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地球信息科学学报 >

2016 , Vol. 18 >Issue 11: 1537 - 1543

DOI: https://doi.org/10.3724/SP.J.1047.2016.01537

高速公路对路域植被影响的时空格局变化遥感监测分析

  • 郭云开 , 1 ,
  • 苟叶培 , 2*, *
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  • 1. 长沙理工大学,长沙 410076
  • 2. 成都市国土规划地籍事务中心,成都 610072
*通讯作者：苟叶培（1989-）,男,四川都江堰人,硕士,主要从事植被信息定量反演研究。E-mail: 362760390@qq.com

作者简介：郭云开（1958-）,男,湖南桃源人,博士,主要从事遥感技术理论及其应用的教学与科研工作。E-mail:

收稿日期: 2015-10-19

  要求修回日期: 2015-12-25

  网络出版日期: 2016-11-20

基金资助

国家自然科学基金项目（41471421、41171397）

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Analysis of Expressway′s Impact on Vegetation in Its Spatial and Temporal Variation Based on Remote Sensing Monitoring

  • GUO Yunkai , 1 ,
  • GOU Yepei , 2, *
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  • 1. Changsha University of Science & Technology, Changsha 410076, China;
  • 2. Chengdu City Land Planning Cadastral Affairs Center, Chengdu 610072, China
*Corresponding author: GOU Yepei, E-mail:

Received date: 2015-10-19

  Request revised date: 2015-12-25

  Online published: 2016-11-20

Copyright

《地球信息科学学报》编辑部 所有
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摘要

植被覆盖动态监测及与生态环境变化的响应是陆地生态系统研究的重要内容。本文以不同时相的TM遥感影像为信息源,基于辐射传输模型PROSAIL,定量反演了不同时期高速公路两侧植被的叶面积指数,以反演的叶面积指数作为路域植被生长状况的监测指标,对实验区路域植被的生长状况进行了时空格局变化的动态遥感监测研究。研究表明：① 在时间上,路域植被的生长状况在高速公路开通后5年内受到的影响相对最大;② 在空间上,离高速公路越近的植被的生长状况受到的影响较大,离高速公路越远的植被的生长状况受到的影响较小。本研究为监测路域植被生长状况提供了重要的方法,为指导路域植被的生态恢复与保护提供了可靠的基础。

关键词： 叶面积指数(LAI); PROSAIL模型; 路域植被; 定量遥感

本文引用格式

郭云开 , 苟叶培 . 高速公路对路域植被影响的时空格局变化遥感监测分析[J]. 地球信息科学学报, 2016 , 18(11) : 1537 -1543 . DOI: 10.3724/SP.J.1047.2016.01537

Abstract

Vegetation dynamics and their coupled relations with ecology are current research hot spots in exploring how the terrestrial ecosystems respond to the climate systems. We have obtained the vegetation leaf area index on both sides for the expressway in the research area, based on the simulations which use the PROSAIL and TM images as the information source. We studied the dynamic changes of road vegetation growth status with LAI from aspects of time and space. The results are shown as follows. (1) The temporal change of vegetation growth pattern in the expressway region shows that: the temporal growth condition of the roadside vegetation is influenced heavily in the first 5-year period after a new expressway has opened. (2) The spatial change of vegetation growth pattern in the expressway region shows that: the spatial growth condition of the roadside vegetation is heavily impacted within the regions that are closer to the expressway, while the vegetation located far from the expressway is mildly affected. Generally, this research provides reliable basic data for guiding the vegetation restoration and protection.

