基于光谱特征变量的湿地典型植物生态类型识别方法--以北京野鸭湖湿地为例  

Identifying typical plant ecological types based on spectral characteristic variables: a case study in Wild Duck Lake wetland,Beijing

作　　者：林川[1,2,3,4] 宫兆宁[1,2,3,4] 赵文吉[1,2,3,4] 樊磊[1,2,3,4] 

机构地区：[1]首都师范大学资源环境与旅游学院 [2]三维信息获取与应用教育部重点实验室 [3]资源环境与地理信息系统北京市重点实验室 [4]北京市城市环境过程与数字模拟国家重点实验室培育基地,北京100048

出　　处：《生态学报》2013年第4期1172-1185,共14页Acta Ecologica Sinica

基　　金：863计划课题(2012AA12A308);国家青年基金项目(41101404);国家基础测绘项目(2011A2001);北京市教委科技计划面上项目(KM201110028013);国土资源部重点实验室开放基金(KLGSIT2013-04)

摘　　要：光谱特征变量的选择对于湿地植被识别的精度和效率有着直接的影响作用。以华北地区典型的淡水湿地——野鸭湖湿地为研究区,采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植物的冠层光谱。以野外高光谱数据为基础,首先利用一阶导数与包络线去除的方法,分析和对比不同植物生态类型的光谱特征,选定了用于识别植物生态类型的光谱特征变量,选定的8个光谱特征变量为红边位置WP_r、红边幅值Dr、绿峰位置WP_g、绿峰幅值Rg、510 nm附近的吸收深度DEP-510和吸收面积AREA-510、675 nm附近的吸收深度DEP-675和吸收面积AREA-675。其中,7种植物生态类型的一阶导数光谱特征差异较小,吸收特征差异性相对较大。除WP_r和WP_g外,沉水植物Rg和Dr平均值最低,湿生植物的Rg平均值最高,达到0.164,栽培植物的Dr平均值最高,达到0.012。7种植物生态类型在675 nm附近的DEP-675和AREA-675均高于510 nm附近的DEP-510与AREA-510,除去栽培植物,随着水分梯度的变化,其他6种植物生态类型的吸收深度和吸收面积都表现出先升高后降低的趋势。然后利用单因素方差分析(One-way ANOVA)验证了所选光谱特征变量的区分度,在P≤0.01的置信水平下,选取的8个光谱特征变量都能够较好的区分7种植物生态类型,区分度的最小值为13,最大值为18,并且吸收特征参数的区分度优于一阶导数参数。最后应用非线性的反向传播人工神经网络(BP-ANN)与线性判别分析(FLDA)的类型识别方法,利用选定的8个光谱特征变量进行湿地植物生态类型识别,取得了较好的识别精度,两种方法的总分类精度分别达到85.5%和87.98%。单因素方差分析(One-way ANOVA)和不同分类器的分类精度表明,所选的8个光谱特征变量具有一定的普适性和可靠性。Certain spectral characteristic variables have a direct impact to the accuracy and efficiency of identifying the wetland vegetation.The Wild Duck Lake,a typical freshwater wetland of North China,was selected as the study area of this research.The canopy hyperspectral reflectances of typical wetland vegetation were measured by Field-Spec 3 high-spectrum radiometer.In this research,a total of seven vegetation covers were measured.The reflectance spectrum of submerged vegetation species was special because of the influence of water body,suspended solid in water,etc.The reflectance spectra of the other six vegetation covers were similar.But different vegetation cover had different feature,such as plant morphology,water content and chlorophyll content so that they had their own spectral reflectance characteristics.Thus,based on the canopy spectral reflectance,first-derivative and continuum removal were applied to analyze and contrast spectral features of different vegetation types.The spectral characteristic variables were selected for identifying plant ecological.The research established eight distinct spectral characteristic variables（red edge position WP_r,red edge amplitude Dr,green peak position WP_g,green peak amplitude Rg,absorption depth around 510 nm and 675 nm,absorption area around 510 nm and 675 nm） that have importance for mapping vegetation cover types in the Wild Duck Lake wetland.In addition,the absorption features of seven vegetation types were larger differences than first-derivative spectral features.Excepting WP_r and WP_g,the average of Rg and Dr of submerged plant were minimum,the average of Rg of hygrophilous plant was maximum（0.164） and the average of Dr of cultivated plant was maximum（0.012）.The DEP-675 and AREA-675 around 675 nm of seven vegetation types were higher than the DEP-510 and AREA-510 around 510 nm.Absorption depth and absorption area of other six vegetation types had shown fall after rise along with the water environment gradient change except cultivated plant.Then we

关 键 词：湿地植物生态类型 高光谱 光谱特征变量 单因素方差分析(One-way ANOVA) 非线性的反向传播人工神经网络(BP-ANN) 线性判别分析(FLDA) 

分 类 号：Q948[生物学—植物学]