Algorithm for leaf area index inversion in the Great Xing'an Mountains using MISR data and spatial scaling for the validation
-
摘要: 叶面积指数(LAI)是气候研究和生态研究中重要的植被冠层结构参数,遥感技术为快速获取大面积叶面积指数提供了有效途径。大兴安岭地区是我国重要的生态功能区,本文以大兴安岭为研究区域,根据森林林分特征,采用基于物理过程的4-Scale几何光学模型,利用多角度MISR遥感数据反演该区域叶面积指数数据。几何光学模型特点在于参数具有物理意义,考虑地面反射的热点效应,模型反演过程不依赖于样本数据适用于大区域反演研究,MISR数据提供同一区域多角度遥感数据,有效解决了单一观测角度植被指数和叶面积指数函数关系饱和点低的问题。由于地面验证数据空间尺度无法满足MISR数据的空间分辨率,本文采用TM数据对样地实测叶面积指数数据进行尺度转换,针对不用坡向叶面积指数空间异质性进行分析,讨论不同空间分辨率验证数据的合理性,研究表明大兴安岭区域使用600m空间分辨率验证数据对MISR数据反演结果检验最优,该分辨率下叶面积指数变化随空间尺度变化趋于稳定,并较好地避免了2种遥感数据几何配准带来的误差。结果表明:4-Scale几何光学模型适用于我国大兴安岭地区森林叶面积指数反演,实验中MISR数据反演叶面积指数的平均绝对误差为25.6%、均方根误差为0.622。本研究为大兴安岭地区叶面积指数大区域快速定量反演提供了研究基础。Abstract: Leaf Area Index (LAI) is an important parameter of vegetation canopy structure in the research of climate and ecology. Remote sensing technology provides an effective method for rapid acquisition of large-area leaf area index. The Great Xing'an Mountains are an important ecological function area of China, where the present study was conducted. According to the different forest characteristics, we used 4-scale geometrical optics model based on physical process. Simultaneously, we used the multi-perspectives MISR remote sensing data to inverse the leaf area index of this region. Geometrical optics model characterized by parameters have physical significance which considers the hot-spot effect of the ground reflection, and modelling inversion process is independent on sample data, suitable for inversion in a large area. The MISR remote sensing data provide multiple perspectives in the same region, which effectively address the question that LAI can only be observed at a single angle and the question of low level saturation point in the LAI function relationship. Because the scale of ground validation data cannot meet the spatial resolution requirement of the MISR data, TM data were used to scale transformation for plot-measured leaf area index data. We analyzed the heterogeneity of LAI in different slopes and discussed the validation data rationality at different spatial resolutions. Our study shows that the validation data at a 600m spatial resolution can obtain optimal inversion result of MISR data, and at such a resolution, the change of LAI with spatial scale tends to stabilize and successfully avoids the error caused by the geometric registration of the two remote sensing data. The results of our study showed that: 4-scale geometry model is suitable for LAI inversion in the Great Xing'an Mountains, MISR-inversed mean absolute error of LAI is 25.6% and RMSE (the root-mean-square error) is 0.622. This research provides foundation for rapid, quantitative inversion of LAI in the Great Xing'an Mountains.
-
-
[1] YANG F, SUN J L, ZHANG B, et al. Assessment of MODIS LAI product accuracy based on the PROSAIL model, TM and field measurements[J]. Transactions of the CSAE, 2010, 26(4): 192-197.
[1] 杨飞, 孙九林, 张柏, 等. 基于PROSAIL模型及TM与实测数据的MODIS LAI精度评价[J]. 农业工程学报, 2010, 26(4):192-197. [2] SONNENAG O, TALBOT J, CHEN J M, et al. Using direct and indirect measurements of leaf area index to characterize the shrub canopy in an ombrotrophic peatland[J]. Agricultural and Forest Meteorology, 2007, 144(3): 200-212.
[2] LIU J Y, TANG X G, CHANG S Z, et al. Application of remote sensing to inverse the forest leaf area index and regional estimation[J]. Remote Sensing Technology and Application, 2014, 29(1):18-25.
[3] CHEN J M, BLACK T A. Defining leaf area index for non-flat leaves[J]. Plant, Cell & Environment, 1992, 15(4): 421-429.
[3] HUANG M, JI J J. The spatial-temporal distribution of leaf area index in China: a comparison between ecosystem modeling and remote sensing reversion[J]. Acta Ecologica Sinica, 2010, 30(11):3057-3064.
[4] LIU Y, LIU R G, CHEN J M, et al. Current status and perspectives of leaf area index retrieval from optical remote sensing data[J]. Journal of Geo-Information Science, 2013, 15(5):734-743.
[4] 刘婧怡, 汤旭光, 常守志, 等. 森林叶面积指数遥感反演模型构建及区域估算[J]. 遥感技术与应用, 2014,29(1):18-25. [5] XU X R. Physical principles of remote-sensing[M]. Beijing: Peking University Press, 2005.