Key words： Leaf Area Index (LAI); PROSAIL model; road vegetation; remote sensing

1 引言

近30年来,中国公路交通基础设施的显著改善有力地推动了中国经济发展。然而,高速公路的开通运营对周边的植被产生了较严重的影响,制约了中国高等级公路的可持续发展。因此,开展路域植被生长状况的动态监测就变得尤为重要。
传统的路域植被生态环境监测主要是到现场实地调查,这种方法费时费力且对于大区域植被监测是不可行的,而遥感的快速发展使其对大区域植被的监测成为可能。随着中国航天航空事业的快速发展,遥感技术的发展已十分成熟,渗入国民经济的各个领域,成为对地观测的有力工具。遥感以其时空分辨率高、覆盖范围广等优点,为陆地植被的分布、季节变化及年际间的变化提供了有效的手段[1-2]。通过学者研究发现许多生物、物理和生态过程都与叶面积指数有关[3-4],因此,叶面积指数已成为一个重要的植物学参数和评价指标,并被学者广泛的应用于农业、林业、生物学以及生态学等领域。研究表明,植被生长的环境受到影响后,区域内叶面积指数会明显降低[5-9]。因此,通过监测叶面积指数的变化情况来反映路域植被生态环境的变化情况,对生态环境监测具有重要的意义。目前,经验统计和物理机理方法是获取植被叶面积指数的主要方法。经验统计方法是通过建立实测数据与遥感影像光谱信息之间的经验模型来实现叶面积指数的反演。因此,该方法工作量大,并且建立的模型使用范围有限,不便于做时空二维扩展分 析[10-13],给区域相对较大的路域植被叶面积指数的反演带来极大的困难。而物理机理模型以数学的方式描述叶片、冠层、叶片-冠层生理、生化参数与辐射能量之间的物理机理作用过程,模型可以定量地估算植被生物物理和生物化学参数,物理过程严密[13-14]。物理机理模型因其适用范围广、受背景和植被类型影响小、模型的输入参数能较快的获取等特点,特别适用于路域这种区域大的植被环境的动态监测[8,15]。
目前,已有学者利用经验模型对路域植被环境开展了研究,但经验模型反演的植被信息精度不高,进而间接地影响了环境监测评价的准确性。因此,本文利用辐射传输模型PROSAIL模拟植被冠层光谱,根据模拟的冠层反射率反演不同时期的路域植被叶面积指数。最后,利用统计数据方法分析道路两侧不同区域内的植被叶面积指数在时间和空间格局上的变化情况,进而分析高速公路开通后对两侧植被的影响情况。本文研究结果对区域性植被的恢复和保护工作注入强有力的推进剂,并推动两型社会的建设。

2 模型与方法

2.1 模型

PROSAIL模型是在PROSPECT和SAIL模型基础上建立包含化学组分含量的耦合模型。通过改进Allen平板模型,得到辐射传输模型PROSPECT。PROSPECT将叶片视为一个表面粗糙的均匀平板,并将非致密型叶片当做由N层平板和N-1层空气组成,现在N已经被扩展到实数范围内,N实际描述的是叶片内部的结构[16]。模型模拟叶片在400-2500 nm波长范围内的反射和透射光谱特性。简化公式如式（1）-（4）所示。
x = t av ( a , n ) （1）
y = x ( t av ( 90 , n ) - 1 ) + 1 - t av ( a , n ) （2）
R N , a = x R N , 90 + y （3）
T N , a = x T N , 90 （4）
式中： a 是最大入射角; t av ( a , n ) 是所有入射和折射方向的平均辐射在平板表面的透射率; n 是叶肉界面折射指数; N 是叶片叶肉结构参数; R N , a 是最大入射角 a 和叶片叶肉结构参数为 N 时的叶片反射率; T N , a 是最大入射角 a 和叶片叶肉结构参数为 N 时的透射率。
SAIL模型是一个冠层二向反射率模型,即给定冠层结构参数和环境参数,可以计算任意太阳高度和观测方向的冠层反射率。其假设植物冠层是由方位随机分布的水平、均一及无限扩展的各向同性叶片组成的混合体,进而实现植被冠层反射率的模拟[15]。模型将叶片模型耦合到冠层模型中实现整个冠层的生化组分含量的反演[17]。简化公式如式（5）-（8）所示。
d E s / dx = k E s （5）
d E - / dx = - S E s + a E - - σ E + （6）
d E + / dx = S ' E s + σ E - - a E + （7）
d E o / dx = ω E s + v E - + u E + - k E o （8）
式中： k 为直射辐射的消弱系数; E s 为由上而下传输的直射辐射通量密度; a 为消光系数; σ 为背向散射系数;S为同向直射辐射的散射系数; S ′ 为背向直射辐射的散射系数; E o 为观测方向上的通量密度; E - 和 E + 代表向下或向上传输的辐射通量密度; ω 、 v 、 u 为由 E + 、 E - 、 E s 向观测方向上传输的辐射亮度的转化系数。模型通过解辐射传输方程（式（5）-（8））获得植被冠层反射率。

