|
扩展功能 |
|
| 本文信息 | |
 |
Supporting info |
|
PDF(330KB) |
|
[HTML全文] |
|
参考文献[PDF] |
|
参考文献 |
| 服务与反馈 | |
|
加入引用管理器 |
|
引用本文 |
|
Email Alert |
|
文章反馈 |
|
浏览反馈信息 |
| 本文关键词相关文章 | |
|
海草 |
|
高光谱 |
|
光学浅水 |
|
导数光谱 |
|
覆盖率 |
| 本文作者相关文章 | |
| PubMed | |
以三亚湾泰莱藻为例, 对海草光谱特征进行分析。结果表明, 450—780nm是海草光谱主要敏感波段, 其波段内的导数光谱是海草叶面积大小序列辨别的有效依据。一阶导数获得的红边与叶片叶绿素a浓度密切相关。 海草在625nm、675nm 两处出现明显的导数特征峰, 两峰峰值相差较大; 其优势特征峰分布在550nm、700nm、750—780nm。实际应用中, 此特征可作为海草底质分类的识别条件, 与海草覆盖率和光谱特征的关系相结合, 可对大量的遥感数据进行识别, 从而达到大尺度遥感监测海草分布和动态变化的目的。
The authors use the spectrum of Thalassia to analyze the optical properties of seagrass substrates. The results show that in the range of 450?780nm, the derivative spectral reflectance of seaweed can reflect the variability of leaf area index with high accuracy. Red edge calculated by the first order derivative spectrometry is a good indicator for chlorophyll concentration. In the derivative spectra, there are two obvious peaks at 625nm and 675nm, and the difference between the two peak values is large for seagrass. Other dominated peaks appear at 550nm, 700nm, and 750?780nm. Combining with the relationship between seagrass coverage rate and the spectral properties, the properties of peak distribution can be utilized to classify plenty of re-mote sensing data sets in order to monitor large scale spatio-temporal dynamic patterns.
国家自然科学基金(40876092); 国家高技术研究发展计划项目(2006AA09Z155); 广东省科技计划项目(2008B030303026); 广东
省自然科学基金团队项目(8351030101000002); 中国科学院南海海洋研究所知识创新工程领域前沿项目(LYQY200701)
[1] ROUGH G J, RUNNING S, MATAON P.What does remote sensing do for ecology[J]. Ecology, 1991, 72(6): 1981-1922.
[2] ACKLESON S G, KLEMAS V. Remote sensing of sub-merged aquatic vegetation in Lower Chesapeake Bay: A comparison of LandSat MSS to TM imagery[J]. Remote Sensing of Environment, 1987, 22: 235-248.
[3] JENSEN J R. Measurement of seasonal and yearly aquatic macrophyte changes in a reservoir using multidate SPOT panchromatic data[J]. Technical Papers, American Society for Photogrammetry and Remote Sensing, 1992, 1: 167-176.
[4] JENSEN J R, NARUMALANI S, WEATHERBEE O, et al. Measurement of seasonal and yearly cattail and waterlily changes using multidate SPOT panchromatic data[J]. Photo-grammetric Engineering and Remote Sensing, 1993, 59: 519-525.
[5] KIRKMAN H. Baseline and monitoring methods for sea-grass meadows[J]. Environment Management, 1996, 47: 191-201.
[6] NOHARA S. A study on annual changes in surface cover of floating-leaved plants in a lake using aerial photography[J]. Vegetation, 1991, 97: 125-136.
[7] WELCH R, REMILLIARD R R, SLACK R B. Remote sensing and geographic information system techniques for aquatic resource evaluation[J]. Photogrammetrie Engineering and Remote Sensing, 1988, 54: 177-185.
[8] ORTH R J, MOORE K A. Chesapeake Bay: an unprece-dented decline in submerged aquatic vegetation[J]. Science, 1983, 222: 51-53.
[9] HAN L, RUNDQUIST D. The response of both surface reflectance and the underwater light field to various levels of suspended sediments: Preliminary results[J]. Photogrammet-ric Engineering and Remote Sensing, 1994, 60: 1463-1471.
[10] 吴长山, 项月琴. 利用高光谱数据对作物群体叶绿素密度估算的研究[J]. 遥感学报, 2000, 4(3): 228-232.
[11] 陈清莲. 利用直方图方法处理二类水体剖面数据研究[J]. 海洋技术, 2005, 24(3): 116-119.
[12] FYFE S K, DEKKER A G. Seagrass species—are they spec-trally distinct?[J], International Geoscience and Remote Sensing Symposium, 2001. 6: 2740-2742.
[13] BECKER B L, DAVID P L, QI J G. Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis[J]. Remote Sensing of Environment, 2005, 97(2): 238-248.