Topic Editors

Forest and Wood Science Center, Department of Forest Sciences, Federal University of Paraná (UFPR), Curitiba, Brazil
Department of Geography, University of California, Berkeley, CA, USA
Geography Department, Sultan Qaboos University, Muscat 123, Oman
Department of Geography, Faculty of Social and Management Sciences, University of Buea, P.O. BOX 63, Buea, Southwest Region, Cameroon
Dr. Ana Novo
CINTEX, Geotech Group, Natural Resources and Environmental Engineering Forestry Engineering School, University of Vigo, A Xunqueira, 36005 Pontevedra, Spain

Individual Tree Detection (ITD) and Its Applications

Abstract submission deadline
closed (31 March 2024)
Manuscript submission deadline
30 June 2024
Viewed by
4515

Topic Information

Dear Colleagues,

Over the past couple of years, applications of individual tree detection (ITD) have become prevalent in numerous sectors, including forestry, biodiversity conservation, sustainability, land-use–land-cover change, climate change mitigation, and water management. One major catalyst for this trend was the proliferation of low-cost unmanned aerial vehicles (UAVs), along with advanced machine learning algorithms, that made ITD at multiple scales possible with high accuracy. Nonetheless, further research is required to explore several unexplored areas, such as the extraction of forest attributes from dense forest canopy structures, biomass mapping in arid environments, the development of transferrable ITD algorithms, and the detection of species-specific ITD paradigms, as well as the detection of mangrove distribution and the establishment of long-term databases that allow seasonality studies. This is a multidisciplinary topic with a focus on ITD, and this Topic intends to publish research, perspectives, or review articles that promote and support advancements in remote sensing applications dealing with tree-level research activities. The areas of interest include but are not limited to:

  • Tree-level attributes estimation and characterization;
  • Three-dimensional (3D) forest structure analysis;
  • Development of novel ITD algorithms;
  • Biomass modeling and validation;
  • ITD using different remotely sensed data types;
  • Machine learning and deep learning applications for ITD;
  • Exploring UAV-LiDAR (light detection and ranging) and data fusion approaches.

Dr. Ana Paula Dalla Corte
Dr. Midhun (Mikey) Mohan
Dr. Meshal M. Abdullah
Dr. Ewane Basil Ewane
Dr. Ana Novo
Topic Editors

Keywords

  • forest monitoring
  • forest attributes, mensuration and modeling
  • individual tree crown delineation
  • species classification
  • biomass mapping
  • 3D point clouds
  • unmanned aerial vehicles
  • light detection and ranging
  • multispectral and hyperspectral data
  • satellite remote sensing
  • data fusion
  • machine learning
  • deep learning

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Drones
drones
4.8 6.1 2017 17.9 Days CHF 2600 Submit
Forests
forests
2.9 4.5 2010 16.9 Days CHF 2600 Submit
Land
land
3.9 3.7 2012 14.8 Days CHF 2600 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (4 papers)