2.2 叶面积指数反演方法

PROSAIL模型的输入参数包括冠层生理生化参数、环境参数和其他参数3部分。这些参数的确定主要根据实地测量、实验室分析、成像时间以及观测条件进行取值。
将实地获取的生理生化、环境等参数输入PROSAIL模型中,通过模型的计算便得到植被冠层反射率,从而完成从地表植被理化、几何参数和光谱特性到植被冠层反射率获取的过程。而遥感影像通过辐射校正、几何校正和大气校正,得到地表植被冠层反射率,从而将遥感影像与植被参数LAI通过物理过程联系起来,完成模型模拟过程,流程如图1所示。
Fig. 1 Technical route

图1 技术路线

3 实验区叶面积指数反演

3.1 研究区概况

潭耒高速公路是京珠高速湘潭至耒阳高速公路段。南起于湘潭马家河,北至耒阳市陈家坪。于1997年7月潭耒高速公路开工建设,2000年12月26日建成通车,是贯通南北的重要交通枢纽工程。研究路段选在湘潭市郊外长12 km路段,该路段车流量较大,日均车流量在8万辆以上。实验区路段两侧植被覆盖度较大,植被类型主要为常绿阔叶林樟树、灌木丛等植被。路段两侧居民区较少,因而人类活动对该区域的植被生长状况影响不大。本文选择该路段两侧外各1.5 km内为研究区,研究区面积为37.00 km2。实验区如图2所示。
Fig. 2 Research area

图2 研究区

3.2 实验数据处理

本文采用5期美国陆地卫星（Landsat）TM数据（30 m × 30 m）,过境日期分别是2001年10月28日、2003年11月8日、2005年10月30日、2007年11月3日和2010年11月7日。遥感影像获取日期最早为10月28日,最晚为11月8日,时间差较小,可以排除时间差异对不同时期影像反演叶面积指数带来的影响。而且选取的遥感影像质量良好,图像清晰,无条带,云、雾对影像的影响较小。影像数据均摄于植被生长状况相同时期,可较好地对比分析公路运营后对路域植被生长状况的影响情况。
本文采用GIS专业软件裁剪出实验区,并利用遥感专业处理软件（ERDAS）对实验区进行标准化处理（云、水、裸地等地物的去除）。运用多项式方法和地面像控点（GCP）对影像进行几何校正处理,以消除影像的几何误差。为了使影像具有相同的几何精度,需将不同时期的影像配准到同一坐标系下。利用精度较高的FLAASH模型对遥感影像进行处理,消除大气产生的误差[18-19]。
Fig. 3 Simulated canopy reflectances under different LAIs

图3 不同LAI下模拟的冠层反射率

3.3 叶面积指数反演

通过对PROSAIL模型输入参数的敏感性分析,设置叶绿素步长为5,范围为35~70;叶面积指数步长为0.5,范围为0.5~6,并将影像对应时期野外采集的主要树种理化信息及LOPEX93数据库的相关数据输入PROSAIL模型。具体输入参数为：野外实测干物质平均含量为0.0134 μg·cm-2、叶片等效水厚度为0.0198 cm和平均叶倾角为40°,结构参数N是模型的假设参数,查阅相关文献确定该参数为1.44。通过TM影像的头文件可以获得方位角、太阳天顶角、观测天顶角和方位角等物理输入参数。结合模型与输入参数模拟了不同叶面积指数的冠层光谱反射率,模拟结果如图3所示。通过输入不同步长的叶绿素、叶面积指数参数,构建不同时相的LAI查找表。最后,对预处理后的不同时期遥感影像与对应查找表进行匹配,得到不同时期的叶面积指数,反演结果如图4所示。
Fig. 4 Pictures of LAI distribution in different years

图4 不同年份叶面积指数分级图

3.4 模型模拟结果验证

将已有研究区2010年11月7日实验叶面积指数数据作为验证数据。研究区内共有8个采样点,采用“星地同步”的方法,获取了实验区LAI数据。根据实验数据验证反演的结果,如图5所示。2010年11月7日叶面积指数反演的均方根误差为0.19,R2为0.89,反演精度较高。
Fig. 5 The inversion LAI and the measured LAI

图5 反演的LAI与实测LAI

4 实验结果与分析

根据气象局提供的资料,2001、2003、2005、2007和2010年的年降雨量为1340 mm左右,年均降雨量变化幅度在34 mm以内,平均温度为16.8 ℃,年均气温变化幅度在0.3 ℃以内,可以看出研究区不同年份同期气候差异不大,对植被生长变化的影响有限。

