Estudo das variações da reflectância de imagens RapidEye em função dos parâmetros da modelagem topográfica no Parque Estadual do Turvo, Rio Grande do Sul

2015 
At leaf scale, the spectral features of vegetation are mainly associated to photosynthetic pigment, leaf internal structure and water concentration, allowing the use of remote sensing techniques to monitoring forest areas. However, external factors such as topographic characteristics affect the spectral response of the target. Thus, the objective of the study was to evaluate the reflectance of a forest area as a function of local topographic variation. The study was carried out in the Parque Estadual do Turvo (PET), in South Brazil, using RapidEye images. The methodology included the use of Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM). Topographic variables as slope, aspect and topographic illumination factor were obtained using ASTER GDEM. For a local evaluation, two RapidEye images from June 28, 2012 and October 17, 2012 were acquired. The results showed that the red edge and near infrared bands presented a higher dependence on topographic variables, considering their higher coefficient of variation. North oriented slopes presented greater values of reflectance due to orthogonal illumination. Because the RapidEye images are widely used for several applications, caution is recommended when one considers studying areas with highly undulated topography to correct for topographic effects. These preliminary results require more studies including field inspection integration to remote sensing derived results. Palavras-chave: sensoriamento remoto, estudos florestais, fator de iluminacao, geometria de iluminacao, relevo, remote sensing, forest studies, topographic lighting factor, illumination geometry, relief.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    3
    References
    0
    Citations
    NaN
    KQI
    []