Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

Lyu, Xin; Li, Xiaobing; Dang, Dongliang; Dou, Huashun; Wang, Kai; Lou, Anru

doi:10.3390/rs14051096

Cited by 44 publications

(25 citation statements)

References 104 publications

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“…As stated in Almaki et al [2], the fourth industrial revolution (4IR) is bringing new opportunities to employ advanced technology to achieve an efficient and sustainable means of boosting crop productivity. For instance, unmanned aerial vehicles (UAVs) open new paths that advance the solution of existing problems, such as for package delivery in logistics [3], in the field of agriculture 4.0 [4], Industry 4.0 [5], artificial intelligence [6], the Internet of Things (IoT) [7], remote sensing [8] and many others. In precision agriculture, UAVs are widely used to map the spatial and temporal variability of various parameters in the cultivation environment, helping producers in decision-making and consequently improving agricultural management [9,10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

et al. 2023

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Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.

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Section: Introductionmentioning

confidence: 99%

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

et al. 2023

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“…The use of aerial drones for wildlife monitoring has increased exponentially in the past decade [1][2][3][4][5][6][7]. These drones, also known as uncrewed aerial vehicles (UAVs), unmanned aerial systems (UASs), and remotely piloted aircraft systems (RPASs), can carry a variety of sensors, including high-resolution visible-light-cameras (RGB) and thermal infrared (TI) cameras.…”

Section: Introductionmentioning

confidence: 99%

“…These drones, also known as uncrewed aerial vehicles (UAVs), unmanned aerial systems (UASs), and remotely piloted aircraft systems (RPASs), can carry a variety of sensors, including high-resolution visible-light-cameras (RGB) and thermal infrared (TI) cameras. As the technologies advance and the price of these drones and sensors drops, they become more accessible to conservation biologists, wildlife managers, and other professionals working with wildlife monitoring [2][3][4][5]. The prospects of drones in wildlife monitoring have already been proven to save time, create better imagery and spatial data for especially cryptic and nocturnal animals [8,9], and reduce the risks and hazards for the observer [10,11].…”

Section: Introductionmentioning

confidence: 99%

Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

Povlsen,

Bruhn,

Durdevic

et al. 2023

Drones

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Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models’ precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.

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“…Another important application of UAS imagery is in grassland degradation monitoring, including studies concerning degradation of total vegetation (biomass and productivity), degradation of vegetation structure (reduction of plant height and increase of weeds), processes that pose several challenges to grassland management and sustainable development (Lyu et al, 2020). The hyperspectral and hyperspatial remote sensing technology can further obtain more-in-depth data information and help in monitoring of grassland vegetation structure (Lyu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Medeiros

Morellato²,

Silva³

2023

Front. Environ. Sci.

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Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland.

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Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

Cited by 44 publications

References 104 publications

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

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