UAV-Based Forest Health Monitoring: A Systematic Review

Ecke, Simon; Dempewolf, Jan; Frey, Julian; Schwaller, Andreas; Endres, Ewald; Klemmt, Hans-Joachim; Tiede, Dirk; Seifert, Thomas

doi:10.3390/rs14133205

Cited by 106 publications

(70 citation statements)

References 215 publications

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“…Despite these challenges, it is important to acknowledge the intrinsic advantages of multispectral and hyperspectral sensors. They can provide detailed information about different types of vegetation cover, plant health, environmental stress, and other crucial aspects of forests (Hänsch & Hellwich 2021; Li et al 2021; Ecke et al 2022).…”

Section: Discussionmentioning

confidence: 99%

How have RPAS helped monitor forests and what can we apply in forest restoration monitoring?

Sinegalia,

Santoro,

Molin

2023

Restoration Ecology

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We are currently experiencing an unprecedented moment in forest restoration, where monitoring activities in recovery areas play a vital role in problem identification and method adaptation. However, it is crucial to shift away from expensive traditional methodologies and develop technologies that enable large‐scale monitoring using more accessible and cost‐effective tools. In an effort to provide an evidence‐based perspective, we conducted a systematic review of how remotely piloted aircraft systems equipped with various sensors have been employed for forest monitoring. We consulted three databases and included 53 articles in our review. The results revealed a trend toward research in tropical ecosystems, with forest structure being the most frequently assessed attribute, and canopy height being the most widely measured structural indicator. Red‐green‐blue sensors were commonly used, both individually and in combination with others, and there was a noticeable shift toward the use of light detection and ranging. Data validation primarily relied on forest inventory methods, often involving comparisons of outputs from different sensors and the use of artificial intelligence algorithms. Despite the wide range of studies utilizing sensor‐equipped drones to analyze forest attributes, there is a notable scarcity of research specifically addressing the application of these technologies in forest restoration monitoring. Filling this research gap is essential, as employing techniques that enable large‐scale monitoring, such as aerial photogrammetry and remote sensing, aligns with current trends and contributes to global commitments to environmental restoration and conservation.

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Section: Discussionmentioning

confidence: 99%

How have RPAS helped monitor forests and what can we apply in forest restoration monitoring?

Sinegalia,

Santoro,

Molin

2023

Restoration Ecology

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“…Weed mapping and management [ 6 ] Irrigation management [ 7 ] Corps spraying [ 8 ] health monitoring of Vegetation and detection of diseases [ 9 ] growth monitoring of Vegetation and estimation of yield [ 10 ]…”

Section: Drone Application In Pa Referencementioning

confidence: 99%

Review on Drone application methodologies in agriculture precision

Wadod,

Mohammed

2023

IOP Conf. Ser.: Earth Environ. Sci.

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In recent times, Drone use has increased significantly in the field of precise agriculture. These aircraft, which are equipped with thermal and multi-spectrum cameras, allow us to increase agricultural production in proportion to the increasing demand of the population and monitor crops grown in large areas, which is a very difficult task given the large areas of fields and the standards required for the successful cultivation of the crop in terms of soil moisture and fertility, proximity to water sources, and the possibility of its delivery In a way that reduces the waste of this water. Fields are at risk of crop damage due to diseases to crops caused by insects and pests, as drones can be used to spray pesticides and fertilizers in a way that reduces the health risks to people when spreading them manually. In this research paper, we will address several previous research related to the use of sensor and drone techniques in the field of precision agriculture and their efficiency in helping to sustain production and increase crop yields.

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“…Therefore, the accurate diagnosis and continuous monitoring of A. angustifolia in forest remnants are crucial for environmental preservation. With technological advances, Unmanned Aerial Vehicles (UAVs), or drones, have increasingly become versatile and accessible tools for research in this eld, enabling the mapping and monitoring of trees and forests (SAAD et al, 2021;ECKE et al, 2022;CUNHA NETO et al, 2023). Additionally, computer vision techniques used to analyze images from remote sensors, particularly those based on deep learning, more speci cally Convolutional Neural Networks (CNNs), have shown their potential in recognizing tree species in forest regions (KNAUER et al, 2019;SANTOS et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Identification of Araucaria angustifolia trees in an urban forest fragment using UAV images and YOLOv7 structure

Correa,

Prandini,

Cysneiros

et al. 2024

Preprint

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This study addresses the identification of individuals of the Araucaria angustifolia species in urban forest fragments, specifically in the Mixed Ombrophilous Forest (FOM) in Curitiba, Paraná, Brazil. The aim of the study is to use UAV images and the computer vision technique of the YOLOv7 model to detect individuals of A. angustifolia. The FOM is essential for local biodiversity conservation and human well-being but faces challenges due to urban sprawl and the conversion of land use to agriculture. The species is critically endangered, requiring actions and strategies for its conservation. The study highlights the role of Unmanned Aerial Vehicles (UAVs) and deep learning techniques, such as Convolutional Neural Networks (CNNs), in identifying tree species in urban ecosystems. YOLOv7, an architecture based on CNNs, was chosen because of its detection capacity. YOLOv7 is especially effective at detecting a wide variety of objects, including people, vehicles, animals, household objects, road signs and much more, making it an ideal choice for identifying tree species in urban environments. The data was obtained by a DJI Mavic 3 UAV. Utilizing a UAV, the study area of the urban forest was flown over, generating an orthomosaic that was subsequently divided into 14 parts for training, validation, and testing. The YOLOv7 model was trained with the images to detect A. angustifolia trees present in the area. The results show that model achieved a precision of 79.3%, recall of 86.8%, and Mean Average Precision of 87% during training. Comparative analysis with forest inventory data reveals promising performance in detecting A. angustifolia trees. The average confidence of the model's classification was 76.18 ± 12.88%, with 80.81% being the most frequent classification for the median result. The present study uses the effective integration of UAV technology, YOLOv7 model with deep learning technique to detect and assess tree species in urban ecosystems. This approach provides an important tool for conservation strategies aimed at assessing and managing the tree biodiversity in urban forest remnants.

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UAV-Based Forest Health Monitoring: A Systematic Review

Cited by 106 publications

References 215 publications

How have RPAS helped monitor forests and what can we apply in forest restoration monitoring?

How have RPAS helped monitor forests and what can we apply in forest restoration monitoring?

Review on Drone application methodologies in agriculture precision

Identification of Araucaria angustifolia trees in an urban forest fragment using UAV images and YOLOv7 structure

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