Satellite-based terrestrial production efficiency modeling

Production efficiency models (PEMs) are based on the theory of light use efficiency (LUE) which states that a relatively constant relationship exists between photosynthetic carbon uptake and radiation receipt at the canopy level. Challenges remain however in the application of the PEM methodology to global net primary productivity (NPP) monitoring. The objectives of this review are as follows: 1) to describe the general functioning of six PEMs (CASA; GLO-PEM; TURC; C-Fix; MOD17; and BEAMS) identified in the literature; 2) to review each model to determine potential improvements to the general PEM methodology; 3) to review the related literature on satellite-based gross primary productivity (GPP) and NPP modeling for additional possibilities for improvement; and 4) based on this review, propose items for coordinated research

Inter-individual variability in discourse informativeness in elderly populations.

An increasing number of studies focus on discourse production in patients with neurodegenerative diseases and underline its clinical usefulness. However, if this is to be used as a clinical tool, one needs to consider how normal discourse varies within cognitively unimpaired elderly populations. In the current study, the aim has been to investigate discourse macrolinguistic variability. For this, 123 participants aged between 55 and 84 were recruited. A cluster analysis of their discourse macrolinguistic features was conducted. Then, cluster characterisation based on socio-demographic and linguistic performance was tested (fluency, naming, syntax and spelling). This method aims to identify various profiles of speaker and informativeness and then see if inter-individual variability may be related to socio-demographic and/or linguistic aspects. Four clusters of informativeness were found but no socio-demographic features appeared significant. The fourth cluster, defined as 'off topic', had lower performance during linguistic tasks than others and thus the boundary between normality and pathology should be questioned

turtle_image_metadata

This file contains all turtle detections along with relevant metadata such as the image the detection is in, that image's file location, aircraft information, and precise turtle location in each image in both GPS and pixel locations. Also included are many validated false positives with the same metadata but a label of 0 instead of certain or positive turtle

Data from: A convolutional neural network for detecting sea turtles in drone imagery

1. Marine megafauna are difficult to observe and count because many species travel widely and spend large amounts of time submerged. As such, management programs seeking to conserve these species are often hampered by limited information about population levels. 2. Unoccupied aircraft systems (UAS, aka drones) provide a potentially useful technique for assessing marine animal populations, but a central challenge lies in analyzing the vast amounts of data generated in the images or video acquired during each flight. Neural networks are emerging as a powerful tool for automating object detection across data domains and can be applied to UAS imagery to generate new population-level insights. To explore the utility of these emerging technologies in a challenging field setting, we used neural networks to enumerate olive ridley turtles (Lepidochelys olivacea) in drone images acquired during a mass-nesting event on the coast of Ostional, Costa Rica. 3. Results revealed substantial promise for this approach; specifically, our model detected 8% more turtles than manual counts while effectively reducing the manual validation burden from 2,971,554 to 44,822 image windows. Our detection pipeline was trained on a relatively small set of turtle examples (N=944), implying that this method can be easily bootstrapped for other applications, and is practical with real-world UAS datasets. 4. Our findings highlight the feasibility of combining UAS and neural networks to estimate population levels of diverse marine animals and suggest that the automation inherent in these techniques will soon permit monitoring over spatial and temporal scales that would previously have been impractical

duke_turtles_r1

This is the second subset of images for the paper. Image metadata as well as turtle locations can be found in turtle_image_metadata.csv

duke_turtles

This is the first subset of images for the paper. Image metadata as well as turtle locations can be found in turtle_image_metadata.csv