The index outperforms traditional metrics, offering more accurate and comprehensive forest loss mapping and providing vital information for forest management and disaster recovery.
Forests play a vital role in maintaining biodiversity and sequestering carbon, but they are highly vulnerable to disturbances such as snow and ice storms. These events can cause significant damage, including broken branches, uprooted trees, and long-term impacts like increased susceptibility to pests. Traditional methods for assessing forest loss, such as measuring canopy cover and tree height, often fail to capture the vertical structural changes caused by these storms. While remote sensing technologies like satellite imagery and optical methods have been used to monitor forest changes, they mainly focus on horizontal shifts in the canopy and overlook crucial vertical changes. LiDAR technology, on the other hand, excels in capturing vertical distribution changes, making it a powerful tool for accurately assessing forest damage after snow and ice storms. Efficiently evaluating such damage is essential for effective recovery planning and forest management.
A study (DOI: 10.1016/j.plaphe.2025.100057) published in Plant Phenomics on 27 May 2025 by Yuanyong Dian’s team, Huazhong Agricultural University, introduces LFSCI as a more accurate and comprehensive method for assessing forest loss caused by snow and ice storms, offering improved detection of vertical structural changes compared to traditional metrics.
The study introduced LFSCI to assess forest loss from snow and ice storms using bitemporal UAV LiDAR point data. The research evaluated the effectiveness of LFSCI in detecting changes in forest structure compared to traditional metrics like canopy cover (CC), leaf area index (LAI), and tree height (TH). The analysis of LFSCI mapping showed a clear correlation with changes in CC and LAI, but a weaker correlation with TH. The study revealed that 84.2% of the forests in the study area were impacted by the storms, with LFSCI values ranging from 0 to 20. In comparison to other metrics, LFSCI was more effective in detecting various types of damage, including cases where damage occurred in the sub-canopy or where branches were bent. LFSCI demonstrated superior performance in identifying forest loss patterns, outperforming CC, LAI, and TH, especially in cases of partial canopy loss or structural damage. Further analysis showed that LFSCI had a positive correlation with CC and LAI changes, while TH changes did not correlate strongly with other metrics. Additionally, the study found that higher point densities in LiDAR data improved the accuracy of LFSCI, with a point density of 50 pt/m² recommended for effective snow and ice storm impact detection. The research also analyzed the influence of vegetation type, tree height, and terrain on forest loss severity, with results indicating that taller trees and certain vegetation types, like pure broad-leaved forests, were more susceptible to storm damage. Overall, LFSCI proved to be a more reliable tool for assessing forest loss compared to traditional methods, highlighting its potential for post-storm evaluations and forest management.
LFSCI represents a significant advancement in the ability to assess forest damage from snow and ice storms. By capturing both horizontal and vertical structural changes in forests, LFSCI provides a more accurate and comprehensive understanding of storm-induced forest loss compared to traditional methods. The results from this study offer valuable insights for forest management and disaster recovery, emphasizing the need for improved monitoring techniques that can withstand the challenges posed by natural disturbances.
###
References
DOI
10.1016/j.plaphe.2025.100057
Original URL
https://doi.org/10.1016/j.plaphe.2025.100057
Funding information
This research was funded by the National Natural Science Foundation of China (Grant number 32071683) and the Project of Fundamental Research Funds for the Central Universities (2662023YLPY003).
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.