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Integrated Measurements for Precision 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: 31 July 2024 | Viewed by 7667

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Prof. Dr. Jia Wang

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Guest Editor

The College of Forestry, Beijing Forestry University, Beijing, China
Interests: forest inventory; remote sensing; 3S; LiDAR; smart forestry

Prof. Dr. Weiheng Xu

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Guest Editor

College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China
Interests: forestry equipment and informatization; intelligent processing and application of remote sensing big data; regional ecological remote sensing

Prof. Dr. Jincheng Liu

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College of Natural Resources and Environment, Northwest A&F University, Yangling, China
Interests: forest informatics; remote sensing; geographic information system; 3D point cloud; vegetation spatial configuration; forestry carbon sink

Dr. Zhichao Wang

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Guest Editor

Mapping and 3S Technology Center, Beijing Forestry University, Beijing, China
Interests: forest inventory; computational virtual measurement; LiDAR; environmental sensor

Prof. Dr. Stelian Alexandru Borz

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Guest Editor

Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Şirul Beethoven 1, 500123 Brasov, Romania
Interests: wood and biomass supply chain optimization; sensor technology; transport optimization; forest planning
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Special Issue Information

Dear Colleagues,

Forests are integral to global climate, biodiversity, and human health, making it more important than ever to accurately assess and monitor their condition and health. This Special Issue entitled "Integrated Measurements for Precision Forestry" seeks to bring together research on the development and implementation of integrated approaches to precision forestry.

Precision forestry is the application of high-precision measurements and methods to inform decisions related to the management of forests. It is important to incorporate multiple types of measurements in order to accurately understand forest conditions. We invite papers that focus on the reporting of new and advanced methods for the field survey of forest sample plots, as well as advances in remote sensing methods that monitor forests. Examples of topics that could be covered in this Special Issue include, but are not limited to, the following:

Development of integrated models for precision forestry;
Reporting new forest measurement instruments;
Use of unmanned aerial vehicles (UAVs) and other remote sensing techniques for precision forestry;
Use of geographic information systems (GIS) and other geospatial technologies for precision forestry;
Development and evaluation of new field survey methods for forest monitoring;
Application of precision forestry techniques to specific forest types and ecosystems;
Impact of precision forestry on forest policy and management.

We welcome articles from a variety of disciplines, including forestry, ecology, geomatics, and engineering. We also encourage submissions from industry, government, and academic researchers.

Prof. Dr. Jia Wang
Prof. Dr. Weiheng Xu
Prof. Dr. Jincheng Liu
Dr. Zhichao Wang
Prof. Dr. Stelian Alexandru Borz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

precision forestry
smart forestry
instrument
sensor
forest monitoring
ecology

Published Papers (8 papers)

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Research

27 pages, 16005 KiB

Open AccessArticle

A Small Target Tea Leaf Disease Detection Model Combined with Transfer Learning

by Xianze Yao, Haifeng Lin, Di Bai and Hongping Zhou

Forests 2024, 15(4), 591; https://doi.org/10.3390/f15040591 - 25 Mar 2024

Viewed by 719

Abstract

Tea cultivation holds significant economic value, yet the leaves of tea plants are frequently susceptible to various pest and disease infestations. Consequently, there is a critical need for research focused on precisely and efficiently detecting these threats to tea crops. The investigation of a model capable of effectively identifying pests and diseases in tea plants is often hindered by challenges, such as limited datasets of pest and disease samples and the small size of detection targets. To address these issues, this study has chosen TLB, a common pest and disease in tea plants, as the primary research subject. The approach involves the application of transfer learning in conjunction with data augmentation as a fundamental methodology. This technique entails transferring knowledge acquired from a comprehensive source data domain to the model, aiming to mitigate the constraints of limited sample sizes. Additionally, to tackle the challenge of detecting small targets, this study incorporates the decoupling detection head TSCODE and integrates the Triplet Attention mechanism into the E-ELAN structure within the backbone to enhance the model’s focus on the TLB’s small targets and optimize detection accuracy. Furthermore, the model’s loss function is optimized based on the Wasserstein distance measure to mitigate issues related to sensitivity in localizing small targets. Experimental results demonstrate that, in comparison to the conventional YOLOv7 tiny model, the proposed model exhibits superior performance on the TLB small sample dataset, with precision increasing by 6.5% to 92.2%, recall by 4.5% to 86.6%, and average precision by 5.8% to 91.5%. This research offers an effective solution for identifying tea pests and diseases, presenting a novel approach to developing a model for detecting such threats in tea cultivation. Full article