[5] 黄玫, 季劲钧. 中国区域植被叶面积指数时空分布:机理模型模拟与遥感反演比较[J]. 生态学报, 2010, 30(11):3057-3064. [6] 刘洋, 刘荣高, 陈镜明,等.叶面积指数遥感反演研究进展与展望[J]. 地球信息科学学报, 2013, 15(5):734-743. [6] WAN H W, WANG J D, LIANG S L, et al.Estimating leaf area index by fusing MODIS and MISR data [J]. Spectroscopy and Spectral Analysis, 2009, 29(11):3106-3111.
[7] ZHANG Y Y. The research on the remote sensing model for forest biomass in Daxing'an Mountains[D]. Harbin:Northeast Forestry University, 2009.
[7] 徐希孺. 遥感物理[M].北京:北京大学出版社,2005. [8] CHEN M, DENG F. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2230-2238.
[8] ZOU J, YAN G J. Optical methods for in situ measuring leaf area index of forest canopy[J]. Chinese Journal of Applied Ecology, 2010, 21(11):2971-2979.
[9] YU Y, FAN W Y, YANG X G. Comparisons of three models for vegetation canopy bi-directional reflectance distribution function[J]. Chinese Journal of Plant Ecology, 2012,36(1):55-62.
[9] FANG H, LIANG S, KUUSK A. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model[J]. Remote Sensing of Environment, 2003, 85(3): 257-270.
[10] 万华伟, 王锦地, 梁顺林,等.联合MODIS与MISR遥感数据估算叶面积指数[J]. 光谱学与光谱分析, 2009, 29(11):3106-3111. [10] XIANG H B, GUO Z H, ZHAO Z Q,et al. Estimating method of forest leaf area index on different space scales[J]. Scientia Silvae Sinicae, 2009, 45(6): 139-144.
[11] 张元元. 大兴安岭地区森林生物量遥感模型的研究[D].哈尔滨: 东北林业大学, 2009. [11] JIAO Z M. Research on retrieval and spatial scaling of leaf area index from remote sensing images[D]. Beijing:Beijing Forestry University,2014.
[12] DEMAREZ V, DUTHOIT S, BARET F, et al. Estimation of leaf area and clumping indexes of crops with hemispherical photographs[J]. Agricultural and Forest Meteorology, 2008, 148(4): 644-655.
[13] 邹杰, 阎广建. 森林冠层地面叶面积指数光学测量方法研究进展[J]. 应用生态学报, 2010,21(11): 2971-2979. [14] KOKHANOVSKY A A, BREON F M, CACCIARI A, et al. Aerosol remote sensing over land: a comparison of satellite retrievals using different algorithms and instruments[J]. Atmospheric Research, 2007, 85(3): 372-394.
[15] STENBERG P, RAUTIAINENI M, MANNINEN T, et al. Boreal forest leaf area index from optical satellite images: model simulations and empirical analyses using data from central Finland[J]. Boreal Environment Research, 2008, 13(5): 433-443.
[16] CHEN J M, LI X, NILSON T, et al. Recent advances in geometrical optical modelling and its applications[J]. Remote Sensing Reviews, 2000, 18(2-4): 227-262.
[17] 于颖, 范文义, 杨曦光. 三种植被冠层二向反射分布函数模型的比较[J]. 植物生态学报, 2012, 36(1): 55-62. [18] BROWN L, CHEN J M, LEBLANC S G, et al. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis[J]. Remote Sensing of Environment, 2000, 71(1): 16-25.
[19] DENG F, CHEN M, PLUMMER S, et al. Algorithm for global leaf area index retrieval using satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2219-2229.
[20] ROUJEAN J L, LEROG M, DESCHAMPS D Y. A bidinectional reflectance model of the earth's surface for the correction of remote sensing data[J]. Journal of Geophysical Research: Atmospheres, 1992, 97: 20455-20468.
[21] CHEN J M, CIHLAR J. A hotspot function in a simple bidirectional reflectance model for satellite applications[J]. Journal of Geophysical Research: Atmospheres, 1997, 102: 25907-25913.
[22] 向洪波, 郭志华, 赵占轻, 等. 不同空间尺度森林叶面积指数的估算方法[J]. 林业科学, 2009, 45(6): 139-144. [23] 焦志敏. 植被叶面积指数遥感反演及空间尺度转换研究[D].北京:北京林业大学, 2014. [24] CHEN J M, PAVLIC G, BROWN L, et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements[J]. Remote Sensing of Environment, 2002, 80(1): 165-184.
-
期刊类型引用(1)
1. 徐媛，陈锦玲，陈玉梅，李璐璐，李惠敏，秦新民. 干旱胁迫下花生转录组与miRNA测序及相关基因的表达. 贵州农业科学. 2021(01): 1-9 . 
百度学术
其他类型引用(3)
计量
- 文章访问数: 1671
- HTML全文浏览量: 177
- PDF下载量: 29
- 被引次数: 4
下载:
