4种常用植被指数的地形效应评估教程

1007-4619 (2013) 01-0210-25 Journal of Remote Sensing 遥感学报

Received: 2012-01-05; Accepted: 2012-05-17

Foundation: National Basic Research Program of China (973 Program) (No. 2010CB833503, 2010CB950702); National High Technology Research and Development Program of China (863 Program) (No. 2009AA122103); Priority Academic Program Development of Jiangsu Higher Education Institutions

First author biography: ZHU Gaolong (1974— ), male, associate professor, Ph.D. His research interests are retrieving biophysical parameters of vege-tation covers using multi-angle remote sensing data. E-mail: zhugaolong@

Corresponding author biography: JU Weimin (1963— ), male, professor, His research interests are ecology environmental remote sensing and global change. E-mail: juweimin@

Evaluation of topographic effects on four commonly used

vegetation indices

ZHU Gaolong 1, 2, LIU Yibo 1, JU Weimin 1, CHEN Jingming 1, 3

1. International Institute for Earth System Science , Nanjing University , Nanjing 210093, China ;

2. Department of Geography, Minjiang University , Fuzhou 350108, China ;

3. Department of Geography , University of Toronto , Toronto , Ontario , Canada M5S 3G3

Abstract: Vegetation Indices (VIs) derived from remotely sensed data have been developed to monitor the Earth’s vegetation

cover. However, the topographic influence on VIs is an inevitable issue and is usually neglected in their large scale applications. In this study, the topographic effects on four commonly used vegetation indices, including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Reduced Simple Ratio (RSR), and Modified Normalized Difference Vegetation Index (MNDVI), derived from Landsat TM data over a mountainous forest area are evaluated. Two simple methods, the cosine correction and C-correction models, with different treatments of the influence of the diffused irradiance on reflectance, are used to remove the topographic effects on selected VIs. The results indicate that the reflectance in the Near Infrared (NIR) and Short Wave Infrared (SWIR) bands are more sensitive to topographical variations than that in the red band. Diffused radiance from the sky in the red band can moderate the variations of red band reflectance with topography, while this moderation is weak in the NIR and SWIR bands. The topography affects strongly vegetation indices which are not expressed as band ratios, such as RSR and MNDVI, resulting in negative biases on Sun-facing slopes and positive biases on Sun-backing slopes. As the slope increases, these biases increase rapidly. Therefore, the topographic effects should be carefully removed before using these non-band-ratio vegetation indices for vegetation parameter retrieval. Vegetation indices which are expressed as band ratios, such as SR, NDVI, can greatly reduce the noise caused by topographical variations. However, these indices still include significant topographic ef-fects on steep slopes. SR is more sensitive to topographical variations on steep slopes than NDVI. The C-correction model is much better than the cosine correction model in removing topographic effects on VIs, especially on steep slopes. Key words: vegetation index, topographic effect, topographic correction, band ratio CLC number: TP702 Document code: A

1 INTRODUCTION

Vegetation Indices (VIs) derived from remotely sensed data have been widely used for monitoring the Earth’s vegetation at local, regional, continental, and global scales. The VIs have been proved to be better than a single spectral band for estimating the biophysical parameters of vegetation, including leaf area index (LAI), fractional vegetation cover, biomass, and photosynthetic activity (Clevers, 1989; Myneni & Williams, 1994; Chen, et al., 2006). In addition to vegetation changes, there are a number of factors that also influence VIs, including soil background, atmospheric conditions, topography, illumination and viewing geometry, and sensor calibration (LePrieur, et al., 1994; Chen, 1996). These factors very often cause unknown noises in VIs and impact the effectiveness of their applications. A perfect VI should enhance its sensitivity to vegetation change and minimize the noises caused by other factors. Several Soil Adjusted Vegetation Index (SAVI) family indices have been proposed to reduce the influence of soil background (Huete, 1988; Baret & Guyot, 1991; Qi, et al., 1994; Gilabert, et al., 2002). Modified Normalized Difference Vegetation Index (MNDVI) and Reduced Simple Ratio (RSR) which combine the reflectances in the Red, Near Infrared (NIR), and Short Wave Infrared (SWIR) bands are able to reduce the background effects (Nemani, et al., 1993; Brown, et al., 2000). Global Environment Monitoring Index(GEMI) is designed to minimize atmospheric noise (Pinty & Verstrate, 1992). Enhanced Vegetation Index (EVI) can reduce both the effects of atmospheric condition and soil background (Liu &

Citation format: Zhu G L, Liu Y B, Ju W M, Chen J M. 2013. Evaluation of topographic effects on four commonly used vegetation indices. Journal of Remote Sensing, 17(1): 210–234