Please wait a minute...
img

官方微信

遥感技术与应用  2012, Vol. 27 Issue (5): 722-727    DOI: 10.11873/j.issn.1004-0323.2012.5.722
模型与反演     
区域森林生物量遥感信息模型构建研究
程鹏飞1,王金亮2,徐 申2,程 峰3,王小花4
(1.云南省一九八煤田地质勘探队,云南 昆明 650208;2.云南师范大学旅游与地理科学学院,云南 昆明 650092;3.中国科学院对地观测与数字地球科学中心,北京 100094;4.重庆市梁平县规划与地理信息中心,重庆 405227)
A Study on Remote Sensing Information Model of Regional Forest Biomass
Cheng Pengfei1,Wang Jinliang2,Xu Shen2,Cheng Feng3,Wang Xiaohua4
(1.Yunnan 198 Coal Geological Exploration Team,Kunming 650208,China;2.College of Tourism & Geographical Sciences,Yunnan Normal University,Kunming 650500,China;3.Center for Earth Observation and Digital Earth Chinese Academy of Sciences,Beijing 100094,China;4.Planning and Geographic Information Center of Liangping,Chongqing  405227,China)
 全文: PDF(1905 KB)  
摘要:

森林是陆地生态系统中最大的碳汇,在调节全球碳平衡、减缓大气CO2等方面具有不可替代的作用。森林生物量是陆地生态系统碳循环过程中最主要的参数,准确估算森林生物量及森林的变动引起的生物量变化受到科学家的普遍关注,并成为碳循环科学研究中的焦点。以生态敏感区滇西北香格里拉县为研究区,在野外森林样方调查数据的支持下,综合3S技术、地理学、生态学、气象学等相关知识,筛选了9个植被指数、2波段灰度值、生长季降水、生长季积温、生长季总辐射量、海拔、坡度、坡向、坡位和土壤有机质含量等多个因子,组合成遥感综合因子层、地理综合因子层与水、光、热共同构成变量,建立了区域森林生物量估算模型,并进行了检验,模型的R、R2、aR2及F统计量分别为0.809、0.655、0.661、101.436;样地实测值与模型估测值建立线性回归方程常数项(a)和回归系数(b)分别为0.09和1.021;用22个野外实测样点生物量数据对估算模型进行独立性检验,平均估算精度达到76.43%。说明模型的估算精度总体稳定,基本满足生物量估算精度要求,可用于该区域的森林生物量估算研究。

关键词: 生物量估算遥感信息模型香格里拉    
Abstract:

The forest is the largest carbon sink in terrestrial ecosystems.It has an irreplaceable role in adjusting the global carbon balance and slowing down CO2.Forest biomass is the most important parameters in the terrestrial ecosystem carbon cycle and becomes more and more universal concern by the scientists in forest biomass estimation and changes and the focus of carbon cycle research.Taking the ecologically sensitive areas in Northwest Yunnan Shangri-La County as a study area,in the support of survey data in the wild forest,combined with 3S technology, geography, ecology, meteorology and other related knowledge,the variable of 9 vegetation index,2-band gray data,growth season precipitation,growth season accumulated temperature,growth season total radiation,elevation,slope,aspect,slope position and soil organic matter content were selected,which combine the layer of remote sensing integrated factors and the layer of geographic comprehensive factor with water,light,heat into the variables,and then establish the regional forest biomass estimation model and be tested.The statistic of model R,R2,aR2 and F is 0.809,0.655,0.661 and 101.436.The linear regression equation constant (a) and regression coefficient (b) which established by sample measured data and model is 0.09,1.021.The independence test of estimation model had done by 22 biomass data of field sample;the average estimation accuracy is 76.43%.The result shows that the estimation accuracy of the model is generally stable,and basically meets the accuracy requirements of biomass estimation.It can be used to the study of estimating the forest biomass in this area.

Key words:  Biomass estimates    Remote sensing information model    Shangri-La
收稿日期: 2011-10-31 出版日期: 2012-10-17
:  TP 79  
基金资助:

国家自然科学基金项目(40861009)。

作者简介: 程鹏飞(1983-),男,陕西咸阳人,硕士,主要从事遥感与GIS应用研究。Email:xilin100@163.com。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

程鹏飞,王金亮,徐 申,程 峰,王小花. 区域森林生物量遥感信息模型构建研究[J]. 遥感技术与应用, 2012, 27(5): 722-727.

Cheng Pengfei,Wang Jinliang,Xu Shen,Cheng Feng,Wang Xiaohua. A Study on Remote Sensing Information Model of Regional Forest Biomass. Remote Sensing Technology and Application, 2012, 27(5): 722-727.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2012.5.722        http://www.rsta.ac.cn/CN/Y2012/V27/I5/722

