Tree Species Classification in Subtropical Natural Forests Using High-Resolution UAV RGB and SuperView-1 Multispectral Imageries Based on Deep Learning Network Approaches: A Case Study within the Baima Snow Mountain National Nature Reserve, China

Chen, Xianggang; Shen, Xin; Cao, Lin

doi:10.3390/rs15102697

Cited by 6 publications

(3 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In com-parison to existing studies that have primarily focused on different plant types in similar natural environments, our research looks at applying UAVs and MS technology to classify the specific plant species of BLP. Comparing our findings to existing studies, our research builds upon the foundation laid by prior researchers [53,54,75] in utilising MS sensor technology and UAVs for ecological mapping using different DL models. Our findings reveal that the DL U-Net model outperformed other models, exhibiting promising results in the classification of BLP during testing.…”

Section: Discussionmentioning

confidence: 64%

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

Amarasingam,

Vanegas,

Hele

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species.

show abstract

Section: Discussionmentioning

confidence: 64%

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

Amarasingam,

Vanegas,

Hele

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Active data include light detection and ranging (LiDAR) and synthetic aperture radar (SAR). The primary UAV data are LiDAR [13][14][15], HIS [16,17], MSI [7,18], and the red, green, and blue image (RGB) [19][20][21][22]. Auxiliary data encompass a range of variables, including elevation, slope, slope direction, temperature, and precipitation.…”

Section: Remote Sensing Data For Ts Classificationmentioning

confidence: 99%

A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods

Zhong,

Dai,

Fang

et al. 2024

Forests

View full text Add to dashboard Cite

Timely and accurate information on tree species is of great importance for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. A review of the remote sensing data and deep learning tree species classification methods is lacking in its analysis of unimodal and multimodal remote sensing data and classification methods in this field. To address this gap, we search for major trends in remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss some limitations of tree species classification.

show abstract

“…Over the past decade, the use of unmanned aerial vehicles (UAVs) to assess forest regeneration, monitor forest health and identify individual trees has grown rapidly due to the advantages of low cost, high efficiency, and high precision [14]. Furthermore, recent technological advances integrating UAV imagery and machine learning algorithms have provided an efficient method for tree species mapping.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Distribution of High-Value Broadleaf Tree Crowns through Unmanned Aerial Vehicle Image Analysis Using Deep Learning

Htun,

Owari,

Tsuyuki

et al. 2024

Algorithms

View full text Add to dashboard Cite

High-value timber species with economic and ecological importance are usually distributed at very low densities, such that accurate knowledge of the location of these trees within a forest is critical for forest management practices. Recent technological developments integrating unmanned aerial vehicle (UAV) imagery and deep learning provide an efficient method for mapping forest attributes. In this study, we explored the applicability of high-resolution UAV imagery and a deep learning algorithm to predict the distribution of high-value deciduous broadleaf tree crowns of Japanese oak (Quercus crispula) in an uneven-aged mixed forest in Hokkaido, northern Japan. UAV images were collected in September and October 2022 before and after the color change of the leaves of Japanese oak to identify the optimal timing of UAV image collection. RGB information extracted from the UAV images was analyzed using a ResU-Net model (U-Net model with a Residual Network 101 (ResNet101), pre-trained on large ImageNet datasets, as backbone). Our results, confirmed using validation data, showed that reliable F1 scores (>0.80) could be obtained with both UAV datasets. According to the overlay analyses of the segmentation results and all the annotated ground truth data, the best performance was that of the model with the October UAV dataset (F1 score of 0.95). Our case study highlights a potential methodology to offer a transferable approach to the management of high-value timber species in other regions.

show abstract

Tree Species Classification in Subtropical Natural Forests Using High-Resolution UAV RGB and SuperView-1 Multispectral Imageries Based on Deep Learning Network Approaches: A Case Study within the Baima Snow Mountain National Nature Reserve, China

Cited by 6 publications

References 75 publications

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods

Mapping the Distribution of High-Value Broadleaf Tree Crowns through Unmanned Aerial Vehicle Image Analysis Using Deep Learning

Contact Info

Product

Resources

About