, Vlek P, 2005. An appraisal of global wetland area and its organic carbon stock. General Article, 88(1): 25–35.

    Google Scholar 

  • Morgan J L, Gergel S E, Coops N C, 2010. Aerial photography: a rapidly evolving tool for ecological management. Bioscience, 60(1): 47–59. doi: 10.1525/bio.2010.60.1.9

    Article  Google Scholar 

  • Nagendra H, Lucas R, Honrado J P et al., 2013. Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Original Research Article Ecological Indicators, 33: 45–59. doi: 10.1016/j.ecolind.2012.09.014

    Article  Google Scholar 

  • Nakaegawa T, 2012. Comparison of water-related land cover types in six 1-km global land cover databases. Journal of Hydrometeorology, 13(2): 649–664. doi: 10.1175/JHM-D-10-05036.1

    Article  Google Scholar 

  • Nayak S R, Sahai B, 1985. Coastal Morphology: a case-study of the Gulf of Khambhat (Cambay). International Journal of Remote Sensing, 6(3–4): 559–567. doi: 10.1080/01431168 508948478

    Article  Google Scholar 

  • Ning Jia, Zhang Shuwen, Cai Hongyan et al., 2012. A comparative analysis of the MODIS land cover databases and Global land cover datasets in Heilongjiang Basin. Journal of Geo-information Science, 14(2): 240–249. (in Chinese)

    Article  Google Scholar 

  • Niu Z G, Gong P, Cheng X et al., 2009. Geographical characteristics of China’s wetland derived from remotely sensed data. Science in China Series D: Earth Sciences, 52(6): 723–738. doi: 10.1007/s11430-009-0075-2

    Article  Google Scholar 

  • Ormsby J P, Blanchard B J, Blanchard A J, 1985. Detection of lowland flooding using active microwave systems. Photogrammetric Engineering and Remote Sensing, 51(3): 317–329.

    Google Scholar 

  • Ouyang Z T, Zhang M Q, Xie X et al., 2011. A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants. Ecological Informatics, 6(2): 136–146. doi: 10.1016/j.ecoinf.2011.01.002

    Article  Google Scholar 

  • Petus C, Lewis M, White D, 2013. Monitoring temporal dynamics of Great Artesian Basin wetland vegetation, Australia, using MODIS NDVI. Ecological Indicators, 34: 41–52. doi: 10.1016/j.ecolind.2013.04.009

    Article  Google Scholar 

  • Phinn S, Hess L, Finlayson C M, 1999. An assessment of the usefulness of remote sensing for wetland inventory and monitoring in Australia. In: Finlayson C M and Spiers A G (eds.). Techniques for Enhanced Wetland Inventory and Monitoring. Supervising Scientist Report 147, Canberra, Australian Capital Territory, Australia.

    Google Scholar 

  • Place J L, 1985. Mapping of forested wetland: use of Seasat RADAR images to complement conventional sources. The Professional Geographer, 37(4): 463–469. doi: 10.1111/j.0033-0124.1985.00463.x

    Article  Google Scholar 

  • Poulin B, Davranche A, Lefebvre G, 2010. Ecological assessment of Phragmites australis wetland using multi-season SPOT-5 scenes. Remote Sensing of Environment, 114(7): 1602–1609. doi: 10.1016/j.rse.2010.02.014

    Article  Google Scholar 

  • Prigent C, Papa F, Aires F et al., 2007. Global inundation dynamics inferred from multiple satellite observations, 1993–2000. Journal of Geophysical Research, 112: 305–317. doi: 10.1029/2006JD007847

    Article  Google Scholar 

  • Ramsar Convention Secretariat, 2010. Wetland Inventory: A Ramsar Framework for Wetland Inventory and Ecological Character Description. Ramsar Handbooks for the Wise Use of wetland, 4th edn., Vol. 15. Gland, Switzerland. http://www. doe.ir/portal/theme/talab/0DB/2-BS/INV/SO/bs-inv-so-bk-gud-V15-2010.pdf.

