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Novel Applications of UAV Imagery for Forest Science

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 9562

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Dr. Ehsan Khoramshahi

E-Mail Website1 Website2
Guest Editor

1. Finnish Geospatial Research Institute (FGI), National Land Survey of Finland (NLS), Vuorimiehentie 5, FI-02150 Espoo, Finland
2. Metropolia University of Applied Sciences, Leiritie 1, Fi-01600 Vantaa, Finland
Interests: remote sensing; photogrammetry; forest science; deep learning; image processing; computer vision

Dr. Francisco Javier Mesas Carrascosa

E-Mail Website
Guest Editor

Department of Graphic Engineering and Geomatics, Higher Technical School of Agricultural Engineering, University of Córdoba, 14071 Córdoba, Spain
Interests: UAV; remote sensing; photogrammetry; cloud computing
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Special Issue Information

Dear Colleagues,

Forest management and precision agriculture is an important multidisciplinary field that has recently attracted a lot of attention from research communities. One of the earliest developments in forest science was the forest inventory, dating back to the 19th century. Until recently, field work and airborne photogrammetry were among the main tools used in forest science to collate these inventories. However, these tools are expensive and time-consuming. As light-weight unmanned aerial vehicles (UAVs) have been developed in recent decades, their potential usage in forest science has been widely researched. High spatial resolution UAV imaging has become a standard data provider for many scientific forest activities. Recent advances in accessible and fast communication protocols, light-weight and powerful onboard computing devices, cloud computing platforms, analytical methods such as Bayesian methods or deep convolutional neural networks, and novel sensorial developments provide remarkable opportunities for the use of UAVs in forest science. Nowadays, UAVs are the first choice for many tasks in forest science that require the vast measurement of large areas.

This Special Issue aims to collect studies related to forest observation and monitoring that employ UAVs for capturing data. We place no limitations on the sensorial configuration of the UAVs; therefore, a wide range of sensors, including RGB cameras, lidars, GNSS, IMU, and hyper-spectral cameras are accepted. We also welcome research works that cover different aspects of challenges involving UAVs as data collectors. These topics could cover anything from employing low-cost uncalibrated sensors to costly state-of-the-art sensors, or concern problems such as communication, synchronization, localization, hardware design, onboard or cloud design, etc. We will also accept comprehensive literature reviews on the mentioned topics.

Articles may address, but are not limited, to the following topics:

Forest inventory;
Biomass estimation;
Deforestation;
Real-time mapping of forests;
Large-scale mapping of forests by UAV clusters;
Tree type classification in forests;
Forest fire detection;
Forest tree disease detection.

Suggested themes and article types for submissions:

Original research articles about novel aspects of employing UAVs in forest science, or any article that addresses a challenge in this selected topic;
Literature reviews about the application of UAVs in forest science.

Dr. Ehsan Khoramshahi
Dr. Francisco Javier Mesas Carrascosa
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

forest inventory
UAV
deforestation
forest planning
structure from motion
3D point cloud
hyper-spectral imaging

Published Papers (6 papers)

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31 pages, 14190 KiB

Open AccessArticle

Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved Yolov5 Model and Transfer Learning

by Huanyu Yang, Jun Wang and Jiacun Wang

Remote Sens. 2023, 15(23), 5527; https://doi.org/10.3390/rs15235527 - 27 Nov 2023

Viewed by 1161

Abstract

Forest fires pose severe challenges to forest management because of their unpredictability, extensive harm, broad impact, and rescue complexities. Early smoke detection is pivotal for prompt intervention and damage mitigation. Combining deep learning techniques with UAV imagery holds potential in advancing forest fire smoke recognition. However, issues arise when using UAV-derived images, especially in detecting miniature smoke patches, complicating effective feature discernment. Common deep learning approaches for forest fire detection also grapple with limitations due to sparse datasets. To counter these challenges, we introduce a refined UAV-centric forest fire smoke detection approach utilizing YOLOv5. We first enhance anchor box clustering through K-means++ to boost the classification precision and then augment the YOLOv5 architecture by integrating a novel partial convolution (PConv) to trim down model parameters and elevate processing speed. A unique detection head is also incorporated to the model to better detect diminutive smoke traces. A coordinate attention module is embedded within YOLOv5, enabling precise smoke target location and fine-grained feature extraction amidst complex settings. Given the scarcity of forest fire smoke datasets, we employ transfer learning for model training. The experimental results demonstrate that our proposed method achieves 96% AP₅₀ and 57.3% AP_50:95 on a customized dataset, outperforming other state-of-the-art one-stage object detectors while maintaining real-time performance. Full article

(This article belongs to the Special Issue Novel Applications of UAV Imagery for Forest Science)

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27 pages, 5790 KiB

Open AccessEditor’s ChoiceArticle

A New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings

by Mohammad Imangholiloo, Ville Luoma, Markus Holopainen, Mikko Vastaranta, Antti Mäkeläinen, Niko Koivumäki, Eija Honkavaara and Ehsan Khoramshahi

Remote Sens. 2023, 15(21), 5233; https://doi.org/10.3390/rs15215233 - 03 Nov 2023