[1]Luo Yunjian,Zhang Xiaoquan,Wang Xiaoke,et al.Forest Biomass Estimation Methods and Their Prospects[J].Scientia Silvae Sinicae,2009,45(8):129-134.[罗云建,张小全,王效科,等.森林生物量的估算方法及其研究进展[J].林业科学,2009,45(8):129-134.]
[2]Ma Wei,Sun Yujun.Forest Biomass in China[J].World Forestry Research,2009,22(5):71-76.[马炜,孙玉军.我国的森林生物量研究[J].世界林业研究,2009,22(5):71-76.]
[3]Chen Yufeng.The Possible Effects of Climate Change on Forest Vegetation-methods of Research based on GIS[J].Acta Geographica Sinica,1995,50(3):42-46.[陈育峰.气候变化对森林植被的可能影响—GIS支持下的方法研究[J].地理学报,1995,50(3):42-46.]
[4]Ma Ainai.Remote Sensing Information Models and Geographical Mathematics[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2000,25(2):10-15.[马蔼乃.遥感信息模型与地理数学[J].北京大学学报(自然科学版),2000,25(2):10-15.]
[5]Wang Hongyan,Gao Zhihai,Wan Fengyu,et al.Estimation of Vegetation Biomass Using SPOT5 Satellite Images in Fengning Country,Hebei Province[J].Remote Sensing Technology and Application,2010,25(5):639-646.[王红岩,高志海,王琫瑜,等.基于SPOT5遥感影像丰宁县植被地上生物量估测研究[J].遥感技术与应用,2010,25(5):639-646.]
[6]Ma Ainai.Remote Sensing Information Models[M].Beijing:Peking University Press,1997:57-103.[马蔼乃.遥感信息模型[M].北京:北京大学出版社,1997:57-103.]
[7]Li Shuang,Qian Lexiang,Ding Shengyan.Object-Oriented Remote Sensing Information Models of Geography[J].Geography and Territorial Research,2002,18(2):27.[李爽,钱乐祥,丁圣彦.面向对象的地理遥感信息模型 [J].地理学与国土研究,2002,18(2):27.]
[8]Liu Wanru,Xu Xinzhi,Gao Shanghua.Probability and Statistics[M].Beijing:Higher Education Press,1993:262-266.[刘婉如,徐信之,高尚华.概率与统计[M].北京:高等教育出版社,1993:262-266.]
[9]Song Farong.Planning and Design of Forest Resources Investigation Report of Tibetan Autonomous Prefecture[R].Dali Yunnan,2006.[宋发荣.迪庆藏族自治州森林资源规划设计调查报告[R].云南大理,2006.]
[10]Wang Yu.Agro-climatic Resources of Yunnan Province and Zoning[M].Beijing:China Meteorological Press,1990:13-118[王宇.云南省农业气候资源及区划[M].北京:气象出版社,1990:13-118.]
[11]China Meteorological Administration.The Historical Meteorological Data Query[DB/OL].http://www.cma.gov.cn/lssjcx/,2009-11-20.[中国气象局.历史气象数据查询[DB/OL].http://www.cma.gov.cn/lssjcx/,2009-11-20.]
[12]Tong Huijie,Feng Zhongke,Luo Xu,et al.Correlations between Forest Biomass and Remote Sensing Informaton[J].Journal of Beijing Forestry University,2007,29(2):156-159.[仝慧杰,冯仲科,罗旭,等.森林生物量与遥感信息的相关性[J].北京林业大学学报,2007,29(2):156-159.]
[13]Huang Chun,Shi Benjun.Number of Composite Model to Predict Stand Volume Growth Process[J].Forest Inventory and Planning,1996,3(1):16-18.[黄春,施本俊.用多项复合模型预测林分蓄积生长过程[J].云南林业调查规划,1996,3(1):16-18.]
[14]Qin Chengzhi,Zhu Axing,Li Baolin,et al.Taxonomy of Slope Positions and Quantification of Their Spatial Distribution Information[J].Geomatics and Information Science of Wuhan University,2009,34(3):374-377.[秦承志,朱阿兴,李宝林,等.坡位的分类及其空间分布信息的定量化[J].武汉大学学报(信息科学版),2009,34(3):374-377.]
[15]Tong Huijie.Methods of Modeling Forest Biomass based on Remote Sensing Information[D].Beijing:Beijing Forestry University,2007.[仝慧杰.森林生物量遥感反演建模基础与方法研究[D].北京:北京林业大学,2007.]
[16]Li Xueping.A Study of Scaling Method to Obtain Index Weight by Analytic Hierarchy Process[J].Journal of BU PT(Social Sciences Edition),2001,3(1):25-27.[李学平.用层次分析法求指标权重的标度方法的探讨[J].北京邮电大学学报(社会科学版),2001,3(1):25-27.]
[17]Lu Wendai,Wu Xizhi.SPSS for Windows Statistical Analysis[M].Beijing:Publishing House of Electronics Industry,2008:345.[卢纹岱,吴喜之.SPSS for Windows 统计分析[M].北京:电子工业出版社,2008:345.]
[18]Li Yamin.A Study on Community Biomass and Standing Wood Weight Form of the Natural Conifer Forest Dominated by Larix Principis-rupprechtii[D].Beijing:China Agricultural University,2005.[李亚民.庞泉沟自然保护区华北落叶松林生物量研究[D].北京:中国农业大学,2005.]

[1] 王金亮,曾浩,王艳英,刘广杰. 基于小波分析的TM 遥感图像超分辨率重建[J]. 遥感技术与应用, 2016, 31(3): 476-480.
[2] 张正健,李爱农,边金虎,赵伟,南希,靳华安,谭剑波. 基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究[J]. 遥感技术与应用, 2016, 31(1): 51-62.
[3] 王金亮,程峰,王成,陈联君,王小花. 基于ICESat-GLAS数据估算复杂地形区域森林蓄积量潜力初探—以云南香格里拉县为例[J]. 遥感技术与应用, 2012, 27(1): 45-50.