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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 (30 June 2024) | Viewed by 8345

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Special Issue Editors

Dr. Linyuan Li

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Guest Editor

The College of Forestry, Beijing Forestry University, Beijing 100083, China
Interests: near-surface, UAV, and satellite remote sensing of vegetation; low-cost techniques; advanced image processing; vegetation biophysical and biochemical variables; plant traits; BRDF measurement; modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Jie Shao

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Guest Editor

Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: forest inventory; UAV; LiDAR remote sensing; point cloud processing

Dr. Hao Yang

E-Mail Website
Guest Editor

1. National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 10089, China
2. Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: orchard monitoring; crop phenotyping; LiDAR; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

UAV remote sensing technology at small spatial scales has substantially enhanced the measurement efficiency for forest stands and even for individual trees, and verified its reliability at larger scales. This technology offers opportunities for testing fundamental theories and generating new insights into forest management, forest ecology, and tree phenotyping. Recent advances in UAV applications in forestry have emerged as part of a novel interdisciplinary domain encompassing tree physiology, radiative transfer modeling, 3D characterization, computer vision, and data science. This Special Issue invites contributions on UAV remote sensing of forest ecosystems using imaging technologies (RGB, multispectral/hyperspectral sensor, and thermal sensor), photogrammetry, and LiDAR observations. The topics include, but are not limited to:

Multi-modal and multi-source UAV data acquisition and processing;
New theories, models, and algorithms to estimate structural, biophysical, and biochemical variables (or plant functional traits);
Using the UAV observations to address or explore forest ecology problems (e.g., biodiversity and functional variability);
Synergetic use of UAV and satellite data to optimize forest inventory;
Investigations of uncertainties and errors during UAV observations and data processing, such as radiometric corrections and geometric corrections.

Dr. Linyuan Li
Dr. Jie Shao
Dr. Hao Yang
Guest Editors

Manuscript Submission Information

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Keywords

forest ecosystem
forest inventory
UAV remote sensing
physical-based modelling
machine learning

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Published Papers (5 papers)

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Research

15 pages, 5873 KiB

Open AccessArticle

Biomass Inversion of Highway Slope Based on Unmanned Aerial Vehicle Remote Sensing and Deep Learning

by Guangcun Hao, Zhiliang Dong, Liwen Hu, Qianru Ouyang, Jian Pan, Xiaoyang Liu, Guang Yang and Caige Sun

Forests 2024, 15(9), 1564; https://doi.org/10.3390/f15091564 - 5 Sep 2024

Cited by 2 | Viewed by 1066

Abstract

Biomass can serve as an important indicator for measuring the effectiveness of slope ecological restoration, and unmanned aerial vehicle (UAV) remote sensing provides technical support for the rapid and accurate measurement of vegetation biomass on slopes. Considering a highway slope as the experimental area, in this study, we integrate UAV data and Sentinel-2A images; apply a deep learning method to integrate remote sensing data; extract slope vegetation features from vegetation probability, vegetation indices, and vegetation texture features; and construct a slope vegetation biomass inversion model. The R² of the slope vegetation biomass inversion model is 0.795, and the p-value in the F-test is less than 0.01, which indicates that the model has excellent regression performance and statistical significance. Based on laboratory biomass measurements, the regression model error is small and reasonable, with RMSE = 0.073, MAE = 0.064, and SE = 0.03. The slope vegetation biomass can be accurately estimated using remote-sensing images with a high precision and good applicability. This study will provide a methodological reference and demonstrate its application in estimating vegetation biomass and carbon stock on highway slopes, thus providing data and methodological support for the simulation of the carbon balance process in slope restoration ecosystems. Full article

(This article belongs to the Special Issue UAV Application in Forestry)

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24 pages, 44227 KiB

Open AccessArticle

Assessment of Trees’ Structural Defects via Hybrid Deep Learning Methods Used in Unmanned Aerial Vehicle (UAV) Observations

by Qiwen Qiu and Denvid Lau

Forests 2024, 15(8), 1374; https://doi.org/10.3390/f15081374 - 6 Aug 2024

Cited by 2 | Viewed by 1629

Abstract

Trees’ structural defects are responsible for the reduction in forest product quality and the accident of tree collapse under extreme environmental conditions. Although the manual view inspection for assessing tree health condition is reliable, it is inefficient in discriminating, locating, and quantifying the defects with various features (i.e., crack and hole). There is a general need for investigation of efficient ways to assess these defects to enhance the sustainability of trees. In this study, the deep learning algorithms of lightweight You Only Look Once (YOLO) and encoder-decoder network named DeepLabv3+ are combined in unmanned aerial vehicle (UAV) observations to evaluate trees’ structural defects. Experimentally, we found that the state-of-the-art detector YOLOv7-tiny offers real-time (i.e., 50–60 fps) and long-range sensing (i.e., 5 m) of tree defects but has limited capacity to acquire the patterns of defects at the millimeter scale. To address this limitation, we further utilized DeepLabv3+ cascaded with different network architectures of ResNet18, ResNet50, Xception, and MobileNetv2 to obtain the actual morphology of defects through close-range and pixel-wise image semantic segmentation. Moreover, the proposed hybrid scheme YOLOv7-tiny_DeepLabv3+_UAV assesses tree’s defect size with an averaged accuracy of 92.62% (±6%). Full article

(This article belongs to the Special Issue UAV Application in Forestry)

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20 pages, 5937 KiB

Open AccessArticle

Cunninghamia lanceolata Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

by Luyue Zhang, Xiaoyu Su, Huan Liu, Yueqiao Zhao, Wenjing Gao, Nuo Cheng and Riwen Lai

