Pu Ruiliang, Gong Peng, Yu Qian
Department of Geography, University of South Florida, 4202 E. Fowler Ave., NES 107, Tampa, FL 33620 USA.
State Key Lab of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing, Applications, Chinese Academy of Sciences, and Beijing Normal University, Beijing China 100101; Center for Assessment and Monitoring of Forest and Environmental Res.
Sensors (Basel). 2008 Jun 6;8(6):3744-3766. doi: 10.3390/s8063744.
In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data.
在本研究中，对用于绘制森林郁闭度（CC）和叶面积指数（LAI）的三种传感器的能力进行了对比分析。这三种传感器分别是搭载于EO - 1卫星上的高光谱成像仪（Hyperion）和先进陆地成像仪（ALI）以及陆地卫星7号增强型专题绘图仪升级版（ETM +）。在美国加利福尼亚大学伯克利分校的布洛杰特森林研究站共收集了38个针叶混交林CC测量值和38个LAI测量值。分析方法包括：（1）提取光谱植被指数（VIs）、光谱纹理信息和最大噪声分数（MNFs）；（2）建立多元预测模型；（3）预测并绘制基于像元的CC和LAI值；（4）用经过实地验证的照片判读CC和LAI值对绘制的CC和LAI结果进行验证。实验结果表明，Hyperion数据在绘制森林CC和LAI方面最为有效（CC绘制精度（MA） = 76.0%，LAI MA = 74.7%），其次是ALI数据（CC MA = 74.5%，LAI MA = 70.7%），而ETM +数据效果最差（CC MA = 71.1%，LAI MA = 63.4%）。该分析表明，Hyperion传感器优于其他两种传感器：ALI和ETM +。这是因为它具有高光谱分辨率，带有丰富的细微光谱信息；它的短波红外数据可用于构建受大气影响较小的最优VIs；并且与其他两种传感器相比，它有更多可用的MNFs可供选择来建立预测模型。与ETM +数据相比，ALI数据在绘制森林CC和LAI方面更好，这是因为ALI数据的波段更多且信噪比高于ETM +数据。
