Data Archive

HF322

R Code and Images for Developing a Spatial Concordance Coefficient at Harvard Forest 2010

Related Publications

Data

hf322-01: R code
hf322-02: canopy images
hf322-03: simulations R code

Overview

Lead: Ronny Vallejos, Aaron Ellison, Andrew Richardson
Investigators: Javier Perez
Contact: Information Manager
Start date: 2010
End date: 2010
Status: complete
Location: Prospect Hill Tract (Harvard Forest)
Latitude: +42.53 to +42.55 degrees
Longitude: -72.20 to -72.17 degrees
Elevation: 280 to 420 meter
Datum: WGS84
Taxa: Pinus strobus (white pine), Quercus rubra (red oak)
Release date: 2023
Language: English
EML file: knb-lter-hfr.322.6
DOI: digital object identifier
EDI: data package
DataONE: data package
Related links:

Study type: short-term measurement, modeling
Research topic: ecological informatics and modelling
LTER core area: primary production
Keywords: canopies, imagery, phenology, spatial variability
Abstract:
Concordance correlation coefficients have been developed in a variety of different contexts. This problem has been widely addressed in a non-spatial context, but here we consider a coefficient that for a fixed spatial lag allows the comparison of two spatial sequences (e.g., images). We define a spatial concordance coefficient for second-order stationary processes.
Methods:
The proposed spatial concordance coefficient was explored for the bivariate Mat\'ern and Wendland covariance functions. The asymptotic normality of a sample version of the spatial concordance coefficient for an increasing domain sampling framework was established for the Wendland covariance function. Finally, we developed a local approach for estimating the concordance that uses local spatial models on non-overlapping windows.

As an illustrative example, we applied this methodology to two similar images of a deciduous forest canopy at Harvard Forest. The images were recorded with different cameras but similar fields-of-view and within minutes of each other. Our analysis showed that the local approach helped to explain a percentage of the non-spatial concordance and to learn about the decaying of it as a function of the spatial lag.
Organization: Harvard Forest. 324 North Main Street, Petersham, MA 01366, USA. Phone (978) 724-3302. Fax (978) 724-3595.
Project: The Harvard Forest Long-Term Ecological Research (LTER) program examines ecological dynamics in the New England region resulting from natural disturbances, environmental change, and human impacts. (ROR).
Funding: National Science Foundation LTER grants: DEB-8811764, DEB-9411975, DEB-0080592, DEB-0620443, DEB-1237491, DEB-1832210.
Use: This dataset is released to the public under Creative Commons CC0 1.0 (No Rights Reserved). Please keep the dataset creators informed of any plans to use the dataset. Consultation with the original investigators is strongly encouraged. Publications and data products that make use of the dataset should include proper acknowledgement.
License: Creative Commons Zero v1.0 Universal (CC0-1.0)
Citation: Vallejos R, Ellison A, Richardson A. 2023. R Code and Images for Developing a Spatial Concordance Coefficient at Harvard Forest 2010. Harvard Forest Data Archive: HF322 (v.6). Environmental Data Initiative: https://doi.org/10.6073/pasta/f54733ad85e8037fffc14820ef6f4bf6.

Detailed Metadata

hf322-01: R code

Compression: none
Format: R script
Type: script

hf322-02: canopy images

Compression: zip
Format: jpeg, png
Type: image

hf322-03: simulations R code

Compression: none
Format: R script
Type: script