Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2012, Vol. 24 Issue (3) : 38-43     DOI: 10.6046/gtzyyg.2012.03.08
Retrieval of Wetland Vegetation Biomass in Poyang Lake Based on Quad-polarization Image
LIU Ju1,2, LIAO Jing-juan1, SHEN Guo-zhuang1
1. Center for Earth Observation and Digital Earth Chinese Academy of Sciences, Beijing 100094, China;
2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
Download: PDF(1375 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  The Poyang Lake is the largest freshwater lake in China as well as an internationally important wetland. Long-term quantitative study of vegetation biomass in this area helps deepen our understanding of regional and global carbon balance. The authors investigated the approach and method of Radarsat-2C-Band quad-polarization imagery for biomass retrieval in wetland vegetation. The vegetation canopy scattering model was modified and used to simulate the backscattering characteristics. Polarization decomposition was adopted to prepare the testing data with the model output for BP neural network. After obtaining the retrieval values of vegetation biomass, the values were compared with the filed-measured values. The results show that the introduction of the output data of vegetation canopy scattering model and polarimetric decomposition technique to the BP neural network algorithm could reduce the retrieval error effectively, and that the Quad-polarization imagery has broad application prospect in the field of biomass retrieval.
Keywords fractal      remote sensing prospecting      lithological classification      alteration information extraction      fractal dimension spectra     


Issue Date: 20 August 2012
E-mail this article
E-mail Alert
Articles by authors
ZHENG Gui-xiang
CHI Tian-he
LIN Qi-zhong
Cite this article:   
ZHENG Gui-xiang,CHI Tian-he,LIN Qi-zhong. Retrieval of Wetland Vegetation Biomass in Poyang Lake Based on Quad-polarization Image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 38-43.
URL:     OR
[1] Zhang X.On the Estimation of Biomass of Submerged Vegetation Using Landsat Thematic Mapper (TM) Imagery:A Case Study of the Honghu Lake,P R China[J].International Journal of Remote Sensing,1998,19(1):11-20.
[2] Thenkabail P S,Smith R B,Pauw D E.Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics[J].Remote Sens Environ,2000,71(2):158-182.
[3] Lu D S.The Potential and Challenge of Remote Sensing-based Biomass Estimation[J].International Journal of Remote Sensing,2006,27(7):1297-1328.
[4] Steininger M K.Satellite Estimation of Tropical Secondary Forest Above Ground Biomass Data from Brazil and Bolivia[J].International Journal of Remote Sensing,2000,21(6/7):1139-1157.
[5] Lu D S,Batistella M.Exploring TM Image Texture and Its Relationships with Biomass Estimation in Rondnia,Brazilian Amazon [J].Acta Amazonica,2005,35(2):249-257.
[6] Shao Y,Liao J J,Wang C Z.Analysis of Temporal Radar Backscatter of Rice:A Comparison of SAR Observations with Modeling Results[J].Can J Remote Sens,2002,28(2):128-138.
[7] Le T,Ribbes F,Wang L F,et al.Rice Crop Mapping and Monitoring Using ERS-1 Data Based on Experiment and Modeling Results[J].IEEE Trans Geosci Remote Sens,1997,35(1):41-56.
[8] Shao Y,Fan X T,Liu H,et al.Rice Monitoring and Production Estimation Using Multitemporal Radarsat[J].Remote Sens Environ,2001,76(3):310-325.
[9] Inoue Y,Kurosu T,Maeno H,et al.Season-long Daily Measurements of Multifrequency (Ka,Ku,X,C,and L) and Full-polarization Backscatter Signatures over Paddy Rice Field and Their Relationship with Biological Variables[J].Remote Sens Environ,2002,81(3):194-204.
[10] Shen S H,Yang S B,Li B B,et al.A Scheme for Regional Rice Yield Estimation Using Envisat ASAR Data[J].Sci China Ser D:Earth Sci,2009,52(8):1183-1194.
[11] ULander L M,Sandberg G,Soj M.Biomass Retrieval Algorithm Based on P-band Biosar Experiments of Boreal Forest[C]//IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2011:4245-4248.
[12] Fan W,Chao W,Hong Z,et al.Rice Crop Monitoring in South China with Radarsat-2 Quad-polarization SAR Data[J].IEEE Geoscience and Remote Sensing Letters,2011,8(2):196-200.
