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Applications of Artificial Intelligence in Forestry

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 25144

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Prof. Dr. Guojie Wang

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Guest Editor

School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: climate change; water cycle; vegetation change; remote sensing; vegetation–climate interactions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zengxin Zhang

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Guest Editor

College of Forestry, Nanjing Forestry University, Nanjing 210037, China
Interests: forest meteorology; forest hydrology; climate change; forest carbon estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in big data in Earth observations have fostered interdisciplinary studies of forest dynamics and management, as well as their interactions with the environment. Artificial intelligence (AI) provides an interesting and efficient solution for big data applications in forestry. AI-based approaches, e.g., a variety of deep learning models, are currently mainly dedicated to forest monitoring, assessment, mapping, and predictions, e.g., using satellite remote sensing images, for smart decision making in forest management. In such cases, deep learning models have indicated excellent performances. In the era of big data, there are emerging opportunities to utilize deep learning models to improve our understanding of forest dynamics and forest–climate interactions in the warming environment, and explainable artificial intelligence methods can be used to obtain explanations of the model results. Therefore, original research papers using AI approaches to improve our understanding of forestry are welcome in this special collection.

Topics may include but are by no means limited to:

- Forest mapping and change detection;

- Forest disturbance and damage assessment;

- Forest threat and health monitoring;

- Ecosystem service assessment;

- Forest carbon estimation;

- Smart decision system of forest management;

- Wildfire risk assessment and prediction;

- Forest meteorology;

- Forest–climate interactions.

Prof. Dr. Guojie Wang
Prof. Dr. Zengxin Zhang
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

forestry
artificial intelligence
remote sensing
meteorology
hydroecology
forest hydrology
climate change

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Related Special Issue

Applications of Artificial Intelligence in Forestry: 2nd Edition in Forests

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Research

19 pages, 4899 KiB

Open AccessArticle

The Many Shades of the Vegetation–Climate Causality: A Multimodel Causal Appreciation

by Yuhao Shao, Daniel Fiifi Tawia Hagan, Shijie Li, Feihong Zhou, Xiao Zou and Pedro Cabral

Forests 2024, 15(8), 1430; https://doi.org/10.3390/f15081430 - 14 Aug 2024

Viewed by 831

Abstract

The causal relationship between vegetation and temperature serves as a driving factor for global warming in the climate system. However, causal relationships are typically characterized by complex facets, particularly within natural systems, necessitating the ongoing development of robust approaches capable of addressing the challenges inherent in causality analysis. Various causality approaches offer distinct perspectives on understanding causal structures, even when experiments are meticulously designed with a specific target. Here, we use the complex vegetation–climate interaction to demonstrate some of the many facets of causality analysis by applying three different causality frameworks including (i) the kernel Granger causality (KGC), a nonlinear extension of the Granger causality (GC), to understand the nonlinearity in the vegetation–climate causal relationship; (ii) the Peter and Clark momentary conditional independence (PCMCI), which combines the Peter and Clark (PC) algorithm with the momentary conditional independence (MCI) approach to distinguish the feedback and coupling signs in vegetation–climate interaction; and (iii) the Liang–Kleeman information flow (L-K IF), a rigorously formulated causality formalism based on the Liang–Kleeman information flow theory, to reveal the causal influence of vegetation on the evolution of temperature variability. The results attempt to capture a fuller understanding of the causal interaction of leaf area index (LAI) on air temperature (T) during 1981–2018, revealing the characteristics and differences in distinct climatic tipping point regions, particularly in terms of nonlinearity, feedback signals, and variability sources. This study demonstrates that realizing a more holistic causal structure of complex problems like the vegetation–climate interaction benefits from the combined use of multiple models that shed light on different aspects of its causal structure, thus revealing novel insights that are missing when we rely on one single approach. This prompts the need to move toward a multimodel causality analysis that could reduce biases and limitations in causal interpretations. Full article

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

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28 pages, 5695 KiB

Open AccessArticle

Multi-Agent Reinforcement Learning for Stand Structure Collaborative Optimization of Pinus yunnanensis Secondary Forests

by Shuai Xuan, Jianming Wang, Jiting Yin, Yuling Chen and Baoguo Wu

Forests 2024, 15(7), 1143; https://doi.org/10.3390/f15071143 - 30 Jun 2024

Viewed by 917

Abstract

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China. Full article

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

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16 pages, 7035 KiB

Open AccessArticle

A Lightweight Pine Wilt Disease Detection Method Based on Vision Transformer-Enhanced YOLO

by Quanbo Yuan, Suhua Zou, Huijuan Wang, Wei Luo, Xiuling Zheng, Lantao Liu and Zhaopeng Meng

Forests 2024, 15(6), 1050; https://doi.org/10.3390/f15061050 - 18 Jun 2024

Cited by 2 | Viewed by 1108

Abstract

Pine wilt disease (PWD) is a forest disease characterized by rapid spread and extremely high lethality, posing a serious threat to the ecological security of China’s forests and causing significant economic losses in forestry. Given the extensive forestry area, limited personnel for inspection and monitoring, and high costs, utilizing UAV-based remote sensing monitoring for diseased trees represents an effective approach for controlling the spread of PWD. However, due to the small target size and uneven scale of pine wilt disease, as well as the limitations of real-time detection by drones, traditional disease tree detection algorithms based on RGB remote sensing images do not achieve an optimal balance among accuracy, detection speed, and model complexity due to real-time detection limitations. Consequently, this paper proposes Light-ViTeYOLO, a lightweight pine wilt disease detection method based on Vision Transformer-enhanced YOLO (You Only Look Once). A novel lightweight multi-scale attention module is introduced to construct an EfficientViT feature extraction network for global receptive field and multi-scale learning. A novel neck network, CACSNet(Content-Aware Cross-Scale bidirectional fusion neck network), is designed to enhance the detection of diseased trees at single granularity, and the loss function is optimized to improve localization accuracy. The algorithm effectively reduces the number of parameters and giga floating-point operations per second (GFLOPs) of the detection model while enhancing overall detection performance. Experimental results demonstrate that compared with other baseline algorithms, Light-ViTeYOLO proposed in this paper has the least parameter and computational complexity among related algorithms, with 3.89 MFLOPs and 7.4 GFLOPs, respectively. The FPS rate is 57.9 (frames/s), which is better than the original YOLOv5. Meanwhile, its [email protected]:0.95 is the best among the baseline algorithms, and the recall and [email protected] slightly decrease. Our Light-ViTeYOLO is the first lightweight method specifically designed for detecting pine wilt disease. It not only meets the requirements for real-time detection of pine wilt disease outbreaks but also provides strong technical support for automated forestry work. Full article

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

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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 2117

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 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)

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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

Cited by 3 | Viewed by 2023

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 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)

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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

Cited by 1 | Viewed by 1502

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 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)

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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 8 | Viewed by 1579

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 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)

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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

Cited by 5 | Viewed by 1541

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 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)

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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 3 | Viewed by 4880

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 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)

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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 8 | Viewed by 1615

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 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)

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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 - 9 Jun 2023

Cited by 3 | Viewed by 2205

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 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)

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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 3 | Viewed by 2012

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 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)

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