Reeds and suaeda biomass estimation model based on HJ-1 hyperspectal image in the Yellow River Estuary

doi:10.3969/j.issn.1001-909X.2014.04.004

Abstract

Abstract: Wetland vegetation biomass is the basic information of wetland ecological assessment, protection and utilization. Remote sensing has become one of the most efficient technologies of wetland biomass monitoring. Utilizing the HJ-1 hyperspectral remote sensing image that acquired in September 2013 and the coinstantaneous field survey data, the biomass estimation capabilities of 7 kinds of narrow-band vegetation indices and 2 kinds of red edge position indices of reeds and suaeda in the Yellow River Estuary have been studied. The results reveal that (1) In single variable estimation model case, for reeds, the SRI index with 635 nm and 827 nm bands and REP_ linear interpolation index get the best R² measures, and for suaeda, the NDVI and SRI indices with 692 nm and 807 nm bands and OSAVI index get the best biomass estimation results. (2) In multiple variable case, for reeds and suaeda, the R² measure get 0.71 and 0.66 respectively.

Key words: biomass remote sensing, HJ-1 hyperspectral image, vegetation indices, red edge position indices

CLC Number:

TP75

REN Guang-bo , ZHANG Jie , WANG Wei-qi , GENG Yan-jie , CHEN Yan-jun , MA Yi. Reeds and suaeda biomass estimation model based on HJ-1 hyperspectal image in the Yellow River Estuary[J]. Journal of Marine Sciences, 2014, 32(4): 27-34.

