Search Results (124)

Accurate quantification of above-ground biomass (AGB) and carbon sequestration is vital for monitoring terrestrial ecosystem dynamics, informing climate policy, and supporting carbon neutrality initiatives. However, conventional methods—ranging from manual field surveys to remote sensing techniques based solely on 2D vegetation indices—often fail to capture the intricate spectral and structural heterogeneity of forest canopies, particularly at fine spatial resolutions. To address these limitations, we introduce ForestIQNet, a novel end-to-end multimodal deep learning framework designed to estimate AGB and associated carbon stocks from UAV-acquired imagery with high spatial fidelity. ForestIQNet combines dual-stream encoders for processing multispectral UAV imagery and a voxelized Canopy Height Model (CHM), fused via a Cross-Attentional Feature Fusion (CAFF) module, enabling fine-grained interaction between spectral reflectance and 3D structure. A lightweight Transformer-based regression head then performs multitask prediction of AGB and CO₂e, capturing long-range spatial dependencies and enhancing generalization. Proposed method achieves an R² of 0.93 and RMSE of 6.1 kg for AGB prediction, compared to 0.78 R² and 11.7 kg RMSE for XGBoost and 0.73 R² and 13.2 kg RMSE for Random Forest. Despite its architectural complexity, ForestIQNet maintains a low inference cost (27 ms per patch) and generalizes well across species, terrain, and canopy structures. These results establish a new benchmark for UAV-enabled biomass estimation and provide scalable, interpretable tools for climate monitoring and forest management. Full article

Fish kills are sudden mass mortalities that occur in freshwater and marine systems worldwide. Fish kill surveys are essential for assessing the ecological and economic impacts of fish kill events, but are often labor-intensive, time-consuming, and spatially limited. This study aims to address these challenges by exploring the application of unoccupied aerial systems (or drones) and deep learning techniques for coastal fish carcass detection. Seven flights were conducted using a DJI Phantom 4 RGB quadcopter to monitor three sites with different substrates (i.e., sand, rock, shored Sargassum). Orthomosaics generated from drone imagery were useful for detecting carcasses washed ashore, but not floating or submerged carcasses. Single shot multibox detection (SSD) with a ResNet50-based model demonstrated high detection accuracy, with a mean average precision (mAP) of 0.77 and a mean average recall (mAR) of 0.81. The model had slightly higher average precision (AP) when detecting large objects (>42.24 cm long, AP = 0.90) compared to small objects (≤14.08 cm long, AP = 0.77) because smaller objects are harder to recognize and require more contextual reasoning. The results suggest a strong potential future application of these tools for rapid fish kill response and automatic enumeration and characterization of fish carcasses. Full article

Wildlife monitoring has entered a transformative era with the convergence of drone technology and artificial intelligence (AI). Drones provide access to remote and dangerous habitats, while AI unlocks the potential to process vast amounts of wildlife data. This synergy is reshaping wildlife monitoring, offering novel solutions to tackle challenges in species identification, animal tracking, anti-poaching, population estimation, and habitat analysis. This paper conducts a comprehensive literature review to examine the recent advancements in drone and AI systems for wildlife monitoring, focusing on two critical dimensions: (1) Methodologies, algorithms, and applications, analyzing the AI techniques employed in wildlife monitoring, including their operational frameworks and real-world implementations. (2) Challenges and opportunities, identifying current limitations, including technical hurdles and regulatory constraints, as well as exploring the untapped potential in drone and AI integration to enhance wildlife monitoring and conservation efforts. By synthesizing these insights, this paper will provide researchers with a structured framework for leveraging drone and AI systems in wildlife monitoring, identifying best practices and outlining actionable pathways for future innovation in the field. Full article

