| 多源遥感数据支持下的区域性森林冠层高度估测 |
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| 引用本文： | 胡凯龙,刘清旺,崔希民,庞勇,穆喜云.多源遥感数据支持下的区域性森林冠层高度估测[J].武汉大学学报(信息科学版),2018,43(2):289. |
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| 作者姓名： | 胡凯龙 刘清旺 崔希民 庞勇 穆喜云 |
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| 作者单位： | 1.中国矿业大学(北京)地球科学与测绘工程学院, 北京, 100083 |
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| 基金项目： | 国家高技术研究发展计划2013AA12A302国家重点基础研究发展计划13CB733404 |
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| 摘 要： | 结合机载LiDAR数据，提出了一种改进的GLAS光斑点冠层高度地形校正模型，以校正后的GLAS光斑点作为输入样本，结合MODIS遥感影像，利用支持向量回归（SVR）的方法对研究区森林冠层高度进行分生态区估测，并利用野外调查数据和机载LiDAR冠层高度结果对估测结果进行验证。结果显示:研究区的坡度等级直接影响GLAS光斑点森林冠层高度估测精度，改进的地形校正模型可以较好的减小坡度对GLAS光斑点森林冠层高度估测的影响，模型精度RMSE稳定在3.25~3.48 m；不同生态分区的SVR模型估测精度较为稳定，其RMSE=6.41~7.56 m；与算数平均高相比，样地的Lorey's高与制图结果拟合最好，不同生态分区平均估测精度为80.3%。机载LiDAR冠层高度结果的验证平均精度为79.5%，和Lorey's高验证结果呈现较好的一致性。
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| 关 键 词： | 冠层高度 坡度 多源遥感数据 地形校正 支持向量回归 生态分区 |
| 收稿时间： | 2016-05-11 |
Regional Forest Canopy Height Estimation Using Multi-source Remote Sensing Data |
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| Affiliation: | 1.College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China2.Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China3.Institute of Forestry Ecology, Chifeng Academy of Forestry, Chifeng 024000, China |
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| Abstract: | In this paper, a method which based on multi-source remote sensing data was designed to estimate forest canopy height. An improved topographic correction model which was used for calculating based-GLAS canopy height was put forward with airborne LiDAR data, since surface topography directly impacts vegetation LiDAR waveforms and vegetation height retrieval. Then, with corrected GLAS footprints as input samples, forest canopy height in different ecological zones was estimated by support vector regression (SVR) model combining with MODIS imagery. Lastly, the accuracy of estimation results was verified using field survey data and airborne LiDAR canopy height. The result show: slope levels in the study area directly impacted based-GLAS canopy height estimation accuracy. The improved topographic correction model could relieve the influence of topography on based-GLAS canopy height estimation, and the RMSE of estimation result ranged from 3.25m to 3.48m. In different ecological zones, SVR model estimation accuracy was stable and the RMSE ranged from 6.41m to 7.56m. Compared with arithmetic mean height, Lorey's height was closest to the estimation result and the average estimated accuracy of 80.3% in different ecological zones. The average estimated accuracy verified with airborne LiDAR metric height was 79.5% and consistence with Lorey's height. |
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