Datasets

creator: Songlin Fei, 0000-0003-2772-0166

Total is 24 Results
Invasive Species Identification from High-resolution 4-band Multispectral Imagery - Data

10.4231/QNEQ-WG69

Bina Thapa ORCID logo , Cameron Wingren ORCID logo , Christopher Ardohain ORCID logo , Songlin Fei ORCID logo

07/02/2024

Data associated with invasive species (Callery Pear) identification in the greater New York City metropolitan area.

Convolutional neural network Deep learning Callery pear Negative training data Tree phenology

Spring and autumn phenology data from a northern red oak (Quercus rubra) common garden in West Lafayette, IN from 2013-2020.

10.4231/C2WH-Y183

Jeffrey S Dukes ORCID logo , Jonathan Knott , Liang Liang , Robert Swihart ORCID logo , Songlin Fei ORCID logo

06/13/2022

The files in this repository contain autumn and spring phenology data from a northern red oak (Quercus rubra) common garden at Martell Forest, West Lafayette, IN.

common garden diameter Forestry and Natural Resources northern red oak oak phenology provenance test Quercus rubra

Localization of Urban Trees across the USA with Generative AI

10.4231/7AK0-1P48

Adnan Firoze ORCID logo , Akshaj Uppala , Bedrich Benes ORCID logo , Brady Hardiman , Daniel Aliaga , Lindsay Darling , Raymond Yeh , Songlin Fei ORCID logo

12/12/2023

We use deep learning to provide a novel solution to map all trees on both public and private lands across 330 United States (U.S.) cities. This repository contains the dataset and code base.

Computer Science Computer Vision Ecology Pattern Recognition urban forestry urban tree

Spatial patterns of discovery points and invasion hotspots of non-native forest pests

10.4231/7YT5-ET33

Andrew M. Liebhold , Samuel F. Ward ORCID logo , Songlin Fei ORCID logo

07/05/2019

Spreadsheets and spatial data analyzed in "Spatial patterns of discovery points and invasion hotspots of non-native forest pests"

Forest Insects Forest Pathogens Forestry and Natural Resources Invasion Biology Invasive Species Spatial Ecology

Functional traits of non-native woody plants in the United States

10.4231/1YW7-DE10

Basil Iannone , Gabriela C Nunez-Mir , Qinfeng Guo , Songlin Fei ORCID logo

06/10/2019

The dataset includes 45 functional traits of over 800 nonnative woody plant species in the U.S.

Forestry and Natural Resources Functional Traits Invasive Species

Invasive forest pest distributions and their impacts on forest biomass

10.4231/82EJ-B095

Andrew Liebhold , Christopher Oswalt , Randall Morin , Songlin Fei ORCID logo

06/19/2019

Three sets of data are included in this publication: forest inventory data, pest distribution data, and host tree list by pest. In addition, SQL code on how to run analyses with the data sets are also included.

Biomass Carbon Exotic Invasion Forestry and Natural Resources Invasion Invasive Insects Invasive Pests Mortality

Distributional shifts of regional forest communities in the eastern United States

10.4231/F0BT-GN15

Christopher M. Oswalt , Jonathan Knott , Michael A. Jenkins , Songlin Fei ORCID logo

07/24/2019

This dataset contains the distribution of forest communities across the eastern United States at T1 (1980-1995) and T2 (2015-2017) and the species comprising these communities.

Climate Change Forest Inventory and Analysis Forestry and Natural Resources Latent Dirichlet Allocation Regional Forest Communities Spatial Shifts

Structural diversity and ecosystem function

10.4231/N88M-7A62

Brady Hardiman , Elizabeth A Larue ORCID logo , Jessica Elliott , Songlin Fei ORCID logo

10/14/2019

Codes and data for published article: LaRue, EA, BS Hardiman, JM Elliott, S Fei. 2019. Structural diversity as a predictor of ecosystem function. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ab49bb

Ecosystem Function Forestry and Natural Resources Invasion Biology LiDAR Macrosystems biology NEON RStudio Structural diversity

Key ecological research concepts between 1980 and 2016

10.4231/R7KD1W40

Songlin Fei ORCID logo

08/21/2018

Occurrence and co-occurrence frequency of major concepts in ecological research between 1980 and 2016.

Big Literature Content Analysis Ecological concepts Forestry and Natural Resources

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