不同方法提取的快鸟影像信息估算刺槐林有效叶面积指数的精度比较

doi:10.11707/j.1001-7488.20150904

摘要/Abstract

摘要：

[目的] 合理利用高分辨率影像空间信息可提高森林参数的估算精度,本研究在前人基础上进一步细化,探索高分辨率影像的光谱与空间信息在提高森林有效叶面积指数(LAIe)估算精度上的组合规律,以期为高分辨率影像对森林参数的估算和森林健康评价研究提供参考和基础数据。[方法] 以黄土高原渭北地区刺槐人工林为研究对象,野外测定76块刺槐人工林样地的LAIe,并分别提取高分辨率快鸟影像全色数据的7种纹理指数(角二阶矩阵ASM、同质性HOM、相关性COR、对比度CON、非相似度DIS、变化量VAR、熵ENT)和多光谱数据的7种光谱信息(近红外波段b₄、土壤调节植被指数SAVI、修正的土壤植被指数MSAVI、非线性植被指数NLI、改进型土壤大气修正植被指数EVI、差值植被指数DVI、归一化植被指数NDVI),通过栅格运算得到光谱-纹理组合参数,利用一元线性回归模型、二次多项式模型、乘幂模型和指数模型分别建立光谱-纹理组合参数、纹理参数与刺槐人工林LAIe的关系方程,计算比较光谱-纹理与纹理参数对刺槐人工林LAIe的估算精度和均方根误差(RMSE),揭示Quickbird影像光谱信息与纹理信息在提高森林LAIe估算精度上的组合规律。[结果] ASM,HOM,COR与任意植被指数结合后,估算精度均比相应纹理指数高; CON,DIS和VAR与部分植被指数结合后,估算精度比相应纹理指数高; 相反,ENT与任意植被指数结合,光谱-纹理组合参数的估算精度均小于纹理指数。二次多项式模型和指数模型对LAIe估算的决定系数略高于一元线性回归模型和乘幂模型。[结论] 利用高分辨率影像的纹理信息和光谱信息估算刺槐人工林LAIe时,将空间信息加入光谱信息,可有效估算森林LAIe且能够得到较高的森林LAIe估算精度; 但并非任意纹理指数与植被指数结合对森林LAIe的估算精度均高于纹理指数,且估算模型对精度有一定影响。本文的研究表明,综合利用高分辨率影像的空间信息和光谱信息,并选择合适的光谱-纹理组合参数和估算方程,有利于区域尺度森林参数的精确估计和反演。

关键词: 有效叶面积指数, 光谱-纹理, 纹理, 高分辨率影像, 刺槐林

Abstract:

[Objective] The spatial information of high resolution remote sensing image can improve the estimation accuracy of forestry parameters. This study precisely explored the combinational rule of spectral and spatial information with high resolution remote sensing in order to improve the effective leaf area index (LAIe) based on the existing research. Obtained results can be provide evidence and data for estimation of forestry parameters and assessments of forestry health. [Method] The black locust (Robinia pseudoacacia) plantations located in Weibei area of Loess Plateau were chosen as research objects. The LAIe values of 76 plots were measured. We also extracted seven textural parameters of panchromatic data including ASM, HOM, COR, CON, DIS, VAR, ENT and seven spectral parameters of multi-spectral image including b₄, SAVI, MSAVI, NLI, EVI, DVI, NDVI from Quickbird imagey with high resolution. The combined spectral-textural indices of Quickbird imagery were obtained using method of raster operation. Four different techniques, including simple linear regression model, quadratic regression model, power model and exponential model, were developed to describe the relationship between image parameters and field measurements of LAIe. The predicted accuracy of combined spectral-textural index and sole texture parameter was compared to reveal the role of combined spectral index and texture parameters used for LAIe retrieval. [Result] The LAIe estimation accuracy was improved when ASM, COR and HOM were combined with SVIs. To a certain extent, the accuracy of SVIs to estimate LAIe was improved with the combination of CON, DIS, VAR and SVIs. The combination of HOM, ASM and COR with SVIs gained the higher r² than those achieved using HOM, ASM or COR alone. The performances of CON, DIS and VAR were improved when combining with partly SVIs. The combination of Entropy data with SVIs invariably yielded adjusted r² values that were lower than those achieved using ENT alone. Quadratic regression model and exponential model exhibited higher r² values than power model and simple linear regression model slightly.[Conclusion] The combination of spectral and special information can improve the accuracy of LAIe estimation effectively when the high-resolution image was used to invert LAIe of black locust plantations. However, not all combined spectral and textural information can obtained higher accuracy comparing to the solely textural information. The model types influenced the accuracy of LAIe estimation slightly. Our results showed that comprehensive use of spatial and spectral information and appropriate selection of model was beneficial to accurate estimation and inversion of forestry parameters.

