Topographic Correction of ZY-3 Satellite Images and Its Effects on Estimation of Shrub Leaf Biomass in Mountainous Areas

The availability of ZY-3 satellite data provides additional potential for surveying, mapping, and quantitative studies. Topographic correction, which eliminates the terrain effect caused by the topographic relief, is one of the fundamental steps in data preprocessing for quantitative analysis of vegetation. In this paper, we rectified ZY-3 satellite data using five commonly used topographic correction models and investigate their impact on the regression estimation of shrub forest leaf biomass obtained from sample plots in the study area. All the corrections were assessed by means of: (1) visual inspection (2) reduction of the standard deviation (SD) at different terrain slopes (3) correlation analysis of different correction results. Best results were obtained from the Minnaert+SCS correction, based on the non-Lambertian reflection assumption. Additional analysis showed that the coefficient correlation of the biomass fitting result was improved after the Minnaert+SCS correction, as well as the fitting precision. The R2 has increased by 0.113 to reach 0.869, while the SD (standard deviation) of the biomass dropped by 21.2%. Therefore, based on the facts, we conclude that in the region with large topographic relief, the topographical correction is essential to the estimation of the biomass.

[1]  Guoqing Sun,et al.  Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data , 2010 .

[2]  D. Civco Topographic normalization of landsat thematic mapper digital imagery , 1989 .

[3]  Trevor Moffiet,et al.  Evaluation of Different Topographic Corrections for Landsat TM Data by Prediction of Foliage Projective Cover (FPC) in Topographically Complex Landscapes , 2013, Remote. Sens..

[4]  Emilio Chuvieco,et al.  Aboveground biomass assessment in Colombia: a remote sensing approach. , 2009 .

[5]  C. Justice,et al.  The topographic effect on spectral response from nadir-pointing sensors , 1980 .

[6]  N. Goel,et al.  Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation , 1994 .

[7]  P. Teillet,et al.  On the Slope-Aspect Correction of Multispectral Scanner Data , 1982 .

[8]  Craig A. Coburn,et al.  SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[9]  S. Ekstrand,et al.  Landsat TM-based forest damage assessment : correction for topographic effects , 1996 .

[10]  Wanchang Zhang,et al.  A simple empirical topographic correction method for ETM+ imagery , 2009 .

[11]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[12]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[13]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[14]  H. Du,et al.  Pixel-based Minnaert correction method for reducing topographic effects on a landsat 7 ETM+ image , 2008 .

[15]  Steven M. Manson,et al.  A comparison of illumination geometry-based methods for topographic correction of QuickBird images of an undulant area , 2008 .

[16]  John R. Dymond,et al.  Correction of the topographic effect in remote sensing , 1999, IEEE Trans. Geosci. Remote. Sens..

[17]  A. Gillespie,et al.  Topographic Normalization of Landsat TM Images of Forest Based on Subpixel Sun–Canopy–Sensor Geometry , 1998 .

[18]  J. Estornell,et al.  Estimation of biomass and volume of shrub vegetation using LiDAR and spectral data in a Mediterranean environment , 2012 .

[19]  Simplification and Modification of a Physical Topographic Correction Algorithm for Remotely Sensed Data , 2008 .

[20]  Javier Estornell,et al.  Estimation of shrub biomass by airborne LiDAR data in small forest stands , 2011 .

[21]  The study of vegetation biomass inversion based on the HJ satellite data in Yellow River wetland , 2013 .

[22]  Massimo Vincini,et al.  An empirical topographic normalization method for forest TM data , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[23]  C. Conese Topographic normalization of TM scenes through the use of an atmospheric correction method and digital terrain model , 1993 .

[24]  Anthony Yeh,et al.  Inventory of mangrove wetlands in the Pearl River Estuary of China using remote sensing , 2006 .

[25]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[26]  Guo Wei-hua Foliar phenotypic plasticity of a warm-temperate shrub,Vitex negundo var.heterophylla,to different light environments in the field , 2011 .

[27]  Liu Ping-xiang An Improved Topographic Correction Approach for Satellite Image , 2005 .

[28]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[29]  J. Chen Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[30]  R. Crippen Calculating the vegetation index faster , 1990 .

[31]  Manfred Ehlers,et al.  Photogrammetric Engineering and Remote Sensing , 2007 .

[32]  Emilio Chuvieco,et al.  International Journal of Applied Earth Observation and Geoinformation , 2011 .

[33]  J. Nichol,et al.  Topographic correction for differential illumination effects on IKONOS satellite images , 2004 .

[34]  D. Reeder Topographic correction of satellite images: Theory and application , 2002 .

[35]  Hermann Kaufmann,et al.  Comparison of Topographic Correction Methods , 2009, Remote. Sens..

[36]  Zhang Wan-chang Comparison test and research progress of topographic correction on remotely sensed data , 2008 .

[37]  N. H. Brogea,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2022 .

[38]  T. Lin,et al.  The Lambertian assumption and Landsat data. , 1980 .