Forests 2024, 15(6), 965; https://doi.org/10.3390/f15060965 - 31 May 2024

Viewed by 1336

Abstract

This study aimed to streamline the determination of chlorophyll content in Cunninghamia lanceolate while achieving precise measurements of canopy chlorophyll content. Relative chlorophyll content (SPAD) in the Cunninghamia lanceolate canopy were assessed in the study area using the SPAD-502 portable chlorophyll meter, alongside spectral data collected via onboard multispectral imaging. And based on the unmanned aerial vehicle (UAV) multispectral collection of spectral values in the study area, 21 vegetation indices with significant correlation with Cunninghamia lanceolata canopy SPAD (CCS) were constructed as independent variables of the model’s various regression techniques, including partial least squares regression (PLSR), random forests (RF), and backpropagation neural networks (BPNN), which were employed to develop a SPAD inversion model. The BPNN-based model emerged as the best choice, exhibiting test dataset coefficients of determination (R²) at 0.812, root mean square error (RSME) at 2.607, and relative percent difference (RPD) at 1.942. While the model demonstrated consistent accuracy across different slope locations, generalization was lower for varying slope directions. By creating separate models for different slope directions, R² went up to about 0.8, showcasing favorable terrain applicability. Therefore, constructing inverse models with different slope directions samples separately can estimate CCS more accurately. Full article

(This article belongs to the Special Issue UAV Application in Forestry)

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23 pages, 19881 KiB

Open AccessArticle

Identification of Damaged Canopies in Farmland Artificial Shelterbelts Based on Fusion of Unmanned Aerial Vehicle LiDAR and Multispectral Features

by Zequn Xiang, Tianlan Li, Yu Lv, Rong Wang, Ting Sun, Yuekun Gao and Hongqi Wu

Forests 2024, 15(5), 891; https://doi.org/10.3390/f15050891 - 20 May 2024

Cited by 1 | Viewed by 1513

Abstract

With the decline in the protective function for agricultural ecosystems of farmland shelterbelts due to tree withering and dying caused by pest and disease, quickly and accurately identifying the distribution of canopy damage is of great significance for forestry management departments to implement dynamic monitoring. This study focused on Populus bolleana and utilized an unmanned aerial vehicle (UAV) multispectral camera to acquire red–green–blue (RGB) images and multispectral images (MSIs), which were fused with a digital surface model (DSM) generated by UAV LiDAR for feature fusion to obtain DSM + RGB and DSM + MSI images, and random forest (RF), support vector machine (SVM), maximum likelihood classification (MLC), and a deep learning U-Net model were employed to build classification models for forest stand canopy recognition for the four image types. The model results indicate that the recognition performance of RF is superior to that of U-Net, and U-Net performs better overall than SVM and MLC. The classification accuracy of different feature fusion images shows a trend of DSM + MSI images (Kappa = 0.8656, OA = 91.55%) > MSI images > DSM + RGB images > RGB images. DSM + MSI images exhibit the highest producer’s accuracy for identifying healthy and withered canopies, with values of 95.91% and 91.15%, respectively, while RGB images show the lowest accuracy, with producer’s accuracy values of 79.3% and 78.91% for healthy and withered canopies, respectively. This study presents a method for identifying the distribution of Populus bolleana canopies damaged by Anoplophora glabripennis and healthy canopies using the feature fusion of multi-source remote sensing data, providing a valuable data reference for the precise monitoring and management of farmland shelterbelts. Full article

(This article belongs to the Special Issue UAV Application in Forestry)

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20 pages, 7321 KiB

Open AccessArticle

Identification of Larch Caterpillar Infestation Severity Based on Unmanned Aerial Vehicle Multispectral and LiDAR Features

by Sa He-Ya, Xiaojun Huang, Debao Zhou, Junsheng Zhang, Gang Bao, Siqin Tong, Yuhai Bao, Dashzebeg Ganbat, Nanzad Tsagaantsooj, Dorjsuren Altanchimeg, Davaadorj Enkhnasan, Mungunkhuyag Ariunaa and Jiaze Guo

Forests 2024, 15(1), 191; https://doi.org/10.3390/f15010191 - 17 Jan 2024

Cited by 4 | Viewed by 1995

Abstract

Utilizing UAV remote sensing technology to acquire information on forest pests is a crucial technical method for determining the health of forest trees. Achieving efficient and precise pest identification has been a major research focus in this field. In this study, Dendrolimus superans (Butler) was used as the research object to acquire UAV multispectral, LiDAR, and ground-measured data for extracting sensitive features using ANOVA and constructing a severity-recognizing model with the help of random forest (RF) and support vector machine (SVM) models. Sixteen sensitive feature sets (including multispectral vegetation indices and LiDAR features) were selected for training the recognizing model, of which the normalized differential greenness index (NDGI) and 25% height percentile were the most sensitive and could be used as important features for recognizing larch caterpillar infestations. The model results show that the highest accuracy is SVM_VI+LIDAR (OA = 95.8%), followed by SVM_VI, and the worst accuracy is RF_LIDAR. For identifying healthy, mild, and severely infested canopies, the SVM_VI+LIDAR model achieved 90%–100% for both PA and UA. The optimal model chosen to map the spatial distribution of severity at the single-plant scale in the experimental area demonstrated that the severity intensified with decreasing elevation, especially from 748–758 m. This study demonstrates a high-precision identification method of larch caterpillar infestation severity and provides an efficient and accurate data reference for intelligent forest management. Full article

(This article belongs to the Special Issue UAV Application in Forestry)

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