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

Gao, Mengtan; Zhao, Wenji; Gong, Zhaoning; Gong, Huili; Chen, Zheng; Tang, Xinming

doi:10.3390/rs6042745

Cited by 43 publications

(33 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The improved cosine correction shows a small overcorrection in Figures 5d,l, and 6d,l, which is caused by the Lambertian reflectance assumptions. Many studies have reported similar behavior in the cosine correction method [10,13,14,63]. Among the five methods investigated here, the …”

Section: Evaluation Of Topographic Correction Methodsmentioning

confidence: 88%

“…Our results indicate that the C-correction, SEC, and VECA methods show the best performance with both DEMs. The improved cosine correction consistently overcorrects the imagery, as a result of the Lambertian reflectance assumptions [10,13,14,63]. Additionally, the algorithm for this method considers the mean illumination for forest strata instead of regression parameters calculated from the illumination and uncorrected reflectance [5].…”

Section: Topographic Correction and Demsmentioning

confidence: 99%

See 1 more Smart Citation

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

et al. 2017

View full text Add to dashboard Cite

Abstract:The accurate mapping and monitoring of forests is essential for the sustainable management of forest ecosystems. Advancements in the Landsat satellite series have been very useful for various forest mapping applications. However, the topographic shadows of irregular mountains are major obstacles to accurate forest classification. In this paper, we test five topographic correction methods: improved cosine correction, Minnaert, C-correction, Statistical Empirical Correction (SEC) and Variable Empirical Coefficient Algorithm (VECA), with multisource digital elevation models (DEM) to reduce the topographic relief effect in mountainous terrain produced by the Landsat Thematic Mapper (TM)-5 and Operational Land Imager (OLI)-8 sensors. The effectiveness of the topographic correction methods are assessed by visual interpretation and the reduction in standard deviation (SD), by means of the coefficient of variation (CV). Results show that the SEC performs best with the Shuttle Radar Topographic Mission (SRTM) 30 m × 30 m DEM. The random forest (RF) classifier is used for forest classification, and the overall accuracy of forest classification is evaluated to compare the performances of the topographic corrections. Our results show that the C-correction, SEC and VECA corrected imagery were able to improve the forest classification accuracy of Landsat TM-5 from 78.41% to 81.50%, 82.38%, and 81.50%, respectively, and OLI-8 from 81.06% to 81.50%, 82.38%, and 81.94%, respectively. The highest accuracy of forest type classification is obtained with the newly available high-resolution SRTM DEM and SEC method.

show abstract

Section: Evaluation Of Topographic Correction Methodsmentioning

confidence: 88%

Section: Topographic Correction and Demsmentioning

confidence: 99%

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Как сообщается в работе [9], топографическая коррекция, которая устраняет различные эффекты, вызванные наклоном местности, является важным этапом в предварительной обработке бортовой инфор-мации.…”

Section: анализ литературных данных и постановка проблемыunclassified

“…Согласно [9], топографическая коррекция пред-ставляет собой транспортирование радиационных яр-костных данных или данных об отраженной радиации пикселей на наклонной плоскости в данные другой опорной плоскости с целью устранения топографиче-ских эффектов. При этом, один и тот же объект имею-щий различные азимутальные координаты по Солнцу должен иметь одинаковые спектральные реакции.…”

Section: анализ литературных данных и постановка проблемыunclassified

Optimization of slant scanning in the systems of combined two-step location

Абдуллаев¹,

Асадов²

2015

EEJET

View full text Add to dashboard Cite

“…Previous studies have compared these models under different conditions [1], [6], [7], but their performance is site dependent, and currently, no topographic normalization model is generally accepted. Good performance appears to only occur in relatively small study sites when using one global parameter for all pixels from one band image [1] or when complex prestratification approaches were used under conditions where the relationship between the spectral signature and an IC can be assumed topography specific or illumination specific [8], or land cover specific [9].…”

Section: Introductionmentioning

confidence: 99%

Local Parameter Estimation of Topographic Normalization for Forest Type Classification

Fuchs

Fehrmann

et al. 2015

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

Radiometric distortions caused by rugged terrain make the classification of forest types from satellite imagery a challenge. Various band-specific topographic normalization models are expected to eliminate or reduce these effects. The quality of these models also depends on the approach to estimate empirical parameters. Generally, a global estimation of these parameters from a whole satellite image is simple, but it may tend to overcorrection, particularly for larger areas. A land-cover-specific method usually performs better, but it requires obtaining a priori land classification, which presents another challenge in many cases. Empirical parameters can be directly estimated from local pixels in a given window. In this letter, we propose and evaluate a central-pixel-based parameter estimation method for topographic normalization using local window pixels. We tested the method with Landsat 8 imagery and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) in very rough terrain with diverse forest types. Visual comparison and statistical analyses showed that the proposed method performed better at a range of window sizes compared with an uncorrected image or with a global parameter estimation approach. The intraclass spectral variability of each forest type has been reduced significantly, and it can yield higher accuracy of forest type classification. The proposed method does not require the a priori knowledge of land covers. Its simplicity and robustness suggest that this method has the potential to be a standard preprocessing approach for optical satellite imagery, particularly for rough terrain. Index Terms-Advanced Spaceborne Thermal Emission andReflection Radiometer Global Digital Elevation Model (ASTER GDEM), empirical parameter estimation, Landsat 8, local window, topographic normalization. 1545-598X

show abstract

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

Cited by 43 publications

References 26 publications

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

Optimization of slant scanning in the systems of combined two-step location

Local Parameter Estimation of Topographic Normalization for Forest Type Classification

Contact Info

Product

Resources

About