Observing cloud lifecycles and obtaining measurements on cloud features are significant problems in atmospheric cloud research. Scanning radars have been the most capable instruments to provide such measurements, but they have shortcomings when it comes to spatial and temporal resolution. High spatial and temporal resolution is particularly important to capture the variations in developing convections. Stereo photogrammetry can complement scanning radars with the potential to observe clouds as distant as tens of kilometers and to provide high temporal and spatial resolution, although it comes with the calibration challenges peculiar to various outdoor settings required to collect measurements on atmospheric clouds. This work explores the use of stereo photogrammetry in atmospheric cloud research, focusing on tracking vertical motion in developing convections. Calibration challenges and strategies to overcome these challenges are addressed within two different stereo settings in Miami, Florida and in the plains of Oklahoma. A feature extraction and matching algorithm is developed and implemented to identify cloud features of interest. A two-level resolution hierarchy is exploited in feature extraction and matching. 3D positions of cloud features are reconstructed from matched pixel pairs, and cloud tops of developing turrets in shallow to deep convection are tracked in time to estimate vertical accelerations. Results show that stereophotogrammetry provides a useful tool to observe cloud lifecycles and track the vertical acceleration of turrets exceeding 10 km height.
Optic surveillance is an important part of monitoring environmental changes in various ecological settings.
Although remote sensing provides extensive data, its resolution is yet not sufficient for scientific research focusing on small spatial scale landscape variations. We are interested in exploiting high resolution image data to observe and investigate the landscape variations at a small spatial scale arctic corridor in Barrow, AK, as part of the DOE Next-Generation Ecosystem Experiments (NGEE-Arctic). A 35 m transect is continuously imaged by two separate pole mounted consumer grade stationary cameras, one capturing in NIR and the other capturing in visible range, starting from June to August in 2014. Surface and subsurface features along this 35 m transect are also sampled by electrical resistivity tomography (ERT), temperature loggers and water content reflectometers. We track the behavioral change along this transect by collecting samples from the pole images and look for a relation between the image features and electrical conductivity. Results show that the correlation coefficient between inferred vegetation indices and soil electrical resistivity (closely related to water content) increased during the growing season, reaching a correlation of 0.89 at the peak of the vegetation. To extrapolate such results to a larger scale, we use a high resolution RGB map of a 500x40 m corridor at this site, which is occasionally obtained using a low-altitude kite mounted consumer grade (RGB) camera. We introduce a segmentation algorithm that operates on the mosaic generated from the kite images to classify the landscape features of the corridor.