Research

17 pages, 5903 KiB

Open AccessArticle

Enhancing Tree Species Identification in Forestry and Urban Forests through Light Detection and Ranging Point Cloud Structural Features and Machine Learning

by Steffen Rust and Bernhard Stoinski

Forests 2024, 15(1), 188; https://doi.org/10.3390/f15010188 - 17 Jan 2024

Viewed by 1037

Abstract

As remote sensing transforms forest and urban tree management, automating tree species classification is now a major challenge to harness these advances for forestry and urban management. This study investigated the use of structural bark features from terrestrial laser scanner point cloud data [...] Read more.

As remote sensing transforms forest and urban tree management, automating tree species classification is now a major challenge to harness these advances for forestry and urban management. This study investigated the use of structural bark features from terrestrial laser scanner point cloud data for tree species identification. It presents a novel mathematical approach for describing bark characteristics, which have traditionally been used by experts for the visual identification of tree species. These features were used to train four machine learning algorithms (decision trees, random forests, XGBoost, and support vector machines). These methods achieved high classification accuracies between 83% (decision tree) and 96% (XGBoost) with a data set of 85 trees of four species collected near Krakow, Poland. The results suggest that bark features from point cloud data could significantly aid species identification, potentially reducing the amount of training data required by leveraging centuries of botanical knowledge. This computationally efficient approach might allow for real-time species classification. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

20 pages, 24540 KiB

Open AccessArticle

FireViT: An Adaptive Lightweight Backbone Network for Fire Detection

by Pengfei Shen, Ning Sun, Kai Hu, Xiaoling Ye, Pingping Wang, Qingfeng Xia and Chen Wei

Forests 2023, 14(11), 2158; https://doi.org/10.3390/f14112158 - 30 Oct 2023

Viewed by 1213

Abstract

Fire incidents pose a significant threat to human life and property security. Accurate fire detection plays a crucial role in promptly responding to fire outbreaks and ensuring the smooth execution of subsequent firefighting efforts. Fixed-size convolutions struggle to capture the irregular variations in [...] Read more.

Fire incidents pose a significant threat to human life and property security. Accurate fire detection plays a crucial role in promptly responding to fire outbreaks and ensuring the smooth execution of subsequent firefighting efforts. Fixed-size convolutions struggle to capture the irregular variations in smoke and flames that occur during fire incidents. In this paper, we introduce FireViT, an adaptive lightweight backbone network that combines a convolutional neural network (CNN) and transformer for fire detection. The FireViT we propose is an improved backbone network based on MobileViT. We name the lightweight module that combines deformable convolution with a transformer as th DeformViT block and compare multiple builds of this module. We introduce deformable convolution in order to better adapt to the irregularly varying smoke and flame in fire scenarios. In addition, we introduce an improved adaptive GELU activation function, AdaptGELU, to further enhance the performance of the network model. FireViT is compared with mainstream lightweight backbone networks in fire detection experiments on our self-made labeled fire natural light dataset and fire infrared dataset, and the experimental results show the advantages of FireViT as a backbone network for fire detection. On the fire natural light dataset, FireViT outperforms the PP-LCNet lightweight network backbone for fire target detection, with a 1.85% increase in mean Average Precision (mAP) and a 0.9 M reduction in the number of parameters. Additionally, compared to the lightweight network backbone MobileViT-XS, which similarly combines a CNN and transformer, FireViT achieves a 1.2% higher mAP while reducing the Giga-Floating Point Operations (GFLOPs) by 1.3. FireViT additionally demonstrates strong detection performance on the fire infrared dataset. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

18 pages, 5249 KiB

Open AccessArticle

Future Reductions in Suitable Habitat for Key Tree Species Result in Declining Boreal Forest Aboveground Biomass Carbon in China

by Bin Zhu, Zengxin Zhang, Rui Kong, Meiquan Wang, Guangshuai Li, Xiran Sui and Hui Tao

Forests 2023, 14(10), 2053; https://doi.org/10.3390/f14102053 - 13 Oct 2023

Viewed by 1034

Abstract

China’s forest ecosystem plays a crucial role in carbon sequestration, serving as a cornerstone in China’s journey toward achieving carbon neutrality by 2060. Yet, previous research primarily emphasized climate change’s influence on forest carbon sequestration, neglecting tree species’ suitable area changes. This study [...] Read more.

