Geoffrey A. Fricker, Jeffrey A. Wolf, Sassan S. Saatchi, and Thomas W. Gillespie. 2015. Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing. Ecological Applications 25:17761789. http://dx.doi.org/10.1890/14-1593.1


Supplement

The spatial and non-spatial data sets as well as the R code to perform Ordinary Least Squares and Generalized Least Squares regression analysis.
Ecological Archives A025-109-S1.

Copyright


Authors
File list (downloads)
Description


Author(s)

Geoffrey A. Fricker
Department of Geography, University of California, Los Angeles
1255 Bunche Hall, Box 951524
Los Angeles, CA 90095 USA
E-mail: africker@g.ucla.edu

Jeffrey A. Wolf
Department of Ecology and Evolutionary Biology, University of California, Los Angeles
621 Charles E. Young Drive South
Los Angeles, CA 90095 USA
E-mail: wolfjeff@ucla.edu

Sassan S. Saatchi
Jet Propulsion Laboratory, California Institute of Technology
4800 Oak Grove Drive
Pasadena, CA 91109 USA
E-mail: Sasan.S.Saatchi@jpl.nasa.gov

Thomas W. Gillespie
Department of Geography, University of California, Los Angeles
1255 Bunche Hall, Box 951524
Los Angeles, CA 90095 USA
E-mailtg@geog.ucla.edu


File list

TABLES_CODE.zip (MD5: bb78bef8ed07b50f56555a594dc9ad74)

200.csv (MD5: f3dd49b436b548e88cb366831697f346)

100.csv (MD5: 45342b9bd19db40c8f4c4480c618d613)

10.csv (MD5: 302504c6368dd71845d2302421f3f466)

10_100.csv (MD5: 5a76d3d07dfa88d08d0323875db55a49)

10_200.csv (MD5: 7d2c830a789b2b84c5764de1988967d5)

d100.csv (MD5:f2c2bdc846d38b27f4c7dc0c31c8771f )

d50.csv (MD5: 12863fe859f675c87899531cefad2a85)

d20.csv (MD5: 60e8a63bc51ec2ae19b0fd08552673f6)

QB_BCI_predictions_grid_rsvars.csv (MD5: fd5584e3971f69ba1f2d7a26b6747992)

2015_fricker_predictions_fid.csv (MD5: 73f69524912cec54b7c0a860de6aff80)

2015_02_03_bci_main.r (MD5: c40a6770943c7106be689ddc94408154)

2015_02_03_ols_gls_optimization_Fig5.r (MD5: b4b9417dfe1350d9d5e2e2bf14a40e40)

2015_02_03_bci_ols_predictions.r (MD5: adb311fff7ffb47a225e1ab2b53be5ba)

2015_02_03_multiscale_analysis.r (MD5: 2d32b9e8804d75b047c7b9d65d1b60f8)

bci50ha_100m.shp (MD5: 109b6fe73fee786b168fa48271d92ff6)

bci50ha_50m.shp (MD5: 36b610e1cb4589787b26226a2e01b9e9)

bci50ha_20m.shp (MD5: 30e23f05c86a56acebc1dad921162422)

c7_t_10mm.shp (MD5: 565f38ecdbcc55e9db4e2529aaf3c7f2)

QB_BCI_prediction_grid.shp (MD5: 702cd736606b196fc4a0b6b2b3818609)

QB_BCI_gls_ols_predictions.shp (MD5: 702cd736606b196fc4a0b6b2b3818609)

Description

TABLES_CODE.zip is an archive containing all data and code described below (20 files)

200.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for stems greater than 200 mm dbh.

100.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for stems greater than 100 mm dbh.

10.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for all stems.

10_100.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for stems smaller than 100 mm dbh.

10_200.csv is a comma separated file which includes all response and predictor variables at the 1-ha spatial scale for stems smaller than 200 mm dbh.

d100.csv is a comma separated file for the multi-scale analysis which includes all response and predictor variables at the 100 × 100 m subplot scale (same contents as 10.csv).

d50.csv is a comma separated file for the multi-scale analysis which includes all response and predictor variables at the 50 × 50 m subplot scale.

d20.csv is a comma separated file for the multi-scale analysis which includes all response and predictor variables at the 20 × 20 m subplot scale.

QB_BCI_predictions_grid_rsvars.csv is a comma separated file which contains a value for remote sensing variables (used in the predictive models) for each 1-ha grid cell (‘fid’ corresponds with the QB_BCI_prediction_grid.shp).

2015_fricker_predictions_fid.csv is a comma separated file which contains a prediction for each tree size class for the prediction grid over BCI.

2015_02_03_bci_main.r is the r-code for reading in all data files and creating correlation matrices. Also contains the base linear model used for the OLS regression models.

2015_02_03_ols_gls_optimization_Fig5.r is the r-code for performing the Generalize Least Squares (GLS) spatial model optimization.

2015_02_03_bci_ols_predictions.r is the r-code for making the Ordinary Least Squares (and one GLS) prediction across BCI.

2015_02_03_multiscale_analysis.r is the r-code for the multi-scale analysis

Bci50ha_100m.shp is the GIS shapefile of the 50 ha forest dynamics plot at the 100 × 100 m subplot scale.

Bci50ha_50m.shp is the GIS shapefile of the 50 ha forest dynamics plot at the 50 × 50 m subplot scale.

Bci50ha_20m.shp is the GIS shapefile of the 50 ha forest dynamics plot at the 20 × 20 m subplot scale.

C7_t_10mm.shp is the GIS shapefile of the tree data for all stems greater than 10 mm dbh for the 50 ha forest dynamics plot.

QB_BCI_prediction_grid.shp is the GIS shapefile which was used to make predictions across BCI (1 ha scale).

QB_BCI_gls_ols_predictions.shp is the GIS shapefile is the GIS shapefile containing the predictions across BCI (1 ha scale).