4.1 路域植被LAI变化的时间格局分析

将反演所得不同时相的叶面积指数图进行处理,分别求取不同时相的平均叶面积指数,结果如图6所示。从图6可看出,在2001-2005年,平均叶面积指数变化最大,叶面积指数从0.542下降到0.513,降幅达5.59%;而2005-2010年,平均叶面积指数变化较小,维持在0.515左右。由以上变化可看出,高速公路开通5年内,由于道路的开通,植被的生长状况逐年变差,之后,植被适应了环境,植被受影响相对变小。
Fig. 6 Changes of the average LAI onboth sides of the highway

图6 高速公路两侧植被平均LAI变化情况

4.2 路域植被LAI变化的空间格局分析

分别提取5个时相道路两侧0~100 m、0~200 m、0~300 m、0~400 m、0~500 m区域内的平均LAI,以式（9）计算不同区域内平均LAI的离散度。
f = ∑ i = 1 n ( x - x i ) 2 n （9）
式中： x 表示区域内5个时相评价LAI的算术平均值; x i 表示区域不同时相的平均LAI,依次计算5个时相的LAI离散度。从图7可看出,在道路300 m范围内的LAI离散度相对最大。因此,在对反演的不同年限叶面积指数图进行处理时,以300 m为区域,分别提取研究区高速公路两侧0~300 m、300~600 m、600~900 m、900~1200 m、1200~1500 m区域的影像,并求取不同区域内的叶面积指数平均值,结果如图8所示。
Fig. 7 Dispersion of LAI in different regions

图7 不同区域内LAI的离散度

Fig. 8 Changes of the average LAI indifferent regions of road

图8 道路两侧不同区域平均LAI变化情况

从图8可看出,高速公路开通后,2001-2005年,道路两侧0~300 m区域内平均叶面积指数变化较大,由0.581下降到0.418,下降了0.087,降幅达15.00%。在2005-2010年,平均叶面积指数反而上升,由0.418上升到0.488,上升了0.070。道路两侧300~600 m区域内,2001-2005年平均叶面积指数由0.542下降到0.510,下降了0.032,在降幅达5.93%。2005-2010年平均叶面积指数有所上涨,但涨幅不大。而在道路两侧600~1500 m区域内,2001-2010年平均叶面积指数几乎没有变化。道路两侧0~300 m区域的植被平均叶面积指数在2001-2005年明显降低,说明公路开通后汽车排放的有害气体对植被的生长带来了较大影响,使植被出现了胁迫生长,进而导致植被叶面积指数降低。而在2005-2010年,平均叶面积指数增加,但没超过2001年均值,说明植被在经过一段时间后适应了这种生长环境,叶面积指数没有继续减少。300~600 m区域的叶面积指数也在2001-2005年发生了变化,但没有0~300 m区域受到的影响大。叶面积指数在600~1500 m区域没有变化,说明汽车尾气对此区域植被影响较小。图9反映了高等级公路开通运营对道路两侧不同区域内的植被影响情况。
Fig. 9 Impact of highway on the vegetation from both sides

图9 高速公路对两侧植被影响情况

5 结束语

高速公路两侧植被环境监测对于气候变化、生态环境演变和全球碳循环研究具有重要意义。本文运用PROSAIL模型对高速公路两侧植被的叶面积指数进行定量反演,研究分析了道路两侧不同区域内的平均叶面积指数随着时间变化而变化的规律。实验研究表明,高速公路的营运对道路两侧 0~300 m区域影响最大,两侧300~600 m区域受影响居其次,而600~1500 m区域基本没有影响。路域植被在高速公路建成后前5年内受到的影响相对最大,之后植被逐渐适应这种胁迫环境,受到的影响也逐渐降低。本文采用的动态监测方法可操作性强,技术路线明确,在道路两侧植被环境评价中具有重要的实际应用价值,可为中国路域植被治理、修复工作提出有针对性的指导,从而促进中国高速公路的可持续发展。

The authors have declared that no competing interests exist.

