With the launch of the Sentinel-2 satellite constellation, offering freely available multispectral data at the global scale at 10-20 m spatial resolution, remote sensing has entered the era of big data, enabling high-frequency monitoring of land cover and biophysical change. Combined with Landsat-8 data the global median average revisit interval has become less than 3 days, creating an archive of image data for terrestrial monitoring that is unprecedented. Upcoming spaceborne hyperspectral sensors like the Environmental Mapping and Analysis Program (EnMAP) and the Surface Biology and Geology mission (SBG) will provide image spectroscopy data at 30m resolution and may serve urban applications requiring information on land cover composition that goes beyond the typical VIS mapping done with multispectral data. Complementary to these medium-resolution data, high spatial resolution data sources, such as the WorldView sensor series, or the recent PlanetScope constellation, consisting of 120+ CubeSat miniature satellites providing high frequency, global coverage of RGB and NIR imagery at a resolution of 3m, offer interesting prospects for urban mapping at high spatial and temporal resolution. With the increasing amount of imagery that is becoming available, there is a growing need for developing (semi-)automated mapping approaches that require less user intervention, able to deliver good mapping accuracies in the absence of site-specific training data. The research presented in this chapter focuses on the potential of spectral libraries consisting of image spectra extracted from high spatial resolution hyperspectral data, and their role in the development of automated multi-sensor, multi-scale, and multi-site mapping approaches.
|Titel||Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment|
|ISBN van elektronische versie||978-1-119-62585-8|
|ISBN van geprinte versie||978-1-119-62584-1|
|Status||Published - okt 2021|