我们的网站为什么显示成这样?

可能因为您的浏览器不支持样式,您可以更新您的浏览器到最新版本,以获取对此功能的支持,访问下面的网站,获取关于浏览器的信息:

|Table of Contents|

基于双源遥感数据的杉木林分蓄积量估测模型研究(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2016年05期
Page:
107-114
Column:
研究论文
publishdate:
2016-09-30

Article Info:/Info

Title:
Study on the model for estimating forest volume of Chinese fir based on bi-source remote sensing data
Article ID:
1000-2006(2016)05-0107-08
Author(s):
YANG MingWANG YuetingZHANG Xiaoli*
Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083,China
Keywords:
forest volume estimation ZY-3 satellite Alos Palsar texture feature backscatter coefficient
Classification number :
S758.5+1
DOI:
10.3969/j.issn.1000-2006.2016.05.017
Document Code:
A
Abstract:
In order to improve the precision of forest volume estimation, the Chinese fir(Cunninghamia lanceolata)stands of state-owned forest farm in Jiangle County,Sanming City,Fujian Province were selected as the study object, and the high resolution images of ZY-3 and the images of Alos Palsar were selected as the remotely sensed data sources. The polarization radar parameters with high correlation and the texture parameters of the optimal window were combined for the volume inversion. Eight texture features of ZY-3 high resolution image were extracted by the gray level co-occurrence matrix under 5 kinds of window sizes including 3×3,5×5,7×7,9×9 and 11×11 pixels. Meanwhile, the backscatter coefficients in HH and HV polarization modes were derived from Alos Palsar images. Furthermore, ratio of the two backscatter coefficients above was computed. The texture features from 5 different windows were used as independent variable in the inversion of forest volume respectively by using stepwise regression analysis to find the optimal window. Then, the correlation between backscatter coefficients from different polarization modes and the forest volume was computed. The results showed that for the inversion model based on ZY-3 images, the optimal window was the size of 5×5, the value of multiple correlation coefficient reached to 0.869, with a root mean square error 23.38 m3/hm2 and a total estimation accuracy of 80.32%. While for the inversion model integrating the ratio of backscatter coefficients from Palsar with the texture features of optimal window from ZY-3, the value of multiple correlation coefficient reached to 0.901, with the root mean square error 22.32 m3/hm2 and the estimation accuracy of total forest volume 85.42%.The results suggests using bi-source remote sensing data can produce a higher precision of volume estimation on average.

