Building a Forward-Mode Three-Dimensional Reflectance Model for Topographic Normalization of High-Resolution (1–5 m) Imagery: Validation Phase in a Forested Environment

Couturier, Stéphane; Gastellu-Etchegorry, Jean-Philippe; Martin, Emmanuel; Patiño, P.

doi:10.1109/tgrs.2012.2226593

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…On the other hand, for bare soil areas (Figure 5c,f,h), it can be seen that this improvement is not so obvious, as even the correction by no-slope classification seems slightly better in the Landsat OLI image from 7 March 2015 ( Figure 5i). This fact may seem evident since, according to previous works, the SCS + C topographic correction model is the appropriated framework for forested scenes [6,15,16]. Regarding this fact, Gu and Gillespie [17] suggested the performance of separate corrections using a sun-canopy-sensor model for forest areas and the C-correction model for non-forest areas [2].…”

Section: Quantitative Validationmentioning

confidence: 91%

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Topographic Correction to Landsat Imagery through Slope Classification by Applying the SCS + C Method in Mountainous Forest Areas

Vázquez-Jiménez

Calcerrada

Ramos-Bernal

et al. 2017

IJGI

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The aim of the topographic normalization of remotely sensed imagery is to reduce reflectance variability caused by steep terrain and thus improve further processing of images. A process of topographic correction was applied to Landsat imagery in a mountainous forest area in the south of Mexico. The method used was the Sun Canopy Sensor + C correction (SCS + C) where the C parameter was differently determined according to a classification of the topographic slopes of the studied area in nine classes for each band, instead of using a single C parameter for each band. A comparative, visual, and numerical analysis of the normalized reflectance was performed based on the corrected images. The results showed that the correction by slope classification improves the elimination of the effect of shadows and relief, especially in steep slope areas, modifying the normalized reflectance values according to the combination of slope, aspect, and solar geometry, obtaining reflectance values more suitable than the correction by non-slope classification. The application of the proposed method can be generalized, improving its performance in forest mountainous areas.

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Section: Quantitative Validationmentioning

confidence: 91%

“…Topographic correction methods applied in remote sensing comprise cosine, Minnaert, Civco two-stages, statistical-empirical, and Factor C, which have been extensively studied [4][5][6][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Topographic Correction to Landsat Imagery through Slope Classification by Applying the SCS + C Method in Mountainous Forest Areas

Vázquez-Jiménez

Calcerrada

Ramos-Bernal

et al. 2017

IJGI

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“…These methods can be grouped into three categories based on their degree of complexity and data requirements [7]: simple empirical methods, semi-empirical methods, and physically-based methods. Simple empirical methods are based on band ratios and lack of physical meaning, while physically-based TOC methods perform better but they are complex and require many parameters [8]. Finally, semi-empirical methods consist of a photometric function tuned by an empirical coefficient [9], and they have gained popularity because of their balance between complexity and performance.…”

Section: Introductionmentioning

confidence: 99%

The Added Value of Stratified Topographic Correction of Multispectral Images

2016

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Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results.

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“…Considering the different bidirectional reflectance distribution functions (BRDFs) of the canopy in inclined and horizontal surfaces, many BRDF-based correction models have been developed [19][20][21], such as the Minnaert correction [22], sun-canopy-sensor (SCS) correction [23,24], SCS + C correction [25], adaptive shade compensation (ASC) model [26], gamma method [27], Sandmeier model [28], Santini model [29], BRDF-based atmospheric and topographic correction (BRATC) model [30], approach based on path length correction (PLC) [31], and the four-scale bidirectional reflection model of slope geometric optical model [32,33]. These BRDF-based correction models have a solid physical basis [34,35]; however, the ill-posed nature of the inverse problem and complicated parameters counteract their advantage in physics and constrain their practical applications [36]. What is more, these models made little progress in removal of the cast shadow in rugged terrains.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Monitoring for Mountainous Regions Using a New Integrated Topographic Correction (ITC) of the SCS + C Correction and the Shadow-Eliminated Vegetation Index

et al. 2022

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The mountainous vegetation is important to regional sustainable development. However, the topographic effect is the main obstacle to the monitoring of mountainous vegetation using remote sensing. Aiming to retrieve the reflectance of frequently-used red–green–blue and near-infrared (NIR) wavebands of rugged mountains for vegetation mapping, we developed a new integrated topographic correction (ITC) using the SCS + C correction and the shadow-eliminated vegetation index. The ITC procedure consists of image processing, data training, and shadow correction and uses a random forest machine learning algorithm. Our study using the Landsat 8 OLI multi-spectral images in Fujian province, China, showed that the ITC achieved high performance in topographic correction of regional mountains and in transferability from the sunny area of a scene to the shadow area of three scenes. The ITC-corrected multi-spectral image with an NIR–red–green composite exhibited flat features with impressions of relief and topographic shadow removed. The linear regression of corrected waveband reflectance vs. the cosine of the solar incidence angle showed an inclination that nearly reached the horizontal, and the coefficient of determination decreased to 0.00~0.01. The absolute relative errors of the cast shadow and the self-shadow all dramatically decreased to the range of 0.30~6.37%. In addition, the achieved detection rate of regional vegetation coverage for the three cities of Fuzhou, Putian, and Xiamen using the ITC-corrected images was 0.92~6.14% higher than that using the surface reflectance images and showed a positive relationship with the regional topographic factors, e.g., the elevation and slope. The ITC-corrected multi-spectral images are beneficial for monitoring regional mountainous vegetation. Future improvements can focus on the use of the ITC in higher-resolution imaging.

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Building a Forward-Mode Three-Dimensional Reflectance Model for Topographic Normalization of High-Resolution (1–5 m) Imagery: Validation Phase in a Forested Environment

Cited by 13 publications

References 32 publications

Topographic Correction to Landsat Imagery through Slope Classification by Applying the SCS + C Method in Mountainous Forest Areas

Topographic Correction to Landsat Imagery through Slope Classification by Applying the SCS + C Method in Mountainous Forest Areas

The Added Value of Stratified Topographic Correction of Multispectral Images

Vegetation Monitoring for Mountainous Regions Using a New Integrated Topographic Correction (ITC) of the SCS + C Correction and the Shadow-Eliminated Vegetation Index

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