Decline in Thermal Habitat Conditions for the Endangered Delta Smelt as Seen from Landsat Satellites (1985-2019).

This study uses Landsat 5, 7, and 8 level 2 collection 2 surface temperature to examine habitat suitability conditions spanning 1985-2019, relative to the thermal tolerance of the endemic and endangered delta smelt (Hypomesus transpacificus) and two non-native fish, the largemouth bass (Micropterus salmoides) and Mississippi silverside (Menidia beryllina) in the upper San Francisco Estuary. This product was validated using thermal radiometer data collected from 2008 to 2019 from a validation site on a platform in the Salton Sea (RMSE = 0.78 °C, r = 0.99, R2 = 0.99, p < 0.01, and n = 237). Thermally unsuitable habitat, indicated by annual maximum water surface temperatures exceeding critical thermal maximum temperatures for each species, increased by 1.5 km2 yr-1 for the delta smelt with an inverse relationship to the delta smelt abundance index from the California Department of Fish and Wildlife (r = -0.44, R2 = 0.2, p < 0.01). Quantile and Theil-Sen regression showed that the delta smelt are unable to thrive when the thermally unsuitable habitat exceeds 107 km2. A habitat unsuitable for the delta smelt but survivable for the non-natives is expanding by 0.82 km2 yr-1. Warming waters in the San Francisco Estuary are reducing the available habitat for the delta smelt.

A framework for estimating potential fluid flow from digital imagery.

Given image data of a fluid flow, the flow field, , governing the evolution of the system can be estimated using a variational approach to optical flow. Assuming that the flow field governing the advection is the symplectic gradient of a stream function or the gradient of a potential function-both falling under the category of a potential flow-it is natural to re-frame the optical flow problem to reconstruct the stream or potential function directly rather than the components of the flow individually. There are several advantages to this framework. Minimizing a functional based on the stream or potential function rather than based on the components of the flow will ensure that the computed flow is a potential flow. Next, this approach allows a more natural method for imposing scientific priors on the computed flow, via regularization of the optical flow functional. Also, this paradigm shift gives a framework--rather than an algorithm--and can be applied to nearly any existing variational optical flow technique. In this work, we develop the mathematical formulation of the potential optical flow framework and demonstrate the technique on synthetic flows that represent important dynamics for mass transport in fluid flows, as well as a flow generated by a satellite data-verified ocean model of temperature transport.