为了提高森林蓄积量估测精度,以福建省三明市将乐县国有林场中杉木林作为试验区,选择资源3号卫星多光谱高分辨率影像及Alos Palsar影像为数据源,将相关性较高的极化雷达参数与最优窗口下的纹理参数相结合,协同两种遥感数据反演蓄积量。利用灰度共生矩阵分别提取高分辨率影像在3×3、5×5、7×7、9×9和11×11的5组窗口大小下8种纹理特征信息,提取Alos Palsar影像双极化方式下后向散射系数并进行比值运算。采用多元逐步回归分析方法,分别利用5组纹理特征信息反演杉木林蓄积量,找出最优窗口; 检测不同极化方式下后向散射系数与蓄积量之间相关性。结果表明,单数据源反演蓄积量模型中,5×5窗口反演效果最好,模型复相关系数R=0.869,均方根误差σRMSE=23.38 m3/hm2,蓄积量总体的估测精度为80.32%; 多数据源反演蓄积量模型中,两种极化方式下的后向散射系数比值与高分影像纹理特征参数结合后,反演模型的效果更好,模型中R=0.901,σRMSE=22.32 m3/hm2,蓄积量总体估测精度达到85.42%。研究表明,基于多数据源数据的森林蓄积量反演精度更高,结果更准确。
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.
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[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.
基金
收稿日期:2015-10-23 修回日期:2016-07-05
基金项目:国家高技术研究发展计划(2012AA102001)
第一作者:杨铭(59231516@qq.com)。*通信作者:张晓丽(zhang-xl@263.net),教授。
引文格式:杨铭,王月婷,张晓丽. 基于双源遥感数据的杉木林分蓄积量估测模型研究[J]. 南京林业大学学报(自然科学版),2016,40(5):107-114.
PDF(2155158 KB)