[13] 黎夏,刘凯,王树功.珠江口红树林湿地演变的遥感分析[J].地理学报,2006,61(1):26-34. Li X,Liu K,Wang S G.Mangrove Wetland Changes in the Pearl River Estuary Using Remote Sensing[J].Acta Geographica Sinica,2006,61(1):26-34(in Chinese with English Abstract).
[14] 黎夏,叶嘉安,王树功,等.红树林湿地植被生物量的雷达遥感估算[J].遥感学报,2006,10(3):387-396. Li X,Ye J A,Wang S G,et al.Estimating Mangrove Wetland Biomass Using Radar Remote Sensing[J].Journal of Remote Sensing,2006,10(3):387-396(in Chinese with English Abstract).
[15] Benson M,Pierce L,Bergen K,et al.Forest Structure Estimation Using SAR,LiDAR,and Optical Data in the Canadian Boreal Forest[C]//IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2011:2609-2612.
[16] Yang S B,Zhao X Y,Li B,et al.Interpreting Radarsat-2 Quad-polarization SAR Signatures from Rice Paddy Based on Experiments[J].IEEE Geoscience and Remote Sensing Letters,2011,9(1):60-69.
[17] Zhang Y,Wang C Z,Wn J P,et al.Mapping Paddy Rice with Multitemporal ALOS/PALSAR Imagery in Southeast China[J].Int J Remote Sens,2009,30(23):6301-6315.
[18] Stauer S,Kugler F,Lee S K,et al.Polarimetric Decomposition for Forest Biomass Retrieval[C]//IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2010,4780-4783.
[19] McDonald K C,Dobson M C,Ulaby F T.Using MIMICS to Model L-band Multiangle and Multitemporal Backscatter from a Walnut Orchard[J].IEEE Transactions on Geoscience and Remote Sensing,1990,28(4):477-491.
[20] Ulaby F T,Sarabandi K,McDonald K,et al.Michigan Microwave Canopy Scattering Model[J].Int J Remote Sense,1990,11(7):1223-1253.
[21] Attema E P W,Ulaby F T.Vegetation Modeled as a Water Cloud [J].Radio Science,1978,13(2):357-364.
[22] De Roo R D,Du Y,Ulaby F T,et al.A Semi-empirical Backscattering Model at L-band and C-band for a Soybean Canopy with Soil Moisture Inversion[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(4):864-872.
[23] Le T T,Laur H,Mougin E,et al.Multitemporal and Dual-polarization Observations of Agricultural Vegetation Covers by X-band SAR Images[J].IEEE Transactions on Geoscience and Remote Sensing,1989,27(6):709-718.
[24] 张远.微波遥感水稻种植面积提取、生物量反演与稻田甲烷排放模拟[D].浙江:浙江大学,2008. Zhang Y.Acreage Extraction and Biomass Estimation of Paddy Rice Based on Microwave Remote Sensing and Methane Emissions Simulation from Paddy Field[D].Zhejiang:Zhejiang University,2008.(in Chinese with English Abstract)
[25] 彭映辉,简永兴,李仁东.鄱阳湖平原湖泊水生植物群落的多样性[J].中南林学院学报,2003,23(4):22-27. Peng Y H,Jian Y X,Li R D.Community Diversity of Aquatic Plants in the Lakes of Poyang Plain District of China[J].Journal of Central South Forestry University,2003,23(4):22-27(in Chinese with English Abstract).
[26] Karam M A,Amar F,Fung A K,et al.A Microwave Polarimetric Scattering Model for Forest Canopies Based on Vector Radiative Transfer Theory[J].Remote Sens Environ,1995,53(1):16-30.
[27] Wang C Z,Wu J P,Zhang Y,et al.Characterizing L-band Scattering of Paddy Rice in Southeast China with Radiative Transfer Model and Multitemporal ALOS/PALSAR Imagery[J].IEEE Trans Geosci Remote Sens,2009,47(4):990-995.
[28] Freeman A,Durden S L.A Three-component Scattering Model for Polaimetric SAR data[J].IEEE Trans Geosci Remote Sens,1998,36(3):963-973.