References

[1] RICHARDSON A, WIEGAND C, ARKIN G, et al. Remotely-sensed spectral indicators of sorghum development and their use in growth modeling[J]. Agricultural Meteorology,1982,26(1):11-23.
[2] EVERITT J, ESCOBAR D, RICHARDSON A. Estimating grassland phytomass production with near-infrared and mid-infrared spectral variables[J]. Remote Sensing of Environment,1989,30(3):257-261.
[3] ANDERSON G, HANSON J, HAAS R. Evaluating Landsat Thematic Mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands[J]. Remote Sensing of Environment,1993,45(2):165-175.
[4] WYLIE B, MEYER D, TIESZEN L, et al. Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study[J]. Remote Sensing of Environment,2002,79(2):266-78.
[5] LU Deng-sheng. The potential and challenge of remote sensing-based biomass estimation[J]. International Journal of Remote Sensing,2006,27(7):1 297-1 328.
[6] CHO M, SKIDMORE A. Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy[J]. International Journal of Remote Sensing,2009,30(2):499-515.
[7] ROUSE J. Monitoring the vernal advancement and retrogradation(greenwave effect) of natural vegetation[R]//NASA/GSFC Type III Final Report. Greenbelt, MD,1974:371-373.
[8] TUCKER C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment,1979,8(2):127-150.
[9] WANG Hong, LI Xiao-bing, YU Hong-jing. Monitoring growing season of typical steppe in northern China based on NOAA/AVHRR NDVI data[J]. Acta Phytoecologica Sinica,2006,30(3): 356-374.
王宏,李晓兵,余弘婧.基于NOAA/AVHRR NDVI监测中国北方典型草原的生长季及变化[J].植物生态学报,2006,30(3):365-374.
[10] TAO Wei-guo, XU Bin, LIU Li-jun, et al. Yield estimation model for different utilization status grassland based on remote sensing data[J]. Chinese Journal of Ecology,2007,26(3):332-337.
陶伟国,徐斌,刘丽军,等.不同利用状况下草原遥感估产模型[J].生态学杂志,2007,26(3):332-337.
[11] KLEMAS V. Remote sensing of coastal wetland biomass: An overview [J]. Journal of Coastal Research,2013,29(5):1 016-1 028.
[12] HUETE A, JACKSON R. Soil and atmosphere influences on the spectra of partial canopies [J]. Remote Sensing of Environment,1988,25(1):89-105.
[13] KAUFMAN Y J, TANRE D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS [J]. Geoscience and Remote Sensing, IEEE Transactions on,1992,30(2):261-270.
[14] QI Jia-guo, CABOT F, MORAN M, et al. Biophysical parameter estimations using multidirectional spectral measurements[J]. Remote Sensing of Environment,1995,54(1):71-83.
[15] TODD S, HOFFER R, MILCHUNAS D. Biomass estimation on grazed and ungrazed rangelands using spectral indices[J]. International Journal of Remote Sensing,1998,19(3):427-438.
[16] MYNENI R B, HALL F G, SELLERS P J, et al. The interpretation of spectral vegetation indexes[J]. Geoscience and Remote Sensing, IEEE Transactions on,1995,33(2):481-486.
[17] SELLERS P. Canopy reflectance, photosynthesis and transpiration[J]. International Journal of Remote Sensing,1985,6(8):1 335-1 372.
[18] GAO Xiang, HUETE A R, NI Wen-ge, et al. Optical-biophysical relationships of vegetation spectra without background contamination[J]. Remote Sensing of Environment,2000,74(3):609-620.
[19] BLACKBURN G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches[J]. Remote Sensing of Environment,1998,66(3):273-285.
[20] THENKABAIL P S, SMITH R B, DE PAUW E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics[J]. Remote Sensing of Environment,2000,71(2):158-182.
[21] GILABERT M A, GANDÍA S, MELIA J. Analyses of spectral-biophysical relationships for a corn canopy [J]. Remote Sensing of Environment,1996,55(1):11-20.
[22] MUTANGA O, SKIDMORE A K. Narrow band vegetation indices overcome the saturation problem in biomass estimation[J]. International Journal of Remote Sensing,2004,25(19):3 999-4 014.
[23] ADAM E, MUTANGA O, RUGEGE D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review[J]. Wetlands Ecology and Management,2010,18(3):281-296.
[24] WANG Ye-qiao. Remote sensing of coastal environment[M]. London: CRC Press,2010:261-280.
[25] PENGRA B W, JOHNSTON C A, LOVELAND T R. Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor[J]. Remote Sensing of Environment,2007,108(1):74-81.
[26] ROSSO P, USTIN S, HASTINGS A. Mapping marshland vegetation of San Francisco Bay, California, using hyperspectral data[J]. International Journal of Remote Sensing,2005,26(23):5 169-5 191.
[27] WANG Ye-qiao. Remote sensing of coastal environment[M]. London: CRC Press,2010:61-78.
[28] USTIN S L, ROBERTS D A, GAMON J A, et al. Using imaging spectroscopy to study ecosystem processes and properties[J]. BioScience,2004,54(6):523-534.
[29] HIRANO A, MADDEN M, WELCH R. Hyperspectral image data for mapping wetland vegetation[J]. Wetlands,2003,23(2):436-448.
[30] COHEN W B. Response of vegetation indices to changes in three measures of leaf water stress [J]. Photogrammetric Engineering & Remote Sensing,1991,57(2):195-202.
[31] GUPTA R, VIJAYAN D, PRASAD T. New hyperspectral vegetation characterization parameters[J]. Advances in Space Research,2001,28(1):201-206.
[32] BROGE N H, LEBLANC E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J]. Remote Sensing of Environment,2001,76(2):156-172.
[33] KIM M S, DAUGHTRY C, CHAPPELLE E, et al. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation[C]//Proc. ISPRS'94, Val d'Isere, France,1994:299-306.
[34] DAUGHTRY C, WALTHALL C, KIM M, et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J]. Remote Sensing of Environment,2000,74(2):229-239.
[35] DAWSON T, CURRAN P. Technical note A new technique for interpolating the reflectance red edge position[J]. International Journal of Remote Sensing,1998,19(11):2 133-2 139.
[36] CURRAN P J. Imaging spectrometry[J]. Progress in Physical Geography,1994,18(2):247-266.
[37] GOBRON N, PINTY B, VERSTRAETE M M. Theoretical limits to the estimation of the leaf area index on the basis of visible and near-infrared remote sensing data[J]. Geoscience and Remote Sensing, IEEE Transactions on,1997,35(6): 1 438-1 445.