Cited by 1 | Viewed by 994

Abstract

Tree species information is important for forest management, especially in seedling stands. To mitigate the spectral admixture of understory reflectance with small and lesser foliaged seedling canopies, we proposed an image pre-processing step based on the canopy threshold (C_th) applied on drone-based multispectral images prior to feeding classifiers. This study focused on (1) improving the classification of seedlings by applying the introduced technique; (2) comparing the classification accuracies of the convolutional neural network (CNN) and random forest (RF) methods; and (3) improving classification accuracy by fusing vegetation indices to multispectral data. A classification of 5417 field-located seedlings from 75 sample plots showed that applying the C_th technique improved the overall accuracy (OA) of species classification from 75.7% to 78.5% on the C_th-affected subset of the test dataset in CNN method (1). The OA was more accurate in CNN (79.9%) compared to RF (68.3%) (2). Moreover, fusing vegetation indices with multispectral data improved the OA from 75.1% to 79.3% in CNN (3). Further analysis revealed that shorter seedlings and tensors with a higher proportion of C_th-affected pixels have negative impacts on the OA in seedling forests. Based on the obtained results, the proposed method could be used to improve species classification of single-tree detected seedlings in operational forest inventory. Full article

(This article belongs to the Special Issue Novel Applications of UAV Imagery for Forest Science)

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25 pages, 9441 KiB

Open AccessArticle

Tree Stem Detection and Crown Delineation in a Structurally Diverse Deciduous Forest Combining Leaf-On and Leaf-Off UAV-SfM Data

by Steffen Dietenberger, Marlin M. Mueller, Felix Bachmann, Maximilian Nestler, Jonas Ziemer, Friederike Metz, Marius G. Heidenreich, Franziska Koebsch, Sören Hese, Clémence Dubois and Christian Thiel

Remote Sens. 2023, 15(18), 4366; https://doi.org/10.3390/rs15184366 - 05 Sep 2023

Cited by 1 | Viewed by 1637

Abstract

Accurate detection and delineation of individual trees and their crowns in dense forest environments are essential for forest management and ecological applications. This study explores the potential of combining leaf-off and leaf-on structure from motion (SfM) data products from unoccupied aerial vehicles (UAVs) equipped with RGB cameras. The main objective was to develop a reliable method for precise tree stem detection and crown delineation in dense deciduous forests, demonstrated at a structurally diverse old-growth forest in the Hainich National Park, Germany. Stem positions were extracted from the leaf-off point cloud by a clustering algorithm. The accuracy of the derived stem co-ordinates and the overall UAV-SfM point cloud were assessed separately, considering different tree types. Extracted tree stems were used as markers for individual tree crown delineation (ITCD) through a region growing algorithm on the leaf-on data. Stem positioning showed high precision values (0.867). Including leaf-off stem positions enhanced the crown delineation, but crown delineations in dense forest canopies remain challenging. Both the number of stems and crowns were underestimated, suggesting that the number of overstory trees in dense forests tends to be higher than commonly estimated in remote sensing approaches. In general, UAV-SfM point clouds prove to be a cost-effective and accurate alternative to LiDAR data for tree stem detection. The combined datasets provide valuable insights into forest structure, enabling a more comprehensive understanding of the canopy, stems, and forest floor, thus facilitating more reliable forest parameter extraction. Full article

(This article belongs to the Special Issue Novel Applications of UAV Imagery for Forest Science)

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21 pages, 8626 KiB

Open AccessArticle

A Novel Deep Multi-Image Object Detection Approach for Detecting Alien Barleys in Oat Fields Using RGB UAV Images

by Ehsan Khoramshahi, Roope Näsi, Stefan Rua, Raquel A. Oliveira, Axel Päivänsalo, Oiva Niemeläinen, Markku Niskanen and Eija Honkavaara

Remote Sens. 2023, 15(14), 3582; https://doi.org/10.3390/rs15143582 - 17 Jul 2023

Cited by 2 | Viewed by 1781

Abstract

Oat products are significant parts of a healthy diet. Pure oat is gluten-free, which makes it an excellent choice for people with celiac disease. Elimination of alien cereals is important not only in gluten-free oat production but also in seed production. Detecting gluten-rich crops such as wheat, rye, and barley in an oat production field is an important initial processing step in gluten-free food industries; however, this particular step can be extremely time consuming. This article demonstrates the potential of emerging drone techniques for identifying alien barleys in an oat stand. The primary aim of this study was to develop and assess a novel machine-learning approach that automatically detects and localizes barley plants by employing drone images. An Unbiased Teacher v2 semi-supervised object-detection deep convolutional neural network (CNN) was employed to detect barley ears in drone images with a 1.5 mm ground sample distance. The outputs of the object detector were transformed into ground coordinates by employing a photogrammetric technique. The ground coordinates were analyzed with the kernel density estimate (KDE) clustering approach to form a probabilistic map of the ground locations of barley plants. The detector was trained using a dataset from a reference data production site (located in Ilmajoki, Finland) and tested using a 10% independent test data sample from the same site and a completely unseen dataset from a commercial gluten-free oats production field in Seinäjoki, Finland. In the reference data production dataset, 82.9% of the alien barley plants were successfully detected; in the independent farm test dataset, 60.5% of the ground-truth barley plants were correctly recognized. Our results establish the usefulness and importance of the proposed drone-based ultra-high-resolution red–green–blue (RGB) imaging approach for modern grain production industries. Full article