References

[1] 国庆喜, 张锋. 基于遥感信息估测森林的生物量[J]. 东北林业大学学报, 2003,31(2):13-16. Doi:10.13759/ j.cnki.dlxb.2003.02.006. Guo Q X, Zhang F. Estimation of forest biomass based on remote sensing [J]. Journal of Northeast Forestry University, 2003, 31(2):13-16.
[2] 余坤勇, 林芳, 刘健,等. 基于RS的闽江流域马尾松林分蓄积量估测模型研究[J]. 福建林业科技, 2006, 33(1):16-19. Doi: 10.13428/j.cnki.fjlk.2006.01.004. Yu K Y, Lin F, Liu J, et al. Study on estimating model of Pinus massoniana stand volume in Minjiang Watershed based on RS technologies [J]. Journal of Fujian Forestry Science and Technology, 2006, 33(1):16-19.
[3] 赵良平. 森林生态系统健康理论的形成与实践[J]. 南京林业大学学报(自然科学版),2007,31(3):1-7.Doi: 10.3969/j.jssn.1000-2006.2007.03.001. Zhao L P. Development and application of forest ecosystem health theory in forest ecological construction of China [J]. Journal of Nanning Forestry University(Natural Sciences Edition), 2007,31(3):1-7.
[4] Hame T, Salli A, Andersso K, et al. A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data[J]. International Journal of Remote Sensing,1997,18(15):3211-3243. Doi: 10.1080/014311697217053.
[5] Trotter C M, Dymond J R, Goulding C J. Estimation of timber volume in a coniferous plantation forest using LANDSAT TM[J]. International Journal of Remote Sensing,1997,18(10):2209-2223.Doi:10.1080/014311697217846.
[6] Lu D S, Batistella M. Exploring TM image texture and its relationships with biomass estimation in Rondonia, Brazilian Amazon[J]. Acta Amazonica, 2005,35(2): 261-268. Doi: 10.1590/S0044-59672005000200015.
[7] 王妮, 彭世揆, 刘斌,等. 近10年江苏宿迁森林蓄积量变化的定量遥感监测[J]. 南京林业大学学报(自然科学版),2013,37(5):65-69. Doi:10.3969/j.issn.1000 -2006.2013.05.013. Wang N, Peng S K, Liu B,et al. A study on detecting the changes of the forest volume of Suqian in Jiangsu based on the quantitative remote sensing during 2000-2010[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2013,37(5): 65-69.
[8] 王小青. 基于遥感技术的小黑杨木材产量和质量预测[D]. 北京:中国林业科学研究院, 2007. Wang X Q. Prediction of wood production and quality of Populus Xiaohei based on remote sensing technology[D].Beijing: Chinese Academy of Forestry, 2007.
[9] 王佳, 宋珊芸, 刘霞,等. 结合影像光谱与地形因子的森林蓄积量估测模型[J]. 农业机械学报, 2014, 45(5):216-220. Doi:10.6041/j.issn.1000-1298.2014.05. 033. Wang J, Song S Y, Liu X,et al. Forest volume estimation model using spectra and topographic factors of ZY-3 image[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014,45(5):216-220.
[10] 陈尔学. 合成孔径雷达森林生物量估测研究进展[J]. 世界林业研究,1999,12(6):18-23. Doi:10.13348/j.cnki. sjlyyj.1999.06.004. Chen E X. Development of forest biomass estimation using SAR data[J]. World Forestry Research, 1999, 12(6):18-23.
[11] 范凤云, 陈尔学, 李世明. ALOS PALSAR极化数据对山区森林蓄积量的敏感性评价[C]//南宁: 中国林业学术大会, 2009. Fan F Y, Chen E X, Li S M. Evaluation of sensitivity of ALOS PALSAR polarimetric data to forest volume in hilly region[C]//Nanjing: China Forestry Academic Conference, 2009.
[12] 杨永恬, 李增元, 陈尔学,等. 基于ALOS PALSAR数据的森林蓄积量估测技术研究[J]. 林业资源管理, 2010, 2(1):113-117. Doi:10.13466/j.cnki.lyzygl.2010. 01.016. Yang Y T, Li Z Y, Chen E X, et al. Forest volume estimation method based on ALOS PALSAR data[J]. Forest Resources Management, 2010, 2(1):113-117.
[13] Ma X Q, Heal K V, Liu A Q, et al. Nutrient cycling and distribution in different-aged plantations of Chinese fir in southern China[J]. Forest Ecology and Management, 2007,243(1): 61-74. Doi: 10.1016/j.foreco.2007.02.018.
[14] 刘龙飞, 陈云浩, 李京. 遥感影像纹理分析方法综述与展望[J]. 遥感技术与应用, 2003, 18(6):441-447. Doi: 10.3969/j.issn.1004-0323.2003.06.015. Liu L F, Chen Y H, Li J. Texture analysis methods used in remote sensing images[J]. Remote Sensing Technology and Application, 2003, 18(6):441-447.
[15] 王昆, 张晓丽, 王珊,等. 鹫峰地区QuickBird影像纹理特征与生物量估测关系初探[J]. 地理与地理信息科学, 2013, 29(3):52-55. Doi:10.7702/dlydlxxkx2013 0312. Wang K, Zhang X L, Wang S, et al. Study on the relationship between texture of QuickBird image and biomass estimation in area of Jiufeng[J]. Geography and Geo-Information Science, 2013, 29(3):52-55.
[16] Imhoff M L. Radar backscatter and biomass saturation: ramifications for global biomass inventory[J]. IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):511-518.Doi:10.1109/36.377953.
[17] Imhoff M L. Radar backscatter/biomass saturation: observations and implications for global biomass assessment[J]. International Geoscience & Remote Sensing Symposium, 1993, 1(1):43-45. Doi:10.1109/ IGARSS.1993.322465.
[18] 吴达胜.基于多源数据和神经网络模型的森林资源蓄积暈动态监测[D].杭州:浙江大学,2013. Wu D S. Dynamic monitoring for volume of the forest resources based on multi-source data and neural network[D].Hangzhou:Zhejiang University,2013.
[19] Ulander L M H. Radiometric slope correction of synthetic-aperture radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(5):1115-1122. Doi: 10.1109/36.536527.
[20] 王登峰,杨志刚,魏安世. 纹理信息在遥感影像分类中的应用[J]. 南京林业大学学报(自然科学版),2010,34(3):97-100. Doi:10.3969/j.issn.1000-2006. 2010.03.020. Wang D F, Yang Z G, Wei A S. Application of texture information on classification of remote sensing imagery[J]. Journal of Nanjing Forestry University(Natural Sciences Edition), 2010,34(3):97-100.
[21] Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, 3(6): 610-621. Doi:10.1109/tsmc.1973.4309314.
[22] 李明诗, 谭莹, 潘洁,等. 结合光谱、纹理及地形特征的森林生物量建模研究[J]. 遥感信息, 2006, 21(6):6-9. Doi: 10.3969/j.issn.1000-3177.2006.06.003. Li M S, Tan Y, Pan J, et al. Modeling forest aboveground biomass by combining the spectrum, textures with topographic features[J]. Remote Sensing Information, 2006, 21(6): 6-9,66.
[23] 赵红平. EViews6软件的逐步回归分析模块在多重共线性教学中的应用[J]. 贵州教育学院学报,2009,20(12):31-34. Doi:10.3969/j.issn.1674-7798.2009.12.009. Zhao H P. The Application of the stepwise regression program of EViews6 in the teaching of Multi-collinearity[J]. Journal of Guizhou Education Institute, 2009, 20(12):31-34.
[24] Latifur R S, Janet E N. Improved forest biomass estimates using ALOS AVNIR-2 texture indices[J]. Remote Sensing of Environment, 2011, 115(4):968-977. Doi: 10.1016/j.rse.2010.11.010.
[25] Thenkabail P S, Stucky N, Griscom B W, et al. Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data[J]. International Journal of Remote Sensing, 2004, 42(25):5447-5472. Doi:10.1080/014311 60412331291279.
[26] 王晓宁, 徐天蜀, 李毅. 利用ALOS PALSAR双极化数据估测山区森林蓄积量模型[J]. 浙江农林大学学报, 2012, 29(5):667-670. Wang X N, Xu T S, Li Y. Estimating forest volume in hilly regions with the ALOS PALSAR model's dual polarization data[J]. Journal of Zhejiang A & F University, 2012, 29(5):667-670.
[27] 宋茜, 范文义. 大兴安岭植被生物量的ALOS PALSAR估算[J]. 应用生态学报, 2011, 22(2):303-308. Doi: 10.13287/j.1001-9332.2011.0074. Song Q, Fan W Y. ALOS PALSAR estimation of vegetation biomass in Daxing'anling region[J]. Chinese Journal of Applied Ecology, 2011,22(2):303-308.

Last Update: 2016-10-30