A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory

Zhu, Yanyan; Li, Dongni; Fan, Jiangchuan; Zhang, Huaiqing; Eichhorn, Markus P.; Wang, Xiangjun; Yun, Ting

doi:10.3389/fpls.2023.1109443

Cited by 12 publications

(5 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These findings align with the findings reported by others [48][49][50]. The subsequent examination of the effects of these factors on poplar growth unveiled notable discrepancies in how stand density, rainfall, altitude, and temperature at various levels influenced the growth rate of poplar trees across different sizes [51]. For ease of describing poplar growth, we classified the trees into three diameter ranges according to their breast diameters: small (5-15 cm), medium (15-30 cm), and large (exceeding 30 cm).…”

Section: Discussionsupporting

confidence: 89%

Constructing a Model of Poplus spp. Growth Rate Based on the Model Fusion and Analysis of Its Growth Rate Differences and Distribution Characteristics under Different Classes of Environmental Indicators

Zhang,

Liu,

Feng

et al. 2023

Forests

View full text Add to dashboard Cite

Poplar (Poplus spp.) is an important forest species widely distributed in China of great significance in identifying factors that clearly influence its growth rate in order to achieve effective control of poplar growth. In this study, we selected 16 factors, including tree size, competition, climate, location, topography, and soil characteristics, to construct linear regression (LR), multilayer perceptron (MLP), k-nearest neighbor regression (KNN), gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN) models based on the poplar growth rate. Using model fusion methods, the fitting accuracy and estimation capability were improved. The relative importance of each variable in estimating the poplar growth rate was analyzed using the permutation importance evaluation. The results showed the following: (1) the model fusion approach significantly improved the estimation accuracy of the poplar growth rate model with an R2 of 0.893; (2) the temperature and precipitation exhibited the highest importance in poplar growth; (3) the forest stand density, precipitation, elevation, and temperature had significant variations in growth rates among different-sized poplar trees within different ranges; (4) low-forest stand density, high-precipitation, low-elevation, and high-temperature environments significantly increased the poplar growth rate and had a larger proportion of large-sized individuals with high growth rates. In conclusion, environmental factors significantly influence poplar growth, and corresponding planting and protection measures should be tailored to different growth environments to effectively enhance the growth of poplar plantations.

show abstract

Section: Discussionsupporting

confidence: 89%

Constructing a Model of Poplus spp. Growth Rate Based on the Model Fusion and Analysis of Its Growth Rate Differences and Distribution Characteristics under Different Classes of Environmental Indicators

Zhang,

Liu,

Feng

et al. 2023

Forests

View full text Add to dashboard Cite

show abstract

“…But the processing of forest fire images presents unique challenges. The complex backgrounds, inadequate contrast, visual obstructions, and irregular shapes of fires all increase the difficulty of image segmentation [3]. These factors lead to the inefficiency of traditional image processing methods in accurately identifying and segmenting fire areas, failing to meet the needs for rapid response [4,5].…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Mu,

Ou,

Chen

et al. 2024

Drones

View full text Add to dashboard Cite

Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learning-based forest fire surveillance has significantly accelerated. Nevertheless, the integration of graph convolutional networks (GCNs) in forest fire detection remains relatively underexplored. In this context, we introduce a novel superpixel-based graph convolutional network (SCGCN) for forest fire image segmentation. Our proposed method utilizes superpixels to transform images into a graph structure, thereby reinterpreting the image segmentation challenge as a node classification task. Additionally, we transition the spatial graph convolution operation to a GraphSAGE graph convolution mechanism, mitigating the class imbalance issue and enhancing the network’s versatility. We incorporate an innovative loss function to contend with the inconsistencies in pixel dimensions within superpixel clusters. The efficacy of our technique is validated on two different forest fire datasets, demonstrating superior performance compared to four alternative segmentation methodologies.

show abstract

“…Forests play a vital role in the land ecosystem of the Earth. They are indispensable for conserving biodiversity, protecting watersheds, capturing carbon, mitigating climate change effects [1,2], maintaining ecological balance, regulating rainfall patterns, and ensuring the stability of large-scale climate systems [3,4]. As a result, the timely and precise monitoring and mapping of forest cover has emerged as a vital aspect of sustainable forest management and the monitoring of ecosystem transformations [5].…”

Section: Introductionmentioning

confidence: 99%

Semi-FCMNet: Semi-Supervised Learning for Forest Cover Mapping from Satellite Imagery via Ensemble Self-Training and Perturbation

et al. 2023

View full text Add to dashboard Cite

Forest cover mapping is of paramount importance for environmental monitoring, biodiversity assessment, and forest resource management. In the realm of forest cover mapping, significant advancements have been made by leveraging fully supervised semantic segmentation models. However, the process of acquiring a substantial quantity of pixel-level labelled data is prone to time-consuming and labour-intensive procedures. To address this issue, this paper proposes a novel semi-supervised-learning-based semantic segmentation framework that leverages limited labelled and numerous unlabelled data, integrating multi-level perturbations and model ensembles. Our framework incorporates a multi-level perturbation module that integrates input-level, feature-level, and model-level perturbations. This module aids in effectively emphasising salient features from remote sensing (RS) images during different training stages and facilitates the stability of model learning, thereby effectively preventing overfitting. We also propose an ensemble-voting-based label generation strategy that enhances the reliability of model-generated labels, achieving smooth label predictions for challenging boundary regions. Additionally, we designed an adaptive loss function that dynamically adjusts the focus on poorly learned categories and dynamically adapts the attention towards labels generated during both the student and teacher stages. The proposed framework was comprehensively evaluated using two satellite RS datasets, showcasing its competitive performance in semi-supervised forest-cover-mapping scenarios. Notably, the method outperforms the fully supervised approach by 1–3% across diverse partitions, as quantified by metrics including mIoU, accuracy, and mPrecision. Furthermore, it exhibits superiority over other state-of-the-art semi-supervised methods. These results indicate the practical significance of our solution in various domains, including environmental monitoring, forest management, and conservation decision-making processes.

show abstract

A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory

Cited by 12 publications

References 62 publications

Constructing a Model of Poplus spp. Growth Rate Based on the Model Fusion and Analysis of Its Growth Rate Differences and Distribution Characteristics under Different Classes of Environmental Indicators

Constructing a Model of Poplus spp. Growth Rate Based on the Model Fusion and Analysis of Its Growth Rate Differences and Distribution Characteristics under Different Classes of Environmental Indicators

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Semi-FCMNet: Semi-Supervised Learning for Forest Cover Mapping from Satellite Imagery via Ensemble Self-Training and Perturbation

Contact Info

Product

Resources

About