(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)

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26 pages, 13389 KiB

Open AccessArticle

Assessing the Distribution and Driving Effects of Net Primary Productivity along an Elevation Gradient in Subtropical Regions of China

by Bo Xu, Zhongke Feng, Yuan Chen, Yuchen Zhou, Yakui Shao and Zhichao Wang

Forests 2024, 15(2), 340; https://doi.org/10.3390/f15020340 - 09 Feb 2024

Cited by 1 | Viewed by 767

Abstract

Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m⁻² a⁻¹, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle. Full article

(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)

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23 pages, 16013 KiB

Open AccessArticle

Predicting Sub-Forest Type Transition Characteristics Using Canopy Density: An Analysis of the Ganjiang River Basin Case Study

by Yuchen Zhou, Juhua Hu, Mu Liu and Guanhong Xie

Forests 2024, 15(2), 274; https://doi.org/10.3390/f15020274 - 31 Jan 2024

Viewed by 680

Abstract

In the process of societal development, forest land categories often conflict with other land use types, leading to impacts on the ecological environment. Therefore, research on changes in forest land categories has increasingly become a globally focused topic. To anticipate potential forest ecological security issues under urbanization trends, studies on regional land use simulation become more important. This paper, based on land use data from the Ganjiang River basin, analyzes the distribution characteristics and changing trends of land use types from 2000 to 2020. Using the CA-Markov model, it predicts the land use pattern of the basin in 2040 and analyzes the transfer characteristics of forest land categories. The conclusions indicate that, between 2000 and 2020, the most significant trend in land use evolution was the transfer between various subcategories of forest land, especially frequent in the high-altitude mountainous areas in the southern and western parts of the basin. The land use pattern prediction model constructed in this paper has a kappa index of 0.92, indicating high accuracy and reliability of the predictions. In 2040, the most significant land evolution phenomenon would be from forest land to arable land to construction land, particularly pronounced around large cities. Over the next 20 years, the focus of land use evolution may shift from the southern part of the basin to the central and northern parts, with urban expansion possibly becoming the main driving force of land use changes during this period. Forest land restoration work is an effective method to compensate for the loss of forest land area in the Ganjiang River basin, with key areas for such work including Longnan, Yudu, Xingguo, Ningdu, Lianhua, and Yongxin counties. Full article

(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)

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18 pages, 16167 KiB

Open AccessArticle

Utilizing Grid Data and Deep Learning for Forest Fire Occurrences and Decision Support: A Case Study in the Ningxia Hui Autonomous Region

by Yakui Shao, Qin Zhu, Zhongke Feng, Linhao Sun, Peng Yue, Aiai Wang, Xiaoyuan Zhang and Zhiqiang Su

Forests 2023, 14(12), 2418; https://doi.org/10.3390/f14122418 - 12 Dec 2023

Viewed by 910

Abstract

In order to investigate the geographical distribution of forest fire occurrences in the Ningxia Hui Autonomous Region, this study employs advanced modeling techniques, utilizing diverse data sources, including fuel, Gross Domestic Product (GDP), population, meteorology, buildings, and grid data. This study integrates deep learning Convolutional Neural Networks (CNNs) to predict potential fire incidents. The research findings can be summarized as follows: (i) The employed model exhibits very good performance, achieving an accuracy of 84.35%, a recall of 86.21%, and an Area Under the Curve (AUC) of 87.67%. The application of this model significantly enhances the reliability of the forest fire occurrence model and provides a more precise assessment of its uncertainty. (ii) Spatial analysis shows that the risk of fire occurrence in most areas is low-medium, while high-risk areas are mainly concentrated in Longde County, Jingyuan County, Pengyang County, Xiji County, Yuanzhou District, Tongxin County, Xixia District, and Yinchuan City, which are mostly located in the southern, southeastern, and northwestern regions of Ningxia Hui Autonomous Region, with a total area of 2191.2 square kilometers. This underscores the urgent need to strengthen early warning systems and effective fire prevention and control strategies in these regions. The contributions of this research include the following: (i) The development of a highly accurate and practical provincial-level forest fire occurrence prediction framework based on grid data and deep learning CNN technology. (ii) The execution of a comprehensive forest fire prediction study in the Ningxia Hui Autonomous Region, China, incorporating multi-source data, providing valuable data references, and decision support for forest fire prevention and control. (iii) The initiation of a preliminary systematic investigation and zoning of forest fires in the Ningxia Hui Autonomous Region, along with tailored recommendations for prevention and control measures. Full article

