基于TAVI的山区毛竹林LAI反演分析

doi:10.3724/SP.J.1047.2015.00500

地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (4): 500-504.doi: 10.3724/SP.J.1047.2015.00500

• • 上一篇

基于TAVI的山区毛竹林LAI反演分析

江洪¹(), 张兆明², 汪小钦¹, 何国金²

1. 福州大学空间数据挖掘与信息共享教育部重点实验室福建省空间信息工程研究中心, 福州 350002
2. 中国科学院遥感与数字地球研究所,北京 100094

收稿日期:2014-02-20 修回日期:2014-04-13 出版日期:2015-04-10 发布日期:2015-04-10
作者简介:
作者简介：江洪(1975-),男,福建永安人,博士,副研究员。研究方向为遥感技术与应用及电子政务等。E-mail:jh910@fzu.edu.cn
基金资助:
国家科技支撑计划项目“南方红壤水土流失综合监测”(2013BAC08B01);福建省自然科学基金项目“基于地形调节植被指数的毛竹林叶面积指数遥感反演研究”(2011J01267)

Bamboo Forest LAI Retrieval and Analysis in Mountainous Area Based on TAVI

JIANG Hong^1,^*(), ZHANG Zhaoming², WANG Xiaoqin¹, HE Guojin²

1. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Spatial Information Research Centre of Fujian Province, Fuzhou University, Fuzhou 350002, China
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

Received:2014-02-20 Revised:2014-04-13 Online:2015-04-10 Published:2015-04-10
Contact: JIANG Hong E-mail:jh910@fzu.edu.cn
About author:
*The author: SHEN Jingwei, E-mail:jingweigis@163.com

摘要/Abstract

摘要：

本文采用地形调节植被指数（TAVI）,以RapidEye高分辨率多光谱遥感影像为数据源,对福建省永安市毛竹林山区进行了叶面积指数（LAI）地面实测、遥感建模及反演分析。通过TAVI与归一化植被指数（NDVI）、比值植被指数（RVI）的对比研究,结果表明：（1）毛竹林实测LAI与TAVI、NDVI和RVI线性回归的决定系数（R²）分别为0.6085、0.3156和0.4092,最佳非线性回归的R²分别提高到0.6624、0.5280和0.6497。LAI与NDVI或RVI非线性（U曲线）模型可以很好地解释LAI-VI的散点分布规律,但难以解决LAI-VI间因地形影响导致的“同物异谱”和“异物同谱”问题,因此,在山区大面积推广应用需慎重。（2）通过实测LAI的验证表明,LAI-TAVI回归模型可有效避免因地形影响导致的“同物异谱”和“异物同谱”问题。TAVI具有良好的削减地形影响作用,可用于山区植被LAI的遥感反演。

关键词: 叶面积指数, 地形调节植被指数, 同物异谱

Abstract:

As one of the key biophysical parameters in the bamboo forest evaluation, the leaf area index (LAI) retrieval from remote sensing data has always been challenged by the topographic effect in mountainous area. In this paper, the topographic-adjusted vegetation index (TAVI) was proposed to eliminate the topographic influence for the bamboo forest LAI derivation in Yongan city, Fujian, based on the Rapideye high spatial resolution satellite imagery. Normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were also utilized in the statistical analysis with respect to LAI for comparison with TAVI. The regression results indicate: (1) LAI is more linearly correlated with TAVI than NDVI or RVI. R² (coefficient of determination) of the linear regression between LAI and TAVI, NDVI, RVI are 0.6085, 0.3156, and 0.4092 respectively. For the optimal non-linear fitting model, the corresponding R² had increased to 0.6624, 0.5280 and 0.6497 respectively. Although the quadratic polynomial regression model can well explain the relationship between LAI and NDVI or RVI, it can hardly illustrate the typical phenomenon of "same object with different spectra" and "different objects with same spectrum" that resulted from topographic effect. (2) Both the LAI-TAVI regression models and the in-situ measurement demonstrate that the proposed method can effectively avoid the above problems with a correlation coefficient (r) of 0.7674 between in-situ and the simulated LAI, and a RMSE of 0.3403. In conclusion, TAVI shows good capability to alleviate the topographic effect and can be effectively applied to the LAI retrieval of the bamboo forest in mountainous area.

