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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 7702

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Special Issue Editors

Dr. Akash Anand

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Guest Editor

Department of Forestry and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA
Interests: forestry; remote sensing; UAV; machine learning

Dr. Prashant Srivastava

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Guest Editor

Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India
Interests: microwave active and passive; optical/IR; hydrology; soil moisture; sensitivity and uncertainty analysis; artificial intelligence; geospatial technology; classification methods; simulation and modelling
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Dr. Sanjeev Kumar Srivastava

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Guest Editor

School of Science, Technology & Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia
Interests: forestry; remote sensing; LIDAR; UAV

Dr. Ram Avtar

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Guest Editor

Graduate School of Environmental Science, Hokkaido University, Sapporo, Japan
Interests: UAV; earth observation and forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation and unmanned aerial vehicle (UAV) applications are rapidly expanding and revolutionizing our understanding of the natural sciences. The recent growing availability of Earth observation remote sensing imagery from various sensors has allowed regional and global studies on a high-frequency basis. On the other hand, UAV platforms provide a unique opportunity to acquire low-cost imagery at fine spatial and temporal resolutions from local to regional scales. In order to better understand forests and the problems associated with preserving them as ecosystems, carbon sinks, and renewable energy resources, both platforms play a huge role.

In this Special Issue, we intend to provide a unique collection of original research works in the field of forestry that address novel approaches using remote sensing data at a global, regional and local scale. In particular, we encourage authors to demonstrate the enormous possibilities of advanced methods and technologies for applications in forest resource management.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Image classification and change detection;
Image processing and pattern recognition;
Feature extraction and object-based image analysis;
Image fusion for forest monitoring;
Machine learning and deep learning approaches;
Large-scale forest monitoring using LiDAR data (specially GEDI and ICESat-2 missions) for forest inventory and monitoring;
UAV-image-derived forest stand metrics (forest yield, biomass, tree density, etc.);
Detection capabilities of UAV multispectral data (individual species classification and characterization).

Dr. Akash Anand
Dr. Prashant Srivastava
Dr. Sanjeev Kumar Srivastava
Dr. Ram Avtar
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 cover change
forest inventory
UAV multispectral and LiDAR data
data fusion
object-based image classification
canopy characterization
high-resolution spatial imagery
machine learning
image processing
trend analysis

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28 pages, 13248 KiB

Open AccessArticle

A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis

by Kim Penglase, Tom Lewis and Sanjeev K. Srivastava

Remote Sens. 2023, 15(10), 2621; https://doi.org/10.3390/rs15102621 - 18 May 2023

Cited by 4 | Viewed by 1655

Abstract

Increased demand for sustainable timber products has resulted in large investments in agroforestry in Australia, with plantations growing various Pinus species, selected to suit a plantation’s environment. Juvenile Pinus species have a low fire tolerance. With Australia’s history of wildfires and the likelihood of climate change exacerbating that risk, the potential for a total loss of invested capital is high unless cost-effective targeted risk minimisation is part of forest management plans. Based on the belief that the understory profiles within the juvenile plantations are a major factor determining fuel hazard risks, an accurate assessment of these profiles is required to effectively mitigate those risks. At present, assessment protocols are largely reliant on ground-based observations, which are labour-intensive, time consuming, and expensive. This research project investigates the effectiveness of using geospatial analysis of drone-derived photographic data collected in the commercial pine plantations of south-eastern Queensland as a cost-saving alternative to current fuel hazard risk assessment practices. Understory composition was determined using the supervised classification of orthomosaic images together with derivations of canopy height models (CHMs). The CHMs were subjected to marker-controlled watershed segmentation (MCWS) analysis, isolating and removing the plantation pine trees, enabling the quantification of understory fuel profiles. The method used proved highly applicable to immature forest environments with minimal canopy closure, but became less reliable for close canopied older plantations. Full article

(This article belongs to the Special Issue Earth Observation and UAV Applications in Forestry)

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13 pages, 15758 KiB

Open AccessArticle

Application of YOLOv5 for Point Label Based Object Detection of Black Pine Trees with Vitality Losses in UAV Data

by Peter Hofinger, Hans-Joachim Klemmt, Simon Ecke, Steffen Rogg and Jan Dempewolf

