Automated Author Profile

Le Saint, Théo

Littoral, Environnement, Télédétection, Géomatique

Current S-Index

1.0

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.5

Average Dataset Index per dataset

Total Datasets

2

Total datasets for this author

Average FAIR Score

13.5%

Average FAIR Score per dataset

Total Citations

1

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

A spatio-temporal dataset for ecophysiological monitoring of urban trees

A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured in situ and in the laboratory; (ii) plant area density measured in situ under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.The repository comprised 3 files : DATASET_PART1.csv : This file contains leaf trait measurementsDATASET_PART2.csv : This file contains plant area density measurements DATASET_PART3.gpkg : This file contains two spatial vector layers: (1) CROWN_EXTENT that is a polygon layer describing tree crowns and (2) TRUNK_LOCATION that is a point layer describing tree location.More details on the study site, protocols and data can be found in the following reference:Théo Le Saint, Jean Nabucet, Cécile Sulmon, Julien Pellen, Karine Adeline, Laurence Hubert-Moy, A spatio-temporal dataset for ecophysiological monitoring of urban trees, Data in Brief, Volume 57, 2024, 111010, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.111010.

Authors

  • Le Saint, Théo ;
  • Nabucet, Jean ;
  • Sulmon, Cécile ;
  • Pellen, Julien ;
  • Adeline, Karine ;
  • Hubert-Moy, Laurence
0 Citations0 Mentions13% FAIR0.3 Dataset Index
10.5281/zenodo.12751352July 2024

A spatio-temporal dataset for ecophysiological monitoring of urban trees

A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured in situ and in the laboratory; (ii) plant area density measured in situ under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.The repository comprised 3 files : DATASET_PART1.csv : This file contains leaf trait measurementsDATASET_PART2.csv : This file contains plant area density measurements DATASET_PART3.gpkg : This file contains two spatial vector layers: (1) CROWN_EXTENT that is a polygon layer describing tree crowns and (2) TRUNK_LOCATION that is a point layer describing tree location.More details on the study site, protocols and data can be found in the following reference:Théo Le Saint, Jean Nabucet, Cécile Sulmon, Julien Pellen, Karine Adeline, Laurence Hubert-Moy, A spatio-temporal dataset for ecophysiological monitoring of urban trees, Data in Brief, Volume 57, 2024, 111010, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.111010.

Authors

  • Le Saint, Théo ;
  • Nabucet, Jean ;
  • Sulmon, Cécile ;
  • Pellen, Julien ;
  • Adeline, Karine ;
  • Hubert-Moy, Laurence
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.5281/zenodo.12751353July 2024