Evaluating the reliability of bi-temporal canopy height model generated from airborne laser scanning for monitoring forest growth in boreal forest region

被引:3
作者
Yu, Zhexiu [1 ]
Qi, Jianbo [2 ]
Zhao, Xun [1 ]
Huang, Huaguo [1 ,3 ,4 ]
机构
[1] Beijing Forestry Univ, State Key Lab Efficient Prod Forest Resources, Beijing 100083, Peoples R China
[2] Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China
[3] Beijing Forestry Univ, State Forestry & Grassland Adm, Key Lab Forest Resources & Environm Management, Beijing 100083, Peoples R China
[4] Beijing Forestry Univ, Coll Forestry, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-temporal; forest canopy height change; canopy height model; spatial scale; MULTITEMPORAL LIDAR; ABOVEGROUND BIOMASS; CARBON DYNAMICS; DENSITY; TREES; SIZE;
D O I
10.1080/17538947.2024.2345725
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The discrepancies in data across different phases and the unexplored optimal spatial resolution present challenges when using multi-temporal canopy height models to accurately discern actual forest growth. In this study, we evaluated the reliability of bi-temporal CHMs to characterize growth changes in a boreal natural forest over a four-year period. A maximum mosaic method was introduced to construct a CHM from various flight strips, aimed at minimizing data alignment errors. Subsequently, the canopy height and height changed derived from six different height percentile metrics and five spatial resolutions were evaluated. The results showed that higher resolution (e.g., < 2 m) and lower height metrics (e.g., 85th height percentile) consistently underestimated. Tree growth correlation with individual segmentation surpassed field-measurements at all resolutions and height metrics. for the optimal resolution and height metrics, the results suggest that using a 95th height percentile the 10 m scale effectively represents both canopy height (R-2012(2) = 0.95, RMSE2012 = 0.88 m, rRMSE(2012 ) = 5.83%; R-2016(2) = 0.96, RMSE2016 = 1.11 m, rRMSE(2016) = 6.91%) and height changes (R-2 = 0.59, RMSE = 0.86 m, rRMSE = 18.38%). This study demonstrates the necessity of carefully evaluating data characteristics and resolutions when employing multi-temporal CHM for forest dynamics monitoring.
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页数:29
相关论文
共 71 条
[71]   Research on volume prediction of single tree canopy based on three-dimensional (3D) LiDAR and clustering segmentation [J].
Zhou, Hang ;
Zhang, Junxiong ;
Ge, Luzhen ;
Yu, Xiaowei ;
Wang, Yonglin ;
Zhang, Chunlong .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2021, 42 (02) :738-755