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.