Individual Tree Detection (ITD) and Its Applications

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

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 (5 papers)

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All Drones Forests Land Remote Sensing Sustainability

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18 pages, 5573 KiB  

Open AccessArticle

Effects of Illumination Conditions on Individual Tree Height Extraction Using UAV LiDAR: Pilot Study of a Planted Coniferous Stand

by Tianxi Li, Jiayuan Lin, Wenjian Wu and Rui Jiang

Forests 2024, 15(5), 758; https://doi.org/10.3390/f15050758 - 26 Apr 2024

Viewed by 273

Abstract

Tree height is one of the key dendrometric parameters for indirectly estimating the timber volume or aboveground biomass of a forest. Field measurement is time-consuming and labor-intensive, while unmanned aerial vehicle (UAV)-borne LiDAR is a more efficient tool for acquiring tree heights of [...] Read more.

Tree height is one of the key dendrometric parameters for indirectly estimating the timber volume or aboveground biomass of a forest. Field measurement is time-consuming and labor-intensive, while unmanned aerial vehicle (UAV)-borne LiDAR is a more efficient tool for acquiring tree heights of large-area forests. Although individual tree heights extracted from point cloud data are of high accuracy, they are still affected by some weather and environment factors. In this study, taking a planted M. glyptostroboides (Metasequoia glyptostroboides Hu & W.C. Cheng) stand as the study object, we preliminarily assessed the effects of various illumination conditions (solar altitude angle and cloud cover) on tree height extraction using UAV LiDAR. The eight point clouds of the target stand were scanned at four time points (sunrise, noon, sunset, and night) in two consecutive days (sunny and overcast), respectively. The point clouds were first classified into ground points and aboveground vegetation points, which accordingly produced digital elevation model (DEM) and digital surface model (DSM). Then, the canopy height model (CHM) was obtained by subtracting DEM from DSM. Subsequently, individual trees were segmented based on the seed points identified by local maxima filtering. Finally, the individual tree heights of sample trees were separately extracted and assessed against the in situ measured values. As results, the R² and RMSEs of tree heights obtained in the overcast daytime were commonly better than those in the sunny daytime; the R² and RMSEs at night were superior among all time points, while those at noon were poorest. These indicated that the accuracy of individual tree height extraction had an inverse correlation with the intensity of illumination. To obtain more accurate tree heights for forestry applications, it is best to acquire point cloud data using UAV LiDAR at night, or at least not at noon when the illumination is generally strongest. Full article

(This article belongs to the Topic Individual Tree Detection (ITD) and Its Applications)

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21 pages, 68245 KiB  

Open AccessArticle

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, Yingbo Lu, Chang Xu and Ran Kang

Forests 2024, 15(5), 737; https://doi.org/10.3390/f15050737 (registering DOI) - 23 Apr 2024

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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)

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15 pages, 13555 KiB  

Open AccessArticle

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

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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  

Open AccessArticle

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

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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  

Open AccessArticle

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 991

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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