基于全极化SAR影像的双台河口湿地分类及其变化分析
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摘要
为了研究辽东湾双台河口湿地变化原因,对该地区全极化SAR影像进行湿地分类.引入Freeman分解建模全极化SAR影像,得到二面角散射、体散射、表面散射的散射功率,利用SVM分类法对散射机制假彩色影像进行分类,进而确定该地区的湿地分布情况.通过对比分析2007年与2016年的湿地分布情况,同时考虑区域特性和数据的可获取性,选取盘锦市年降水量、年均气温、年径流量、海平面高度、城市建成面积、原油年产量、水产品年产量和逐年GDP等8个驱动因子,研究双台河口湿地变化的驱动机制.结果表明:(1)全极化SAR影像分类结果总精度达到80. 25%,较光学影像的分类精度提升7. 43%,并且可将芦苇湿地进一步细分为芦苇池塘和芦苇草甸;(2)2016年双台河口各湿地面积占比依次为草本沼泽(28. 54%)>浅海水域(22. 44%)>灌丛沼泽(16. 88%)>水产养殖厂(9. 54%)>河流(8. 91%)>稻田(7. 58%)>淤泥质沙滩(6. 11%);(3)不同湿地的变化原因有所差异,驱动因子也不尽相同,如水产品年产量增加是水产养殖厂转化为淤泥质沙滩的直接驱动因子,而气温上升等自然因素和城市建成区面积增长等社会因素是草本沼泽退化和变化的主要驱动因子.研究显示,全极化SAR影像较光学影像更适合湿地分类,而双台河口湿地自然湿地减少和人工湿地增加是自然和社会因素共同作用的结果.
To study changes of the Shuangtai Estuary Wetland,full-PolSAR image of the region was classified. Scattering powers of three scattering mechanisms( double-bounce scatter,volume scatter,surface scatter) were obtained by the Freeman decomposition and visualized as false color images. The false color images were classified by SVM classifier to determine the distribution of wetlands in the Shuangtai estuary area. Considering the relevance to wetland change and the data availability,we selected 8 driving factors( annual precipitation,average annual temperature,annual runoff,sea level,urban built-up area,annual crude oil production,annual output aquatic products and GDP of Panjin) to study the mechanisms of Shuangtai estuary wetland changes. As a result:( 1) The total classification accuracy is 80. 25% which is higher than the Landsat-8 image 7. 43%. Meanwhile,full-PolSAR image can be used to obtain better classification results based on the differences of the scatter mechanisms among all the objects derived from the Freeman decomposition and the proposed method can not only classify wetland from full-PolSAR image but also subtly sub-classify the reed wetland into meadow reed and reed pond to comprehensively reflect the spatial distribution of various wetlands.( 2) The area ratios for marsh,shallow sea waters,shrub swamp,reservoir pond,rivers,rice and silt sandy beaches paddies were 28. 54%,22. 44%,16. 88%,9. 54%,8. 91%,7. 58%,6. 11%.( 3) The results demonstrated that the main driving factors of the different types of wetland varied. For example,the increase in the annual output of aquatic products was a direct driving factor for the conversion of ponds to silt sandy beaches;natural factors such as rising temperatures and social factors such as increase in the urban development were the main driving factors for the degradation and changes of marshes. The study shows that the full-PolSAR image is more suitable for wetland classification than optical image. The natural and social factors are the main reasons of the Shuangtai Estuary wetland changes.
To study changes of the Shuangtai Estuary Wetland,full-PolSAR image of the region was classified. Scattering powers of three scattering mechanisms( double-bounce scatter,volume scatter,surface scatter) were obtained by the Freeman decomposition and visualized as false color images. The false color images were classified by SVM classifier to determine the distribution of wetlands in the Shuangtai estuary area. Considering the relevance to wetland change and the data availability,we selected 8 driving factors( annual precipitation,average annual temperature,annual runoff,sea level,urban built-up area,annual crude oil production,annual output aquatic products and GDP of Panjin) to study the mechanisms of Shuangtai estuary wetland changes. As a result:( 1) The total classification accuracy is 80. 25% which is higher than the Landsat-8 image 7. 43%. Meanwhile,full-PolSAR image can be used to obtain better classification results based on the differences of the scatter mechanisms among all the objects derived from the Freeman decomposition and the proposed method can not only classify wetland from full-PolSAR image but also subtly sub-classify the reed wetland into meadow reed and reed pond to comprehensively reflect the spatial distribution of various wetlands.( 2) The area ratios for marsh,shallow sea waters,shrub swamp,reservoir pond,rivers,rice and silt sandy beaches paddies were 28. 54%,22. 44%,16. 88%,9. 54%,8. 91%,7. 58%,6. 11%.( 3) The results demonstrated that the main driving factors of the different types of wetland varied. For example,the increase in the annual output of aquatic products was a direct driving factor for the conversion of ponds to silt sandy beaches;natural factors such as rising temperatures and social factors such as increase in the urban development were the main driving factors for the degradation and changes of marshes. The study shows that the full-PolSAR image is more suitable for wetland classification than optical image. The natural and social factors are the main reasons of the Shuangtai Estuary wetland changes.