(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)

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19 pages, 11208 KiB

Open AccessArticle

Spatiotemporal Patterns and Risk Zoning of Wildfire Occurrences in Northeast China from 2001 to 2019

by Aiai Wang, Dongyou Zhang, Zhongke Feng, Xueying Li and Xiangyou Li

Forests 2023, 14(12), 2350; https://doi.org/10.3390/f14122350 - 29 Nov 2023

Viewed by 857

Abstract

Wildfires, a recurring and persistent natural disaster, present direct threats to both ecological balance and human safety. Despite the northeastern region of China boasting abundant forest resources, it grapples with a significant wildfire issue. This study, focused on China’s northeastern region, employs sophisticated methodologies, including the Mann–Kendall (MK) mutation test, sliding t-test, and geographical heat maps, to unveil the spatial distribution and temporal trends of wildfires. Furthermore, a random forest model is utilized to develop a wildfire susceptibility map, enabling an in-depth analysis of the relationships between various potential factors and wildfires, along with an assessment of the significance of these driving factors. The research findings indicate that wildfires in the northeastern region exhibit distinct seasonality, with the highest occurrences in the autumn and spring and fewer incidents in the summer and winter. Apart from the spring season, historical wildfires show a decreasing trend during other seasons. Geographically, wildfires tend to cluster, with over half of the high-risk areas concentrated at the junction of the Greater Khingan Mountains and Lesser Khingan Mountains in the northeastern region. The random forest model assumes a pivotal role in the analysis, accurately identifying both natural and human-induced factors, including topography, climate, vegetation, and anthropogenic elements. This research further discloses that climate factors predominantly influence wildfires in the northeastern region, with sunshine duration being the most influential factor. In summary, this study highlights the variation in various wildfire-driving factors, providing the basis for tailored management strategies and region-specific fire prevention. Through a comprehensive analysis of the spatiotemporal patterns of wildfires and associated risk factors, this research offers valuable insights for mitigating wildfire risks and preserving the region’s ecological integrity. Full article

(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)

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17 pages, 4795 KiB

Open AccessArticle

Conservation Effectiveness Assessment of the Three Northern Protection Forest Project Area

by Yakui Shao, Yufeng Liu, Tiantian Ma, Linhao Sun, Xuanhan Yang, Xusheng Li, Aiai Wang and Zhichao Wang

