Multitemporal evaluation of topographic normalization methods on deciduous forest tm data

Vincini, Massimo; Frazzi, Ermes

doi:10.1109/tgrs.2003.817416

Cited by 33 publications

(21 citation statements)

References 7 publications

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“…TM1 was the only band in the study of Ekstrand (2006) where topography had no clear effect. The c-correction technique and other topographic correction techniques have proven to effectively remove the trend of increasing reflectance with increasing illumination (Meyer et al, 1993;Vincini and Frazzi, 2003;Huang et al, 2008;Soenen et al, 2008;Wu et al, 2008). This characteristic feature of topographic correction technique remained unaffected in our modified version of the c-correction method.…”

Section: Influence On Individual Band Reflectancementioning

confidence: 90%

“…They influence any image processing technique based on individual band reflectance, such as Land Use Land Cover (LULC) classifications (Bishop and Colby, 2002;Riano et al 2003;Mitri and Gitas, 2004). Hence, a range of topographic normalization techniques have been developed with the prime goal to detrend the illumination-reflectance relationship (Teillet et al, 1982;Civco, 1989;Vincini and Frazzi, 2003;Kobayashi and Sanga-Ngoie, 2008). Topographic effects in ratioimages based analysis, however, are assumed to be minimal (Song and Woodcock, 2003) and are not considered in most studies using the NBR to assess fire severity.…”

Section: Introductionmentioning

confidence: 99%

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Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

Veraverbeke¹,

Verstraeten²,

Lhermitte³

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

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The influence of illumination effects on the optimality of the dNBR (differenced Normalized Burn Ratio) was evaluated for the case of the 2007 Peloponnese (Greece) wildfires using a pre/post-fire Landsat TM (Thematic Mapper) image couple. Well illuminated pixels (south and south-east facing slopes) exhibited more optimal displacements in the bi-spectral feature space than more shaded pixels (north and north-west exposed slopes). Moreover, pixels experiencing small image-to-image differences in illumination obtained a higher optimality than pixels with a relatively large difference in illumination. To correct for illumination effects, the c-correction method and a modified c-correction technique were applied. The resulting median dNBR optimality of uncorrected, c-corrected and modified c-correction data was respectively 0.58, 0.60 and 0.71 (differences significant for p<0.001). The original ccorrection method improved the optimality of badly illuminated pixels while deteriorating the optimality of well illuminated pixels. In contrast, the modified c-correction technique improved the optimality of all the pixels while retaining the prime characteristic of topographic correction techniques, i.e. detrending the illumination-reflectance relationship. For a minority of the data, for shaded pixels and/or pixels with a high image-to-image difference in illumination, the original c-correction outperformed the modified c-correction technique. In this study conducted in rugged terrain and with a bi-temporal image acquisition scheme that substantially deviated from the ideal anniversary date scheme the modified ccorrection technique resulted in a more reliable change detection.

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Section: Influence On Individual Band Reflectancementioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

Veraverbeke¹,

Verstraeten²,

Lhermitte³

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

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“…Using this sample, the c-parameters can be determined and the Ccorrection applied to the corresponding image subset. This strategy for determining the empirical c-parameter --based on a sample stratified by cosine of i and with a power allocation of observations --may also be useful in determination of other empirical parameters such as the Minnaert constant k (Smith et al 1980), the b of b-correction (Vincini and Frazzi 2003), and C in SCS+C (Soenen et al 2005). …”

Section: Summary Of Suggested Methods To Determine the Empirical Parammentioning

confidence: 99%

“…Unequal variance in the data (in addition to non-linearity) is an issue occasionally reported in other topographic normalization studies, and in some cases has been addressed by linearizing the spectral data (Vincini and Frazzi 2003). The cause of unequal variance and non-linearity should be identified, however.…”

Section: Importance Of Vegetation Type Specific Normalizationmentioning

confidence: 98%

“…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%

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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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Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale

et al. 2011

Environ Monit Assess

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Both the net primary productivity (NPP) and the normalized difference vegetation index (NDVI) are commonly used as indicators to characterize vegetation vigor, and NDVI has been used as a surrogate estimator of NPP in some cases. To evaluate the reliability of such surrogation, here we examined the quantitative difference between NPP and NDVI in their outcomes of vegetation vigor assessment at a landscape scale. Using Landsat ETM+ data and a process model, the Boreal Ecosystem Productivity Simulator, NPP distribution was mapped at a resolution of 90 m, and total NDVI during the growing season was calculated in Heihe River Basin, Northwest China in 2002. The results from a comparison between the NPP and NDVI classification maps show that there existed a substantial difference in terms of both area and spatial distribution between the assessment outcomes of these two indicators, despite that they are strongly correlated. The degree of difference can be influenced by assessment schemes, as well as the type of vegetation and ecozone. Overall, NDVI is not a good surrogate of NPP as the indicators of vegetation vigor assessment in the study area. Nonetheless, NDVI could serve as a fairish surrogate indicator under the condition that the target region has low vegetation cover and the assessment has relatively coarse classification schemes (i.e., the class number is small). It is suggested that the use of NPP and NDVI should be carefully selected in landscape assessment. Their differences need to be further evaluated across geographic areas and biomes.

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Multitemporal evaluation of topographic normalization methods on deciduous forest tm data

Cited by 33 publications

References 7 publications

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

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

Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale

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