| 基于不同立地质量的杉木生物量遥感估测 |
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| 引用本文： | 温小荣,孟雪,刘俊,陈树,林国忠,佘光辉,刘雪惠,郜昌建.基于不同立地质量的杉木生物量遥感估测[J].林业科学研究,2016,29(4):494-499. |
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| 作者姓名： | 温小荣 孟雪 刘俊 陈树 林国忠 佘光辉 刘雪惠 郜昌建 |
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| 作者单位： | 南京林业大学南方现代林业协同创新中心, 江苏 南京 210037;南京林业大学林学院, 江苏 南京 210037;南京林业大学南方现代林业协同创新中心, 江苏 南京 210037;南京林业大学林学院, 江苏 南京 210037;南京林业大学南方现代林业协同创新中心, 江苏 南京 210037;南京林业大学林学院, 江苏 南京 210037;南京林业大学南方现代林业协同创新中心, 江苏 南京 210037;南京林业大学林学院, 江苏 南京 210037;南京林业大学南方现代林业协同创新中心, 江苏 南京 210037;南京林业大学林学院, 江苏 南京 210037;南京林业大学南方现代林业协同创新中心, 江苏 南京 210037;南京林业大学林学院, 江苏 南京 210037;南京林业大学南方现代林业协同创新中心, 江苏 南京 210037;南京林业大学林学院, 江苏 南京 210037;南京林业大学南方现代林业协同创新中心, 江苏 南京 210037;南京林业大学林学院, 江苏 南京 210037 |
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| 基金项目： | 国家948计划项目（2013-4-63）；南京林业大学科技创新基金项目（CX2011-24）；江苏省林业三新工程（LYSX［2015］19）；江苏高校优势学科建设工程自助项目（PAPD）。 |
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| 摘 要： | 目的]研究不同立地质量对杉木生物量遥感估测精度的影响,为进一步提高和完善森林生物量遥感监测体系提供一种新的思路和方法。方法]以2007年建德市森林资源二类调查数据和TM影像为研究材料,采用蓄积量—生物量换算因子连续函数法计算杉木林生物量和地位级法评价立地质量等级,比较杉木立地质量好、中等、差和不分地位等级4种生物量遥感估测模型,并进行精度检验。结果]表明:(1)以TM遥感影像主成分分析中第一主成分为自变量的模型拟合效果最好,决定系数R2均在0.69以上,最高0.855。(2)利用预留独立样本对模型精度进行验证,不分地位级总体估测精度为87.78%,分立地质量等级好、中、差3种类型总体估测精度分别为97.37%、95.82%、98.23%。分不同立地质量类型可以提高杉木生物量遥感估测精度。结论]研究结果为森林生物量遥感估测提供一种改进的思路,且为提高森林生物量和碳储量遥感估测精度提供一种参考方法。
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| 关 键 词： | TM影像 森林生物量 立地等级 一元线性回归 |
| 收稿时间： | 2015/11/18 0:00:00 |
Site-Based Remote Sensing Estimation of Chinese Fir Biomass |
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| WEN Xiao-rong,MENG Xue,LIU Jun,CHEN Shu,LIN Guo-zhong,SHE Guang-hui,LIU Xue-hui and GAO Chang-jian.Site-Based Remote Sensing Estimation of Chinese Fir Biomass[J].Forest Research,2016,29(4):494-499. |
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| Authors: | WEN Xiao-rong MENG Xue LIU Jun CHEN Shu LIN Guo-zhong SHE Guang-hui LIU Xue-hui and GAO Chang-jian |
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| Institution: | Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China |
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| Abstract: | Objective] To understand the influence of site quality in the remote sensing Chinese fir biomass estimation. Method] Based on the forest resource management inventory data and TM image of Jiande city obtained in 2007, the biomass of Chinese fir was calculated by forest volume-biomass conversion factor continuous function method and the site quality was evaluated by site class method. Four biomass estimation models for different site classes (good, moderate, poor and no ranking) were compared and the accuracy of them was tested. Result] (1) The performance of regression model based on the first principal component analysis of TM remote sensing image is the best, the determination coefficients R2 is higher than 0.69 and the maximum is 0.855. (2) Verifying the model accuracy by reserved independent samples, the whole model accuracy without site class is 87.78% and the accuracies of good, moderate, poor site quality models are respectively 97.37%, 95.82%, and 98.23%. Conclusion] Distinguishing different site qualities could improve remote sensing estimation precision of Chinese fir biomass. The research results provide with an improved method for the remote sensing estimation of forest biomass, and a reference for improving the remote sensing estimation accuracy of forest biomass and carbon storage. |
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| Keywords: | TM image forest biomass site class simple linear regression |
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