(This article belongs to the Special Issue Novel Applications of UAV Imagery for Forest Science)

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21 pages, 7117 KiB

Open AccessArticle

Modeling Carbon Emissions of Post-Selective Logging in the Production Forests of Ulu Jelai, Pahang, Malaysia

by Siti Nor Maizah Saad, Wan Shafrina Wan Mohd Jaafar, Hamdan Omar, Khairul Nizam Abdul Maulud, Aisyah Marliza Muhmad Kamarulzaman, Esmaeel Adrah, Norzalyta Mohd Ghazali and Midhun Mohan

Remote Sens. 2023, 15(4), 1016; https://doi.org/10.3390/rs15041016 - 12 Feb 2023

Cited by 2 | Viewed by 1823

Abstract

Harvested timber and constructed infrastructure over the logging area leave massive damage that contributes to the emission of anthropogenic gases into the atmosphere. Carbon emissions from tropical deforestation and forest degradation are the second largest source of anthropogenic emissions of greenhouse gases. Even though the emissions vary from region to region, a significant amount of carbon emissions comes mostly from timber harvesting, which is tightly linked to the selective logging intensity. This study intended to utilize a remote sensing approach to quantify carbon emissions from selective logging activities in Ulu Jelai Forest Reserve, Pahang, Malaysia. To quantify the emissions, the relevant variables from the logging’s impact were identified as a predictor in the model development and were listed as stump height, stump diameter, cross-sectional area, timber volume, logging gaps, road, skid trails, and incidental damage resulting from the logging process. The predictive performance of linear regression and machine learning models, namely support vector machine (SVM), random forest, and K-nearest neighbor, were examined to assess the carbon emission from this degraded forest. To test the different methods, a combination of ground inventory plots, unmanned aerial vehicles (UAV), and satellite imagery were analyzed, and the performance in terms of root mean square error (RMSE), bias, and coefficient of correlation (R²) were calculated. Among the four models tested, the machine learning model SVM provided the best accuracy with an RMSE of 21.10% and a bias of 0.23% with an adjusted R² of 0.80. Meanwhile, the linear model performed second with an RMSE of 22.14%, a bias of 0.72%, and an adjusted R² of 0.75. This study demonstrates the efficacy of remotely sensed data to facilitate the conventional methods of quantifying carbon emissions from selective logging and promoting advanced assessments that are more effective, especially in massive logging areas and various forest conditions. Findings from this research will be useful in assisting the relevant authorities in optimizing logging practices to sustain forest carbon sequestration for climate change mitigation. Full article

(This article belongs to the Special Issue Novel Applications of UAV Imagery for Forest Science)

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9 pages, 12042 KiB

Open AccessTechnical Note

Development of a Monitoring Method Using UAVs That Can Detect the Occurrence of Bark Stripping by Deer

by Hideyuki Niwa, Guihang Dai, Midori Ogawa and Mahito Kamada

Remote Sens. 2023, 15(3), 644; https://doi.org/10.3390/rs15030644 - 21 Jan 2023

Viewed by 1081

Abstract

The occurrence of bark stripping associated with increased deer densities can severely damage forests. Identifying trends in bark stripping is crucial for forest management, but such data are often difficult to obtain through field surveys. Therefore, this study aimed to develop an efficient monitoring method using unmanned aerial vehicles (UAVs) that can detect the occurrence of bark stripping and enable long-term monitoring. The area around the Ochiai Pass in Higashi-Iya Ochiai, Miyoshi City, Tokushima Prefecture, Japan, was selected as the study area for the survey of Abies homolepis, which was found to be significantly bark-stripped by deer in the field. The location and root diameter of A. homolepis were measured, and the percentages of bark stripping and tree growth were visually determined. Simultaneously, normalized difference vegetation index (NDVI) and visible light orthomosaic images were produced using a UAV. A canopy polygon of A. homolepis was created, and the average value of the NDVI within the polygon was calculated. Where the bark stripping rate at the root edge was greater than 75%, the number of “partially dead” and “dead” trees increased significantly, indicating that bark stripping by deer was the primary cause of the death of A. homolepis in Ochiai Pass. In addition, the mean value of the NDVI was significantly lower, with a bark stripping rate of 75% or higher, indicating that the NDVI of the canopy of A. homolepis can be used to estimate individuals with a high bark stripping rate at the root tips, that is, those with a high probability of mortality. Furthermore, by extrapolating the results of the tree-by-tree survey to the nontarget A. homolepis, we detected 46 (8%) A. homolepis with an average NDVI value of 0.8 or less (i.e., those with a bark stripping ratio of 75% or higher and a high probability of mortality). Therefore, the utilization of remote sensing technology via UAVs, as demonstrated in this study, proves to be a potent means for monitoring the incidence of bark stripping. Full article

(This article belongs to the Special Issue Novel Applications of UAV Imagery for Forest Science)

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