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

Vázquez-Jiménez, René; Calcerrada, Raúl Romero; Ramos-Bernal, Rocío N.; Arrogante-Funes, Patricia; Novillo, Carlos J.

doi:10.3390/ijgi6090287

Cited by 29 publications

(18 citation statements)

References 26 publications

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“…A strong and statistically significant relationship between local illumination and pixel value has been reported in the literature (Kobayashi and Sanga-Ngoie 2008, Soenen et al 2008, Wu et al 2008, Mishra et al 2009, Zhang and Gao 2011, Vázquez-Jiménez et al 2017 and is confirmed in this study. The initial objective of this research was to identify which topographic algorithm Fig.…”

Section: Discussionsupporting

confidence: 90%

“…Several topographic correction methods require the satellite imagery to be atmospherically corrected (surface reflectance) (Richter 1997, Riaño et al 2003, Vanonckelen et al 2014, Pimple et al 2017, Vázquez-Jiménez et al 2017, Phiri et al 2018. However, the use of top-of-atmosphere (TOA) reflectance is also fairly common (Richter et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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Topographic Correction of LAPAN-A3/LAPAN-IPB Multispectral Image: A Comparison of Five Different Algorithms

Zylshal

2020

Quaestiones Geographicae

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Reflectance variability in mountainous regions caused by steep slopes can decrease the accuracy of landcover mapping. Topographic correction aims to reduce this effect, and various techniques have been proposed to conduct such correction on satellite imagery. This paper presents the initial results of five different topographic correction techniques applied to LAPAN-A3 multispectral images, namely cosine correction, improved cosine correction, Minnaert correction, modified Minnaert correction and two-stage normalization. The widely-available ALOS World 3D 30 meter DEM was employed, with the evaluation made in a mountainous area in South Sulawesi, Indonesia, located in an ancient volcanic region, with slopes ranging from 0 to 60 degrees. The slope aspect was almost equally distributed in all directions. Visual and statistical analysis was conducted before and after the topographic correction to evaluate the results. Standard deviation (SD) and the coefficient variation (CV) were calculated; the results show that the topographic corrections were able to reduce the effect of shadows and relief. Minnaert correction proved to be the best method in terms of visual appearance and spectral variability reduction.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Topographic Correction of LAPAN-A3/LAPAN-IPB Multispectral Image: A Comparison of Five Different Algorithms

Zylshal

2020

Quaestiones Geographicae

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“…The C parameter used in the topographic correction to improve the correction by moderating the overcorrection of dimly illuminated pixels [60] was determined by linear regressions between the values of illumination and reflectance, according to a classification of the topographic slopes of the studied area [61].…”

Section: Topographic Normalizationmentioning

confidence: 99%

Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

et al. 2018

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Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

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“…Some improved models have been developed to reduce the topographic effects for the regions with complex terrains, such as the sun-canopy-sensor + C (SCSC) model [30,35] and the rotation correction model [36]. Although these models can achieve sound corrections, their performance relies very much on the experimental parameters fitted by the pixel reflectance and the cosine of solar incident angles, such as the factor c for C-Correction (CC) models, factor a for rotation correction models, and constant k for MIN models.…”

Section: Introductionmentioning

confidence: 99%

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

et al. 2019

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Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images.

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

Cited by 29 publications

References 26 publications

Topographic Correction of LAPAN-A3/LAPAN-IPB Multispectral Image: A Comparison of Five Different Algorithms

Topographic Correction of LAPAN-A3/LAPAN-IPB Multispectral Image: A Comparison of Five Different Algorithms

Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

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