Assessing tree height and density of a young forest using a consumer unmanned aerial vehicle (UAV)

Hao, Zhenbang; Lin, Lili; Post, Christopher J.; Jiang, Yusen; Li, Minghui; Wei, Nibin; Yu, Kunyong; Liu, Jian

doi:10.1007/s11056-020-09827-w

Cited by 32 publications

(20 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The flight plan was programmed to acquire the UAV imagery in the study area using the DJI Ground Station Pro application. The flight altitude was 80 m, with an 85% forward overlap rate and 80% side-lap [51]. In addition, the acquired images were directly georeferenced by the global positioning system (GPS) during the flight because the GPS is included in the P4M (https://www.dji.com/p4-multispectral/specs, 6 February 2022).…”

Section: Uav Image Acquisition and Processingmentioning

confidence: 99%

How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation

et al. 2022

Self Cite

View full text Add to dashboard Cite

Monitoring and assessing vegetation using deep learning approaches has shown promise in forestry applications. Sample labeling to represent forest complexity is the main limitation for deep learning approaches for remote sensing vegetation classification applications, and few studies have focused on the impact of sample labeling methods on model performance and model training efficiency. This study is the first-of-its-kind that uses Mask region-based convolutional neural networks (Mask R-CNN) to evaluate the influence of sample labeling methods (including sample size and sample distribution) on individual tree-crown detection and delineation. A flight was conducted over a plantation with Fokienia hodginsii as the main tree species using a Phantom4-Multispectral (P4M) to obtain UAV imagery, and a total of 2061 manually and accurately delineated tree crowns were used for training and validating (1689) and testing (372). First, the model performance of three pre-trained backbones (ResNet-34, ResNet-50, and ResNet-101) was evaluated. Second, random deleting and clumped deleting methods were used to repeatedly delete 10% from the original sample set to reduce the training and validation set, to simulate two different sample distributions (the random sample set and the clumped sample set). Both RGB image and Multi-band images derived from UAV flights were used to evaluate model performance. Each model’s average per-epoch training time was calculated to evaluate the model training efficiency. The results showed that ResNet-50 yielded a more robust network than ResNet-34 and ResNet-101 when the same parameters were used for Mask R-CNN. The sample size determined the influence of sample labeling methods on the model performance. Random sample labeling had lower requirements for sample size compared to clumped sample labeling, and unlabeled trees in random sample labeling had no impact on model training. Additionally, the model with clumped samples provides a shorter average per-epoch training time than the model with random samples. This study demonstrates that random sample labeling can greatly reduce the requirement of sample size, and it is not necessary to accurately label each sample in the image during the sample labeling process.

show abstract

Section: Uav Image Acquisition and Processingmentioning

confidence: 99%

How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, large-scale AGB mapping initiatives pursue characterizing scale-independent LiDAR-derived predictors to develop LiDAR-to-AGB models across scales [10,19]. More specifically, in relation to recent advances in forest monitoring using close-range LiDAR technologies the development of versatile, practical and new cost-effective sensors [13] and platforms [20] has seen a rapid growth, widening the applicability of the emerging LiDAR systems [12,21]. Their emergence has triggered discussions and investigations related to sensor accuracy, sensor types [22] and purpose-adapted surveying methods [23].…”

Section: Introductionmentioning

confidence: 99%

Aboveground biomass estimates from UAV LiDAR improved via contextual learning in a Norway spruce forest