Key words: effective leaf area index(LAIe), spectral-textural information, texture, high resolution imagery, black locust plantation

中图分类号:

S771.8

周靖靖, 赵忠, 刘金良, 赵君, 赵青侠, 刘俊. 不同方法提取的快鸟影像信息估算刺槐林有效叶面积指数的精度比较[J]. 林业科学, 2015, 51(9): 24-34.

Zhou Jingjing, Zhao Zhong, Liu Jinliang, Zhao Jun, Zhao Qingxia, Liu Jun. A Comparison of Different Quickbird Image Information for Estimating the Effective Leaf Area Index of Robinia pseudoacacia Plantations[J]. Scientia Silvae Sinicae, 2015, 51(9): 24-34.

参考文献

柳钦火. 2010. 定量遥感模型、应用及不确定性研究. 北京: 科学出版社.
(Liu Q H. 2010. The study of model, application, uncertainty about quantitative remote sensing. Beijing: Science Press.[in chinese])
石月婵,杨贵军,冯海宽,等. 2012. 北京山区森林叶面积指数季相变化遥感监测. 农业工程学报, 28(15): 133-139.
(Shi Y C, Yang G J, Feng H K, et al. 2012. Remote sensing of seasonal variability monitoring of forest LAI over mountain areas in Beijing. Transactions of the Chinese Society of Agricultural Engineering, 28(15): 133-139.[in chinese])
张晶晶, 赵忠, 宋西德, 等. 2010. 渭北黄土高原人工刺槐林植物多样性动态. 西北植物学报, 30(12): 2490-2496.
(Zhang J J, Zhao Z, Song X D, et al. 2010. Biodiversity dynamics of artificial Robinia pseudoacacia forest in Weibei Loess Plateau. Acta Botanica Boreali-Occidentalia Sinica, 30(12): 2490-2496.[in chinese])
赵安玖, 杨长青, 廖承云. 2014. 基于影像纹理特征的川西南山地常绿阔叶林有效叶面积指数的空间分析.应用生态学报, 25(11): 3237-3246
(Zhao A J, Yang C Q, Liao C Y. 2014. Spatial analysis of LAIe of montane evergreen broad-leaved forest in southwest Sichuan, Northwest China, based on image texture. Chinese Journal of Applied Ecology, 25(11): 3237-3246.[in chinese])
郑元. 2010. 刺槐光合生理特征与固碳能力研究. 杨凌: 西北农林科技大学博士学位论文.
(Zheng Y. 2010. A study of photosynthetic and physiological characteristics and carbon fixation capacity of black locust (Robinia pseudoacacia). Yangling: PhD thesis of Northwest A&F University.[in chinese])
周慧, 赵忠, 周靖靖, 等. 2011. 黄土高原区不同密度刺槐林冠层结构特征及月动态变化. 林业科技开发, 25(5): 16-20.
(Zhou H, Zhao Z, Zhou J J, et al. 2011. Canopy structure of black locust plantations with different densities and monthly dynamics in the Loess Plateau. China Forestry Science and Technology, 25(5): 16-20.[in chinese])
周靖靖,赵忠,刘金良,等. 2014. 基于快鸟影像纹理特性的刺槐林叶面积指数估算. 应用生态学报, 25(5): 1266-1274.
(Zhou J J, Zhao Z, Liu J L, et al. 2014. Estimating leaf area index of black locust (Robinia pseudoacacia L.) plantations based on texture parameters of Quickbird imagery. Chinese Journal of Applied Ecology, 25(5): 1266-1274.[in chinese])
Baret F, Guyot G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote sensing of environment, 35(2): 161-173.
Barr A G, Black T, Hogg E, et al. 2004. Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agricultural and Forest Meteorology, 126(3): 237-255.
Chen J M, Black T A. 1992. Defining leaf area index for non-flat leaves. Plant Cell and Environment, 15(4): 421-429.
Colombo R, Bellingeri D, Fasolini D, et al. 2003. Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing of Environment, 86(1): 120-131.
Gebreslasie M T, Ahmed F B, van Aardt J A N. 2011. Extracting structural attributes from IKONOS imagery for Eucalyptus plantation forests in KwaZulu-Natal, South Africa, using image texture analysis and artificial neural networks. International Journal of Remote Sensing, 32(22): 7677-7701.
Gray J, Song C. 2012. Mapping leaf area index using spatial, spectral and temporal information from multiple sensors. Remote Sensing of Environment, 119(16): 173-183.