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DOI

[2]
孟丹,李小娟,宫辉力,等.京津冀地区NDVI变化及气候因子驱动分析[J].地球信息科学学报. 2015,17(8):1001-1007.植被覆盖动态监测及与气候变化的响应,是陆地生态系统研究的重要内容.本文以2001-2013年间京津冀地区MOD 13A3月合成NDVI数据,结合生长季的降水和气温资料,运用偏相关和复相关分析、趋势分析方法,研究了该区域NDVI的变化特征和空间分布,以及其区域植被覆盖变化的气候驱动力.结果表明,该区域NDVI最大值在13a间缓慢增加,植被覆盖呈现改善趋势;NDVI和生长季降雨量及平均气温的平均偏相关系数分别为0.20和-0.14,表明在年际变化水平上,京津冀地区NDVI总体与降水量呈正相关,与平均气温呈负相关,且降水对NDVI的影响大于温度对NDVI的影响.对植被覆盖驱动分区得出,降水和气温驱动型占区域面积的5.68％;单独降水驱动型和气温驱动型分别占4.51％、0.18％;区域内植被覆盖变化主要受非气候因子驱动型为主,所占比例为89.63％,表明人类活动对植被变化的影响巨大.

DOI

[ Meng D, Li X J, Gong H L, et al. Analysis of spatial-temporal change of NDVI and its climatic driving factors in Beijing-Tianjin-Hebei metropolis circle from 2001 to 2013[J]. Journal of Geo-information Science, .2015,17(8):1001-1007. ]

[3]
姚延娟,陈良富,柳钦火,等.基于波谱知识库的MODIS叶面积指数反演及验证[J],遥感学报,2006,10(6):869-878.目前用物理模型反演叶面积指数普遍存在缺少先验知识的状况，如何获得准确的先验知识是遥感走向应用的一个关键环节。中国典型地物标准波谱数据库就是结合国家重大行业中的应用需求，研究制定地物波谱获取与分析的技术规范和数据标准，建立典型地物标准波谱数据库。从波谱数据库提取模型反演所需要的先验知识，实现了基于SAIL模型的MODIS数据（经过几何纠正与大气纠正）叶面积指数的反演。另外，基于TM数据，对MODIS混合像元进行了分解，用纯像元的叶面积指数与实测数据进行对比验证，同时，反演结果与NASA的LAI产品也进行了对比，结果表明基于波谱库的先验知识可以有效的提高叶面积指数的反演精度。

DOI

[ Yao Y J, Chen L F, Liu Q H, et al. Inversion and validation of leaf area index based on the spectral & knowledge database using MODIS data[J]. Journal of Remote Sensing, 2006,10(6):869-878. ]

[4]
吴彤,倪绍祥,李云梅.基于植被信息遥感反演的东亚飞蝗监测研究[J].地理与地理信息科学,2006,22(2):25-29.植被是东亚飞蝗发生和成灾的重要指示因子。运用遥感技术对植被生长进行监测,对东亚飞蝗的预测和防治具有重要意义。以河北省黄骅市为研究区,利用实地获取的植被冠层孔隙度数据反算的LAI数据以及Landsat-5 TM影像提取的各种VI数据,进行了LAI(LAI-2000改进型算法的反算结果)与TM影像上反演的VI之间的相关分析。结果表明,RDVI最适合反映研究区植被生长状况。分析RDVI与飞蝗发生面积的关系,发现两者呈负线性相关,即随着RDVI减小,飞蝗的发生面积呈线性增大。

DOI

[ Wu T, Ni S X, Li Y M.Research on the monitoring oriental migratory locust based on remote sensing retrieval of vegetation information[J]. Geography and Geo-Information Science, 2006,22(2):25-29. ]

[5]
王希群,马屡一,贾忠奎,等.叶面积指数的研究和应用进展[J].生态学杂志,2005,24(5):537-541.对叶面积指数(LAI)提出50多年来,在植物光合作用、蒸腾作用、联系光合和蒸腾的关系和构成生产力基础的研究,在林分、景观以及地区尺度上对碳、能量、水分通量的研究,借助遥感技术建立森林生态系统的生长模型以及研究森林生态系统的能量和水分交换等方面的研究和应用进展进行了综述.LAI作为进行植物群体和群落生长分析的一个重要参数,已在农业、果树业、林业以及生物学、生态学等领域得到广泛应用.

