Monitoring of Monthly Height Growth of Individual Trees in a Subtropical Mixed Plantation Using UAV Data

Tang

et al. 2023

Drones

Self Cite

Light Detection and Ranging (LiDAR) points and high-resolution RGB image-derived points have been successfully used to extract tree structural parameters. However, the differences in extracting individual tree structural parameters among different tree species have not been systematically studied. In this study, LiDAR data and images were collected using unmanned aerial vehicles (UAVs) to explore the differences in digital elevation model (DEM) and digital surface models (DSM) generation and tree structural parameter extraction for different tree species. It was found that the DEMs generated based on both forms of data, LiDAR and image, exhibited high correlations with the field-measured elevation, with an R2 of 0.97 and 0.95, and an RMSE of 0.24 and 0.28 m, respectively. In addition, the differences between the DSMs are small in non-vegetation areas, whereas the differences are relatively large in vegetation areas. The extraction results of individual tree crown width and height based on two kinds of data are similar when all tree species are considered. However, for different tree species, the Cinnamomum camphora exhibits the greatest accuracy in terms of crown width extraction, with an R2 of 0.94 and 0.90, and an RMSE of 0.77 and 0.70 m for LiDAR and image points, respectively. In comparison, for tree height extraction, the Magnolia grandiflora exhibits the highest accuracy, with an R2 of 0.89 and 0.90, and an RMSE of 0.57 and 0.55 m for LiDAR and image points, respectively. The results indicate that both LiDAR and image points can generate an accurate DEM and DSM. The differences in the DEMs and DSMs between the two data types are relatively large in vegetation areas, while they are small in non-vegetation areas. There are significant differences in the extraction results of tree height and crown width between the two data sets among different tree species. The results will provide technical guidance for low-cost forest resource investigation and monitoring.

Section: Introductionmentioning

confidence: 99%

Study on the Differences between the Extraction Results of the Structural Parameters of Individual Trees for Different Tree Species Based on UAV LiDAR and High-Resolution RGB Images

Tang

et al. 2023

Drones

Self Cite

“…For example, Krause et al [19] employed UAV images for individual tree height extraction, resulting in an RMSE of 0.48 m when compared to the measured data. Tang et al [20] used multi-temporal UAV-RGB stereo images to monitor the monthly growth of tree height in subtropical mixed forests. The R 2 of extracting individual tree heights based on UAV-RGB stereo images is 0.99, and the RMSE is 0.36 m. The total growth of tree height within a year is 46.5 cm, and the fastest growth changes occur in May and June.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Seasonal Growth of Eucalyptus Plantation under Different Forest Age and Slopes Based on Multi-Temporal UAV Stereo Images

Tang,

Lei,

You

et al. 2023

Forests

Self Cite

Eucalyptus grows rapidly and can grow up to 10 m per year, making them an important source of wood supply. Guangxi is a typical karst terrain, with hills and plains being the main growth areas for Eucalyptus. The differences in terrain can affect the seasonal growth of Eucalyptus plantations at different forest ages, which in turn affects the stocking of Eucalyptus. Currently, indiscriminate logging and management not only cause resource waste but also bring economic losses to operators. In this study, an unmanned aerial vehicle (UAV) was used to obtain multi-temporal stereo images to investigate the seasonal growth differences in structural parameters of individual Eucalyptus trees under different forest ages and slopes, providing data support for the precise management of Eucalyptus resources. The results showed that the tree height, crown width, diameter at breast height (DBH), and aboveground biomass (AGB) of individual trees, extracted based on UAV stereo images, are highly correlated with the field measured data, with an R2 of 0.99, 0.78, 0.75, and 0.92, and RMSE of 0.21 m, 0.16 m, 1.17 cm, and 3.79 kg/tree, respectively. The growth of Eucalyptus structural parameters varies in different seasons, with faster growth in spring and autumn, accounting for 76.39%, 73.75%, 73.65%, and 73.68% of the total annual growth, respectively. The growth of different structural parameters of individual trees is closely related to forest age, with tree height, crown width, and DBH gradually slowing down with the increase in forest age, while AGB shows a trend of first increasing and then decreasing. The differences in terrain also have a certain impact on the growth of individual Eucalyptus tree structural parameters. For individual 1-year-old and 3-year-old Eucalyptus trees, those located on gentle slopes grow faster in spring and autumn than those located on flat land. However, those located on flat land grow faster in summer and winter than those located on gentle slopes. For tree height, crown width, DBH, and AGB, the maximum annual growth differences between Eucalyptus trees on gentle slopes and flat are 3.17 m, 0.26 m, 1.9 cm, and 9.27 kg/tree, respectively. The results indicate that based on UAV stereo images, the individual tree structural parameters in Eucalyptus plantations under different forest ages and slopes can be extracted, as well as seasonal growth can be monitored, providing data support for logging, planting, and management of Eucalyptus plantations.

“…UAVs have the advantages of a low flight altitude, flexible operation, and low costs [18][19][20][21]. With the development of Structure from Motion (SfM) and multi-view stereo vision technology, high-density point data can be quickly extracted based on stereo overlapping images from UAVs [22,23].…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Aboveground Biomass Estimation Based on UAV Stereo Images in a Eucalyptus Plantation

Liu,

Lei,

You

et al. 2023

Forests

Self Cite

As one of the three fastest-growing tree species in the world, eucalyptus grows rapidly, with a monthly growth rate of up to 1 m and a maximum annual growth rate of up to 10 m. Therefore, ways to accurately and quickly obtain the aboveground biomass (AGB) of eucalyptus in different growth stages at a low cost are the foundation of achieving eucalyptus growth-change monitoring and precise management. Although Light Detection and Ranging (LiDAR) can achieve high-accuracy estimations of individual eucalyptus tree biomasses, the cost of data acquisition is relatively high. While the AGB estimation accuracy of high-resolution images may be affected by a lack of forest vertical structural information, stereo images obtained using unmanned aerial vehicles (UAVs) can not only provide horizontal structural information but also vertical structural information through derived point data, demonstrating strong application potential in estimating the biomass of eucalyptus plantations. To explore the potential of UAV stereo images for estimating the AGB of individual eucalyptus trees and further investigate the impact of stereo-image-derived features on the construction of biomass models, in this study, UAVs equipped with consumer-grade cameras were used to obtain multitemporal stereo images. Different features, such as spectral features, texture, tree height, and crown area, were extracted to estimate the AGB of individual eucalyptus trees of five different ages with three algorithms. The different features extracted based on the UAV images had different effects on estimating AGB in individual eucalyptus trees. By estimating eucalyptus AGB using only spectrum features, we found that tree height had the greatest impact, with its R2 value increasing by 0.28, followed by forest age. Other features, such as spectrum, texture, and crown area, had relatively small effects. For the three algorithms, the estimation accuracy of the CatBoost algorithm was the highest, with an R2 ranging from 0.65 to 0.90, and the normalized root-mean-square error (NRMSE) ranged from 0.08 to 0.15. This was followed by the random forest algorithm. The ridge regression algorithm had the lowest accuracy, with an R2 ranging from 0.34 to 0.82 and an NRMSE value ranging from 0.11 to 0.21. The AGB model that we established with forest age, TH, crown area, and HOM-B feature variables using the CatBoost algorithm had the best estimation accuracy, with an R2 of 0.90 and an NRMSE of 0.08. The results indicated that accurately estimating the AGB of individual eucalyptus trees can be achieved based on stereo images obtained using UAVs equipped with affordable, consumer-grade cameras. This paper can provide methodological references and technical support for estimating forest biomass, carbon storage, and other structural parameters based on UAV images.