China’s forest ecosystem plays a crucial role in carbon sequestration, serving as a cornerstone in China’s journey toward achieving carbon neutrality by 2060. Yet, previous research primarily emphasized climate change’s influence on forest carbon sequestration, neglecting tree species’ suitable area changes. This study combinates the Lund–Potsdam–Jena model (LPJ) and the maximum entropy model (MaxENT) to reveal the coupling impacts of climate and tree species’ suitable area changes on forest aboveground biomass carbon (ABC) in China. Key findings include the following: (1) China’s forests are distributed unevenly, with the northeastern (temperate coniferous broad-leaved mixed forest, TCBMF), southwestern, and southeastern regions (subtropical evergreen broad-leaved forest, SEBF) as primary hubs. Notably, forest ABC rates in TCBMF exhibited a worrisome decline, whereas those in SEBF showed an increasing trend from 1993 to 2012 based on satellite observation and LPJ simulation. (2) Under different future scenarios, the forest ABC in TCBMF is projected to decline steadily from 2015 to 2060, with the SSP5-8.5 scenario recording the greatest decline (−4.6 Mg/ha/10a). Conversely, the forest ABC in SEBF is expected to increase under all scenarios (2015–2060), peaking at 1.3 Mg/ha/10a in SSP5-8.5. (3) Changes in forest ABC are highly attributed to climate and changes in tree species’ highly suitable area. By 2060, the suitable area for Larix gmelinii in TCBMF will significantly reduce to a peak of 65.71 × 10⁴ km² under SSP5-8.5, while Schima superba Gardner & Champ and Camphora officinarum in SEBF will expand to peaks of 94.07 × 10⁴ km² and 104.22 × 10⁴ km², respectively. The geographic detector’s results indicated that the climate and tree species’ suitable area changes showed bi-variate and nonlinear enhanced effects on forest ABC change. These findings would offer effective strategies for achieving carbon neutrality. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

18 pages, 6920 KiB

Open AccessArticle

Assessment of Forest Ecological Function Levels Based on Multi-Source Data and Machine Learning

by Ning Fang, Linyan Yao, Dasheng Wu, Xinyu Zheng and Shimei Luo

Forests 2023, 14(8), 1630; https://doi.org/10.3390/f14081630 - 12 Aug 2023

Cited by 2 | Viewed by 1003

Abstract

Forest ecological function is one of the key indicators reflecting the quality of forest resources. The traditional weighting method to assess forest ecological function is based on a large amount of ground survey data; it is accurate but costly and time-consuming. This study [...] Read more.

Forest ecological function is one of the key indicators reflecting the quality of forest resources. The traditional weighting method to assess forest ecological function is based on a large amount of ground survey data; it is accurate but costly and time-consuming. This study utilized three machine learning algorithms to estimate forest ecological function levels based on multi-source data, including Sentinel-2 optical remote sensing images and digital elevation model (DEM) and forest resource planning and design survey data. The experimental results showed that Random Forest (RF) was the optimal model, with overall accuracy of 0.82, recall of 0.66, and F1 of 0.62, followed by CatBoost (overall accuracy = 0.82, recall = 0.62, F1 = 0.58) and LightGBM (overall accuracy = 0.76, recall = 0.61, F1 = 0.58). Except for the indicators from remote sensing images and DEM data, the five ground survey indicators of forest origin (QI_YUAN), tree age group (LING_ZU), forest category (LIN_ZHONG), dominant species (YOU_SHI_SZ), and tree age (NL) were used in the modeling and prediction. Compared to the traditional methods, the proposed algorithm has lower cost and stronger timeliness. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

15 pages, 4555 KiB

Open AccessArticle

Multiple Defect Classification Method for Green Plum Surfaces Based on Vision Transformer

by Weihao Su, Yutu Yang, Chenxin Zhou, Zilong Zhuang and Ying Liu

Forests 2023, 14(7), 1323; https://doi.org/10.3390/f14071323 - 28 Jun 2023

Viewed by 982

Abstract

Green plums have produced significant economic benefits because of their nutritional and medicinal value. However, green plums are affected by factors such as plant diseases and insect pests during their growth, picking, transportation, and storage, which seriously affect the quality of green plums [...] Read more.