    Google Scholar 

  • Rapinel S, Jan-Bernard B, Johan O et al., 2015. Use of bi-seasonal Landsat-8 Imagery for mapping marshland plant community combinations at the regional scale. Wetland, 35(6): 1043–1054. doi: 10.1007/s13157-015-0693-8

    Article  Google Scholar 

  • Roy D P, Wulder M A, Lovelandc T R et al., 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment, 145: 154–172. doi: 0.1016/j.rse.2014.02.001

    Article  Google Scholar 

  • Sadeghi R, Zarkami R, Sabetraftar K et al., 2012. Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran. Ecological Modelling, 244: 117–126. doi: 10.1016/j.ecolmodel.2012.06.029

    Article  Google Scholar 

  • Sader S A, Ahl D, Liou W S, 1995. Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine. Remote Sensing of Environment, 53(3): 133–144. doi: 10.1016/0034-4257(95)00085-F

    Article  Google Scholar 

  • Salem F, Kafatos M, El-Ghazawi T et al., 2005. Hyperspectral image assessment of oil-contaminated wetland. International Journal of Remote Sensing, 26(4): 811–821. doi: 10.1080/01431160512331316883

    Article  Google Scholar 

  • Schmidt K, Skidmore A, 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment, 85(1): 92–108. doi: 10.1016/S0034-4257(02) 00196-7

    Article  Google Scholar 

  • Schroeder R, Rawlins M A, McDonald K C et al., 2010. Satellite microwave remote sensing of North Eurasian inundation dynamics: development of coarse-resolution products and comparison with high-resolution synthetic aperture radar data. Environmental Research Letters, 5: 015003. doi: 10.1088/1748-9326/5/1/015003

    Article  Google Scholar 

  • Scott D A, Jones T A, 1995. Classification and inventory of wetland: a global overview. Vegetatio, 118(1): 3–16. doi: 10.1007/BF0004518

    Article  Google Scholar 

  • Shapiro C, 1995. Coordination and Integration of Wetland Data for Status and Trends and Inventory Estimates. Federal Geographic Data Committee wetland Subcommittee, Washington, DC. Technical Report 2

    Google Scholar 

  • Silva T S, Costa M P, Melack J M et al., 2008. Remote sensing of aquatic vegetation: theory and applications. Environmental Monitoring and Assessment, 140(1–3): 131–145. doi: 10.1007/s 10661-007-9855-3

    Article  Google Scholar 

  • Stolt M H, Baker J C, 1995. Evaluation of national wetland inventory maps in a heavily forested region in the upper Great Lakes. Wetland, 15(4): 346–353. doi: 10.1672/0277-5212(2000)020

    Article  Google Scholar 

  • Sun Yongjun, Tong Qingxi, Qin Qiming, 2008. The objectoriented method for wetland information extraction. Remote Sengsing for Land & Resources (1): 79–82. (in Chinese)

    Google Scholar 

  • Tiner R W, Lang M W, Klemas V V, 2015. Remote Sensing of Wetland: Applications and Advances. New York: CRC Press, 16–40.

    Google Scholar 

  • Townsend P A, 2000. A quantitative fuzzy approach to assess mapped vegetation classifications for ecological applications. Remote Sensing of Environment, 72(3): 253–267. doi: 10.1016/S0034-4257(99)00096-6

    Article  Google Scholar 

  • Townsend P A, 2002. Relationships between forest structure and the detection of flood inundation in forested wetland using C-band SAR. International Journal of Remote Sensing, 23(3): 443–460. doi: 10.1080/01431160010014738

    Article  Google Scholar 

  • Townsend P A, Walsh S J, 1998. Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing. Geomorphology, 21(3): 295–312. doi: 10.1016/S0169-555X (97)00069-X

    Article  Google Scholar 

  • Töyrä J, Pietroniro A, 2005. Towards operational monitoring of a northern wetland using geomatics-based techniques. Remote Sensing of Environment, 97(2): 174–191. doi: 10.1016/j.rse. 2005.03.012

    Article  Google Scholar 

  • Töyrä J, Pietroniro A, Martz L W et al., 2002. A multi-sensor approach to wetland flood monitoring. Hydrological Processes, 16(8): 1569–1581. doi: 10.1002/hyp.1021

