基于HJ-1A高光谱的黄河口碱蓬和柽柳盖度反演模型研究

任广波; 张杰; 马毅

基于HJ-1A高光谱的黄河口碱蓬和柽柳盖度反演模型研究

国家海洋局第一海洋研究所海洋物理与遥感研究室, 山东青岛 266061

基金项目: 国家自然科学基金青年基金项目(41206172);国家海洋局基本科研业务费项目(GY02-2012G12)。

计量
- 文章访问数: 1319
- HTML全文浏览量: 12
- PDF下载量: 1236
- 被引次数: 0
出版历程
- 收稿日期: 2014-11-04
- 修回日期: 2015-02-17

Suaeda-salsa and tamarisk fractional cover inversion models by HJ-1A hyperspectral remote sensing image in Yellow River Estuary

Laboratory of Marine Physics and Remote Sensing, First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China

摘要

摘要: 碱蓬和柽柳是黄河口湿地典型的盐生植物类型,是多种保护珍禽的主要栖息地,具有景观尺度较小、分布广且多混生的特点。应用覆盖黄河口北部潮滩的HJ-1A高光谱遥感影像,基于现场测量的端元光谱和从遥感影像中使用顺序最大角凸锥法(SAMCC)自动提取的端元光谱,应用线性光谱分解法(LSU)、正交子空间投影法(OSP)、匹配滤波法(MF)、最小能量约束法(CEM)和自适应一致估计法(ACE)5种不同光谱解混方法进行混合像元光谱解混,对比两种方法得到的端元光谱分别对碱蓬和柽柳盖度的反演能力,并给出相应的反演模型。结果显示: (1)现场测量端元光谱取得了较好的碱蓬和柽柳盖度反演结果,其中应用LSU方法的光谱解混结果与现场测量盖度的决定系数对于碱蓬和柽柳分别达到了0.88和0.95;(2)两种端元获取方式的光谱解混结果中,LSU和OSP方法均获得了较高的相关性,ACE解混方法的相关性都最低;(3)SAMCC方法提取端元光谱对柽柳的分解结果与现场测量盖度的相关性远高于碱蓬。
- HJ-1A高光谱 /
- 黄河口湿地 /
- 碱蓬 /
- 柽柳 /
- 植被盖度
Abstract: Suaeda salsa and tamarisk are the typical vegetation in Yellow River Estuary,and also a key habitat for many kinds of rare birds. Suaeda salsa and tamarix,whose landscape scale usually small,widely distributed and often mixed with each other or other vegetation or bare soil in remote sensing images,are difficult to obtain the vegetation fractional cover efficiently,thereby affecting biomass monitoring and ecological assessment. In this study,we acquired the end-members in two ways,the first is measure the vegetation spectrum through field work,and the second way is extract them from the hyperspectral remote sensing image with the SAMCC method. Then we use the two kinds of end-members in the pixel spectral un-mixing process with 5 different algorithms,which are: LUS,OSP,MF,CEM and ACE. At last we evaluate the two kinds of end-members coverage inversion capability of the salsa and Tamarix through correlation analysis with the real coverage which measured in the field work.
- HJ-1A hyperspectral remote sensing /
- Yellow River Estuary wetlands /
- Suaeda salsa /
- tamarisk /
- vegetation fractional cover inversion

HTML全文

参考文献(23)

Gitelson A A,Kaufman Y J,Stark R,et al. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment,2002,80(1): 76-87.

Boyd D S,Foody G M,Ripple W J. Evaluation of approaches for forest covers estimation in the Pacific North west USA,using remote sensing[J]. Applied Geography,2002,22 (4): 375-392.

Choudhury B J,Ahmed N U,Idso S B,et al. Relations between evaporation coefficients and vegetation indices studied by model simulations[J]. Remote Sensing of Environment,1994,50(1): 12-17.

Gutman G,Ignalov A. The derivation of the green vegetation fraction from NOAA /AVHRR data for use in numerical weather prediction models[J]. International Journal of Remote Sensing,1998,19(8): 1533-1543.

