基于光谱特征的沉水植物种类识别研究

摘要/Abstract

摘要： 沉水植物种类的光谱特征识别可为大尺度遥感监测沉水植被提供种类识别的有效参数.本研究利用高光谱地物光谱仪获取上海市郊最大的天然淡水湖泊淀山湖6种典型沉水植物狐尾藻、竹叶眼子菜、金鱼藻、大茨藻、黑藻和苦草群落冠层的反射光谱，分析不同种类沉水植物的原始光谱和一阶导数光谱特征，通过主成分分析（Principal Components Analysis, PCA）筛选对沉水植物光谱种间差异敏感的植被指数和光谱指数，探寻能够有效识别不同种类水生植物的光谱识别方法.研究结果表明：①不同种类沉水植物的光谱曲线形状在可见光和近红外波段近似，但光谱反射率值差异较大;②光谱指数NAV、REP和NGP是识别6种沉水植物最敏感的特征指数，这些指数综合体现和放大了不同种类沉水植物在形态结构、叶绿素含量、光合和保护色素等方面的特征及所栖居的水体环境特征的种间差异.研究结果可为高光谱遥感影像准确解译与提取沉水植物群落组成、分布和生物多样性的动态变化信息提供理论依据和技术支持.

关键词: 沉水植物, 种类识别, 光谱特征, 光谱反射率, 高光谱

Abstract: The among-species spectral characteristics of submerged aquatic vegetation (SAV) could provide effective parameters for species identification to timely monitor the distribution and growth status of SAV using remote sensing technology. In this study, the spectral reflectance of the typical SAV plants were measured using a FieldSpecTM Pro JR Spectroradiometer in Dianshan Lake, the largest natural freshwater lake in Shanghai suburbs, including 6 SAV species Najas marina, Hydrilla verticillata, Myriophyllum spicatum, Ceratophyllum demersum, Vallisneria natans and Potamogeton malaianus. The spectral characteristics of reflectance curves and first derivative curves for different species were analyzed, while vegetation indexes and spectral indexes were screened for identifying species of SAV using Principal Components Analysis (PCA). The results show that the reflectancecurves were similar in shape but much variable in magnitude at the visible and near infrared wavebands for different SAV species. The spectral index NAV, REP and NGP were the most sensitive characteristic index for identifying 6 SAV species. The index NAV, REP and NGP could well reflect and amplify the differences in morphological structure of plant, physiological and biochemical composition among different SAV species and the among-species discrimination of their habitat synthetically. The results from this study could be helpful to accurately interpret the community composition, distribution and biodiversity dynamics of SAV on a large scale from hyperspectral remote sensing image.

Key words: submerged aquatic vegetation, species identification, spectral characteristics, reflectance, hyperspectral

中图分类号:

邹维娜, 张利权, 袁琳. 基于光谱特征的沉水植物种类识别研究[J]. 华东师范大学学报(自然科学版), 2014, 2014(4): 132-140.

ZOU Wei-na, ZHANG Li-quan, YUAN Lin. Study on species identification of submerged aquatic vegetation based on spectral characteristics[J]. Journal of East China Normal University(Natural Sc, 2014, 2014(4): 132-140.

参考文献

［1］吴振斌,邱东茹, 贺锋, 等.沉水植物重建对富营养水体氮磷营养水平的影响［J］.应用生态学报,2003, 14(8): 1351-1353.

［2］ MEERHOFF M, MAZZEO N, MOSS B, et al. The structuring role of free-floating versus submerged plants in a sub tropical shallow lake［J］. Aquatic Ecology, 2003, 37: 377-391.

［3］ WILLIAM F J, BARKO J W, BUTLER M G. Shear stress and sediment resuspension in relation to submersed macrophyte biomass ［J］. Hydrobiologia, 2004, 515: 181-191.

［4］ DOGAN O K, AKYUREK Z, BEKLIOGLU M. Identification and mapping of submerged plants in a shallow lake using quickbird satellite data ［J］. Journal of Environmental Management, 2009, 90: 2138-2143.

［5］ ZHAO D H, JIANG H, YANG T W, et al. Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds ［J］. Journal of Environmental Management, 2012, 95(1): 98-107.

［6］ DAVRANCHE A, LEFEBVRE G, POULIN B. Wetland monitoring using classification trees and SPOT-5 seasonal time series ［J］. Remote Sensing of Environment, 2010, 114(3): 552-562.

［7］ YUAN L, ZHANG L Q. The spectral responses of submerged plant Vallisneria spiralis with varying biomass using spectroradiometer ［J］. Hydrobiologia, 2007, 579: 291-299.

