Spectral normalization of SPOT 4 data to adjust for changing leaf phenology within seasonal forests in Cambodia

Abstract As cloud cover exacerbates the application of optical satellite data for forest monitoring in tropical wet and dry regions during the rainy season, data acquisition is mainly restricted to the dry season. When analyzing wide areas, large numbers of single scenes obtained at different times of the dry season are often handled. Such imagery is characterized by changes of spectral reflectance due to vegetation phenology, varying atmospheric effects and solar geometries. In order to allow batch processing with automatic classification techniques, inter-scene comparability is required and data have to be radiometrically normalized. Cambodia is characterized by a mixture of evergreen, semi-evergreen and deciduous forest types, the latter two experiencing at least partial leaf shedding over the course of the dry season. Using spatial medium resolution SPOT 4 data and a manually delineated base map a season adjustment model was developed. The model is adapting the land cover specific spectral signatures of a slave scene (acquired in the middle of the dry season with its seasonal forests defoliated) to an adjacent master scene (from the beginning of the dry season, showing the same forest types with leafs). The relative position of every pixel reflectance was determined in relation to the mean reflectance and its standard deviation for each land cover type and sensor band of the unadjusted slave scene. For seasonality adjustment these pixel reflectance values were transformed (rescaled) to the corresponding position in spectral space defined by the band mean reflectance and standard deviation derived from the corresponding land cover class of the master scene. While the variability of spectral profiles of the pixels in the slave scene is rescaled, the mean reflectance value of the land cover class in the slave scene is conformed to the mean reflectance of the corresponding land cover class in the master scene. The Transformed Divergence (TD) separability index was used to indicate the performance of the adjustment process by characterizing the spectral distance for each land cover type comparing a reference dataset to the uncorrected and to the seasonality corrected scene respectively. While the TD values of all forest types showed a sharp decline, highlighting the good performance of the model, the TD values of the agriculture/urban class remained high, indicating limited normalization of this heterogeneous land cover type. In order to further demonstrate the performance of the model, an object-based land cover classification was applied to the unadjusted as well as to the corresponding adjusted scene. A comparison of the results showed a highly significant improvement of overall accuracy from 32.2% to 75.8% when applying seasonality adjustment.

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