引文
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[9] YU L,LI M,WANG S,et al.Wetland landscape change in Daliaohe River Basin and the driving factors analysis[J]. Procedia Environmental Sciences,2010,2(6):1255-1264.
[10] GOSSELIN G,TOUZI R,CAVAYAS F.Polarimetric RADARSAT-2wetland classification using the touzi decomposition:case of the lac saint-pierre ramsar wetland[J]. Canadian Journal of Remote Sensing,2014,39(6):491-506.
[11] MORANDEIRA N,GRINGS F,FACCHINETTI C,et al. Mapping plant functional types in floodplain wetlands:an analysis of c-band polarimetric SAR data from RADARSAT-2[J]. Remote Sensing,2016,8(3):1-17.
[12] XIE H,QU L,LIU G.Evolution characteristics and driving forces of wetland changes in the Poyang Lake eco-economic zone of China[J].Scientific Research&Essays,2014,9(2):24-34.
[13] LIU Y,SHENG L,LIU J.Impact of wetland change on local climate in semi-arid zone of northeast China[J]. Chinese Geographical Science,2015,25(3):309-320.
[14]卢善龙,吴炳方,李发鹏.海河流域湿地格局变化分析[J].遥感学报,2011,15(2):349-371.LU Shanlong,WU Bingfang,LI Fapeng. Wetland pattern change in Hai Basin[J].Journal of Remote Sensing,2011,15(2):349-371.
[15]宫宁,牛振国,齐伟,等.中国湿地变化的驱动力分析[J].遥感学报,2016,20(2):172-183.GONG Ning,NIU Zhenguo,QI Wei,et al.Driving forces of wetland change in China[J].Journal of Remote Sensing,2016,20(2):172-183.
[16]孙楠,朱渭宁,程乾.基于多年遥感数据分析长江河口海岸带湿地变化及其驱动因子[J].环境科学学报,2017,37(11):4366-4373.SUN Nan,ZHU Weining,CHENG Qian.Remote sending time-series analysis of wetland variations and driving factors in estuarine and coastal regions of Yangtze River[J].Acta Scientiae Circumstantiae,2017,37(11):4366-4373.
[17]国家林业局调查规划设计院.GB/T 24708—2009湿地分类[S].北京:中国标准出版社,2009.
[18] KALANTARI I,ZAKERI B. A modified polarimetric synthetic aperture radar classifier usingα/A/H technique[J].Electromagnetics,2014,34(5):408-420.
[19] DOULGERIS A,ANFINSEN S,ELTOFT T. Classification with a non-Gaussian model for PolSAR data[J]. IEEE Transactions on Geoscience&Remote Sensing,2008,46(10):2999-3009.
[20] LEE J,POTTIER E. Polarimetric radar imaging:from basics to applications[M]. 2nd ed.Boca Raton,America:Cre Press,2016.
[21] DABBOOR M,HOWELL S,SHOKR M,et al.The jeffries-matusita distance for the case of complex wishart distribution as a separability criterion for fully polarimetric SAR data[J].International journal of remote sensing,2014,35(19):6859-6873.
[22] LIU G,ZHONG H.Nonlocal means filter for polarimetric SAR data despeckling based on discriminative similarity measure[J]. IEEE Geoscience and Remote Sensing Letters,2014,11(2):514-518.
[23] MA X,SHEN H,ZHANG L,et al. Adaptive anisotropic diffusion method for polarimetric SAR speckle filtering[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(3):1041-1050.