［8］ WILLIAMS D J, RYBICKI N B, LOMBANA A V, et al. Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing ［J］. Environmental Monitoring and Assessment, 2003, 81(1-3): 383-392.

［9］ ERIN L H, SHRUTI K, MARGARET E A. Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem ［J］. Remote Sensing of Environment, 2008, 112(11): 4034-4047.

［10］ ARTIGAS F J, YANG J S. Hyperspectral remote sensing of habitat heterogeneity bewteen tide-restrieted and tide-open areas in the NewJersey Meadow lands ［J］.Urban Habitats, 2004, 2(1): 112-129.

［11］ BEGET M E, DI BELLA C M. Flooding: The effect of water depth on the spectral response of grass canopies ［J］. Journal of Hydrology, 2007, 335(3-4): 285-294.

［12］ ZOU W N, YUAN L, ZHANG L Q. Analyzing the spectral response of submerged aquatic vegetation in a eutrophic lake, Shanghai, China ［J］. Ecological Engineering, 2013, 57: 65-71.

［13］ HESTIR E L, KHANNA S, ANDREW M E, et al. Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem ［J］. Remote Sensing of Environment, 2008, 112(11): 4034-4047.

［14］ WANG C, MENENTI M, STOLL M P, et al. Mapping mixed vegetation communities in salt marshes using airborne spectral data ［J］. Remote Sensing of Environment, 2007, 107(4): 559-570.

［15］ FILIPPI A M, JENSEN J R. Fuzzy learning vector quantization for hyperspectral coastal vegetation classification ［J］. Remote Sensing of Environment, 2006, 100(4): 512-530.

［16］ 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.

［17］ ZHANG L Q, GAO Z G, ARMITAGE R, et al. Spectral characteristics of plant communities from salt marshes: A case study from Chongming Dongtan, Yangtze estuary, China ［J］. Frontiers of Environmental Science and Engineering in China, 2008(2): 187-197.

［18］ CHENG X, LI X P. Long-term changes in nutrients and phytoplankton response in Lake Dianshan, a shallow temperate lake in China ［J］. Journal of Freshwater Ecology, 2010, 25(4): 549-554

［19］徐霖林,马长安,田伟,等.淀山湖沉水植物恢复重建对底栖动物的影响［J］复旦学报：自然科学版,2011，50（3） 260-267.

［20］吴迪, 岳峰, 罗祖奎, 等.上海大莲湖湖滨带湿地的生态修复［J］.生态学报， 2011，31(11):2999-3008.

［21］ TUCKER C J. Red and photographic infrared linear combinations for monitoring vegetation ［J］. Remote Sensing of Environment, 1979（8）: 127-150.

［22］邹维娜, 袁琳, 张利权, 等. 盖度与冠层水深对沉水植物水盾草光谱特性的影响［J］, 生态学报, 2012, 32(3): 706-714.

［23］ PINNEL N, HEEGE T, ZIMMERMANN S. Spectral discrimination of submergedmacrophytes in lakes using hyperspectral remote sensing data ［J］. The International Society for Optical Engineering, 2004, 16: 25-29.

［24］ UNDERWOOD E, MULITSCH M, GREENBERG J, et al. Mapping invasive aquatic vegetation in the Sacramento-San Joaquin Delta using hyperspectral imagery ［J］. Environmental Monitoring and Assessment, 2006, 121(1-3): 47-64.

［25］ DURAKO M J. Leaf optical properties and photosynthetic leaf absorptances in several Australian seagrasses ［J］. Aquatic Botany, 2007, 87(1): 83-89.

［26］ ISTVA′ NOVICS V, HONTI M, KOVA′CS A, et al. Distribution of submerged macrophytes along environmental gradients in large, shallow Lake Balaton (Hungary) ［J］. Aquatic Botany, 2008, 88: 317-330.

［27］ BECKER B L, LUSCH D P, QI J. 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.

[1]	李璐含, 束炯, 尹球, 张雷, 刘延安. 基于红外高光谱探测器的大气CO₂反演通道选择[J]. 华东师范大学学报(自然科学版), 2019, 2019(3): 186-198.
[2]	沈振翔, 刘朝顺. 利用CrIS红外高光谱卫星数据反演大气温湿度廓线的研究[J]. 华东师范大学学报(自然科学版), 2019, 2019(3): 199-210.
[3]	房娟, 刘洪英, 李庆利. 基于维度约束的距离测度学习算法[J]. 华东师范大学学报(自然科学版), 2017, 2017(2): 69-74,88.
[4]	束炯;王强;孙娟. 高光谱遥感的应用研究[J]. 华东师范大学学报(自然科学版), 2006, 2006(4): 1-10.