Traditional tsunami vulnerability assessments often rely on empirical models and field surveys, which can be time-consuming and have limited accuracy. In this study, we propose a novel approach that integrates high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry with numerical simulation to improve vulnerability assessment efficacy in Ancon Bay, Lima, Peru, by using the Papathoma Tsunami Vulnerability Assessment (PTVA-4) model. For this purpose, a detailed 3D representation of the study area was generated using UAV-based oblique photogrammetry, enabling the extraction of building attributes. Additionally, a high-resolution numerical tsunami simulation was conducted using the TUNAMI-N2 model for a potential worst-case scenario that may affect the Central Peru subduction zone, incorporating topographic and land-use data obtained with UAV-based nadir photogrammetry. The results indicate that the northern region of Ancon Bay exhibits higher relative vulnerability levels due to greater inundation depths and more tsunami-prone building attributes. UAV-based assessments provide a rapid and detailed method for evaluating building vulnerability. These findings indicate that the proposed methodology is a valuable tool for supporting coastal risk planning and disaster preparedness in tsunami-prone areas. Full article

The Tibetan Plateau is a critical ecological habitat where the overpopulation of plateau pika (Ochotona curzoniae), a keystone species, accelerates grassland degradation through excessive burrowing and herbivory, threatening ecological balance and human activities. To address the inefficiency and high costs of traditional pika burrow monitoring, this study proposes an intelligent monitoring solution that integrates drone remote sensing with deep learning. By combining the lightweight visual Transformer architecture EfficientViT with the hybrid attention mechanism CBAM, we develop an enhanced YOLOv11-AEIT algorithm: (1) EfficientViT is employed as the backbone network, strengthening micro-burrow feature representation through a multi-scale feature coupling mechanism that alternates between local window attention and global dilated attention; (2) the integration of CBAM (Convolutional Block Attention Module) in the feature fusion neck reduces false detections through dual-channel spatial attention filtering. Evaluations on our custom PPCave2025 dataset show that the enhanced model achieves a 98.6% mAP@0.5, outperforming the baseline YOLOv11 by 3.5 percentage points, with precision and recall improvements of 4.8% and 7.2%, respectively. The algorithm enhances efficiency by a factor of 15 compared to manual inspection, while seamlessly meeting real-time drone detection requirements. This approach provides high-precision yet lightweight technical support for plateau ecological conservation and serves as a valuable methodological reference for similar ecological monitoring tasks. Full article

Drones are becoming increasingly valuable tools in wildlife studies due to their ability to access remote areas and offer high-resolution information with minimal human interference. Their application is, however, causing concern regarding wildlife disturbance. This review synthesizes the existing literature on how animals within terrestrial, aerial, and aquatic environments are impacted by drone disturbance in relation to operational variables, sensory stimulation, species-specific sensitivity, and physiological and behavioral responses. We found that drone altitude, speed, approach distance, and noise levels significantly influence wildlife responses, with some species exhibiting increased vigilance, flight responses, or physiological stress. Environmental context and visual cues are also involved in species detection of drones and disturbance thresholds. Although the short-term response to behavior change has been well documented, long-term consequences of repeated drone exposure remain poorly known. This paper identifies the necessity for continued research into drone–wildlife interactions, with an emphasis on the requirement to minimize disturbance by means of improved flight parameters and technology. Full article

Morphological changes during body development measurements are crucial in understanding growth rates, allometric relationships, and sexual dimorphism. Recent advances in drone technology provide a new perspective enabling an indirect, non-invasive morphological assessment of free-ranging cetaceans. In this study, 10 body parameters were measured and examined with drone-based aerial photogrammetry across 82 individual fin whales (Balaenoptera physalus) along the Catalan coast of the Northwestern Mediterranean Sea, between 2021 and 2023. The growth pattern of each body parameter relative to the total length was determined as negative allometry. The developmental changes depicted that the head region at first decreases proportionally until the animal reaches approximately 14 m in length. Then, it remains constant until 18 m, subsequently followed by a relative increase. The difference in the growth rates among the sexes leads to a proportional shift between females and males approximately between 15 and 17 m in length. Overall, males exhibit a more rapid body elongation, along with moderate development of the other body parameters, while females display the contrary. The morphological parameters reveal insights into the population status dynamics and provide information on the reproductive status. These parameters are critical for the proper conservation and management of the local population of the species. Full article

