Quantifying fire-induced changes in ground vegetation using bitemporal terrestrial laser scanning

被引:1
作者
Tienaho, Noora [1 ]
Saarinen, Ninni [1 ]
Yrttimaa, Tuomas [1 ]
Kankare, Ville [1 ]
Vastaranta, Mikko [1 ]
机构
[1] Univ Eastern Finland, Sch Forest Sci, POB 111, Joensuu 80101, Finland
基金
芬兰科学院;
关键词
biomass; boreal forest; controlled burning; forest fires; LiDAR; surface differencing; surface fires; ABOVEGROUND BIOMASS; FOREST STRUCTURE; AIRBORNE LIDAR; NUTRIENT; HEIGHT; STANDS;
D O I
10.14214/sf.23061
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Forest fires pose a significant threat to forest carbon storage and sinks, yet they also play a crucial role in the natural dynamics of boreal forests. Accurate quantification of biomass changes resulting from forest fires is essential for damage assessment and controlled burning evaluation. This study utilized terrestrial laser scanning (TLS) to quantify changes in ground vegetation resulting from low -intensity surface fires. TLS data were collected before and after controlled burnings at eight one -hectare test sites in Scots pine ( Pinus sylvestris L.) dominated boreal forests in Finland. A surface differencing-based method was developed to identify areas exposed to fire. Validation, based on visual interpretation of 1 x 1 m surface patches (n = 320), showed a recall, precision, and F1 -score of 0.9 for the accuracy of identifying burned surfaces. The developed method allowed the assessment of the magnitude of fire -induced vegetation changes within the test sites. The proportions of burned 1 x 1 m areas within the test sites varied between 51-96%. Total volumetric change in ground vegetation was on average -1200 m 3 ha -1 , with burning reducing the vegetation volume by 1700 m 3 ha -1 and vegetation growth increasing it by 500 m 3 ha -1 . Substantial variations in the volumetric changes within and between the test sites were detected, highlighting the complex dynamics of surface fires, and emphasizing the importance of having observations from multiple sites. This study demonstrates that bitemporal TLS measurements provide a robust means for characterizing fire -induced changes, facilitating the assessment of the impact of surface fires on forest ecosystems.
引用
收藏
页数:20
相关论文
共 48 条
  • [41] A Review of the Applications of Remote Sensing in Fire Ecology
    Szpakowski, David M.
    Jensen, Jennifer L. R.
    [J]. REMOTE SENSING, 2019, 11 (22)
  • [42] Vierling LA, 2012, CAN J REMOTE SENS, V38, P709
  • [43] The effects of plant litter on vegetation: a meta-analysis
    Xiong, SJ
    Nilsson, C
    [J]. JOURNAL OF ECOLOGY, 1999, 87 (06) : 984 - 994
  • [44] Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar
    Yao, Tian
    Yang, Xiaoyuan
    Zhao, Feng
    Wang, Zhuosen
    Zhang, Qingling
    Jupp, David
    Lovell, Jenny
    Culvenor, Darius
    Newnham, Glenn
    Ni-Meister, Wenge
    Schaaf, Crystal
    Woodcock, Curtis
    Wang, Jindi
    Li, Xiaowen
    Strahler, Alan
    [J]. REMOTE SENSING OF ENVIRONMENT, 2011, 115 (11) : 2965 - 2974
  • [45] Yrttimaa Tuomas, 2021, Zenodo, DOI 10.5281/ZENODO.5779288
  • [46] Performance of terrestrial laser scanning to characterize managed Scots pine (Pinus sylvestris L.) stands is dependent on forest structural variation
    Yrttimaa, Tuomas
    Saarinen, Ninni
    Kankare, Ville
    Hynynen, Jari
    Huuskonen, Saija
    Holopainen, Markus
    Hyyppa, Juha
    Vastaranta, Mikko
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 168 : 277 - 287
  • [47] Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests
    Yrttimaa, Tuomas
    Saarinen, Ninni
    Kankare, Ville
    Liang, Xinlian
    Hyyppa, Juha
    Holopainen, Markus
    Vastaranta, Mikko
    [J]. REMOTE SENSING, 2019, 11 (12)
  • [48] UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland
    Zhao, Yujin
    Liu, Xiaoliang
    Wang, Yang
    Zheng, Zhaoju
    Zheng, Shuxia
    Zhao, Dan
    Bai, Yongfei
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 101