GLC 2000 First Result Workshop March 2002 Multitemporal compositing Approaches for SPOT-4 VEGETATION Data Ana Cabral 1, Maria J.P.de Vasconcelos 1,2, José M.C.Pereira 1,2, Étienne Bartholomé 3, and Phillipe Mayaux 3 1 Cartography Center, Tropical Research Institute, Portugal 2 Centro de Estudos Florestais, ISA, Portugal 3 Institute for Environment and Sustainability, Joint Research Center, EC

GLC 2000 First Result Workshop March 2002 Why a new multitemporal compositing? To try to reduce spatial and spectral artifacts produced by the application of other compositing algorithms, while effectively removing clouds and shadows; To try to improve the separability between FAO/LCCS legend categories; To reduce intra-category spectral variance, by reducing noise induced by the compositing procedure.

GLC 2000 First Result Workshop March 2002 Compositing approaches MNDVI - Selects the dates with the maximum Normalized Difference Vegetation Index. MNDVI is supposed to minimize cloud cover, enhance the vegetation signal and avoid off-nadir viewing angle data (Holben 1986, Qi and Kerr 1997). Sousa et al, Showed that this procedure can result in cloud retention over certain land cover types. Chilar et al, Showed that it can result in selection of off-nadir data. mRed - Selects pixels with the minimum value in the red channel. As the clouds have much higher reflectance in the red region than vegetation, this procedure reduces the likelihood of retaining cloud pixels (Qi and Kerr, 1997). MNDVI_mSWIR - Applied to SPOT-4 VEGETATION S10 products, can reduce the linear discontinuities between adjacent orbital tracks that result from MNDVI, thus improving spatial homogeneity (Mayaux and Bartholomé 2000).

GLC 2000 First Result Workshop March 2002 Compositing approaches NIRm3 and Albm3 - Based on choosing the third lowest value of NIR or Albedo (mean of the visible Red and NIR bands). This approach is based on the assumption that there is a low likelihood of a cloud shadow to fall on a given pixel more than twice, over a period of one month.

GLC 2000 First Result Workshop March 2002 Quality of the composite images The quality of each method was assessed by: Visual interpretation - Determine the level of cloud removal, retention of cloud shadow, and presence of smoke haze; Morans’I index of spatial autocorrelation - Quantifies the spatial homogeneity of radiometric values; Was calculated with a spatial lag of 2 pixels. Histograms of view zenith angle distributions.

GLC 2000 First Result Workshop March 2002 Results – Visual interpretation MNDVIMNDVI_mSWIRmRed NIRm3 Albm3

GLC 2000 First Result Workshop March 2002 Results – Visual interpretation Albm3MNDVI The best and worst composite image for Southern Africa

GLC 2000 First Result Workshop March 2002 Results – Distribution of sensor view angles MNDVIMNDVI_mSWIRmRed NIRm3Albm3 View zenith angles

GLC 2000 First Result Workshop March 2002 Results bandsAlbm3NIRm3mRedMNDVI_mSWIRMNDVI b b SWIR Mean Morans’I autocorrelation index

GLC 2000 First Result Workshop March 2002 Discussion MNDVI Advantages - Effectivelly removes clouds; - Enhances the vegetation signal. Disadvantages - Produces highly heterogeneous images, with linear discontinuities and a marked mosaic pattern; - High level of radiometric speckle in each single channel; - Shows a relatively strong bias towards selection of off-nadir pixels.

GLC 2000 First Result Workshop March 2002 Discussion mRed Advantage - Effectively removes clouds. Disadvantages - Retains a large amount of cloud shadows, which have very low reflectance in the red region; Cloud Shadows can be confused with burned areas and water bodies; - Shows a high Morans’I value for the red band, but the other bands (NIR and SWIR) are more heterogeneous; - Shows a relatively strong bias towards selection of off-nadir pixels.

GLC 2000 First Result Workshop March 2002 Discussion MNDVI_mSWIR Advantage - Applied over the MNDVI, improves spatial homogeneity. Disadvantages - Cloud shadows and the speckle effects are still visible; - Preserves the deficient observation geometry obtained with MNDVI, showing a relatively strong bias towards selection of off-nadir pixels.

GLC 2000 First Result Workshop March 2002 Discussion Nirm3 and Albm3 Advantages: - Produces visually smoother images without fine-grained spatial heterogeneity (salt-and- pepper pattern) and effectively removes most clouds while avoiding retention of cloud shadows; - Has the highest overall values of Morans’I, in agreement with visual analysis - Sensor view angles of selected pixels are closer to nadir, resulting in substantial improvement in spatial properties of the composites - As the elimination of speckle effect is almost complete, between class spectral separability is expected to improve - A better performance over wetlands and water bodies produces clear images where other algorithms retain cloud and cloud shadows Disadvantage - Retains some clouds over bright green vegetation