The Third Pole region contains vast glaciers, and changes in these glaciers profoundly affect the lives and development of billions of people. Therefore, accurate glacier monitoring in this region is of great scientific and practical significance. Unmanned Aerial Vehicles (UAVs) provide high-resolution observation capabilities and flexible deployment options, effectively overcoming certain limitations associated with traditional in situ and satellite remote sensing observations. Thus, UAV technology is increasingly gaining traction and application in the glaciology community. This review systematically analyzed studies involving UAV technology in Third Pole glaciology research and determined that relevant studies have been performed for a decade (2014–2024). Notably, after 2020, the number of relevant manuscripts has increased significantly. Research activities are biased toward the use of rotary-wing UAVs (63%) and ground control point (GCP) correction methods (67%). Additionally, there is strong emphasis on analyzing glacier surface elevation, surface velocity, and landform evolution. These activities are primarily concentrated in the Himalayan region, with relatively less research being conducted in the western and central areas. UAV technology has significantly contributed to glaciology research in the Third Pole region and holds great potential to enhance the monitoring capabilities in future studies. Full article

Studies of habitat-related behaviour of mammals are time-consuming. This study aims to develop a model for monitoring the behaviour of mammals in different habitat types using drones mounted with thermal cameras in combination with a YOLO object detection model. Red deer (Cervus elaphus) and fallow deer (Dama dama) were used as model species. The data were collected in the nature reserve, Hanstholm, Northern Denmark. The aim is to develop an AI model capable of distinguishing between four behaviours, “foraging”, “locomoting”, “lying” and “standing”, allowing for insights into the rumination and foraging cycle of the two species. At the same time, the behaviour was linked to habitat types by geocoding individuals. The method developed in this study proved to be time-efficient and provided information about how the two deer species used vegetation types and interspecific interaction between the two species. Technical challenges were to follow individuals and the possibility of missing cyclical behaviour. It was found that the degree to which the ungulates actively foraged was significantly different between the two species and that they were clearly geographically separated within the study area. Full article

This study introduces a novel, drone-based approach for the detection and classification of Greater Caribbean Manatees (Trichechus manatus manatus) in the Panama Canal Basin by integrating advanced deep learning techniques. Leveraging the high-performance YOLOv8 model augmented with Sliced Aided Hyper Inferencing (SAHI) for improved small-object detection, our system accurately identifies individual manatees, mother–calf pairs, and group formations across a challenging aquatic environment. Additionally, the use of AltCLIP for zero-shot classification enables robust demographic analysis without extensive labeled data, enhancing model adaptability in data-scarce scenarios. For this study, more than 57,000 UAV images were acquired from multiple drone flights covering diverse regions of Gatun Lake and its surroundings. In cross-validation experiments, the detection model achieved precision levels as high as 93% and mean average precision (mAP) values exceeding 90% under ideal conditions. However, testing on unseen data revealed a lower recall, highlighting challenges in detecting manatees under variable altitudes and adverse lighting conditions. Furthermore, the integrated zero-shot classification approach demonstrated a robust top-2 accuracy close to 90%, effectively categorizing manatee demographic groupings despite overlapping visual features. This work presents a deep learning framework integrated with UAV technology, offering a scalable, non-invasive solution for real-time wildlife monitoring. By enabling precise detection and classification, it lays the foundation for enhanced habitat assessments and more effective conservation planning in similar tropical wetland ecosystems. Full article

Icing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which small control inputs are used to keep the aircraft’s angle of attack and other state variables within a limited range. However, this approach restricts the flight performance of icing aircraft. To address this issue, this paper innovatively proposes a design method of an ice tolerance flight envelope protection control system for a UAV on the base of icing severity detection using a long short-term memory (LSTM) neural network. First, the icing severity is detected using an LSTM neural network without requiring control surface excitation. It relies solely on the aircraft’s historical flight data to detect the icing severity. Second, by modifying the fuzzy risk level boundaries of the icing aircraft flight parameters, a nonlinear mapping relationship is established between the tracking command risk level, the UAV flight control command magnitude, and the icing severity. This provides a safe range of tracking commands for guiding the aircraft out of the icing region. Finally, the ice tolerance flight envelope protection control law is developed, using a nonlinear dynamic inverse controller (NDIC) as the inner loop and a nonlinear model predictive controller (NMPC) as the outer loop. This approach ensures boundary protection for state variables such as the angle of attack and roll angle while simultaneously enhancing the robustness of the flight control system. The effectiveness and superiority of the method proposed in this paper are verified for the example aircraft through mathematical simulation. Full article