Green plums have produced significant economic benefits because of their nutritional and medicinal value. However, green plums are affected by factors such as plant diseases and insect pests during their growth, picking, transportation, and storage, which seriously affect the quality of green plums and their products, reducing their economic and nutritional value. At present, in the detection of green plum defects, some researchers have applied deep learning to identify their surface defects. However, the recognition rate is not high, the types of defects identified are singular, and the classification of green plum defects is not detailed enough. In the actual production process, green plums often have more than one defect, and the existing detection methods ignore minor defects. Therefore, this study used the vision transformer network model to identify all defects on the surfaces of green plums. The dataset was classified into multiple defects based on the four types of defects in green plums (scars, flaws, rain spots, and rot) and one type of feature (stem). After the permutation and combination of these defects, a total of 18 categories were obtained after the screening, combined with the actual situation. Based on the VIT model, a fine-grained defect detection link was added to the network for the analysis layer of the major defect hazard level and the detection of secondary defects. The improved network model has an average recognition accuracy rate of 96.21% for multiple defect detection of green plums, which is better than that of the VGG16 network, the Desnet121 network, the Resnet18 network, and the WideResNet50 network. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

19 pages, 5443 KiB

Open AccessArticle

Improvement and Assessment of Convolutional Neural Network for Tree Species Identification Based on Bark Characteristics

by Zhelin Cui, Xinran Li, Tao Li and Mingyang Li

Forests 2023, 14(7), 1292; https://doi.org/10.3390/f14071292 - 23 Jun 2023

Cited by 2 | Viewed by 3551

Abstract

Efficient tree species identification is of great importance in forest inventory and management. As the textural properties of tree barks vary less notably as a result of seasonal change than other tree organs, they are more suitable for the identification of tree species [...] Read more.

Efficient tree species identification is of great importance in forest inventory and management. As the textural properties of tree barks vary less notably as a result of seasonal change than other tree organs, they are more suitable for the identification of tree species using deep learning models. In this study, we adopted the ConvNeXt convolutional neural network to identify 33 tree species using the BarkNetV2 dataset, compared the classification accuracy values of different tree species, and performed visual analysis of the network’s visual features. The results show the following trends: (1) the pre-trained network weights exhibit up to 97.61% classification accuracy for the test set, indicating that the network has high accuracy; (2) the classification accuracy values of more than half of the tree species can reach 98%, while the confidence level of correct identification (probability ratio of true labels) of tree species images is relatively high; and (3) there is a strong correlation between the network’s visual attractiveness and the tree bark’s biological characteristics, which share similarities with humans’ organization of tree species. The method suggested in this study has the potential to increase the efficiency of tree species identification in forest resources surveys and is of considerable value in forest management. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

18 pages, 5289 KiB

Open AccessArticle

Semi-Supervised Tree Species Classification for Multi-Source Remote Sensing Images Based on a Graph Convolutional Neural Network

by Xueliang Wang, Jian Wang, Zuozheng Lian and Nan Yang

Forests 2023, 14(6), 1211; https://doi.org/10.3390/f14061211 - 11 Jun 2023

Cited by 3 | Viewed by 1136

Abstract

As a current research hotspot, graph convolution networks (GCNs) have provided new opportunities for tree species classification in multi-source remote sensing images. To solve the challenge of limited label information, a new tree species classification model was proposed by using the semi-supervised graph [...] Read more.

As a current research hotspot, graph convolution networks (GCNs) have provided new opportunities for tree species classification in multi-source remote sensing images. To solve the challenge of limited label information, a new tree species classification model was proposed by using the semi-supervised graph convolution fusion method for hyperspectral images (HSIs) and multispectral images (MSIs). In the model, the graph-based attribute features and pixel-based features are fused to deepen the correlation of multi-source images to improve accuracy. Firstly, the model employs the canonical correlation analysis (CCA) method to maximize the correlation of multi-source images, which explores the relationship between information from various sources further and offers more valuable insights. Secondly, convolution calculations were made to extract features and then map graph node fusion, which not only reduces redundancy features but also enhances compelling features. Finally, the relationship between representative descriptors is captured through the use of hyperedge convolution in the training process, and the dominant features on the graph are fully mined. The tree species are classified through two fusion feature operations, leading to improved classification performance compared to state-of-the-art methods. The fusion strategy can produce a complete classification map of the study areas. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

