基于郁闭度联立方程组模型的森林生物量遥感估测

摘要/Abstract

摘要：

以黑龙江省长白山地区遥感影像和122块森林资源连续清查固定样地数据为基础，选择包括各波段灰度值、不同波段灰度值之间的线性和非线性组合、纹理信息以及环境因子在内的171个自变量，分别采用无郁闭度变量常规回归生物量模型、有郁闭度变量常规回归生物量模型和郁闭度联立方程组模型，估算黑龙江省长白山森林生物量，并进行精度评价。结果表明：3种模型中郁闭度联立方程组模型为最优模型，精度最高为83.1%，与其他2个模型相比精度提高6%~7%。本研究可为遥感估算森林生物量提供一种新思路。

关键词: 郁闭度, 生物量, 遥感估算, 联立方程组模型

Abstract:

Forest biomass estimation is the basis of carbon cycle in forest ecosystems and carbon dynamic analysis. Therefore, the accurate estimate of biomass is very important. Establishing biomass models is a major means of biomass estimation on a large scale. Based on remote sensing images of Changbai Mountain region in Heilongjiang province and data from continuous forest inventory of 122 permanent sample plots, 171 independent variables was chosed including options include band grayscale value, the different band combinations between the grey value of linear and nonlinear, texture information and environmental factors. Respectively adopting conventional regression model of biomass without canopy density variable, conventional regression model of biomass with canopy density variable, consociation equations model of biomass and canopy density, forest biomass was calculated in the Changbai Mountain region in Heilongjiang province, and precision evaluation was carried out. Research results showed that: simultaneous equations model of biomass and canopy density was the optimal model, with the accuracy of the model as high as 83.1%, and the precision was increased by 6%-7% compared with the other two models. This study provides a new train of thought for the estimate of forest biomass using the remote sensing.

Key words: canopy density, biomass, remote sensing estimation, simultaneous equations model

中图分类号:

S757

李明泽, 毛学刚, 范文义. 基于郁闭度联立方程组模型的森林生物量遥感估测[J]. 林业科学, 2014, 50(2): 85-91.

Li Mingze, Mao Xuegang, Fan Wenyi. Forest Biomass Estimation Using Remote Sensing Based on Canopy Density Simultaneous Equations Model[J]. Scientia Silvae Sinicae, 2014, 50(2): 85-91.

参考文献

方精云,刘国华,徐嵩龄. 1996. 我国森林植被生物量和生产力. 生态学报,16(4):497-508.
(Fang J Y, Liu G H, Xu S L. 1996. Biomass and net production of forest vegetation in China. Acta Ecologica Sinica, 16(4):497-508. [in Chinese] )
国庆喜,张锋. 2003. 基于遥感信息估测森林的生物量. 东北林业大学学报,31(2):13-16.
(Guo Q X, Zhan F. 2003. Estimation of forest biomass based on remote sensing. Journal of Northeast Forestry University, 31(2):13-16. [in Chinese] )
郭志华,彭少麟,王伯荪. 2002. 利用TM数据提取粤西地区的森林生物量. 生态学报,22(11):1832-1839.
(Guo Z H, Peng S L, Wang B S. 2002. Estimating forest biomass in western guangdong using landsat TM data. Acta Ecologica Sinica, 22(11):1832-1839. [in Chinese] )
蒋延龄,周广胜.2001. 兴安落叶松碳平衡和全球变化影响研究.应用生态学报,12(4):481-483.
(Jiang Y L, Zhou G S. 2001. Carbon equilibrium in Larix gmelinii forest and impact of global change on it. Chinese Journal of Applied Ecology, 12(4):481-483. [in Chinese] )
李健,舒晓波,陈水森. 2005. 基于Landsat TM数据鄱阳湖湿地植被生物量遥感监测模型的建立. 广州大学学报: 自然科学版,(4):32-38.
(Li J, Shu X B, Chen S S. 2005. Establishment of wetland vegetation biomass model by in-situ and remote sensing observation in Poyang Lake area. Journal of Guangzhou University(Natural Science Edition), (4):32-38. [in Chinese] )
李明泽.2010.东北林区森林生物量遥感估算及分析.哈尔滨: 东北林业大学博士学位论文,39-40.
(Li M Z. 2010. Estimation and analysis of forest biomass in Northeast forest region using remote sensing technology. Haerbin: Doctoral Dissertation of Northeast Forestry University, 39-40. [in Chinese] )
郎奎建. 2004.森林生态效益的线性联立方程组模型的研究. 应用生态学报,15(8):1323-1328.
(Lang K J. 2004. Linear consociation equation set model of forest ecological benefits. Chinese Journal of Applied Ecology, 15(8):1323-1328. [in Chinese] )
郎璞玫. 2007. 建立在固定样地上的林分生长线性联立方程组模型研究. 北京林业大学学报,29(1):37-41.
(Lang P M. 2007. Linear simultaneous equations models of stand growth based on the permanent sample plots. Journal of Beijing Forestry University, 29(1):37-41. [in Chinese] )
唐守正. 1986. 多元统计方法.北京: 中国林业出版社.
邢素丽,张广录,刘慧涛,等. 2004. 基于Landsat ETM+数据的落叶松林生物量估算模式. 福建林学院学报,24(2):153-15.
(Xing S L, Zhang G L, Liu H T, et al. 2004. The estimating model of Larix gmelinii forests biomass using landsat ETM data. Journal of Fujian College of Forestry, 24(2):153-15. [in Chinese] )
杨存建,刘纪远,张增详. 2004. 热带森林植被生物量遥感估算探讨. 地理与地理信息科学, 20(6):22-25.
(Yang C J, Liu J Y, Zhang Z X. 2004. Study on the estimation of the tropical forest vegetation biomass by using remote. Geography and Geo-Information Science, 20(6):22-25. [in Chinese] )
杨清培,李鸣光,王伯荪,等. 2003.粤西南亚热带森林演替过程中的生物量与净第一性生产力动态.应用生态学报,4(12):2136-2140.
(Yang Q P, Li M G, Wang B S, et al. 2003. Dynamics of biomass and net primary productivity in succession of south subtropical forests in Southwest Guangdong. Chinese Journal of Applied Ecology, 4(12):2136-2140. [in Chinese] )
赵敏,周广胜. 2004. 基于森林资源清查资料的生物量估算模式及其发展趋势. 应用生态学报,15(8):1469-1472.
(Zhao M, Zhou G S. 2004. Forest inventory data (FID)-based biomass models and their prospects. Chinese Journal of Applied Ecology, 15(8):1469-1472. [in Chinese] )
Dong J R,Kaufmann R K, Myneni R B,et al. 2003. Remote sensing estimations of boreal and temporate forest woody biomass: carbonpools,sources,and sinks. Remote Sensing of Environment,84(3):393-410.
Fang J Y,Wang G G, Liu G H,et al. 1998. Forest biomass of China: An estimation based on the biomass-volume relationship. Ecological Applications,8:1084 -1091.
Hame T,Sallli A,Andersson K,et al. 1997.A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data. International Journal of Remote Sensing,18(15):3211-3243.
Woodwell G M,Whittaker R H, Reiners W A,et al. 1978. The biota and the world carbon budget.Science, 199:141-146.