Jaime¹,

Oehmcke²,

Gharun³

et al. 2024

Preprint

View full text Add to dashboard Cite

Forest structure analyses and biomass prediction systems are key tools for advancing forest trait-based ecology and ecosystem stewardship. The combination of near-field remote sensing techniques---e.g., Unmanned Aerial Vehicles (UAV) and Light Detection and Ranging (LiDAR) systems---with machine-learning methods enhances the accuracy of forest structure analyses and above ground-biomass~(AGB) estimates. In this study, we utilized a UAV-LiDAR system to map the 3D architecture of a monoculture Norway spruce forest in Davos, Switzerland, where a field-based inventory served as ground truth data. The objectives of this effort were (i) to gain insights into variation and gradients of structural traits (i.e., tree height) and (ii) to evaluate whether this knowledge of community structure may prove useful as contextual information to improve predictions of AGB at the individual tree level. To investigate the local association of structural traits, we segmented the point cloud data scene into individual trees and treated tree height as the morphological variable of interest. We then used local indicators of spatial association to determine the extent of significant local context, and defined tree neighborhoods within the forest. For the task of AGB regression, we obtained results of several feature-based regression methods (i.e., AdaBoost, Lasso and Random Forest) and evaluated these based on nested cross-validation.We applied this approach to two separate tree data sets within the same site, one being clustered and continuous, the other discontinuous and scattered in separate sampling plots. In both cases, we found evidence of enhanced AGB prediction performance in context-aware regressions, indicating that gradients in morphological tree traits across the ecosystem proxy for unveiled ecological information that influence tree growth, which can be leveraged to enhance predictions of AGB.

show abstract

“…Existing research has shown various classical algorithms are applicable for individual tree crown extraction on visible light imagery data, such as edge detection, 7 – 9 maximum/minimum detection, 10 , 11 template matching, 12 and threshold segmentation 13 – 15 But environmental factors, such as illumination intensity, 16 non-uniform background, 17 and noise, 18 restrict the applicability of edge detection, maximum/minimum detection, and template matching, causing them to be less effective.…”

Section: Introductionmentioning

confidence: 99%

Individual tree crown extraction of natural elm in UAV RGB imagery via an efficient two-stage instance segmentation model

Yang,

2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

.The advancement of near-ground remote sensing and artificial intelligence techniques has revolutionized field surveys, replacing traditional manual methods. Nevertheless, understanding and exploring the growth patterns and intricate morphology of natural elm tree crowns present significant challenges, especially when attempting to extract their features, which are often susceptible to interference from surrounding grass and vegetation. In addition, existing detection and segmentation models based on convolutional neural networks exhibit redundancies in their network architectures and employ less efficient algorithms, such as mask region-based convolutional neural networks. As a result, these models may not be the most suitable options for analyzing extensive and highly detailed remote-sensing image data. We focus on detecting trees in semi-arid regions and extracting their canopy parameters, such as canopy width and area. A training set is established by outlining a total of 20,594 tree canopies on high-spatial resolution unmanned aerial vehicle images. A two-stage instance segmentation model is proposed to develop a method for individual tree detection and efficient extraction of canopy parameters in complex natural environments. The results demonstrate the method’s capability to accurately detect the location, number, and canopy parameters (e.g., crown width and area) of individual trees in diverse natural scenes. The model achieves a detection speed of 13.3 fps@1024, with the model weight parameters totaling 8.08 M and computation requiring 8.96 Giga floating point operations per seconds (GFLOPs). Moreover, the detection accuracy and segmentation accuracy of individual trees on the validation set are reported as 0.463 and 0.465, respectively. Compared with Mack RCNN and Mask Scoring RCNN, the proposed method reduces the weight parameters and computational complexity of the model by 82.4%, 83.5% and 96.8%, 92.8%, respectively, while increasing the inference speed by 47.4% and 26.3%. This method offers an efficient and accurate solution for obtaining the structural parameters of individual trees.

show abstract

Assessing tree height and density of a young forest using a consumer unmanned aerial vehicle (UAV)

Cited by 32 publications

References 79 publications

How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation

How Does Sample Labeling and Distribution Affect the Accuracy and Efficiency of a Deep Learning Model for Individual Tree-Crown Detection and Delineation

Aboveground biomass estimates from UAV LiDAR improved via contextual learning in a Norway spruce forest

Individual tree crown extraction of natural elm in UAV RGB imagery via an efficient two-stage instance segmentation model

Contact Info

Product

Resources

About