气候变化领域集成服务门户(CCPortal): Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico

CCPortal

DOI	10.1016/j.rse.2020.111940
	Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico
	Feng Y.; Negrón-Juárez R.I.; Chambers J.Q.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. We used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems. © 2020 Elsevier Inc.
语种	英语
scopus关键词	Disasters; Ecosystems; Geomorphology; Hurricane effects; Landforms; Remote sensing; Statistical methods; Digital elevation model; Disturbance patterns; Evergreen forests; Forest disturbances; Forest structure; Natural disasters; Saffir-Simpson scale; Windward surface; Forestry; digital elevation model; disturbance; forest ecosystem; hurricane; landform; Landsat; mangrove; natural disaster; remote sensing; statistical analysis; Puerto Rico; Cocos nucifera
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179243
作者单位	University of California, Department of Geography, Berkeley, CA, United States; Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, CA, United States
推荐引用方式 GB/T 7714	Feng Y.,Negrón-Juárez R.I.,Chambers J.Q.. Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico[J],2020,247.
APA	Feng Y.,Negrón-Juárez R.I.,&Chambers J.Q..(2020).Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico.Remote Sensing of Environment,247.
MLA	Feng Y.,et al."Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico".Remote Sensing of Environment 247(2020).