[1]	薛春泉, 徐期瑚, 林丽平, 何潇, 曹磊, 李海奎. 基于异速生长和理论生长方程的广东省木荷生物量动态预测[J]. 林业科学, 2019, 55(7): 86-94.
[2]	丁令智, 满秀玲, 肖瑞晗, 蔡体久. 寒温带森林根际土壤微生物量碳氮含量生长季内动态变化[J]. 林业科学, 2019, 55(7): 178-186.
[3]	常建国. 油松树干横截面面积年增长量的垂直分布及与材积年增长量和叶量的关系[J]. 林业科学, 2019, 55(6): 55-64.
[4]	李亚藏, 冯仲科. 气候敏感的马尾松生物量相容性方程系统研建[J]. 林业科学, 2019, 55(5): 65-73.
[5]	王怡霖, 王卫锋, 张芸香, 常淑君, 郭晋平. 碧玉杨叶形态结构与生理特性对干旱的响应[J]. 林业科学, 2019, 55(4): 42-50.
[6]	曾伟生, 贺东北, 蒲莹, 肖前辉. 含地域和起源因子的马尾松立木生物量与材积方程系统[J]. 林业科学, 2019, 55(2): 75-86.
[7]	薛春泉, 徐期瑚, 林丽平, 何潇, 罗勇, 赵菡, 曹磊, 雷渊才. 广东主要乡土阔叶树种含年龄和胸径的单木生物量模型[J]. 林业科学, 2019, 55(2): 97-108.
[8]	王波, 李琴, 朱炜, 陈文海, 祝虹梁, 沈泉, 朱安明, 赵建诚. 毛竹林覆盖经营对土壤养分含量、酶活性及微生物生物量的影响[J]. 林业科学, 2019, 55(1): 110-118.
[9]	种培芳, 詹瑾, 贾向阳, 李毅. 模拟CO₂浓度升高及降雨变化对荒漠灌木红砂光合及生长的影响[J]. 林业科学, 2018, 54(9): 27-37.
[10]	韩宗涛, 江洪, 王威, 李增元, 陈尔学, 闫敏, 田昕. 基于多源遥感的森林地上生物量KNN-FIFS估测[J]. 林业科学, 2018, 54(9): 70-79.
[11]	李伟成, 盛海燕, 蒋跃平, 温星. 基塘系统不同竹林土壤CO₂通量特征及其影响因子[J]. 林业科学, 2018, 54(8): 13-22.
[12]	赵嘉诚, 李海奎. 杉木单木和林分水平地下生物量模型的构建[J]. 林业科学, 2018, 54(2): 81-89.
[13]	赵菡, 雷渊才, 符利勇. 江西省不同立地等级的马尾松林生物量估计和不确定性度量[J]. 林业科学, 2017, 53(8): 81-93.
[14]	席本野, 王烨, 贾黎明. 滴灌施肥下施氮量和施氮频率对毛白杨生物量及氮吸收的影响[J]. 林业科学, 2017, 53(5): 63-73.
[15]	郭文霞, 赵志江, 郑娇, 李俊清. 土壤水分和氮素的交互作用对油松幼苗光合和生长的影响[J]. 林业科学, 2017, 53(4): 37-48.