Author(s): Martinuzzi S; Cook B; Helmer E; Keller M; Locke D; Marcano-Vega H; Uriarte M; Morton D
Site ID: US-PR0
Site Name: Island of Puerto Rico
Variables: Aboveground biomass; Remote sensing
Date Range: March 1, 2017 - March 31, 2017
Description: This dataset includes two products from Martinuzzi et al. (2022): "biomass.tif" is a 26-m resolution forest biomass (AGB) map for Puerto Rico derived from NASA G-LiHT lidar data and forest inventory data (FIA plots), in raster format. "input_multivariate_v2.shp" is a point shapefile with information on forest age, substrate, past land use, topographic wetness, slope, and precipitation, for each forest pixel. These two datasets can be used to evaluate spatial patterns of AGB in second-growth forests across transects of lidar data in humid forests of Puerto Rico, and to analyze relationship(s) between AGB and environmental variables. Additional information on these products can be found on the supporting file called "Readme.txt" included within the data archive, as well as in the original manuscript by Martinuzzi et al (2022).
QA/QC: Full QA-QC
Methods Description: The lidar data used here were collected during March 2017 using NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager. G-LiHT data were acquired at a nominal altitude of 335 m using two synchronized Riegl VQ 480i scanning lidars at 300 kHz, providing a density of ≥12 pulses m2, with up to eight returns per pulse. Lidar data were restricted to the central 30° field of view with a resulting swath width of 180 m. We collected lidar data along transects designed to capture FIA plots used for calibration and sample the island’s environmental heterogeneity. The G-LiHT lidar data are available at https://gliht.gsfc.nasa.gov/ -Methods for product "biomass.tif": To map AGB, we first developed a lidar–biomass calibration model based on 56 FIA plots and using ordinary least-squares regression (OLS). The model used AGB as the dependent variable and five lidar metrics as predictor variables. The lidar–biomass calibration model explained 70% of the variation in AGB in the FIA data and had a cross-validated RMSE of 34.2 Mg ha−1. Then, we applied the regression function to the transects of lidar coverage across the island at 26-meter resolution, consistent with the size of the FIA plots. -Methods for product “input_multivariate_v2.shp”. For each biomass pixel we extracted information on forest age (in years), substrate (six classes), past land use (four classes), topographic wetness (continuous), slope (continuous), and precipitation (mm), based on different sources of spatially explicit information. For additional methods information see Martinuzzi et al. (2022), and the "Readme.txt" file included in the data archive.
Access Level: Public
Originating Institution(s): NASA Goddard Space Flight Center; USDA Forest Service; University of Wisconsin-Madison; Jet Propulsion Laboratory
Sponsor Organization(s): U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; NASA; USDA Forest Service, International Institute of Tropical Forestry; US Department of Interior (National Institute of Food and Agriculture).
Contact: Martinuzzi, Sebastian - University of Wisconsin (martinuzzi@wisc.edu)
Version: 1.0
Dataset Citation: Martinuzzi S; Cook B; Helmer E; Keller M; Locke D; Marcano-Vega H; Uriarte M; Morton D (2022): Patterns and controls on island-wide aboveground biomass accumulation in second-growth forests of Puerto Rico. 1.0. NGEE Tropics Data Collection. (dataset). https://doi.org/10.15486/ngt/1873506
Acknowledgement: This work was supported as part of the Next Generation Ecosystem Experiments- Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, through interagency agreements with the US Forest Service (# 89243018SSC000012) and NASA (# 89243018SSC000013). Work by SM was supported by an award from the US Forest Service, International Institute of Tropical Forestry (16- CA- 11132762- 351). Additional funding was provided by the USDA Forest Service, US Department of Interior (National Institute of Food and Agriculture # 2018–67,030-28,124), and NASA. The USDA Forest Service International Institute of Tropical Forestry, Luquillo LTER, and NASA’s Airborne Science Program provided logistical support for G-LiHT data collection. The Caribbean FIA program is conducted by the USDA Forest Service Southern Research Station and International Institute of Tropical Forestry. Work at IITF is done in collaboration with the University of Puerto Rico. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy.
Data Link: Download Dataset
Martinuzzi, S., Cook, B. D., Helmer, E. H., Keller, M., Locke, D., Marcano-Vega, H., Uriarte, M., & Morton, D. C. (2022). Patterns and controls on island-wide aboveground biomass accumulation in second-growth forests of Puerto Rico. Biotropica, 00, 1–14. https://doi.org/10.1111/btp.13122
