Reduction of atmospheric and topographic effect on Landsat TM data for forest classification

Huang, Huabing; Gong, Peng; Clinton, Nicholas; Hui, Fengming

doi:10.1080/01431160802082148

Cited by 55 publications

(31 citation statements)

References 31 publications

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“…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: 71%

“…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%

“…However, topographic corrections of Landsat OLI-8 imagery and their impact on forest classifications have not yet been studied. 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].…”

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: Evaluation Of Topographic Correction Methodsmentioning

confidence: 71%

Section: Topographic Correction and Demsmentioning

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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“…TM1 was the only band in the study of Ekstrand (2006) where topography had no clear effect. The c-correction technique and other topographic correction techniques have proven to effectively remove the trend of increasing reflectance with increasing illumination (Meyer et al, 1993;Vincini and Frazzi, 2003;Huang et al, 2008;Soenen et al, 2008;Wu et al, 2008). This characteristic feature of topographic correction technique remained unaffected in our modified version of the c-correction method.…”

Section: Influence On Individual Band Reflectancementioning

confidence: 90%

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

Veraverbeke¹,

Verstraeten²,

Lhermitte³

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

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The influence of illumination effects on the optimality of the dNBR (differenced Normalized Burn Ratio) was evaluated for the case of the 2007 Peloponnese (Greece) wildfires using a pre/post-fire Landsat TM (Thematic Mapper) image couple. Well illuminated pixels (south and south-east facing slopes) exhibited more optimal displacements in the bi-spectral feature space than more shaded pixels (north and north-west exposed slopes). Moreover, pixels experiencing small image-to-image differences in illumination obtained a higher optimality than pixels with a relatively large difference in illumination. To correct for illumination effects, the c-correction method and a modified c-correction technique were applied. The resulting median dNBR optimality of uncorrected, c-corrected and modified c-correction data was respectively 0.58, 0.60 and 0.71 (differences significant for p<0.001). The original ccorrection method improved the optimality of badly illuminated pixels while deteriorating the optimality of well illuminated pixels. In contrast, the modified c-correction technique improved the optimality of all the pixels while retaining the prime characteristic of topographic correction techniques, i.e. detrending the illumination-reflectance relationship. For a minority of the data, for shaded pixels and/or pixels with a high image-to-image difference in illumination, the original c-correction outperformed the modified c-correction technique. In this study conducted in rugged terrain and with a bi-temporal image acquisition scheme that substantially deviated from the ideal anniversary date scheme the modified ccorrection technique resulted in a more reliable change detection.

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“… To reduce the topographic effect by topographic normalization techniques [1,[11][12][13][14][15][16][17][18][19][20][21];  As an additional -channel‖ increasing forest map accuracy [9,[22][23][24][25];  Combined with expert knowledge or a decision tree enhancing classification accuracy [26][27][28];  Integrating the prior probability of the relationship between elevation and vegetation distributions improving image classification [29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China

Zhang

Coillie

et al. 2014

Remote Sensing

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Abstract:The integration between vegetation data, human disturbance factors, and geo-spatial data (Digital Elevation Model (DEM) and image data) is a particular challenge for vegetation mapping in mountainous areas. The present study aimed to incorporate the relationships between species distribution (or vegetation spatial distribution pattern) and topography and human disturbance factors with remote sensing data, to improve the accuracy of mountain vegetation maps. Two different mountainous areas located in Lancang (Mekong) watershed served as study sites. An Artificial Neural Network (ANN) architecture classification was used as image classification protocol. In addition, canonical correspondence analysis (CCA) ordination was applied to address the relationships between topography and human disturbance factors with the spatial distribution of vegetation patterns. We used ordinary kriging at unobserved locations to predict the CCA scores. The CCA ordination results showed that the vegetation spatial distribution patterns are strongly affected by topography and human disturbance factors. The overall accuracy of vegetation classification was significantly improved by incorporating DEM or four CCA axes as additional channels in both the northern and southern study areas. However, there was no significant difference between using DEM or four CCA axes as extra channels in OPEN ACCESSRemote Sens. 2014, 6 1027 the northern steep mountainous areas because of a strong redundancy between CCA axes and DEM data. In the southern lower mountainous areas, the accuracy was significantly higher using four CCA axes as extra bands, compared to using DEM as an extra band. In the southern study area, the variance of vegetation data explained by human disturbance factors was larger than the variance explained by topographic attributes.

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Reduction of atmospheric and topographic effect on Landsat TM data for forest classification

Cited by 55 publications

References 31 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

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China

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