Haralick R M, Shanmugam K, Dinstein I H. 1973. Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, 6: 610-621.
Kovacs J M, Wang J F, Flores-Verdugo F. 2005. Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific. Extuarine Coastal and Shelf Science, 62(1/2): 377-384.
Kraus T, Schmidt M, Dech S W, et al. 2009. The potential of optical high resolution data for the assessment of leaf area index in East African rainforest ecosystems. International Journal of Remote Sensing, 30(19): 5039-5059.
Nemani R, Pierce L, Running S, et al. 1993. Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed leaf area index estimates. International Journal of Remote Sensing, 14(13): 2519-2534.
Nichol J E, Sarker M L R. 2011. Improved
biomass estimation using the texture parameters of two high-resolution optical sensors. IEEE Transactions on Geoscience and Remote Sensing, 49(3): 930-948.
Ota T, Mizoue N, Yoshida S. 2011. Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution. Journal of Forest Research, 16(6): 432-437.
Ouma Y O, Tateishi R. 2006. Optimization of second-order grey-level texture in high-resolution imagery for statistical estimation of above-ground biomass. Journal of Environmental Informatics, 8(2): 70-85.
Rautiainen M, Heiskanen J, Korhonen L. 2012. Seasonal changes in canopy leaf area index and MODIS vegetation products for a boreal forest site in central Finland. Boreal Environment Research,
17(1): 72-84.
Sarker L R, Nichol J E. 2011. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sensing of Environment, 115(4): 968-977.
Song C, Dickinson M B. 2008. Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index. International Journal of Remote Sensing,
29(19): 5605-5622.
Sonnentag O, Chen J M, Roberts D A, et al. 2007. Mapping tree and shrub leaf area indices in an ombrotrophic peatland through multiple endmember spectral unmixing. Remote Sensing of Environment, 109(3): 342- 360.
Soudani K, Francois C, le Maire G, et al. 2006. Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 102(1/2): 161-175.
Tian Q, Luo Z, Chen J M, et al. 2007. Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images. Journal of Environmental Management, 85: 624-627.
Tillack A, Clasen A, Kleinschmit B, et al. 2014. Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices. Remote Sensing of Environment, 141: 52-63.
Wang Q, Adiku S, Tenhunen J, et al. 2005. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment, 94(2): 244-255.
Wood E M, Pidgeon A M, Radeloff V C, et al. 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sensing of Environment, 121: 516-526.
Wulder M, Franklin S, Lavigne M. 1996. High spatial resolution optical image texture for improved estimation of forest stand leaf area index. Canadian Journal of Remote Sensing, 22(4): 441-449.
Wulder M A, LeDrew E F, Franklin S E, et al. 1998. Aerial image texture information in the estimation of northern deciduous and mixed wood forest leaf area index (LAI). Remote Sensing of Environment, 64(1): 64-76.
Yuan J G, Niu Z, Wang X P. 2009. Atmospheric correction of hyperion hyperspectral image based on FLAASH. Spectroscopy and Spectral Analysis, 29(5): 1181-1185.
Zhou J J, Zhao Z, Zhao Q, et al. 2013. Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data. Journal of Applied Remote Sensing, 7(1): 073484.

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