任广波,张杰,汪伟奇,等. 基于HJ-1高光谱影像的黄河口芦苇和碱蓬生物量估测模型研究[J]. 海洋学研究,2014,32(4): 8-15. Ren Guangbo,Zhang Jie,Wang Weiqi,et al. Reeds and suaeda biomass estimation model based on HJ-1 hyperspectral image in the Yellow River Estuary[J]. Journal of Marine Sciences,2014,32(4):8-15.

傅新,刘高焕,黄翀,等. 湿地翅碱蓬生物量遥感估算模型[J]. 生态学报, 2012,32(17): 5355-5362. Fu Xin,Liu Gaohuan,Huang Chong,et al. Remote sensing estimation models of suaeda biomass in the coastal wetland[J]. Acta Ecologica Sinica,2012,32(17): 5355-5362.

Schmidt K S,Skidmore A K. Spectral discrimination of vegetation types in a coastal wetland[J]. Remote Sensing of Environment,2003,85(1): 92-108.

Silvestri S,Marani M,Marani A. Hyperspectral remote sensing of salt marsh vegetation,morphology and soil topography[J]. Physics and Chemistry of the Earth,Parts A/B/C,2003,28(1/3): 15-25.

Hamada Y,Stow D A,Coulter L L,et al. Detecting tamarisk species in riparian habitats of southern California using high spatial resolution hyperspectral imagery[J]. Remote Sensing of Environment,2007,109(2): 237-248.

Thomson A G,Fuller R M,Yates M G,et al. The use of airborne remote sensing for extensive mapping of intertidal sediments and saltmarshes in eastern England[J]. International Journal of Remote Sensing,2003,24(13): 2717-2737.

Smith G M,Spencer T,Murray A L,et al. Assessing seasonal vegetation change in coastal wetlands with airborne remote sensing: an outline methodology[J]. Mangroves and Salt Marshes,1998,2(1): 15-28.

Thomson J S,Jeffrey T M,Sunil K. Mapping invasive tamarix: a comparison of single-scene and time series analyses of remotely sensed data[J]. Remote Sensing,2009(1): 519-533.

高海亮,顾行发,余涛,等. 基于参考波段的去处HJ-1A HSI图像中条带噪声的方法[J]. 红外,2013,34(3): 7-11. Gao Hailiang,Gu Xingfa,Yu Tao,et al. A reference band based method for removing stripe noise from HJ-1A HIS images[J]. Infrared,2013,34(3): 7-11.

贾德伟,钟仕全,陈燕丽,等. HJ-1A高光谱数据预处理方法研究[J]. 河北遥感,2010(2): 13-15. Jia Dewei,Zhong Shiquan,Chen Yanli,et al. The processing method of HJ-1A hyperspectral remote sensing image[J]. Hebei Remote Sensing,2010(2):13-15.

李俊明,邢秋燕,杨超. 基于森林类型光谱特征的最佳波段选择研究——以HJ-1A高光谱影像为例[J]. 森林工程,2013,29(4): 42-46. Li Junming,Xing Qiuyan,Yang Chao. Study on optical bands selection based on spectral feature of forest types: A case study of HJ-1A hyperspectral image[J]. Forest Engineering,2013,29(4): 42-46.

童庆禧,张兵,郑兰芬. 高光谱遥感——原理、技术与应用[M]. 北京: 高等教育出版社,2006. Tong Qingxi,Zhang Bing,Zheng Lanfen. Hyperspectral Remote Sensing——Theory,Technology and Application[M]. Beijing: High Education Press,2006.