[29] Yamaguchi Y,Moriyama T,Ishido M,et al.Four-component Scattering Model for Polarimetric SAR Image Decomposition[J].IEEE Trans Geosci Remote Sens,2005,43(8):1699-1706.
[30] Freeman A,Durden S L.A Three-component Scattering Model to Describe Polarimetric SAR Data[C]//Proceedings SPIE Conference on Radar Polarimetry,1992:213-225.
[31] Foody G M,Cutler M E,Mcmorrow J,et al.Mapping the Biomass of Bornean Tropical Rain Forest from Remotely Sensed Data[J].Global Ecology and Biogeography,2001,10(4):379-387.
[32] 罗晓曙.人工神经网络理论·模型·单法与应用[M].桂林:广西师范大学出版社,2005. Luo X S.Artificial Neural Network Theory·Model·Algorithm and Application [M].Guilin:Guangxi Normal University Press,2005(in Chinese).
[1] Decai JIANG, Wenji LI, Jingmin LI, Zhaofeng BAI. Extraction of the forest fire region based on the span of ALOS PALSAR by object-oriented analysis[J]. Remote Sensing for Land & Resources, 2019, 31(4): 47-52.
[2] Quan AN, Zhonghua HE, Cuiwei ZHAO, Hong LIANG, Shulin JIAO, Chaohui YANG. GIS-based estimation of fractal dimension and geomorphological development of the water system in the dam construction area[J]. Remote Sensing for Land & Resources, 2019, 31(4): 104-111.
[3] Hui HUANG, Xiongwei ZHENG, Genyun SUN, Yanling HAO, Aizhu ZHANG, Jun RONG, Hongzhang MA. Seismic image classification based on gravitational self-organizing map[J]. Remote Sensing for Land & Resources, 2019, 31(3): 95-103.
[4] Zhan YIN, Lijun ZHANG, Jianliang DUAN, Pei ZHANG. Improvement and application of forced invariance vegetation suppression in southern vegetation area[J]. Remote Sensing for Land & Resources, 2019, 31(2): 82-88.
[5] Yongmei ZHANG, Haiyan SUN, Yulong XU. An improved multispectral image segmentation method based on super-pixels[J]. Remote Sensing for Land & Resources, 2019, 31(1): 58-64.
[6] HAN Haihui, WANG Yilin, YANG Min, REN Guangli, YANG Junlu, LI Jianqiang, GAO Ting. Application of fractal dimension-change point method to the extraction of remote sensing alteration anomaly[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 137-142.
[7] HU Hualong, XUE Wu, QIN Zhiyuan. Extraction of residential area from high resolution images based on wavelet texture and primitive merging[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 21-28.
[8] HAN Haihui, WANG Yilin, REN Guangli, YANG Junlu, LI Jianqiang, YANG Min. Nonlinear analysis method for remote sensing alteration anomalies: A case study of Xinjinchang and Laojinchang in Beishan[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 43-49.
[9] KUAI Kaifu, XU Wenbin, HUANG Zhicai, LI Su. Remote sensing geological characteristics and prospecting of the BIF-type iron deposits in Pilbara Craton of Western Australia[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 93-101.
[10] LIU Lei, ZANG Shuying, SHAO Tiantian, WEI Jinhong, SONG Kaishan. Characterization of lake morphology in China using remote sensing and GIS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 92-98.
[11] ZHOU Lintao, YANG Guofan, ZHAO Fuqiang, DU Juan. Water information extraction from remote sensing image using EMD and fraction method[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 41-45.
[12] JIA Chunyang, LI Weihua, LI Xiaochun. High-resolution remote sensing image segmentation based on weight adaptive fractal net evolution approach[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 22-25.
[13] QIAN Jianping, ZHANG Yuan, ZHAO Xiaoxing, ZHAO Shaojie, LI Chengli. Extraction of linear structure and alteration information based on remote sensing image and ore-prospecting prognosis for Dongwu Banner, Inner Mongolia[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 109-117.
[14] YUAN Xiaoping, LIU Shaofeng, TIAN Guizhong, CHEN Li, YU Jing. Analysis of the fractal dimension in the Golmud River basin based on DEM[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 111-116.
[15] WANG Dong-yin, ZHU Gu-chang, ZHANG Yuan-fei. Spatial Structure Features and Basic Statistic Parameters of Typical Ground Object Spectral Data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 138-145.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech