Geometric and radiometric correction of TM data of mountainous forested areas

Itten, K.I.; Meyer, P.

doi:10.1109/36.239898

Cited by 118 publications

(36 citation statements)

References 5 publications

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“…Remote sensing of vegetation in rugged and mountainous areas is severely affected by topographic effects [1,2]. Environmental factors such as topography, soil, water, and climate influence vegetation distribution in mountainous area [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“… 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%

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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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Section: Introductionmentioning

confidence: 99%

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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“…rotation, curvature, relief). The study area described here lies in a mountainous region, and topographic effects in terms of the earth's curvature, mountain shadow, and clouds represent major obstacles that need to be taken into account (Itten and Meyer 1993).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of state and community/private forests in Punjab, Pakistan using geospatial data and related techniques

et al. 2015

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“…The aim of atmospheric correction was to remove the effects of atmospheric disturbances and noise, and make the corrected images as similar as possible with the IMAGE2000 mosaic. Topographic correction was made using the statisticalempirical correction (Itten and Meyer, 1993) where the effect of topographic variations is determined by computing illumination image using DEM, then computing regression line between image channels and illumination image and correcting image by subtracting the product of illumination image and slope of regression line from original image. Shadow areas were also determined during topographic correction (Hatunen et al, 2008).…”

Section: Satellite Images and Gis Datamentioning

confidence: 99%

Land cover classification of Finnish Lapland using decision tree classification algorithm

Törmä¹

2013

PJF

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Geometric and radiometric correction of TM data of mountainous forested areas

Cited by 118 publications

References 5 publications

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

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

Evaluation of state and community/private forests in Punjab, Pakistan using geospatial data and related techniques

Land cover classification of Finnish Lapland using decision tree classification algorithm

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