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China's Wetland Databases Based on Remote Sensing Technology

YAN Fengqin LIU Xingtu CHEN Jing YU Lingxue YANG Chaobin CHANG Liping YANG Jiuchun ZHANG Shuwen

YAN Fengqin, LIU Xingtu, CHEN Jing, YU Lingxue, YANG Chaobin, CHANG Liping, YANG Jiuchun, ZHANG Shuwen. China's Wetland Databases Based on Remote Sensing Technology[J]. 中国地理科学, 2017, 27(3): 374-388. doi: 10.1007/s11769-017-0872-z
引用本文: YAN Fengqin, LIU Xingtu, CHEN Jing, YU Lingxue, YANG Chaobin, CHANG Liping, YANG Jiuchun, ZHANG Shuwen. China's Wetland Databases Based on Remote Sensing Technology[J]. 中国地理科学, 2017, 27(3): 374-388. doi: 10.1007/s11769-017-0872-z
YAN Fengqin, LIU Xingtu, CHEN Jing, YU Lingxue, YANG Chaobin, CHANG Liping, YANG Jiuchun, ZHANG Shuwen. China's Wetland Databases Based on Remote Sensing Technology[J]. Chinese Geographical Science, 2017, 27(3): 374-388. doi: 10.1007/s11769-017-0872-z
Citation: YAN Fengqin, LIU Xingtu, CHEN Jing, YU Lingxue, YANG Chaobin, CHANG Liping, YANG Jiuchun, ZHANG Shuwen. China's Wetland Databases Based on Remote Sensing Technology[J]. Chinese Geographical Science, 2017, 27(3): 374-388. doi: 10.1007/s11769-017-0872-z

China's Wetland Databases Based on Remote Sensing Technology

doi: 10.1007/s11769-017-0872-z
基金项目: Under the auspices of National Basic Research Program of China (No. 2010CB95090103), Technological Basic Research Program of China (No. 2013FY111800)
详细信息
    通讯作者:

    ZHANG Shuwen. E-mail: zhangshuwen@neigae.ac.cn

China's Wetland Databases Based on Remote Sensing Technology

Funds: Under the auspices of National Basic Research Program of China (No. 2010CB95090103), Technological Basic Research Program of China (No. 2013FY111800)
More Information
    Corresponding author: ZHANG Shuwen. E-mail: zhangshuwen@neigae.ac.cn
  • 摘要: 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.
  • [1] 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
    [2] Bao Y, Ren J, 2011. Wetland landscape classification based on the BP neural network in DaLinor Lake area. Procedia Environmental Sciences, 10: 2360-2366.
    [3] 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
    [4] 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
    [5] 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
    [6] Breiman L, 2001. Random forests. Machine Learning, 45: 5-32. doi: 10.1023/A: 1010933404324
    [7] 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.
    [8] 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
    [9] 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.
    [10] Congalton R G, Green K, 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. New York: CRC Press, 1-25.
    [11] 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.
    [12] 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
    [13] 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
    [14] 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.
    [15] 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.
    [16] 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
    [17] 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
    [18] Dugan P , 1993. Wetland in Danger: A World Conservation Atlas. New York: Oxford University Press, 100-105.
    [19] 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
    [20] 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
    [21] Finlayson C M, Valk A G, 1995. Wetland classification and inventory: a summary. Vegetatio, 118(1-2): 185-192. doi:  10.1007/BF00045199
    [22] 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
    [23] 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
    [24] 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
    [25] 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
    [26] Hall D K, 1996. Remote sensing applications to hydrology: Imaging radar. Hydrological Sciences Journal, 41(4): 609-624. doi:  10.1080/02626669609491528
    [27] 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
    [28] 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
    [29] 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
    [30] Hirano A, Madden M, Welch R, 2003. Hyperspectral image data for mapping wetland vegetation. Wetland, 23(2): 436-448. doi:  10.1672/18-20
    [31] 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
    [32] 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
    [33] 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
    [34] 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
    [35] 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
    [36] 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
    [37] 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
    [38] 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
    [39] 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
    [40] 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
    [41] 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
    [42] 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
    [43] 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
    [44] 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
    [45] Liaw A, Wiener M, 2002. Classification and regression by random forest. R News, 2(3): 18-22.
    [46] 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.
    [47] 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.
    [48] 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
    [49] 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.
    [50] 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)
    [51] 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
    [52] 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.
    [53] 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
    [54] 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
    [55] 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
    [56] 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
    [57] 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
    [58] Mitra S, Wassmann R, Vlek P, 2005. An appraisal of global wetland area and its organic carbon stock. General Article, 88(1): 25-35.
    [59] 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
    [60] 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
    [61] 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
    [62] 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
    [63] 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)
    [64] 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
    [65] 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.
    [66] 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
    [67] 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
    [68] 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.
    [69] 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
    [70] 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
    [71] 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
    [72] 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.
    [73] 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
    [74] 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
    [75] 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
    [76] 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
    [77] 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
    [78] 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
    [79] 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
    [80] Scott D A, Jones T A, 1995. Classification and inventory of wetland: a global overview. Vegetatio, 118(1): 3-16. doi: 10. 1007/BF0004518
    [81] 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
    [82] 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
    [83] 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
    [84] Sun Yongjun, Tong Qingxi, Qin Qiming, 2008. The object- oriented method for wetland information extraction. Remote Sengsing for Land & Resources (1): 79-82. (in Chinese)
    [85] Tiner R W, Lang M W, Klemas V V, 2015. Remote Sensing of Wetland: Applications and Advances. New York: CRC Press, 16-40.
    [86] 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
    [87] 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
    [88] 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
    [89] 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
    [90] 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
    [91] 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
    [92] 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
    [93] Wang Huaqun, 2004. The compilation and drawing of 1:4 000 000 mire map of China. Wetland Science, 2(1): 15-20. (in Chinese)
    [94] Wetland International, 2002. Ramsar Database (RDB). Wetland International, Wageningen, the Netherlands. Available at http:// www.wetland.org/rdb.htm
    [95] 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
    [96] Wilen B O, Bates M K, 1995. The U.S. fish and wildlife service's national wetland inventory project. Vegetatio, 118: 153-169.
    [97] 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
    [98] 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
    [99] 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
    [100] 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
    [101] Zhang Shuqing, 2002. The introduction of China's wetland databases. Scientia Geographica Sinica, 33(11): 189. (in Chinese)
    [102] 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)
    [103] 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
    [104] Zhao Kuiyi, 1999. Chinese Marshes. Beijing: Science Press, 1-10. (in Chinese)
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出版历程
  • 收稿日期:  2016-02-25
  • 修回日期:  2016-06-14
  • 刊出日期:  2017-06-27

China's Wetland Databases Based on Remote Sensing Technology

doi: 10.1007/s11769-017-0872-z
    基金项目:  Under the auspices of National Basic Research Program of China (No. 2010CB95090103), Technological Basic Research Program of China (No. 2013FY111800)
    通讯作者: ZHANG Shuwen. E-mail: zhangshuwen@neigae.ac.cn

摘要: 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.

English Abstract

YAN Fengqin, LIU Xingtu, CHEN Jing, YU Lingxue, YANG Chaobin, CHANG Liping, YANG Jiuchun, ZHANG Shuwen. China's Wetland Databases Based on Remote Sensing Technology[J]. 中国地理科学, 2017, 27(3): 374-388. doi: 10.1007/s11769-017-0872-z
引用本文: YAN Fengqin, LIU Xingtu, CHEN Jing, YU Lingxue, YANG Chaobin, CHANG Liping, YANG Jiuchun, ZHANG Shuwen. China's Wetland Databases Based on Remote Sensing Technology[J]. 中国地理科学, 2017, 27(3): 374-388. doi: 10.1007/s11769-017-0872-z
YAN Fengqin, LIU Xingtu, CHEN Jing, YU Lingxue, YANG Chaobin, CHANG Liping, YANG Jiuchun, ZHANG Shuwen. China's Wetland Databases Based on Remote Sensing Technology[J]. Chinese Geographical Science, 2017, 27(3): 374-388. doi: 10.1007/s11769-017-0872-z
Citation: YAN Fengqin, LIU Xingtu, CHEN Jing, YU Lingxue, YANG Chaobin, CHANG Liping, YANG Jiuchun, ZHANG Shuwen. China's Wetland Databases Based on Remote Sensing Technology[J]. Chinese Geographical Science, 2017, 27(3): 374-388. doi: 10.1007/s11769-017-0872-z
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