Comparison of Topographic Correction Methods

Richter, Rudolf; Kellenberger, Tobias; Kaufmann, Hermann

doi:10.3390/rs1030184

Cited by 198 publications

(134 citation statements)

References 8 publications

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“…Some common Lambertian and non-Lambertian reflection models are presented in Table 2. Law and Nichol's study [21] shows that all methods can be applied to surface reflectance (after atmospheric correction in this paper) or the apparent or top-of-atmosphere (TOA) reflectance. Table 2.…”

Section: Topographic Correction Modelsmentioning

confidence: 99%

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

et al. 2014

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Abstract: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 R 2 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.

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Section: Topographic Correction Modelsmentioning

confidence: 99%

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

et al. 2014

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“…While different semi-empirical methods have been found to have similar results (Meyer et al 1993;Richter et al 2009), there have been general problems such as overcorrection of extreme slopes (Riaño et al, 2003), low correlation between spectral data and illumination angles (Bishop and Colby 2002;Carpenter et al 1999), and unsatisfactory corrections. Among the modifications are slopespecific corrections (Ekstrand 1996;Nichol et al 2006), slope-smoothing (Kobayashi and Sanga-Ngoie 2009), and use of different models for the visible versus infrared bands (Richter et al 2009;Vincini and Frazzi 2003). Some studies have touched upon problems with the empirical parameter, such as Ekstrand (1996) who found an increased k was needed for better correction of the near-infrared (NIR) band, Civco (1989) who found difficulties in normalizing the NIR band in particular, and Gu and Gillespie (1998) who found c-parameters to be so large in general that they "lack an exact physical explanation".…”

Section: Introductionmentioning

confidence: 99%

C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

Reese

Olsson

2011

Remote Sensing of Environment

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Semi-empirical topographic normalization methods (e.g., C-correction) have been widely used to correct illumination differences in optical satellite data. The objective of this study was to examine the precision and accuracy of the C-correction's empirical parameter, c, as a function of the sample from which it was derived. Three sampling methods were compared: a random sample, a sample stratified on north and south aspects, and a sample stratified by cosine of the solar incidence angle, i. In the latter, power allocation was used to determine the quantity of observations for each stratum. Four overlapping satellite images were used (two Landsat 5 TM and two SPOT 5 HRG) with different acquisition dates and large solar zenith angles over an alpine region in Sweden. The sample stratified by cosine of i produced c with the highest precision from repeated trials and had coefficients of determination (R 2 ) twice as high as those from the other sampling methods. Use of power allocation in the cosine of i stratified sample enabled better representation of spectral variability; this was particularly important for the NIR band where the outcome of c differed according to sampling method. Evaluations using t-tests and classification accuracy showed that c derived from the cosine of i stratified sample correctly normalized a larger percentage of the evaluation data. The distribution of cosine of i in the study area, the spectral variability and vegetation types exert influences to consider when sampling to derive c. Although sampling was restricted to alpine vegetation only, some

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“…Hence identifying a suitable topographic correction method is still an unresolved problem. Several studies (Riano et al, 2003;Richter et al, 2009) have assessed different topographic corrections on multispectral data for vegetation studies; however a few studies have comparatively assessed the impact of different topographic corrections on multispectral data in the tropical or subtropical forest conditions. Furthermore, a quantitative assessment in correction accuracy for different topographic corrections on biophysical properties of multispectral remotely sensed data in structurally complex forests is limited.…”

Section: ______________________mentioning

confidence: 99%

Impact of Different Topographic Corrections on Prediction Accuracy of Foliage Projective Cover (Fpc) in a Topographically Complex Terrain

Ediriweera

Pathirana

Danaher³

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Quantitative retrieval of land surface biological parameters (e.g. foliage projective cover [FPC] and Leaf Area Index) is crucial for forest management, ecosystem modelling, and global change monitoring applications. Currently, remote sensing is a widely adopted method for rapid estimation of surface biological parameters in a landscape scale. Topographic correction is a necessary preprocessing step in the remote sensing application for topographically complex terrain. Selection of a suitable topographic correction method on remotely sensed spectral information is still an unresolved problem. The purpose of this study is to assess the impact of topographic corrections on the prediction of FPC in hilly terrain using an established regression model. Five established topographic corrections [C, Minnaert, SCS, SCS+C and processing scheme for standardised surface reflectance (PSSSR)] were evaluated on Landsat TM5 acquired under low and high sun angles in closed canopied subtropical rainforest and eucalyptus dominated open canopied forest, north-eastern Australia. The effectiveness of methods at normalizing topographic influence, preserving biophysical spectral information, and internal data variability were assessed by statistical analysis and by comparing field collected FPC data. The results of statistical analyses show that SCS+C and PSSSR perform significantly better than other corrections, which were on less overcorrected areas of faintly illuminated slopes. However, the best relationship between FPC and Landsat spectral responses was obtained with the PSSSR by producing the least residual error. The SCS correction method was poor for correction of topographic effect in predicting FPC in topographically complex terrain.

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Comparison of Topographic Correction Methods

Cited by 198 publications

References 8 publications

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

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

C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter

Impact of Different Topographic Corrections on Prediction Accuracy of Foliage Projective Cover (Fpc) in a Topographically Complex Terrain

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