[24] FAN W,ZHOU F,TAO M,et al. An automatic ship detection method for PolSAR data based on K-wishart distribution[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2017,10(6):2725-2737.
[25] HULST H. Light scattering by small particles[M]. Hoboken,America:John Wiley,1957.
[26] CLOUDE S,POTTIER E.A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience&Remote Sensing,1996,34(2):498-518.
[27] VAN-ZYL J J. Unsupervised classification of scattering behavior using radar polarimetry data[J]. IEEE Transactions on Geoscience&Remote Sensing,1989,27(1):36-45.
[28] FREEMAN A,DURDEN S L.A three-component scattering model for polarimetric SAR data[J].IEEE Transactions on Geoscience&Remote Sensing,1998,36(3):963-973.
[29] FREEMAN A,DURDEN S L.Three-component scattering model to describe polarimetric SAR data[J]. Proc Spie,1993,1748:213-224.
[30] AGHABABAEE H. Contextual PolSAR image classification using fractal dimension and support vector machines[J]. European Journal of Remote Sensing,2013,46(1):317-332.
[31] ASFARAM A,GHAEDI M,AZQHANDI M H A,et al. Statistical experimental design,least squares-support vector machine(LSSVM)and artificial neural network(ANN)methods for modeling the facilitated adsorption of methylene blue dye[J].Rsc Advances,2016,46(6):40502-40516.
[2]吴海明,袁佐栋,张建,等.规模化人工湿地的温室气体释放通量[J].环境科学研究,2016,29(8):1195-1199.WU Haiming,YUAN Zuodong,ZHANG Jian,et al. Greenhouse gas emissions from large-scale constructed wetlands[J]. Research of Environmental Sciences,2016,29(8):1195-1199.
[3]张菁,李睿华,李杰,等.石灰石和黄铁矿-石灰石人工湿地净化河水的研究[J].环境科学,2013,34(9):3445-3450.ZHANG Jing,LI Ruihua,LI Jie,et al. Limestone and pyritelimestone constructed wetlands for treating river water[J].Environmental Science,2013,34(9):3445-3450.
[4]常洋,王海燕,储昭升,等.硫碳比对芦苇碳源-硫耦合表面流湿地脱氮的影响[J].环境科学研究,2017,30(11):1783-1792.CHANG Yang,WANG Haiyan,CHU Zhaosheng,et al. Influence of sulfur-to-carbon ration on nitrogen removal by phragmites australis and sulfur combined surface flow constructed wetland[J].Research of Environmental Sciences,2017,30(11):1783-1792.
[5]陈炯,贾海峰,杨健,等.基于极化SAR的河流有机物污染监测研究[J].环境科学,2010,31(9):2017-2022.CHEN Jiong,JIA Haifeng,YANG Jian,et al. Monitoring of organic pollutants in river based on polarimetric SAR[J]. Environmental Science,2010,31(9):2017-2022.
[6]宋伟东,杨冬,李恩宝,等.盘锦市湿地信息提取与动态变化监测[J].测绘科学,2016,41(9):60-65.SONG Weidong,YANG Dong,LI Enbao,et al. Wetland information extraction and dynamic monitoring of Panjin[J]. Science of Surveying and Mapping,2016,41(9):60-65.
[7]唐小平,黄桂林.中国湿地分类系统的研究[J].林业科学研究,2003,16(5):531-539.TANG Xiaoping,HUANG Guilin. Study on classification system for wetland types in China[J]. Forest Research,2003,16(5):531-539.
[8] FICKAS K C,COHEN W B,YANG Z.Landsat-based monitoring of annual wetland change in the willamette valley of oregon,USA from1972 to 2012[J].Wetlands Ecology&Management,2016,24(1):73-92.
[9] YU L,LI M,WANG S,et al.Wetland landscape change in Daliaohe River Basin and the driving factors analysis[J]. Procedia Environmental Sciences,2010,2(6):1255-1264.
[10] GOSSELIN G,TOUZI R,CAVAYAS F.Polarimetric RADARSAT-2wetland classification using the touzi decomposition:case of the lac saint-pierre ramsar wetland[J]. Canadian Journal of Remote Sensing,2014,39(6):491-506.