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21 pages, 1859 KiB  
Article
Pine-YOLO: A Method for Detecting Pine Wilt Disease in Unmanned Aerial Vehicle Remote Sensing Images
by Junsheng Yao, Bin Song, Xuanyu Chen, Mengqi Zhang, Xiaotong Dong, Huiwen Liu, Fangchao Liu, Li Zhang, Ying-Bo Lu, Chang Xu and Ran Kang
Forests 2024, 15(5), 737; https://doi.org/10.3390/f15050737 - 23 Apr 2024
Viewed by 53
Abstract
Pine wilt disease is a highly contagious forest quarantine ailment that spreads rapidly. In this study, we designed a new Pine-YOLO model for pine wilt disease detection by incorporating Dynamic Snake Convolution (DSConv), the Multidimensional Collaborative Attention Mechanism (MCA), and Wise-IoU v3 (WIoUv3) [...] Read more.
Pine wilt disease is a highly contagious forest quarantine ailment that spreads rapidly. In this study, we designed a new Pine-YOLO model for pine wilt disease detection by incorporating Dynamic Snake Convolution (DSConv), the Multidimensional Collaborative Attention Mechanism (MCA), and Wise-IoU v3 (WIoUv3) into a YOLOv8 network. Firstly, we collected UAV images from Beihai Forest and Linhai Park in Weihai City to construct a dataset via a sliding window method. Then, we used this dataset to train and test Pine-YOLO. We found that DSConv adaptively focuses on fragile and curved local features and then enhances the perception of delicate tubular structures in discolored pine branches. MCA strengthens the attention to the specific features of pine trees, helps to enhance the representational capability, and improves the generalization to diseased pine tree recognition in variable natural environments. The bounding box loss function has been optimized to WIoUv3, thereby improving the overall recognition accuracy and robustness of the model. The experimental results reveal that our Pine-YOLO model achieved the following values across various evaluation metrics: MAP@0.5 at 90.69%, mAP@0.5:0.95 at 49.72%, precision at 91.31%, recall at 85.72%, and F1-score at 88.43%. These outcomes underscore the high effectiveness of our model. Therefore, our newly designed Pine-YOLO perfectly addresses the disadvantages of the original YOLO network, which helps to maintain the health and stability of the ecological environment. Full article
(This article belongs to the Topic Individual Tree Detection (ITD) and Its Applications)
15 pages, 13555 KiB  
Article
Using UAVs and Machine Learning for Nothofagus alessandrii Species Identification in Mediterranean Forests
by Antonio M. Cabrera-Ariza, Miguel Peralta-Aguilera, Paula V. Henríquez-Hernández and Rómulo Santelices-Moya
Drones 2023, 7(11), 668; https://doi.org/10.3390/drones7110668 - 09 Nov 2023
Viewed by 1317
Abstract
This study explores the use of unmanned aerial vehicles (UAVs) and machine learning algorithms for the identification of Nothofagus alessandrii (ruil) species in the Mediterranean forests of Chile. The endangered nature of this species, coupled with habitat loss and environmental stressors, necessitates efficient [...] Read more.
This study explores the use of unmanned aerial vehicles (UAVs) and machine learning algorithms for the identification of Nothofagus alessandrii (ruil) species in the Mediterranean forests of Chile. The endangered nature of this species, coupled with habitat loss and environmental stressors, necessitates efficient monitoring and conservation efforts. UAVs equipped with high-resolution sensors capture orthophotos, enabling the development of classification models using supervised machine learning techniques. Three classification algorithms—Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood (ML)—are evaluated, both at the Pixel- and Object-Based levels, across three study areas. The results reveal that RF consistently demonstrates strong classification performance, followed by SVM and ML. The choice of algorithm and training approach significantly impacts the outcomes, highlighting the importance of tailored selection based on project requirements. These findings contribute to enhancing species identification accuracy in remote sensing applications, supporting biodiversity conservation and ecological research efforts. Full article
(This article belongs to the Topic Individual Tree Detection (ITD) and Its Applications)
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21 pages, 7339 KiB  
Article
Unsupervised Methodology for Large-Scale Tree Seedling Mapping in Diverse Forestry Settings Using UAV-Based RGB Imagery
by Sadeepa Jayathunga, Grant D. Pearse and Michael S. Watt
Remote Sens. 2023, 15(22), 5276; https://doi.org/10.3390/rs15225276 - 07 Nov 2023
Viewed by 1069
Abstract
Mapping and monitoring tree seedlings is essential for reforestation and restoration efforts. However, achieving this on a large scale, especially during the initial stages of growth, when seedlings are small and lack distinct morphological features, can be challenging. An accurate, reliable, and efficient [...] Read more.
Mapping and monitoring tree seedlings is essential for reforestation and restoration efforts. However, achieving this on a large scale, especially during the initial stages of growth, when seedlings are small and lack distinct morphological features, can be challenging. An accurate, reliable, and efficient method that detects seedlings using unmanned aerial vehicles (UAVs) could significantly reduce survey costs. In this study, we used an unsupervised approach to map young conifer seedlings utilising spatial, spectral, and structural information from UAV digital aerial photogrammetric (UAV-DAP) point clouds. We tested our method across eight trial stands of radiata pine with a wide height range (0.4–6 m) that comprised a total of ca. 100 ha and spanned diverse site conditions. Using this method, seedling detection was excellent, with an overall precision, sensitivity, and F1 score of 95.2%, 98.0%, and 96.6%, respectively. Our findings demonstrated the importance of combining spatial, spectral, and structural metrics for seedling detection. While spectral and structural metrics efficiently filtered out non-vegetation objects and weeds, they struggled to differentiate planted seedlings from regenerating ones due to their similar characteristics, resulting in a large number of false positives. The inclusion of a row segment detection algorithm overcame this limitation and successfully identified most regenerating seedlings, leading to a significant reduction in false positives and an improvement in overall detection accuracy. Our method generated vector files containing seedling positions and key structural characteristics (seedling height, crown dimensions), offering valuable outputs for precision management. This automated pipeline requires fewer resources and user inputs compared to manual annotations or supervised techniques, making it a rapid, cost-effective, and scalable solution which is applicable without extensive training data. While serving as primarily a standalone tool for assessing forestry projects, the proposed method can also complement supervised seedling detection methods like machine learning, i.e., by supplementing training datasets. Full article
(This article belongs to the Topic Individual Tree Detection (ITD) and Its Applications)
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14 pages, 12436 KiB  
Article
Identification of the Forest Cover Growth on Landscape Level from Aerial Laser Scanning Data
by Miroslav Sivák, Miroslav Kardoš, Roman Kadlečík, Juliána Chudá, Julián Tomaštík and Ján Tuček
Land 2023, 12(5), 1074; https://doi.org/10.3390/land12051074 - 16 May 2023
Viewed by 980
Abstract
Aerial laser scanning technology has excellent potential in landscape management and forestry. Due to its specific characteristics, the application of this type of data is the subject of intensive research, with the search for new areas of application. This work aims to identify [...] Read more.
Aerial laser scanning technology has excellent potential in landscape management and forestry. Due to its specific characteristics, the application of this type of data is the subject of intensive research, with the search for new areas of application. This work aims to identify the boundaries of forest stands, and forest patches on non-forest land. The research objectives cover the diversity of conditions in the forest landscapes of Slovakia, with its high variability of tree species composition (coniferous, mixed, deciduous stands), age, height, and stand density. A semi-automatic procedure was designed and verified (consisting of the creation of a digital terrain model, a digital surface model, and the identification of peaks and contours of tree crowns), which allows after identification of homogeneous areas of forest stands and/or forest patches (areas covered with trees species canopy) with selected parameters (height, crown size, gap size), with high accuracy. The applicability of the proposed procedure increases the use of freely available ALS data (provided by the Office of Geodesy, Cartography, and Cadastre of the Slovak Republic) and freely distributable software tools (QGIS, CloudCompare). Full article
(This article belongs to the Topic Individual Tree Detection (ITD) and Its Applications)
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