Evaluating Effects of Post-Fire Climate and Burn Severity on the Early-Term Regeneration of Forest and Shrub Communities in the San Gabriel Mountains of California from Sentinel-2(MSI) Images
[1] Guilin Univ Technol, Coll Environm Sci & Engn, 12 Jiangan St, Guilin 541004, Peoples R China
[2] Guilin Univ Technol, Coll Geomat & Geoinformat, 12 Jiangan St, Guilin 541004, Peoples R China
来源:
FORESTS
|
2022年
/
13卷
/
07期
基金:
中国国家自然科学基金;
关键词:
fire ecology;
burn severity;
post-fire vegetation regeneration;
Sentinel-2;
images;
time-series;
climate factors;
topography;
VEGETATION INDEXES;
TIME-SERIES;
FIRE SEVERITY;
PINE FORESTS;
WILDFIRE;
RECOVERY;
COVER;
RESPONSES;
DYNAMICS;
CLASSIFICATION;
D O I:
10.3390/f13071060
中图分类号:
S7 [林业];
学科分类号:
0829 ;
0907 ;
摘要:
Studying the early changes in post-fire vegetation communities may improve the overall resilience of forests. The necessity for doing so was demonstrated by the Bobcat Fire, which seriously threatened the central San Gabriel Mountains and the Angeles National Forest in California. This study aimed to monitor and quantify the effects of climatological and topographic conditions along with burn severity on early (within 1 year) post-fire forests and shrubs community regeneration. In this study, we used Sentinel-2(MSI) intensive time-series imagery (July 2020-October 2021) to make a confusion matrix combined with 389 vegetation sample points on Google Earth Pro. The overall accuracy (OA) and the Kappa coefficient, calculated from the confusion matrix, were used as evaluation parameters to validate the classification results. With multiple linear regression models and Environmental Systems Research Institute (ESRI) historical images, we analyzed the effects of climate and slope aspects on the regeneration of post-fire forest and shrub communities. We also quantitatively analyzed the regeneration rates based on five burn severity types. The results show that the normalized burning rate (NBR) was the most accurate vegetation classification indicator in this study (OA: 92.3-99.5%, Kappa: 0.88-0.98). The vegetation classification accuracy based on SVM is about 6.6% higher than K-Means. The overall accuracy of the burn area is 94.87%. Post-fire climate factors had a significant impact on the regeneration of the two vegetation communities (R-2: 0.42-0.88); the optimal regeneration slope was 15-35 degrees; and the fire severity changed the original competition relationship and regeneration rate. The results provide four main insights into the regeneration of post-fire vegetation communities: (1) climate factors in the first regenerating season have important impacts on the regeneration of forest and shrub communities; (2) daytime duration and rainfall are the most significant factors for forests and shrubs regeneration; (3) tolerable low burn severity promotes forests regeneration; and (4) forests have a certain ability to resist fires, while shrubs can better tolerate high-intensity fire ecology. This study could support the implementation of strategies for regionalized forest management and the targeted enhancement of post-fire vegetation community resilience.