Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data

Wu, Qiong; Jin, Yi; Fan, Hui

doi:10.1080/01431161.2016.1222101

Cited by 31 publications

(47 citation statements)

References 41 publications

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“…The National Aeronautics and Space Administration (NASA) SRTM 30 m ×30 m (1 arc-second) high-resolution DEM was obtained from the U.S. Geological Survey (USGS) [41] and resampled using a nearest neighborhood transformation [21]. Additionally, the ASTER GDEM has been generated from the stereoscopic ASTER satellite images.…”

Section: Digital Elevation Models (Dem)mentioning

confidence: 99%

“…Additionally, the ASTER GDEM has been generated from the stereoscopic ASTER satellite images. The ASTER GDEM, with 30 m × 30 m (1 arc-second) resolution, was downloaded from the USGS Earth Explorer platform [42] and resampled using a nearest neighborhood transformation [21]. The Japan Aerospace Exploration Agency's Advance Land Observing Satellite "DAICHI" (ALOS) World 3D 30 m DEM was initially considered for this study, but due to the excessive number of missing tiles over the study area, it was not considered further for the analysis.…”

Section: Digital Elevation Models (Dem)mentioning

confidence: 99%

“…Szantoni and Simonetti [5] proposed that the application of topographic corrections with a stratification of LULC types could yield good results on a larger scale and over complex mountainous topography. The most successful topographic correction methods in forested areas are the C-correction [10], the Minnaert correction [18], the Modified Minnaert correction [28], the Statistical Empirical Correction (SEC) [5,21,26,29] and the Variable Empirical Coefficient Algorithm (VECA) [4,30,31], however very few studies have compared their effectiveness and overall accuracy with respect to forest classification [4,11,17,25,31,32].…”

Section: Introductionmentioning

confidence: 99%

“…The most suitable topographic correction method is typically evaluated on a case by case basis, by comparing the performances of different topographic correction methods [21,33]. Vanonckelen et al [32] note that the impact of topographic correction methods on traditional per pixel image classification has not yet been studied, but summarize several studies that examine the effectiveness of topographic corrections on land cover classifications in mountainous terrain.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the extent to which forest classifications could be improved if topographic correction is applied to individual source imagery (Landsat TM-5 and OLI-8) and combined with widely used advanced machine learning Several previous works have found that this topographic effect has a significant impact on forest classifications based on Landsat TM-5 data [12][13][14][15][16][17][18]. Many topographic correction methods have been developed to remove the topographic effects from Landsat TM-5 and OLI-8 imagery, including band rationing, empirical or semi-empirical techniques, and physical-based approaches [4,10,14,[19][20][21][22][23][24]. The empirical and physical methods depend heavily on the input of topographic data, which was not possible for many remote areas of the world until the recent global Shuttle Radar Topographic Mission (SRTM) and Advanced Space-born Thermal Emission and Reflection Radiometers (ASTER) global DEM (GDEM) products [25].…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Digital Elevation Models (Dem)mentioning

confidence: 99%

Section: Digital Elevation Models (Dem)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

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Combining historical aerial photography with machine learning to map landscape change impacts on dry grasslands in the Central Alps

2023

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Context Striking land-use changes after WW II characterize the past century in the European Alps with impact on ecosystems and biodiversity. Documenting land-use changes is often difficult due to limited information from the past. Mapping landscape history with aerial photography can foster the understanding of human-induced changes in vulnerable ecosystems, such as the remnants of dry grasslands in the Central Alps. Objectives We aimed to assess changes in grassland vegetation and their current extent in Val Venosta (European Alps, Italy) in relation to overall landscape settings, anthropogenic drivers of change and the effectiveness of the protected areas. Methods We performed a land-cover classification based on a mixed machine learning approach including several auxiliary classifiers in a random forest model to characterise the extent and state of (dry) grasslands. We calculated landscape metrics between 1945 and 2015 to assess shape-related changes, especially regarding their landscape embedding and the protection status of sites. Results Three main processes related to a changing extent in grassland habitat prevail: (i) agricultural intensification, (ii) settlement expansion at the valley bottom and (iii) forest expansion (afforestation and encroachment due to decreasing pasture activities) on the valley slopes. The remaining grassland habitat is increasingly isolated and fragmented, leaving only few core areas of dry grassland, which tended to be better conserved within protected areas. Conclusion The changes in extent of dry grasslands revealed marked changes. Transformations are assumed to be predominantly caused by human impact and successional changes. Our results confirm the importance of protected area networks. The pronounced landscape changes underline the urgent need for future research with explicit focus on the changes at community level and the underlying causes. Identifying all relevant drivers of change should be a key element in targeted conservation efforts.

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Evaluation of Topographic Correction Effects for Landsat-5 Thematic Mapper Images with Complex Lighting Conditions

Zhou

Zhang

et al. 2020

Earth Sci Inform

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Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data

Cited by 31 publications

References 41 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

Combining historical aerial photography with machine learning to map landscape change impacts on dry grasslands in the Central Alps

Evaluation of Topographic Correction Effects for Landsat-5 Thematic Mapper Images with Complex Lighting Conditions

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