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| 基于TM影像纹理与光谱特征和KNN方法估算5种红树林群落生物量 | |
| 引用本文： | 曹庆先,徐大平,鞠洪波.基于TM影像纹理与光谱特征和KNN方法估算5种红树林群落生物量[J].林业科学研究,2011,24(2):144-150. |
| 作者姓名： | 曹庆先 徐大平 鞠洪波 |
| 作者单位： | 1. 广西红树林研究中心,广西红树林保护重点实验室,广西,北海,536000;中国林业科学研究院热带林业研究所,广东,广州,510520 2. 中国林业科学研究院热带林业研究所,广东,广州,510520 3. 中国林业科学研究院资源信息研究所,北京,100091 |
| 基金项目： | 广西科学院专项"基于遥感影像的红树林生物量、碳贮量的研究"(08YJ16HS01);广西科学基金项目(桂科基0575025) |
| 摘 要： | 为研究红树林生物量的遥感估算方法,本文提取广西和海南部分红树林TM遥感影像光谱及纹理特征,结合同地区地面调查的生物量数据,应用KNN方法,对生物量进行了遥感估算,并和多元逐步回归分析方法比较.研究表明:应用KNN方法估测精度随尺度的增大而增大,且K值取10优于K值取5;在像元尺度上,回归方法估测生物量优于KNN方法. |
| 关 键 词： | 纹理 KNN 生物量估算 均方根误差 平均误差 预估计精度 |
| 收稿时间： | 2010/3/31 0:00:00 |
Biomass Estimation of Five Kinds of Mangrove Community with the KNN Method Based on the Spectral Information and Textural Features of TM Images |
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| CAO Qing-xian,XU Da-ping and JU Hong-bo.Biomass Estimation of Five Kinds of Mangrove Community with the KNN Method Based on the Spectral Information and Textural Features of TM Images[J].Forest Research,2011,24(2):144-150. | |
| Authors: | CAO Qing-xian XU Da-ping and JU Hong-bo |
| Institution: | Guangxi Mangrove Research Center, Guangxi Key Lab for Mangrove Conservation, Beihai 536000, Guangxi, China;Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510520, Guangdong, China;Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510520, Guangdong, China;Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China |
| Abstract: | In order to research the biomass of mangrove based on remote sensing, the biomass of mangrove with the method of KNN was estimated by extracting spectral information and textural features from TM images, combining the field survey biomass data and compared with that of multiple regression analysis. The results showed that with KNN method, the accuracy increased with the extension of the scale, and K=10 was better than K=5 in the accuracy. Estimating biomass of mangrove in the pixel scale, the multiple regression analysis was better than that by using KNN method. |
| Keywords: | texture KNN biomass estimating root-mean-square error mean error predication estimation accuracy |
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