Remote Sens. 2023, 15(8), 1964; https://doi.org/10.3390/rs15081964 - 7 Apr 2023

Cited by 8 | Viewed by 3275

Abstract

Monitoring tree diseases in forests is crucial for managing pathogens, particularly as climate change and globalization lead to the emergence and spread of tree diseases. Object detection algorithms for monitoring tree diseases through remote sensing rely on bounding boxes to represent trees. However, this approach may not be the most efficient. Our study proposed a solution to this challenge by applying object detection to unmanned aerial vehicle (UAV)-based imagery, using point labels that were converted into equally sized square bounding boxes. This allowed for effective and extensive monitoring of black pine (Pinus nigra L.) trees with vitality-related damages. To achieve this, we used the “You Only Look Once’’ version 5 (YOLOv5) deep learning algorithm for object detection, alongside a 16 by 16 intersection over union (IOU) and confidence threshold grid search, and five-fold cross-validation. Our dataset used for training and evaluating the YOLOv5 models consisted of 179 images, containing a total of 2374 labeled trees. Our experiments revealed that, for achieving the best results, the constant bounding box size should cover at least the center half of the tree canopy. Moreover, we found that YOLOv5s was the optimal model architecture. Our final model achieved competitive results for detecting damaged black pines, with a 95% confidence interval of the

F_{1}

score of 67–77%. These results can possibly be improved by incorporating more data, which is less effort-intensive due to the use of point labels. Additionally, there is potential for advancements in the method of converting points to bounding boxes by utilizing more sophisticated algorithms, providing an opportunity for further research. Overall, this study presents an efficient method for monitoring forest health at the single tree level, using point labels on UAV-based imagery with a deep learning object detection algorithm. Full article

(This article belongs to the Special Issue Earth Observation and UAV Applications in Forestry)

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16 pages, 9429 KiB

Open AccessTechnical Note

Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species

by Kirill Korznikov, Dmitriy Kislov, Tatyana Petrenko, Violetta Dzizyurova, Jiří Doležal, Pavel Krestov and Jan Altman

Remote Sens. 2023, 15(18), 4394; https://doi.org/10.3390/rs15184394 - 7 Sep 2023

Cited by 2 | Viewed by 1853

Abstract

The use of drone-borne imagery for tree recognition holds high potential in forestry and ecological studies. Accurate species identification and crown delineation are essential for tasks such as species mapping and ecological assessments. In this study, we compared the results of tree crown recognition across three neural networks using high-resolution optical imagery captured by an affordable drone with an RGB camera. The tasks included the detection of two evergreen coniferous tree species using the YOLOv8 neural network, the semantic segmentation of tree crowns using the U-Net neural network, and the instance segmentation of individual tree crowns using the Mask R-CNN neural network. The evaluation highlighted the strengths and limitations of each method. YOLOv8 demonstrated effective multiple-object detection (F1-score—0.990, overall accuracy (OA)—0.981), enabling detailed analysis of species distribution. U-Net achieved less accurate pixel-level segmentation for both species (F1-score—0.981, OA—0.963). Mask R-CNN provided precise instance-level segmentation, but with lower accuracy (F1-score—0.902, OA—0.822). The choice of a tree crown recognition method should align with the specific research goals. Although YOLOv8 and U-Net are suitable for mapping and species distribution assessments, Mask R-CNN offers more detailed information regarding individual tree crowns. Researchers should carefully consider their objectives and the required level of accuracy when selecting a recognition method. Solving practical problems related to tree recognition requires a multi-step process involving collaboration among experts with diverse skills and experiences, adopting a biology- and landscape-oriented approach when applying remote sensing methods to enhance recognition results. We recommend capturing images in cloudy weather to increase species recognition accuracy. Additionally, it is advisable to consider phenological features when selecting optimal seasons, such as early spring or late autumn, for distinguishing evergreen conifers in boreal or temperate zones. Full article

(This article belongs to the Special Issue Earth Observation and UAV Applications in Forestry)

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