Change detection (CD) is critical for natural disaster detection, monitoring and evaluation. Video satellites, new types of satellites being launched recently, are able to record the motion change during natural disasters. This raises a new problem for traditional CD methods, as they can only detect areas with highly changed radiometric and geometric information. Optical flow-based methods are able to detect the pixel-based motion tracking at fast speed; however, they are difficult to determine an optimal threshold for separating the changed from the unchanged part for CD problems. To overcome the above problems, this paper proposed a novel automatic change detection framework: OFATS (optical flow-based adaptive thresholding segmentation). Combining the characteristics of optical flow data, a new objective function based on the ratio of maximum between-class variance and minimum within-class variance has been constructed and two key steps are motion detection based on optical flow estimation using deep learning (DL) method and changed area segmentation based on an adaptive threshold selection. Experiments are carried out using two groups of video sequences, which demonstrated that the proposed method is able to achieve high accuracy with F1 value of 0.98 and 0.94, respectively.

Dissolved organic carbon (DOC) loading from lands to rivers in response to rainfall events are the major carbon sources to aquatic ecosystem. We used high-resolution measurements to determine the runoff exports of DOC from the Chippewa River and watershed which was largely dominated by agricultural land-uses. It was found that DOC concentrations in spring were higher than in autumn and discharges in spring affected by snowmelt were larger and varied more widely than in autumn in this mixed watershed. We focused on the effect of antecedent 10-day rainfall on DOC variation and exports for the different magnitudes rainfall events. It showed that the antecedent precipitation (field moisture conditions) greatly determined the extent of DOC export. It was also shown that the DOC-Discharge distributions for storm-events were generally asymmetric, whereby they were positively skewed and it was greatly different between different seasons.