Forests 2023, 14(11), 2121; https://doi.org/10.3390/f14112121 - 24 Oct 2023

Cited by 1 | Viewed by 915

Abstract

The Three-North Shelterbelt Project is the largest ecological engineering initiative in China to date, distinguished by its immense scale, extended construction period, and widespread benefits for the population. The gross ecosystem product (GEP) serves as a crucial indicator for assessing ecological benefits. This study focuses on the Three Northern Protection Forest Project Area, utilizing GEP calculations for the years 2000 to 2020. This study evaluates variations in the production values of different ecosystem services to reflect the ecological conservation benefits of the restoration project. Additionally, it analyzes the spatiotemporal evolution and trends of the GEP calculations, offering data references and decision support for the enduring efficacy of ecological restoration projects. The findings are as follows. (i) Between 2000 and 2020, the GEP of the Three-North region exhibited significant growth with continuous enhancement of various ecosystem service functions; the most substantial rate of change was observed in the water conservation function, followed by carbon sequestration and oxygen release, soil retention, windbreak and sand fixation, flood regulation, and environmental purification functions. (ii) The per-unit area value of different ecosystem types generally increased; the forest ecosystem displayed the largest growth rate at 61.18%, followed by shrubland ecosystems at 49.84%. (iii) The spatial distribution of ecosystem service in the Three-North region displayed a clustering trend alongside notable spatial heterogeneity. High-high clustering zones were identified in areas such as the Tianshan Mountains, Altai Mountains, Qilian Mountains, and Greater and Lesser Khingan Mountains. Conversely, low-low clustering areas were scattered, forming patchy distributions in regions like the Tarim Basin, northern Qinghai-Tibet Plateau, and the Hexi Corridor. This study, by analyzing the gross ecosystem product of the Three-North Shelterbelt Project region, unveils the spatial distribution characteristics, trends, and variations in ecosystem service values over the past two decades. It provides data support and decision guidance for the long-term efficacy of future ecological conservation and restoration projects. This study incorporates the GEP accounting method into the assessment of the effectiveness of major conservation projects. Compared to the traditional methods of effectiveness assessment, this represents a significant exploration and innovation. Full article

(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)

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18 pages, 8656 KiB

Open AccessArticle

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

by Jiachen Qian, Di Bai, Wanguo Jiao, Ling Jiang, Renjie Xu, Haifeng Lin and Tian Wang

Forests 2023, 14(10), 2089; https://doi.org/10.3390/f14102089 - 18 Oct 2023

Cited by 1 | Viewed by 1179

Abstract

Forest fires are major forestry disasters that cause loss of forest resources, forest ecosystem safety, and personal injury. It is often difficult for current forest fire detection models to achieve high detection accuracy on both large and small targets at the same time. In addition, most of the existing forest fire detection models are single detection models, and using only a single model for fire detection in a complex forest environment has a high misclassification rate, and the accuracy rate needs to be improved. Aiming at the above problems, this paper designs two forest fire detection models (named WSB and WSS) and proposes an integrated learning-based forest fire detection model (named WSB_WSS), which also obtains high accuracy in the detection of forest fires with large and small targets. In order to help the model predict the location and size of forest fire targets more accurately, a new edge loss function, Wise-Faster Intersection over Union (WFIoU), is designed in this paper, which effectively improves the performance of the forest fire detection algorithm. The WSB model introduces the Simple-Attention-Module (SimAM) attention mechanism to make the image feature extraction more accurate and introduces the bi-directional connectivity and cross-layer feature fusion to enhance the information mobility and feature expression ability of the feature pyramid network. The WSS model introduces the Squeeze-and-Excitation Networks (SE) attention mechanism so that the model can pay more attention to the most informative forest fire features and suppress unimportant features, and proposes Spatial Pyramid Pooling-Fast Cross Stage Partial Networks (SPPFCSPC) to enable the network to extract features better and speed up the operation of the model. The experimental findings demonstrate that the WSB model outperforms other approaches in the context of identifying forest fires characterized by small-scale targets, achieving a commendable accuracy rate of 82.4%, while the WSS model obtains a higher accuracy of 92.8% in the identification of large target forest fires. Therefore, in this paper, a more efficient forest fire detection model, WSB_WSS, is proposed by integrating the two models through the method of Weighted Boxes Fusion (WBF), and the accuracy of detecting forest fires characterized by small-scale targets attains 83.3%, while for forest fires with larger dimensions, the accuracy reaches an impressive 93.5%. This outcome effectively leverages the strengths inherent in both models, consequently achieving the dual objective of high-precision detection for both small and large target forest fires concurrently. Full article

(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)

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20 pages, 3656 KiB

Open AccessArticle

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

by Biao Zhang, Guowei Liu, Zhongke Feng, Mingjuan Zhang, Tiantian Ma, Xin Zhao, Zhiqiang Su and Xiaoyuan Zhang

Forests 2023, 14(10), 2073; https://doi.org/10.3390/f14102073 - 17 Oct 2023

Cited by 1 | Viewed by 834

Abstract

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. Full article

(This article belongs to the Special Issue Integrated Measurements for Precision Forestry)

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