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Disentangling the roles of inter and intraspecific variation on leaf trait distributions across the eastern United States

Functional traits are central to how organisms perform and influence ecosystem function. Although phylogenetic constraints and environmental conditions are both known to affect trait distributions, data limitations have resulted in large scale …

Data Carpentry for Biologists: A semester long Data Carpentry course using ecological and other biological examples

Data Carpentry for Biologists is a semester-long course in best practices for storing, loading, manipulating, and visualizing data using R. The course material includes video demonstrations, lecture notes for live coding demonstrations, links to …

Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches

A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s …

RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation

Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when targets are of irregular shape or difficult to distinguish from …

A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network

Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data …

Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data

Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often …

DeepForest: A Python package for RGB deep learning tree crown delineation

Remote sensing of forested landscapes can transform the speed, scale and cost of forest research. The delineation of individual trees in remote sensing images is an essential task in forest analysis. Here we introduce a new Python package, DeepForest …

Cross-site learning in deep learning RGB tree crown detection

Tree crown detection is a fundamental task in remote sensing for forestry and ecosystem ecology. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with …

Rethinking global carbon storage potential of trees. A comment on Bastin et al.(2019)

Bastin et al. 2019 use two flawed assumptions: 1) that the area suitable for restoration does not contain any carbon currently, and 2) that soil organic carbon (SOC) from increased canopy cover will accumulate quickly enough to mitigate anthropogenic …

A data science challenge for converting airborne remote sensing data into ecological information

Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification …