Engineering topics

Juan Luis Torres-Perez

Publications and source records attributed to Juan Luis Torres-Perez.

3 records

NASA NTRS2021

River streamflow, remotely-sensed water quality, and benthic composition of previously undescribed nearshore coral reefs in northern Puerto Rico

Land-based sediment stress represents a threat to many coral reefs in Puerto Rico primarily as a result of unrestricted land cover/land use changes and poor best management practices. The effects of such stresses have been documented along most coasts around the island. However, little attention has been paid to reefs located on the north coast, and very little is known about their composition and current state. Here, we present a study characterizing riverine inputs, water quality conditions, and benthic composition of two previously undescribed coral reefs (Tómbolo and Machuca reefs) located just eastward of the Río Grande de Manatí outlet in north-central Puerto Rico. This study utilizes a time series of remotely sensed ocean color products (diffuse vertical attenuation coefficient at 490 nm [Kd490] and chlorophyll-a concentration [Chl-a] estimated with data from the Visible Infrared Imaging Radiometer Suite [VIIRS]) to characterize water quality in this coastal region. In general, the months with relatively high mean daily river streamflow also coincide with months having the highest proportion of eastward wave direction, which can promote the eastward influence of river waters towards the two coral reefs sites. Kd490 and Chl-a showed a higher riverine influence closer to the watershed outlet. Kd490 and Chl-a monthly peaks also coincide with river streamflow highs, particularly at those pixels closer to shore. Tómbolo Reef, located farther eastward of the river outlet, shows a well-developed primary reef framework mainly composed of threatened reef-building species (Acropora palmata, Pseudodiploria) and high coral cover (19-51%). The benthos of Machuca Reef, located closer to the river outlet, is dominated by macroalgae with a significantly lower coral cover (0.2-2.7%) mainly composed of “weedy” coral species (Porites astroides, and S. radians). Cover of major benthic components correlates with distance from the river outlet, and with gradients in Kd490 and Chl-a, with higher coral cover and lower macroalgal cover farther from the river outlet. Coral cover at Tómbolo Reef is higher than what has been reported for similar sites around Puerto Rico and other Caribbean islands showing its ecological importance and, as up until now, an unrecognized potential refuge of reef-building threatened coral species.

benthic cover↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