I just attended a conference on multitemporal remote sensing images in Mystic, Connecticut. The conference was already the fifth in a series of workshops focusing on the analysis of time series imagery - for me it was the first one. The topics covered in the meeting were quite diverse, and included also radar images. Various methods for change detection and data mining were presented. There was discussion on cross-calibration of data from different sensors, accuracy assessment and detection of land cover changes and vegetation dynamics (the most interesting part for me). From many of the talks it was evident that we hope to be going towards hypertemporal (and not only hyperspectral) remote sensing - especially if we want to monitor environmental processes continuously and near real-time.
Tiit recommended this conference to me already last winter. I was interested to attend, and so we decided to prepare a presentation on our seasonal reflectance time series of birch stands. We used a radiative transfer model to identify the key factors which influence the seasonal pattern of stand reflectance in medium resolution satellite images (Landsat, SPOT), and briefly compared our results to 'landscape level' MODIS LAI and phenology products.
Tiit recommended this conference to me already last winter. I was interested to attend, and so we decided to prepare a presentation on our seasonal reflectance time series of birch stands. We used a radiative transfer model to identify the key factors which influence the seasonal pattern of stand reflectance in medium resolution satellite images (Landsat, SPOT), and briefly compared our results to 'landscape level' MODIS LAI and phenology products.
