U.S. Department of EnergyOffice of Scientific and Technical Information
LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones
Abstract
Statement of purpose: Cyclones alter the function and composition of tropical forests, making effects of intensifying cyclones on carbon-rich forests a critical topic of study. Here, we quantified cyclone-induced damage and recovery of 21 cyclone disturbances affecting 23 pantropical forest sites between 1988-2017 utilizing leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and transformed NDVI (kNDVI) values from Google Earth Engine. Field observations collected in a meta-analysis (Bomfim et al., 2022, in review) were used to ground-truth and test effects of soil resource availability and disturbance factors on damage and recovery. This meta-analysis also served as the basis to begin vegetation index extraction, utilizing unique site and date combinations, from tropical forests effect by cyclone disturbances. We began collecting NDVI (5km resolution) from the NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), Version 5 data product (Vermote, 2019) for all case studies included, 42. Next, we began extracting Landsat data from Landsat 4, 5, and 8, courtesy of the U.S. Geological Survey, in search of higher resolution data. We selected a 3 by 3 Landsat pixel area, leading to a 90m resolution data extraction. The specific imagery used includes Landsat 4more »
- Authors:
-
- Environmental Engineering Sciences, University of Florida
- Lawrence Berkeley National Laboratory
- University of California Berkeley
- Publication Date:
- Other Number(s):
- NGT0183
- Research Org.:
- Next-Generation Ecosystem Experiments Tropics; Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory; Energy & Resources Group, University of California, Berkeley; Environmental Engineering Sciences, University of Florida; Department of Geography, University of California, Berkeley
- Sponsoring Org.:
- DOE SULI program at LBNL, NGEE-Tropics at LBNL, and NGEE-Tropics at UC, Berkeley
- Subject:
- 54 ENVIRONMENTAL SCIENCES
- OSTI Identifier:
- 1847332
- DOI:
- https://doi.org/10.15486/ngt/1847332
Citation Formats
Bloom, Dellena, Bomfim, Barbara, Kueppers, Lara, and Feng, Yanlei. LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones. United States: N. p., 2022.
Web. doi:10.15486/ngt/1847332.
Bloom, Dellena, Bomfim, Barbara, Kueppers, Lara, & Feng, Yanlei. LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones. United States. doi:https://doi.org/10.15486/ngt/1847332
Bloom, Dellena, Bomfim, Barbara, Kueppers, Lara, and Feng, Yanlei. 2022.
"LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones". United States. doi:https://doi.org/10.15486/ngt/1847332. https://www.osti.gov/servlets/purl/1847332. Pub date:Sat Jan 01 00:00:00 EST 2022
@article{osti_1847332,
title = {LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones},
author = {Bloom, Dellena and Bomfim, Barbara and Kueppers, Lara and Feng, Yanlei},
abstractNote = {Statement of purpose: Cyclones alter the function and composition of tropical forests, making effects of intensifying cyclones on carbon-rich forests a critical topic of study. Here, we quantified cyclone-induced damage and recovery of 21 cyclone disturbances affecting 23 pantropical forest sites between 1988-2017 utilizing leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and transformed NDVI (kNDVI) values from Google Earth Engine. Field observations collected in a meta-analysis (Bomfim et al., 2022, in review) were used to ground-truth and test effects of soil resource availability and disturbance factors on damage and recovery. This meta-analysis also served as the basis to begin vegetation index extraction, utilizing unique site and date combinations, from tropical forests effect by cyclone disturbances. We began collecting NDVI (5km resolution) from the NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), Version 5 data product (Vermote, 2019) for all case studies included, 42. Next, we began extracting Landsat data from Landsat 4, 5, and 8, courtesy of the U.S. Geological Survey, in search of higher resolution data. We selected a 3 by 3 Landsat pixel area, leading to a 90m resolution data extraction. The specific imagery used includes Landsat 4 USGS Landsat 4 TM Collection 1 Tier 1 TOA (top of atmosphere) Reflectance, Landsat 5 USGS Landsat 5 TM (thematic mapper) Collection 1 Tier 1 TOA Reflectance, and Landsat 8 USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance. Within Google Earth Engine, we selected the date and location (latitude and longitude), calculated NDVI, kNDVI, and EVI utilizing Landsat bands (see metadata_NGEE-tropics_cyclones), and extracted post- and pre-cyclone values for each case study to calculate cyclone-induced change in the vegetative index. Due to limited spatial resolution of Landsat remote sensing data, MODIS products were investigated next. First, the MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m product was used to extract 250m EVI and NDVI (Didan, 2015) and then the MCD15A3H.006 MODIS Leaf Area Index/FPAR 4-Day Global 500m product product was used to extract LAI 500m (Myneni et al., 2015). Pre- and post-cyclone values, change in the vegetative index, and standard deviation for all values are included in the main csv (see case_study_data.csv) for all vegetative indices collected, including LAI 500m, EVI 250m, NDVI 250m, NDVI 90m, kNDVI 90m, EVI 90m, and NDVI 5km. Lastly, recovery values were calculated utilizing a standardization method (see metadata_NGEE-tropics_cyclones) and recovery values for MODIS (see MODIS_recovery.csv) and Landsat (Landsat_recovery.csv) data are included.},
doi = {10.15486/ngt/1847332},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jan 01 00:00:00 EST 2022},
month = {Sat Jan 01 00:00:00 EST 2022}
}