[ Wang X Q, Ma L Y, Jia Z K, et al. Research and application advances in leaf area index (LAI)[J]. Chinese Journal of Ecology, 2005,24(5):537-541. ]

[6]
张晓阳,李劲峰.利用垂直植被指数推算作物叶面积系数的理论模式[J].遥感技术与应用, 1995,10(3):13-18. 从作物冠层对光谱的反射特征出发，推导了叶面积系数ＬＡＩ的遥感估算模式，并利用水稻样点观测数据对模式进行了验证，取得了良好效果。

[ Zhang X Y, Li J F.The derivation of a reflectance model for the estimation of leaf area index using perpendicular vegetation[J]. Remote Sensing Technology and Application, 1995,10(3):13-18. ]

[7]
黄文江,王纪华,刘良云,等.冬小麦红边参数变化规律及其营养诊断[J].遥感技术与应用, 2003,8(4):206-211. 研究了冬小麦冠层光谱红边参数随作物生育期的变化规律,并进行了红边参数与各组分间的相关分 析,发现可利用用红边位置反演叶片可溶性糖和叶绿素含量,利用红边振幅反演叶片全氮含量,利用红谷反演叶面积指数.建立了基于红边参数的各组分的统计回归 模型,可为生产上利用遥感手段大面积、无破坏、及时评价冬小麦生长状态及营养诊断提供重要依据.

DOI

[ Huang W J, Wang J H, Liu L Y, et al. The red edge parameters diversification disciplinarian and its application for nutrition diagnosis[J]. Remote Sensing Technology and Application, 2003,8(4):206-211. ]

[8]
杨曦光,范文义,于颖.基于PROSPECT+SAIL模型的森林冠层叶绿素含量反演[J].光谱学与光谱分析,2010,30(11):3022-3026.森林冠层叶绿素含量直接反映着森林的健康和胁迫情况.叶绿素含量的准确估测,更是研究森林生态系统循环模型的关键.文章以PROSPECT+SAIL模型为基础,从物理机理角度反演森林冠层叶绿素含量.首先利用PROSPECT和SAIL模型模拟叶片水平和冠层水平的光谱,并建立叶片水平叶绿素含量的查找表反演叶片叶绿素含量,然后结合森林结构参数Leaf Atea Index(LAI)实现叶片尺度与冠层尺度叶绿素含量的转化,从Hyperion影像反演研究区域冠层水平叶绿素含量.结果表明,叶绿素含量的主要影响波段为400～900 nm;PROSPECT模型模拟的叶片光谱和SAIL模型模拟的冠层光谱均与实测光谱拟合效果较好,相对误差分别为7.06%,16.49%;LAI反演结果的均方根误差RMSE＝0.5426;利用PROSPECT+SAIL模型可以较好地反演森林冠层叶绿素含量,反演精度为77.02%.

DOI

[ Yang X G, Fan W Y, Yu Y.Estimation of forest canopy chlorophyll content based on PROSPECT and SAIL models[J]. Spectroscopy and Spectral Analysis, 2010,30(11):3022-3026. ]

[9]
颜春燕. 遥感提取植被生化组分信息方法与模型研究[D].北京:中国科学院研究生院,2003.

[ Yan C Y.Study on methods and models for vegetation biochemical information retrieval by remote sensing[D]. Beijing: Institute of Remote Sensing Applications, Chinese Academy of Sciences, 2003. ]

[10]
Hui F M, Tian Q J, Jin Z Y, Li H T.Research and quantitative analysis of the correlation between vegetation index and leaf area index[J]. Remote Sensing Information, 2003,2:10-13.Basing on the DN image, apparent reflectance image and atmospheric corrected reflectance image, the NDVI of fir forest from ETM + image over the north of Liping County in Guizhou Province we are calculated. It has been found that the NDVI based on apparent reflectance is 3 - 4 times larger than that based on the DN and the NDVI based on atmospheric corrected reflectance is 4 -5times larger than that based on the apparent reflectance. The research results have also showed that there is a good positive linear correlation between NDVI and ground truth LAI and the removal of influence of remote sensing sensor and atmospheric correction can increase the correlation coefficient. So quantitative analysis of remote sensing information is necessary.

