Monitoring of Monthly Height Growth of Individual Trees in a Subtropical Mixed Plantation Using UAV Data
Monitoring of Monthly Height Growth of Individual Trees in a Subtropical Mixed Plantation Using UAV Data
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利用无人机数据监测亚热带混交林单株树的月高度生长
DOI：
10.3390/rs15020326
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发表时间：
2023-01-01
期刊：
影响因子：
5
通讯作者：
Chen，Jianjun
中科院分区：
文献类型：
--
作者：
Tang，Xu;You，Haotian;Chen，Jianjun
对树木高生长变化的评估可以作为各种生态过程模拟的准确依据。然而，大多数关于树木高度生长变化的研究都是在年度尺度上进行的。这使得很难获得基本数据来校正一年内树木高度生长估计的时间差。在这项研究中，数字高程模型（DEM）的基础上立体图像和光探测和测距（LiDAR）数据获得的无人机（UAV）。采用分水岭分割算法对树冠进行分割，提取树冠内的最大值作为树高。随后，每个树的高度生长的一个月尺度的时间序列提取模拟的时间差校正区域树高估计在一年内。以此验证了月尺度时差改正方法的可行性。结果表明，基于无人机立体影像的DEM与基于无人机LiDAR的DEM具有较好的相关性，相关系数R2 = 0.96，RMSE = 0.28 m。基于无人机影像的冠层高度模型（CHM）提取的树高与实测树高相关性较好，相关系数R2 = 0.99，RMSE = 0.36 m。不考虑树种，全年各月总高生长量为46.53cm。树高生长量变化最显著的是5月（14.26 cm）和6月（14.67 cm）。年高生长量以鹅掌楸最高（58.64 cm），桂花最低（34.00 cm）。通过分析各月树木高生长量的估算值，得出不同树种间存在显著差异的结论。在鹅掌楸树种的情况下，生长季节主要发生在4月至7月。在这个季节，记录了56.92厘米的生长，占年生长量的97.08%。在这种情况下的榕树concinna树种，树高是在一年中的每个月的增长状态。树高生长量估计值的变化以5 ~ 8月较高（生长量为44.24cm，占全年生长量的77.09%）。在将时间差校正应用于区域树木生长估计之后，树木高度生长估计（基于月尺度）的变化的提取结果与UAV图像导出的树木的高度相关。相关系数R2 = 0.99，RMSE = 0.26m。结果表明，在月尺度上的高度增长估计的变化，可以准确地确定采用无人机立体图像。此外，该结果还可以为区域树木生长时差的校正提供基础数据，并进一步为其他森林结构参数的区域时差校正提供技术和方法指导。
The assessment of changes in the height growth of trees can serve as an accurate basis for the simulation of various ecological processes. However, most studies conducted on changes in the height growth of trees are on an annual scale. This makes it difficult to obtain basic data for correcting time differences in the height growth estimates of trees within a year. In this study, the digital elevation models (DEMs) were produced based on stereo images and light detection and ranging (LiDAR) data obtained by unmanned aerial vehicles (UAVs). Individual tree crowns were segmented by employing the watershed segmentation algorithm and the maximum value within each crown was extracted as the height of each tree. Subsequently, the height growth of each tree on a monthly-scale time series was extracted to simulate the time difference correction of regional tree height estimates within a year. This was used to verify the feasibility of the time difference correction method on a monthly scale. It is evident from the results that the DEM based on UAV stereo images was closely related to the DEM based on UAV LiDAR, with correlation coefficients of R2 = 0.96 and RMSE = 0.28 m. There was a close correlation between the tree height extracted from canopy height models (CHMs) based on UAV images and the measured tree height, with correlation coefficients of R2 = 0.99, and RMSE = 0.36 m. Regardless of the tree species, the total height growth in each month throughout the year was 46.53 cm. The most significant changes in the height growth of trees occurred in May (14.26 cm) and June (14.67 cm). In the case of the Liriodendron chinense tree species, the annual height growth was the highest (58.64 cm) while that of the Osmanthus fragrans tree species was the lowest (34.00 cm). By analyzing the height growth estimates of trees each month, it was concluded that there were significant differences among various tree species. In the case of the Liriodendron chinense tree species, the growth season occurred primarily from April to July. During this season, 56.92 cm of growth was recorded, which accounted for 97.08% of the annual growth. In the case of the Ficus concinna tree species, the tree height was in a state of growth during each month of the year. The changes in the height growth estimates of the tree were higher from May to August (44.24 cm of growth, accounting for 77.09% of the annual growth). After applying the time difference correction to the regional tree growth estimates, the extraction results of the changes in the height growth estimates of the tree (based on a monthly scale) were correlated with the height of the UAV image-derived tree. The correlation coefficients of R2 = 0.99 and RMSE = 0.26 m were obtained. The results demonstrate that changes in the height growth estimates on a monthly scale can be accurately determined by employing UAV stereo images. Furthermore, the results can provide basic data for the correction of the time differences in the growth of regional trees and further provide technical and methodological guidance for regional time difference correction of other forest structure parameters.