    Article  Google Scholar 

  • Vierling K T, Vierling L A, Gould W A et al., 2008. Lidar: shedding new light on habitat characterization and modeling. Frontiers in Ecology and the Environment, 6(2): 90–98. doi: 10.1890/070001

    Article  Google Scholar 

  • Walker D A, Raynolds M K, Danïels F J et al., 2005. The circumpolar arctic vegetation map. Journal of Vegetation Science, 16: 267–282. doi: 10.1111/j.1654-1103.2005.tb02365.x

    Article  Google Scholar 

  • Wang Huaqun, 2004. The compilation and drawing of 1:4 000 000 mire map of China. Wetland Science, 2(1): 15–20. (in Chinese)

    Google Scholar 

  • Wetland International, 2002. Ramsar Database (RDB). Wetland International, Wageningen, the Netherlands. Available at http://www.wetland.org/rdb.htm

    Google Scholar 

  • White L, Brisco B, Dabboor M et al., 2015. A collection of SAR methodologies for monitoring wetland. Remote sensing, 7: 7615–7645. doi: 10.3390/rs70607615

    Article  Google Scholar 

  • Wilen B O, Bates M K, 1995. The U.S. fish and wildlife service’s national wetland inventory project. Vegetatio, 118: 153–169.

    Article  Google Scholar 

  • Wilson B A, Rashid H, 2005. Monitoring the 1997 flood in the Red River Valley using hydrologic regimes and RADARSAT imagery. The Canadian Geographer/Le Géographe Canadien, 49(1): 100–109. doi: 10.1111/j.0008-3658.2005.00082.x

    Article  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. doi: 10.1016/j.rse.2006.10.019

    Article  Google Scholar 

  • Yan F Q, Zhang S W, Liu X T et al., 2016. The effects of spatiotemporal changes in land degradation on ecosystem services values in Sanjiang Plain, China. Remote Sensing, 8: 917. doi: 10.3390/rs8110917

    Article  Google Scholar 

  • Zhang C, Xie Z, 2012. Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery. Remote Sensing of Environment, 124: 310–320. doi: 10.1016/j.rse.2012.05.015

    Article  Google Scholar 

  • Zhang Shuqing, 2002. The introduction of China’s wetland databases. Scientia Geographica Sinica, 33(11): 189. (in Chinese)

    Google Scholar 

  • Zhang Shuwen, Yan Fengqin, Yu Lingxue et al., 2013. Application of remote sensing technology to wetland research. Scientia Geographica Sinica, 33(11): 1406–1412. (in Chinese)

    Google Scholar 

  • Zhao D, Jiang H, Yang T et al., 2012. Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds. Journal of Environmental Management, 95(1): 98–107. doi: 10.1016/j.jenvman.2011.10.007

    Article  Google Scholar 

  • Zhao Kuiyi, 1999. Chinese Marshes. Beijing: Science Press, 1–10. (in Chinese)

    Google Scholar 

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Acknowledgements

We would like to thank the China Scholarship Council for the financial support. The paper was also completed with support from the International Center for Climate and Global Change Research and the School of Forestry and Wildlife Sciences, Auburn University (AU), USA. We would like to thank Dr. Tian Hanqin and Dr. Zhang Bowen of AU for their valuable suggestions.

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Authors and Affiliations

  1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China

    Fengqin Yan, Xingtu Liu, Lingxue Yu, Chaobin Yang, Liping Chang, Jiuchun Yang & Shuwen Zhang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Fengqin Yan

  3. International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, 36849, USA

    Fengqin Yan

  4. College of Earth Science, Jilin University, Changchun, 130061, China

    Jing Chen

  5. College of Arts & Sciences, Beijing Union University, Beijing, 100083, China

    Jing Chen

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  1. Fengqin Yan
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Correspondence to Shuwen Zhang.

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Foundation item: Under the auspices of National Basic Research Program of China (No. 2010CB95090103), Technological Basic Research Program of China (No. 2013FY111800)

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Yan, F., Liu, X., Chen, J. et al. China’s wetland databases based on remote sensing technology. Chin. Geogr. Sci. 27, 374–388 (2017). https://doi.org/10.1007/s11769-017-0872-z

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