21 pages, 3059 KiB

Open AccessArticle

Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning

by Jiao Lu, Guofu Wang, Donghan Feng and Isaac Kwesi Nooni

Forests 2023, 14(6), 1201; https://doi.org/10.3390/f14061201 - 09 Jun 2023

Cited by 2 | Viewed by 1397

Abstract

A reliable estimate of the gross primary productivity (GPP) is crucial for understanding the global carbon balance and accurately assessing the ability of terrestrial ecosystems to support the sustainable development of human society. However, there are inconsistencies in variations and trends in current [...] Read more.

A reliable estimate of the gross primary productivity (GPP) is crucial for understanding the global carbon balance and accurately assessing the ability of terrestrial ecosystems to support the sustainable development of human society. However, there are inconsistencies in variations and trends in current GPP products. To improve the estimation accuracy of GPP, a deep learning method has been adopted to merge 23 CMIP6 data to generate a monthly GPP merged product with high precision and a spatial resolution of 0.25°, covering a time range of 1850–2100 under four climate scenarios. Multi-model ensemble mean and the merged GPP (CMIP6_DL GPP) have been compared, taking GLASS GPP as the benchmark. Compared with the multi-model ensemble mean, the coefficient of determination between CMIP6_DL GPP and GLASS GPP was increased from 0.66 to 0.86, with the RMSD being reduced from 1.77 gCm⁻²d⁻¹ to 0.77 gCm⁻²d⁻¹, which significantly reduced the random error. Merged GPP can better capture long-term trends, especially in regions with dense vegetation along the southeast coast. Under the climate change scenarios, the regional average annual GPP shows an upward trend over China, and the variation trend intensifies with the increase in radiation forcing levels. The results contribute to a scientific understanding of the potential impact of climate change on GPP in China. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

16 pages, 10030 KiB

Open AccessArticle

Tree Recognition and Crown Width Extraction Based on Novel Faster-RCNN in a Dense Loblolly Pine Environment

by Chongyuan Cai, Hao Xu, Sheng Chen, Laibang Yang, Yuhui Weng, Siqi Huang, Chen Dong and Xiongwei Lou

Forests 2023, 14(5), 863; https://doi.org/10.3390/f14050863 - 23 Apr 2023

Cited by 1 | Viewed by 1423

Abstract

Tree crown width relates directly to wood quality and tree growth. The traditional method used to measure crown width is labor-intensive and time-consuming. Pairing imagery taken by an unmanned aerial vehicle (UAV) with a deep learning algorithm such as a faster region-based convolutional [...] Read more.

Tree crown width relates directly to wood quality and tree growth. The traditional method used to measure crown width is labor-intensive and time-consuming. Pairing imagery taken by an unmanned aerial vehicle (UAV) with a deep learning algorithm such as a faster region-based convolutional neural network (Faster-RCNN) has the potential to be an alternative to the traditional method. In this study, Faster-RCNN outperformed single-shot multibox detector (SSD) for crown detection in a young loblolly pine stand but performed poorly in a dense, mature loblolly pine stand. This paper proposes a novel Faster-RCNN algorithm for tree crown identification and crown width extraction in a forest stand environment with high-density loblolly pine forests. The new algorithm uses Residual Network 101 (ResNet101) and a feature pyramid network (FPN) to build an FPN_ResNet101 structure, improving the capability to model shallow location feature extraction. The algorithm was applied to images from a mature loblolly pine plot in eastern Texas, USA. The results show that the accuracy of crown recognition and crown width measurement using the FPN_ResNet101 structure as the backbone network in Faster-RCNN (FPN_Faster-RCNN_ResNet101) was high, being 95.26% and 0.95, respectively, which was 4.90% and 0.27 higher than when using Faster-RCNN with ResNet101 as the backbone network (Faster-RCNN_ResNet101). The results fully confirm the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Applications of Artificial Intelligence in Forestry

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI