叶冠尺度野鸭湖湿地植物群落含水量的高光谱估算模型  

Hyperspectral estimation models for plant community water content at both leaf and canopy levels in Wild Duck Lake wetland

作　　者：林川[1,2,3,4] 宫兆宁[1,2,3,4] 赵文吉[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》2011年第22期6645-6658,共14页Acta Ecologica Sinica

基　　金：国际科技合作项目(2010DFA92400);国家科技支撑计划重点项目(2007BAH15B02);北京市科技计划项目(D08040600580801);国家测绘地理信息局科技计划项目2011

摘　　要：利用高光谱遥感技术定量估测野鸭湖湿地植被含水量,对于监测和诊断野鸭湖湿地植被的生理状况及生长趋势具有重要意义,也能够为高光谱遥感影像在野鸭湖湿地植被含水量诊断中的实际应用提供理论依据和技术支持。采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植被冠层和叶片的光谱,并测定了对应的含水量。以上述实测数据为基础,首先以芦苇为例初步探明了不同含水量水平下典型湿地植被冠层和叶片光谱反射率的响应模式,然后采用相关性及单变量线性与非线性拟合分析技术,从冠层和叶片两种层次,对不同尺度下的含水量与"三边"参数及高光谱植被指数进行了分析拟合,并采用交叉检验中的3K-CV方法对估算模型进行了测试和检验,确立了不同尺度下野鸭湖湿地植被含水量的定量监测模型。结果表明:(1)随着含水量水平的增加,芦苇冠层与叶片光谱在可见光波段(350—760 nm)和红外波段(760—2500 nm)的反射率均呈逐渐降低趋势。(2)不同尺度含水量与选取的光谱特征参数整体上相关性较强,与"三边"参数基本上都呈极显著相关,相关系数最大达到0.906;与高光谱指数全部呈极显著相关,相关系数最小为0.455,最大达到0.919,并通过选取不同尺度上相关性最佳的光谱特征参数,分别基于"三边"参数和高光谱植被指数构建了不同尺度下的含水量估算模型。其中,冠层尺度下,黄边面积(SDy)与SRWI(Simple Ratio Water Index)的估算效果最好,估算模型分别为y=-9.462x2-2.671x+0.608和y=0.219e1.010x;叶片尺度下,红边面积(SDr)与WI(Water Index)的估算效果最好,估算模型分别为y=0.562x+0.376和y=2.028x2-0.476x-1.009。通过3K-CV的交叉验证,不同尺度下的含水量估算模型均取得了较为理想的预测精度,预测精度的最小值为94.92%,最大值为97.06%,表明估测模型具有较高的可靠性与普适性。(3)高光谱植被指�Quantitative estimation of vegetation water content with hyperspectral remote sensing technique is of great significance for vegetation physiological status and growth trend monitoring.It also provides a theoretical foundation for actual application of vegetation water content diagnosis using hyperspectral remote sensing images in Wild Duck Lake wetland.The hyperspectral reflectance and corresponding water content of canopy and leaf of typical wetland vegetation were measured by Field-Spec 3 wild high-spectrum radiometer.We used reed as an example to prove the response mode of the spectral reflectance in different water content levels.Then the correlation among water content,trilateral parameters,and hyperspectral vegetation index and hyperspectral estimation models were obtained by using regression and correlation analysis in canopy and leaf levels.In additiona,we made use of Three K-fold Cross Validation to test and inspect the hyperspectral estimation models.The results show： a）The canopy and leaf spectral reflectance of reed in visible bands（350—760nm） and infrared bands（760—2500nm） reflectivity tends to reduce gradually.b） We found strong correlation between the selected spectrum characteristic parameters and different water content scales.For trilateral parameters,they show significant correlation with a maximum correlation coefficient of 0.906.For the hyperspectral vegetation index,they all show significant correlation,with a minimum correlation coefficient of 0.455 and a maximum of 0.919.Based on the trilateral parameters and hyperspectral vegetation index,we choose the spectrum characteristic parameters that have the best correlation in different scales to construct the water content estimation models.SDy and simple ratio water index have the best effect at canopy level;the best models are evaluated and validated as y=-9.462x2-2.671x＋0.608 and y=0.219e1.010x,respectively.SDr and water index have the best effect at leaf level;the best models are evaluated and validated as y=0.562x＋0.

关 键 词：湿地植被 植被含水量 高光谱 “三边”参数 植被指数 北京野鸭湖湿地 

分 类 号：Q948[生物学—植物学]