Key words: leaf area index (LAI), topographic-adjusted vegetation index (TAVI), same object with different spectra

江洪, 张兆明, 汪小钦, 何国金. 基于TAVI的山区毛竹林LAI反演分析[J]. 地球信息科学学报, 2015, 17(4): 500-504.DOI:10.3724/SP.J.1047.2015.00500

JIANG Hong,ZHANG Zhaoming,WANG Xiaoqin,HE Guojin. Bamboo Forest LAI Retrieval and Analysis in Mountainous Area Based on TAVI[J]. Journal of Geo-information Science, 2015, 17(4): 500-504.DOI:10.3724/SP.J.1047.2015.00500

图/表 7

图1

表1

植被指数"

植被指数	公式	备注
归一化差值植被指数（NDVI）	$NDVI = B nir - B r B nir + B r$	Rouse等(1974)^[19]
比值植被指数（RVI）	$RVI = B nir B r$	Jordan等(1969)^[20]
地形调节植被指数（TAVI）	$TAVI = B nir - B r B nir + B r + f Δ M r - B r B r$	江洪等(2010, 2011)^[17-18]
	$SVI = M r - B r B r$

表1

表2

图2

表3

图3

图4

参考文献 20

[1]	Chen J, Black T.Defining leaf area index for non-flat leaves[J]. Plant, Cell and Environment, 1992,15(4):421-429.
[2]	江洪,汪小钦,孙为静.福建省森林生态系统NPP的遥感模拟与分析[J].地球信息科学学报,2010,12(4):580-586.
[3]	Rangaswamy M, Vyjayanthi N, Chandra S J.Estimation of LAI and above-ground biomass in deciduous forests: Western Ghats of Karnataka, India[J]. International Journal of Applied Earth Observation and Geoinformation, 2008,10(2):211-219.
[4]	Gerald M, Dietrich H, Christoph L.Altitudinal change in LAI and stand leaf biomass in tropical montane forests: a transect study in Ecuador and a pan-tropical meta-analysis[J]. Ecosystems, 2007,10(6):924-935.
[5]	郑金双,曹永慧,代全林,等.茶秆毛竹林叶面积指数与生物量关系的研究[J].竹子研究汇刊,2001,20(1):53-57.
[6]	潘寅辉,虞敏之,胡建军,等.四季竹叶面积指数与竹笋产量的关系[J].西南林学院学报,2006,26(5):21-23.
[7]	王超,蒙朝阳,陈洪,等.小蓬竹的叶面积指数测定[J].山地农业生物学报,2007,26(3):277-279.
[8]	郑进煊,董文渊,陈冲,等.海子坪天然毛竹林叶面积指数及秆形结构[J].林业科技开发,2009,23(3):44-48.
[9]	Zhang Z, He G, Wang X, et al.Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery[J]. International Journal of Remote Sensing, 2011,32(19):5365-5379.
[10]	石月婵,杨贵军,冯海宽,等.北京山区森林叶面积指数季相变化遥感监测[J].农业工程学报,2012,28(15):133-139.
[11]	Tian Q, Luo Z, Chen J, et al.Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images[J]. Journal of Environmental Management, 2007,85:624-627.
[12]	王李娟,牛铮,侯学会,等.基于CHRIS数据的新型植被指数的LAI估算研究[J].光谱学与光谱分析,2013,33(4):1082-1086.
[13]	段四波,阎广建,穆西晗,等.基于DEM的山区遥感图像地形校正方法[J].地理与地理信息科学,2007,23(6):18-22.
[14]	高永年,张万昌.遥感影像地形校正研究进展及其比较实验[J].地理研究,2008,27(2):467-477.
[15]	董德进,周国模,杜华强,等.6种地形校正方法对雷竹林地上生物量遥感估算的影响[J].林业科学,2011,47(12):1-8.
[16]	刘玉安,黄波,易成功,等.基于地形校正的植被净初级生产力遥感模拟及分析[J].农业工程学报,2013,29(13):130-141.
[17]	江洪,毛政元,汪小钦.地形调节植被指数及其在森林动态监测中的应用[J].北京林业大学学报,2011,33(5):8-12.
[18]	江洪,汪小钦,吴波,等.地形调节植被指数构建及在植被覆盖度遥感监测中的应用[J].福州大学学报(自然科学版),2010,38(4):527-532.
[19]	Rouse J, Haas J, Shell R, et al.Monitoring vegetation systems in the Great Plains with ERTS[C]. Third Earth Resources Technology Satellite-1 Symposium, 1, 10-14 December, Washington, D.C.: NASA Scientific and Technical Information Office, 1972:309-317.
[20]	Jordan C.Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 1969,50:663-666.

植被指数	斜率（k）	截距（d）	相关系数（r）
NDVI	0.6060	0.3323	0.5774
RVI	0.6677	0.1584	0.6196
TAVI	-0.0442	0.6617	0.0044

基于TAVI的山区毛竹林LAI反演分析

Bamboo Forest LAI Retrieval and Analysis in Mountainous Area Based on TAVI

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回归类型	植被指数	模型	R²	F（F_0.05=3.9）
线性	NDVI	y=3.5554x+0.5478	0.3156	5.53
	RVI	y=2.6755x+2.1746	0.4092	8.31
	TAVI	y=20.252x-13.058	0.6085	18.65
非线性	NDVI	y=39.598x²-58.451x+24.088	0.5280	13.43
	RVI	y=20.591x²-15.73x+5.6826	0.6497	22.26
	TAVI	y=9.1868x^4.8818	0.6624	18.75

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