王彦飞,李云梅,吕恒,等. 环境一号卫星高光谱遥感数据的内陆水质监测适宜性——以巢湖为例[J]. 湖泊科学,2011,23(5): 789-795. Wang Yanfei,Li Yunmei挬敌敶搠楈湥杮獧????剡卬匮??????瑢桩??慴湹愠摡楳慳湥?即祭浥灮潴猠楯畦洠?潡湫?删敷浡潴瑥敲?危敵湡獬楩湴杹???????????は????ね????戠牡?孱??嵲??愠牢獹愠湈祊椭???????敲瑳数捥瑣楴潲湡?愠湩摭?捧汥慲猺猠楁映楣捡慳瑥椠潳湴?潤晹?獯畦戠?灡楫硥攠汃?獡灯敨捵瑛牊慝氮?獊楯杵湲慮瑡畬爠敯獦?楌湡?桥礠灓散物獥灮散捥琬爲愰氱?椬洲愳木攵?猺攠焷甸改渭挷改猵嬮?嵢?￣?愱永瑝椠洠潵爬旘???攠烘慉爱瓾淏攆湻琎?漇暢??汍敝挮琠爗榬挺懑汦??渾本椲渰攱攱爮椠湚杨?啮湧椠療敩牮獧椬瑇祡?漠晌??慮牲祵氮愠湈摹??慲汳瑰楥浣潴牲敡??潉畭湡瑧祥???????扦物?孡??嵯?匠瑡??㈠???灧桥慴渠楄敥???佴汩楯癮楛敍牝???呥潩番物湮敧爺攠瑓???奮??吠桐敲?慳摳愬瀲琰椱瘱攮?换潲栾敛爱改湝挠敉?敳獩瑦椠浌愬瑅潬物?楡獶?瑴桡攠?本敇湡敲特愠汇椬穥整搠?汬椮欠敒汯楬桥漠潯摦?牳慥瑮楳潯?琠敮獯瑩?晥漠物?愠?捹汰慥獲獳?潥晣?桲敡瑬攠牲潥杭敯湴敥漠畳獥?敳湩癮楧爠潯湦洠敮湡瑴獵孲?嵬???整敥敲?区椠杁湰慰汬?偣牡潴捩敯獮猠楴湯朠??整瑲瑩敥牶獡?㈠はて??????????????戠牰?孧?づ嵮??桛慊湝朮???????礠灓敥牮獳灩敮捧琠牯慦氠??浶慩杲楯湮杭?呮整挬栲渰椰焵甬改猵?昲漩爺?匲瀶攴挭琲爷愱氮??敲琾敛挲琰楝漠湖?慮渠摄??氠慍獥獥楲映楆挮愠瑔楨潥渠孥?嵦??乴敩睶?奮潥牳歳??汦甠睳数牥??捲慡牬搠敳浩業捩?偡汲敩湴畹洠?健畡扳汵楲獥桳攠牦獯?㈠ぴと??????????戠牯?嬠??嵰??潳獰敥灣桴????桩慭湡杧???????礠灉敮牴獥灲敮捡瑴物慯汮?楬洠慊杯敵?据污慬猠獯楦映楁捰慰瑬楩潥湤?慅湡摲?摨椠浏敢湳獥楲潶湡慴汩楯瑮礠?牮敤搠畇捥瑯楩潮湦???湡?潩牯瑮栬漲朰漰渶愬永?猱甩戺猠瀳愭挱攷?瀼牢潲樾敛挲琱楝漠湇?慬灬灥牳潰慩捥栠孁?嵒???????呲牡慬渠獭慩捸瑴極潲湥猠?潮湡??敳潩獳挠楯敦渠捭敵?慴湩摳?剥散浴潲瑡敬?却敨湥獲業湡杬?????????????????????Remote Sensing of Environment,1992,42(2): 137-145.

Foody G M,Cox D P. Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership function[J]. International Journal of Remote Sensing,1994,15(3): 619-631.

Boardman J W,Kruse F A,Green R O. Mapping target signatures via partial unmixing of AVIRIS data[C]//In Summaries,Fifth JPL Airborne Earth Science Workshop,JPL Publication 95-1,1995,1: 23-26.

Winter M E. N-finder: An algorithm for fast autonomous spectral end-member determination in hyperspectral data[C]//Proceedings of SPIE,Image Spectrometry. SPIE,1999:266-275.

Bajorski P. Simplex projection methods for selection of end-members in hyperspectral imagery[C]//IEEE International Geoscience and Remote Sensing Symposium, Anchorage Alaska,2004. USA: IEEE Press,2004,5: 3207-3210.

Gruninger J,Ratkowski A,Hoke M. The sequential maximum angle convex cone (SAMCC) end-member model[C]//Proceeding of SPIE,Algorithm for Multi-spectral,Hyper-spectral and Ultra-spectral Imagery. SPIE,2004,5425: 1-14.

Boardman J W. Inversion of imaging spectrometry data using singular value decomposition[C]//Pro

施引文献

资源附件(0)

访问统计

点击查看大图

计量

文章访问数: 1319
HTML全文浏览量: 12
PDF下载量: 1236
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板