[11]
骆社周,程峰,王方建,等.基于TM遥感数据的西藏林芝地区叶面积指数反演[J].遥感技术与应用,2012,2(5):740-745.叶面积指数(LAI)是分析冠层结构最常用的参数之一,它控制着植被的生物、物理过程,如光合、呼吸、蒸腾、碳循环和降水截获等。但是通过野外实测获取大面积的LAI比较困难,通过对西藏林芝地区的TM遥感数据进行处理获取各种植被指数,然后分别与实测LAI建立相应的回归关系,并对不同的回归模型进行分析找出相关性较好、误差较低的回归模型,最后利用该模型对林芝地区的叶面积指数进行制图。通过植被指数与实测LAI进行回归分析建立LAI估算模型,其决定系数最高为R2=0.653,具有较好的相关性。研究结果表明:TM遥感数据可以实现林芝区域LAI估算,能为生态环境研究提供数据支持。

[ Luo S Z, Chen F, Wang F J, et al. Leaf area index inversion based on TM in Linzhi, Tibet[J]. Remote Sensing Technology and Application, 2012,2(5):740-745. ]

[12]
Wu J J, Gao Z H, Li Z Y, et al. Estimation for parse vegetation information in desertification region based on Tiangong-1 hyperspectral image[J]. Spectroscopy and Spectral Analysis, 2014,34(3):751-756.In order to estimate the sparse vegetation information accurately in desertification region, taking southeast of Sunite Right Banner, Inner Mongolia, as the test site and Tiangong-1 hyperspectral image as the main data, sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation index(NDVI) and soil adjusted vegetation index(SAVI), combined with the field investigation data. Then the advantages and disadvantages between them were compared. Firstly, the correlation between vegetation indexes and vegetation coverage under different bands combination was analyzed, as well as the biomass. Secondly, the best bands combination was determined when the maximum correlation coefficient turned up between vegetation indexes (VI) and vegetation parameters. It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7, while that of SAVI could nearly reach 0.8. The center wavelength of red band in the best bands combination for NDVI was 630nm, and that of the near infrared(NIR) band was 910 nm. Whereas, when the center wavelength was 620 and 920 nm respectively, they were the best combination for SAVI. Finally, the linear regression models were established to retrieve vegetation coverage and biomass based on Tiangong-1 VIs. of all models was more than 0.5, while that of the model based on SAVI was higher than that based on NDVI, especially, the of vegetation coverage retrieve model based on SAVI was as high as 0.59. By intersection validation, the standard errors RMSE based on SAVI models were lower than that of the model based on NDVI. The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively, and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region.

DOI PMID

[13]
Jégo G, Elizabeth Pattey E, Liu J G.Using leaf area index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops[J]. Field Crops Research, 2012,131:63-74.Yield and biomass predictions were greatly improved through the re-initialization of seeding date, seeding density and field capacity. Almost no bias was observed, and the relative root mean square error (RMSE%) was 13% for yield and 23% for biomass (versus 22% and 44% without optimization). The improvement in model predictions was particularly noticeable in the case of water-stress conditions or a deficit of growing degree-days, indicating that the method is sensitive to climate variability. The results were very close to the yield and biomass predictions (i.e., RMSE% of 11% for yield and 17% for biomass) obtained with actual management and soil properties. Most of the improved predictions were associated with re-initialization of the seeding date. When only two LAI values were used to re-initialize the seeding date, the RMSE% values for yield and biomass predictions were 15% and 27%, respectively. Finally, we showed that overlaying field boundaries onto soil texture was sufficient to accurately predict yields. The addition of a third layer, based on LAI-homogeneous zones, did not improve yield predictions because the model was not able to capture some of the small within-field yield variations (<0.5聽t聽ha 鈭1 ).

DOI

[14]
Croft H, Chen J M, Zhang Y Q, et al. Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data[J]. Remote Sensing of Environment, 2013,133:128-140.Foliar chlorophyll content in forested ecosystems plays a fundamental role in plant photosynthesis and can indicate vegetation stress and disturbance. However, leaf chlorophyll retrieval is complicated as canopy reflectance in the visible and near-infrared wavelengths is affected by confounding effects not only from leaf pigment concentration but also leaf area index (LAI), canopy architecture, illumination and viewing geometry and understory vegetation. Unlike empirical indices, which are often developed at leaf-level and can be species, site and time specific, a process modelling approach can account for the variation of other variables affecting canopy reflectance; therefore providing a more accurate estimate of chlorophyll content over multiple vegetation species, time-frames and across broader spatial extents. This study used a linked canopy (4-Scale) and leaf (PROSPECT) modelling approach to investigate the ability of radiative transfer models to estimate foliar chemistry for different vegetation types (broadleaf and needle leaf) from optical remote sensing data. Coniferous and deciduous sites were selected in Ontario, Canada, representing different dominant vegetation species, including black spruce ( Picea mariana ), sugar maple ( Acer saccharum ) and trembling aspen ( Populus tremuloides ), and a variety of canopy closures and structures. These sites were sampled over multiple time-frames to collect ground data including leaf area index, leaf reflectance spectra (400鈥2500nm) and laboratory leaf chlorophyll content. Canopy reflectance data were acquired from the Compact Airborne Spectrographic Imager (CASI), Landsat 5 TM and Medium Resolution Imaging Spectrometer (MERIS). The model results show that leaf chlorophyll content derived from satellite images demonstrates a good relationship with measured leaf chlorophyll content, with validation results of R 2 =0.62; p<0.001 (MERIS) and R 2 =0.65; p<0.001 (Landsat 5 TM), and a strong linearity with negligible systematic bias. CASI data gave a regression coefficient of R 2 =0.41 (p<0.05) on a reduced dataset. This research provides theoretical and operational bases for the future retrieval of leaf chlorophyll content across different vegetation species, canopy structures and over broad spatial extents; crucial characteristics for inclusion in photosynthesis and carbon cycle models.

