Milad Hooshyar

Hydrologic controls on junction angle of river networks

The formation and growth of river channels and their network evolution are governed by the erosional and depositional processes operating on the landscape due to the movement of water. The branching angles, i.e., the angle between two adjoining channels, in drainage networks are important features related to the network topology and contain valuable information about the forming mechanisms of the landscape. Based on the channel networks extracted from 1 m Digital Elevation Models of 120 catchments with minimal human impacts across the United States, we show that the junction angles have two distinct modes with α1‐≈49.5° and α2‐≈75.0°. The observed angles are physically explained as the optimal angles that result in minimum energy dissipation and are linked to the exponent characterizing the slope-area curve. Our findings suggest that the flow regimes, debris-flow dominated or fluvial, have distinct characteristic angles which are functions of the scaling exponent of the slope-area curve. These findings enable us to understand the geomorphic signature of hydrologic processes on drainage networks and develop more refined landscape evolution models.

Optical Cloud Pixel Recovery via Machine Learning

Remote sensing derived Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Studies that are heavily dependent on optical sensors are subject to data loss due to cloud coverage. Specifically, cloud contamination is a hindrance to long-term environmental assessment when using information from satellite imagery retrieved from visible and infrared spectral ranges. Landsat has an ongoing high-resolution NDVI record starting from 1984. Unfortunately, this long time series NDVI data suffers from the cloud contamination issue. Though both simple and complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques have limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum using a Random Forest (RF) trained and tested with multi-parameter hydrologic data. The RF-based OCPR model is compared with a linear regression model to demonstrate the capability of OCPR. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance. The RF-based OCPR method achieves a root mean squared error of 0.016 between predicted and observed NDVI reflectance values. The linear regression model achieves a root mean squared error of 0.126. Our findings suggest that the RF-based OCPR method is effective to repair cloudy pixels and provides continuous and quantitatively reliable imagery for long-term environmental analysis.