[11] MORANDEIRA N,GRINGS F,FACCHINETTI C,et al. Mapping plant functional types in floodplain wetlands:an analysis of c-band polarimetric SAR data from RADARSAT-2[J]. Remote Sensing,2016,8(3):1-17.
[12] XIE H,QU L,LIU G.Evolution characteristics and driving forces of wetland changes in the Poyang Lake eco-economic zone of China[J].Scientific Research&Essays,2014,9(2):24-34.
[13] LIU Y,SHENG L,LIU J.Impact of wetland change on local climate in semi-arid zone of northeast China[J]. Chinese Geographical Science,2015,25(3):309-320.
[14]卢善龙,吴炳方,李发鹏.海河流域湿地格局变化分析[J].遥感学报,2011,15(2):349-371.LU Shanlong,WU Bingfang,LI Fapeng. Wetland pattern change in Hai Basin[J].Journal of Remote Sensing,2011,15(2):349-371.
[15]宫宁,牛振国,齐伟,等.中国湿地变化的驱动力分析[J].遥感学报,2016,20(2):172-183.GONG Ning,NIU Zhenguo,QI Wei,et al.Driving forces of wetland change in China[J].Journal of Remote Sensing,2016,20(2):172-183.
[16]孙楠,朱渭宁,程乾.基于多年遥感数据分析长江河口海岸带湿地变化及其驱动因子[J].环境科学学报,2017,37(11):4366-4373.SUN Nan,ZHU Weining,CHENG Qian.Remote sending time-series analysis of wetland variations and driving factors in estuarine and coastal regions of Yangtze River[J].Acta Scientiae Circumstantiae,2017,37(11):4366-4373.
[17]国家林业局调查规划设计院.GB/T 24708—2009湿地分类[S].北京:中国标准出版社,2009.
[18] KALANTARI I,ZAKERI B. A modified polarimetric synthetic aperture radar classifier usingα/A/H technique[J].Electromagnetics,2014,34(5):408-420.
[19] DOULGERIS A,ANFINSEN S,ELTOFT T. Classification with a non-Gaussian model for PolSAR data[J]. IEEE Transactions on Geoscience&Remote Sensing,2008,46(10):2999-3009.
[20] LEE J,POTTIER E. Polarimetric radar imaging:from basics to applications[M]. 2nd ed.Boca Raton,America:Cre Press,2016.
[21] DABBOOR M,HOWELL S,SHOKR M,et al.The jeffries-matusita distance for the case of complex wishart distribution as a separability criterion for fully polarimetric SAR data[J].International journal of remote sensing,2014,35(19):6859-6873.
[22] LIU G,ZHONG H.Nonlocal means filter for polarimetric SAR data despeckling based on discriminative similarity measure[J]. IEEE Geoscience and Remote Sensing Letters,2014,11(2):514-518.
[23] MA X,SHEN H,ZHANG L,et al. Adaptive anisotropic diffusion method for polarimetric SAR speckle filtering[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(3):1041-1050.
[24] FAN W,ZHOU F,TAO M,et al. An automatic ship detection method for PolSAR data based on K-wishart distribution[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2017,10(6):2725-2737.
[25] HULST H. Light scattering by small particles[M]. Hoboken,America:John Wiley,1957.
[26] CLOUDE S,POTTIER E.A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience&Remote Sensing,1996,34(2):498-518.
[27] VAN-ZYL J J. Unsupervised classification of scattering behavior using radar polarimetry data[J]. IEEE Transactions on Geoscience&Remote Sensing,1989,27(1):36-45.
[28] FREEMAN A,DURDEN S L.A three-component scattering model for polarimetric SAR data[J].IEEE Transactions on Geoscience&Remote Sensing,1998,36(3):963-973.
[29] FREEMAN A,DURDEN S L.Three-component scattering model to describe polarimetric SAR data[J]. Proc Spie,1993,1748:213-224.
[30] AGHABABAEE H. Contextual PolSAR image classification using fractal dimension and support vector machines[J]. European Journal of Remote Sensing,2013,46(1):317-332.
[31] ASFARAM A,GHAEDI M,AZQHANDI M H A,et al. Statistical experimental design,least squares-support vector machine(LSSVM)and artificial neural network(ANN)methods for modeling the facilitated adsorption of methylene blue dye[J].Rsc Advances,2016,46(6):40502-40516.
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