DOI

[16]
Zhang Y Q, Chen J M, Miller J R, et al. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery[J]. Remote Sensing of Environment, 2008,112(7):3234.ABSTRACT Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25&ndash;4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical&ndash;optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%&ndash;1.07% and 0.36%&ndash;1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R2 = 0.89 to R2 = 0.97 and discrepancies of 0.02%&ndash;3.63% and 0.24%&ndash;7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R2 = 0.47, RMSE = 4.34 渭g/cm2, and jackknifed RMSE of 5.69 渭g/cm2 for needle chlorophyll content ranging from 24.9 渭g/cm2 to 37.6 渭g/cm2. The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R2 = 0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index.

DOI

[17]
蔡博峰,绍霞.基于PROSPECT+SAIL模型的遥感叶面积指数反演[J].国土资源遥感,2007,72(2):39-43.以PROSPECT+SAIL模型为基础,从物理机理角度反演植 被叶面积指数(LAI).首先,通过FLAASH模型进行大气校正,使得图像像元值表达植被冠层反射率;然后,根据LOPEX 93数据库和JHU光谱数据库选择植物生化参数和光谱数据,以PROSPECT模型模拟出的植物叶片反射率和透射率作为SAIL模型的输入参数,得到植被 冠层反射率,将结果与遥感影像的植被冠层反射率对应,回归出植被LAI;最后,以地面实测数据对遥感反演数据进行验证,并分析了误差的可能来源.

DOI

[ Cai B F, Shao X.Leaf area index retrieval based on remotely sensed data and PROSPECT+SAIL model[J]. Remote Sensing for Land & Resources, 2007,72(2):39-43. ]

[18]
李静,阎广建,穆西晗.面向LAI反演的参数化SAILH模型[J].遥感学报,2010,14(6):1189-1195.建立了一种基于植被冠层辐射传输模型SAILH的参数化模型。该模型首先对SAILH模型中用到的9个中间变量的计算过程进行简化，然后用一个明确的表达式计算光照冠层的单次散射贡献。分别用模拟数据和2008年黑河地区星一机．地同步实验中获取的地面测量数据对该参数化模型的反演精度和效率进行了评价。评价结果表明该参数化模型能在保证反演精度的基础上极大的提高反演效率；利用模拟数据进行的模型稳定性评价表明，参数化模型的稳定性优于SAILH模型。

DOI

[ Li J, Yan G J, Mu X H.A parameterized SAILH model for LAI retrieval[J]. Journal of Remote Sensing, 2010,14(6):1189-1195. ]

[19]
郭云开,曾繁.基于FLAASH与QUAC模型的SPOT5影像大气校正比较[J].测绘通报,2012(11):21-23.卫星遥感影像的大气校正是定量遥感研究的前提与难点之一,大气校正有多种方法和模型。采用FLAASH与QUAC模型对覆盖长株潭地区的SPOT 5遥感影像进行大气校正,进而对校正前后的影像进行视觉、地物光谱曲线对比分析。结果表明,两种模型有其特定的适用范围,均能基本消除大气的影响,能较好地恢复各类地物光谱的典型特征;采用FLAASH模型的精度较QUAC模型的精度高;应用QUAC模型较FLAASH简便,它对输入参数和仪器标定精度的依赖性小。

[ Guo Y K, Zeng F.Atmospheric correction comparison of SPOT 5 image based on FLAASH and QUAC model[J]. Bulletin of Surveying and Mapping, 2012(11):21-23. ]

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