Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment

Meyer, P.; Itten, K.I.; Kellenberger, Tobias; Sandmeier, Stefan; Sandmeier, Ruth

doi:10.1016/0924-2716(93)90028-l

Cited by 245 publications

(230 citation statements)

References 7 publications

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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).…”

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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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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“…El modelo de iluminación se derivó de un modelo digital de elevación (Riaño et al 2003) f o : ángulo de orientación: el ángulo existe entre el vector que señala el norte y la proyección del plano horizontal del vector normal de la superficie en ese punto. La reflectancia del plano normalizado de cada píxel, se obtuvo utilizando el método empírico estadístico, que es una variación del método no lambertiano (Meyer et al 1993) …”

Section: Métodosunclassified

Discriminación de bosques de Araucaria araucana en el Parque Nacional Conguillío, centro-sur de Chile, mediante datos Landsat TM

et al. 2011

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SUMMARYAraucaria forests (Araucaria araucana) have a tremendous ecological relevance; however, the information concerning their spatial distribution is still insufficient. They have only been classified according to small management scales, using satellite photos and images processed through conventional methods. The present study had as its objective to discriminate and characterize types of A. araucana forests in the Conguillío National Park, located in the Southern-Center Chile, through data derived from the Landsat-5 TM satellite and geographic information systems. The normalized difference vegetation index (NDVI) was satisfactorily related with variables corresponding to crown coverage and the diameter at breast height; thus, these index values were incorporated to the classification process. Using the digital elevation model and the NDVI, the effect provoked by the shadow was minimized. Seven types of forests, between dense and semi-dense-open, were discriminated in accordance with the accompanying species. The global reliability of the classification was 83.8 %. The greatest reliability for the producer was for the medium crown density forest of A. araucana -N. dombeyi (B1) (87.5 %); and for the consumer, for the high crown density forests of A. araucana -N. dombeyi (B1) and also for those of medium density (B2) (93 %). It is concluded that incorporating NDVI values and data derived from the digital elevation model to the satellite classification process, it is possible to discriminate araucaria forests with satisfactory reliability in areas of rough relief, which is very useful information for the management of these forestry ecosystems.

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“…This provided bandwise gain and bias coefficients which were applied to each band, resulting in a reflectance normalized image. Topographic normalization was applied to the reflectance normalized images using the C-correction algorithm (Teillet et al 1986;Meyer et al 1993;Ekstrand, 1994). The 50 m DEM used in the topographic correction was interpolated to 10 m resolution using a natural neighbour algorithm (Sibson, 1981;Bater and Coops, 2009) in order to achieve a suitable correction of the 10 m SPOT data, and 60 m for the AWiFS data correction.…”

Section: Relative Reflectance Normalization and Topographic Normalizamentioning

confidence: 99%

Comparison of Resourcesat-1 AWiFS and SPOT-5 data over managed boreal forest stands

Reese

Nilsson

Olsson

2009

International Journal of Remote Sensing

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In this study, the utility of AWiFS data in relation to stem volume estimation for managed boreal forest stands was investigated. Multiple linear regressions were used to predict stem volume (m 3 ha -1 ) with standwise mean spectral values as the independent variables. For comparison, two SPOT-5 images were also used, one with nearly simultaneous acquisition. The adjusted coefficient of determination (R 2 adj ) using AWiFS data to predict stem volume was 0.573 (SE 56.9%) while SPOT had an R 2 adj of 0.598 (SE 55.2%). All bands were negatively correlated, with the SWIR band having the single strongest correlation with stem volume. The best two band predictor of stem volume was the NIR and red band for AWiFS, and the NIR and SWIR bands for SPOT. When stem volume was predicted based on stand size, AWiFS and SPOT produced an R 2 adj of 0.310 and 0.293, respectively, for stands less than 2 ha in size. A steady increase of predictive ability appeared to increase with stand size, with the highest R

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Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment

Cited by 245 publications

References 7 publications

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

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

Discriminación de bosques de Araucaria araucana en el Parque Nacional Conguillío, centro-sur de Chile, mediante datos Landsat TM

Comparison of Resourcesat-1 AWiFS and SPOT-5 data over managed boreal forest stands

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