Thermal imaging drones have transformed wildlife monitoring by facilitating the efficient and noninvasive monitoring of animal populations across large areas. In this study, an optimized flight path design was developed for monitoring wildlife on Guleopdo Island, South Korea using the DJI Mavic 3T drone equipped with a thermal camera. We employed a strata-based sampling technique to reclassify topographical and land cover information, creating an optimal survey plan. Using sampling strata, key waypoints were derived, on the basis of which nine flight paths were designed to cover ~50% of the study area. The results demonstrated that an optimized flight path improved the accuracy of detecting Formosan sika deer (Cervus nippon taiouanus). Population estimates indicated at least 128 Formosan sika deer, with higher detection efficiency observed during cloudy weather. Customizing flight paths based on the habitat characteristics proved crucial for efficient monitoring. This study highlights the potential of thermal imaging drones for accurately estimating wildlife populations and supporting conservation efforts. Full article

Unmanned aerial vehicle (UAV) systems have recently become essential for mapping, surveying, and three-dimensional (3D) modeling applications. These systems are capable of providing highly accurate products through integrated advanced technologies, including a digital camera, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS). UAVs are a cost-effective alternative to traditional aerial photogrammetry, and recent advancements demonstrate their effectiveness in many applications. In UAV-based photogrammetry, ground control points (GCPs) are utilized for georeferencing to enhance positioning precision. The distribution, number, and location of GCPs in the study area play a crucial role in determining the accuracy of photogrammetric products. This research evaluates the accuracy of positioning techniques for image acquisition for photogrammetric production and the effect of GCP distribution models. The camera position was determined using real-time kinematic (RTK), post-processed kinematic (PPK), and precise point positioning-ambiguity resolution (PPP-AR) techniques. In the criteria for determining the GCPs, six models were established within the İstanbul Technical University, Ayazaga Campus. To assess the accuracy of the points in these models, the horizontal, vertical, and 3D root mean square error (RMSE) values were calculated, holding the test points stationary in place. In the study, 2.5 cm horizontal RMSE and 3.0 cm vertical RMSE were obtained with the model containing five homogeneous GCPs by the indirect georeferencing method. The highest RMSE values of all three components in RTK, PPK, and PPP-AR methods were obtained without GCPs. For all six models, all techniques have an error value of sub-decimeter. The PPP-AR technique yields error values that are comparable to those of the other techniques. The PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labor, and higher costs. Full article

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery. Full article

Lythrum salicaria (purple loosestrife) is an invasive species that displaces native wetland flora in the USA. The detection and manual release of biological control agents for L. salicaria is challenging because L. salicaria inhabits many inaccessible areas. This study was conducted to develop aerial systems for the detection of L. salicaria and the release of its natural enemy, Galerucella calmariensis (Coleoptera: Chrysomelidae). We determined the optimal sensors and flight height for the aerial detection of L. salicaria and designed an aerial deployment method for G. calmariensis. Drone-based aerial surveys were conducted at various flight heights utilizing RGB, multispectral, and thermal sensors. We also developed an insect container (i.e., bug ball) for the aerial deployment of G. calmariensis. Our findings indicated that L. salicaria flowers were detectable with an RGB sensor at flight heights ≤ 15 m above the canopy. The post-release mortality and feeding efficiency of G. calmariensis did not significantly differ from the control group (non-aerial release), indicating the feasibility of the targeted release of G. calmariensis. This innovative study establishes a critical foundation for the future development of sophisticated aerial systems designed for the automated detection of invasive plants and the precise release of biological control agents, significantly advancing ecological management and conservation efforts. Full article