Search Results (56)

Daily evapotranspiration (ET) estimated from an unoccupied aerial system (UAS) could help improve irrigation practices, but its spatial resolution needs to be upscaled to coarser pixel resolutions before applying surface energy balance models. The purpose of this study was to evaluate the impact of various energy balance-based aggregation schemes on generating spatially distributed latent heat flux (LE), and, in comparison, to existing occupied aircraft and satellite remote sensing platforms. In 2017, UAS multispectral and thermal imagery, along with ground truth data, were collected at various cotton growth stages. These data sources were combined to model LE using a Two-Source Energy Balance Priestley–Taylor (TSEB-PT) model. Several UAS aggregation schemes were tested, including the mode of aggregation (i.e., input image and output flux) as well as the averaging scheme (i.e., simple aggregation vs. Box–Cox). Results indicate that output flux aggregation with Box–Cox averaging produced the lowest relative upscaling pixel-scale variability in LE and the lowest absolute prediction errors (relative to eddy covariance flux tower measurements). Output flux aggregation with simple averaging was also more accurate in reproducing LE from occupied aircraft and satellite imagery. Although results are limited to a single site, UAS LE estimates were reliably aggregated to coarser pixel resolutions, which made for faster image processing for operational applications. Full article

Vegetated intertidal ecosystems, such as seagrass meadows, salt marshes, and macroalgal beds, are vital for biodiversity, coastal protection, and climate regulation; however, they remain highly vulnerable to anthropogenic and climate-induced stressors. This study aims to assess interannual changes in intertidal vegetation cover along the Portuguese mainland coast from 2015 to 2024 using Sentinel-2 satellite imagery calibrated with high-resolution multispectral unoccupied aerial vehicle (UAV) data, to determine the most accurate index for mapping intertidal vegetation. Among the 16 indices tested, the Atmospherically Resilient Vegetation Index (ARVI) showed the highest predictive performance. Based on a model relating intertidal vegetation cover to this index, an ARVI value greater than or equal to 0.214 was established to estimate the area covered with intertidal vegetation. Applying this threshold to time-series data revealed considerable spatial and temporal variability in vegetation cover, with estuarine systems such as the Ria de Aveiro and the Ria Formosa showing the greatest extents and marked fluctuations. At the national level, no consistent overall trend was identified for the study period. Despite limitations related to satellite image resolution and single-site validation, the results demonstrate the feasibility and utility of combining UAV data and satellite indices for long-term, large-scale monitoring of intertidal vegetation. Full article

We investigated the relationship between foliar blight, tree structure, and spectral signatures in a Pacific Madrone (Arbutus menziesii) orchard in Oregon using unoccupied aerial system (UAS) multispectral imagery and ground surveying. Aerial data were collected under both cloudy and sunny conditions using a six-band sensor (red, green, blue, near-infrared, red edge, and longwave infrared), and ground surveying recorded foliar blight and tree height for 29 trees. We observed band- and index-dependent spectral variation within crowns and between lighting conditions. The Normalized Difference Vegetation Index (NDVI), Modified Simple Ratio Index Red Edge (MSRE), and Red Edge Chlorophyll Index (RECI) showed higher consistency across lighting changes (adjusted R² ≈ 0.95), while the Green Chlorophyll Index (GCI), Modified Simple Ratio Index (MSR), and Green Normalized Difference Vegetation Index (GNDVI) showed slightly lower consistency (adjusted R² ≈ 0.92) but greater sensitivity to blight under cloudy skies. Diffuse skylight increased blue and near-infrared reflectance, reduced red, and enhanced blight detection using GCI, MSR, and GNDVI. Tree height was inversely related to blight presence (p < 0.005), and spectral variation within crowns was significant (p < 0.01), suggesting a role for canopy architecture. The support vector machine classification of tree crowns achieved 92.5% accuracy (kappa = 0.87). 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

Remote sensing observations of snow-covered areas (SCA) are important for monitoring and modeling energy balances, hydrologic processes, and climate change. For an agricultural field, we produced 12 snow cover maps from UAS imagery during an approximately 3-week-long spring snowmelt period. SCA maps were used to characterize snow cover patterns, validate satellite snow cover products, translate satellite Normalized Difference Snow Index (NDSI) to fractional SCA (fSCA), and downscale satellite SCA observations. Compared to manually delineated SCA, the UAS SCA accuracy was 85%, with misclassifications due to shadows, ice, and patchy snow conditions. During snowmelt, UAS-derived maps of bare earth patches exhibited self-similarity, behaving as fractal objects over scales from 0.01 to 100 m². As a validation tool, the UAS-derived SCA showed that satellite snow cover observations accurately captured the fSCA evolution during snowmelt (R² = 0.93−0.98). A random forest satellite downscaling model, trained using 20 m Sentinel-2 NDSI observations and 20 cm vegetation and terrain features, produced realistic (>90% accuracy), high-resolution SCA maps. While similar to traditional Sentinel-2 SCA in most conditions, downscaling snow cover significantly improved performance during periods of patchy snow cover and produced more realistic bare patches. UAS optical sensing demonstrates the potential uses for high-resolution snow cover mapping and recommends future research avenues for using UAS SCA maps. Full article

Quantifying forest structure to assess changing wildfire risk factors is critical as vulnerable areas require mitigation, management, and resource allocation strategies. Remote sensing offers the opportunity to accurately measure forest attributes without time-intensive field inventory campaigns. Here, we quantified forest canopy cover and individual tree metrics across 44 plots (20 m × 20 m) in oak woodlands and mixed-conifer forests in Northern California using structure-from-motion (SfM) 3D point clouds derived from unoccupied aerial systems (UAS) multispectral imagery. In addition, we compared UAS–SfM estimates with those derived using similar methods applied to Airborne Laser Scanning (ALS) 3D point clouds as well as traditional ground-based measurements. Canopy cover estimates were similar across remote sensing (ALS, UAS-SfM) and ground-based approaches (r² = 0.79, RMSE = 16.49%). Compared to ground-based approaches, UAS-SfM point clouds allowed for correct detection of 68% of trees and estimated tree heights were significantly correlated (r² = 0.69, RMSE = 5.1 m). UAS-SfM was not able to estimate canopy base height due to its inability to penetrate dense canopies in these forests. Since canopy cover and individual tree heights were accurately estimated at the plot-scale in this unique bioregion with diverse topography and complex species composition, we recommend UAS-SfM as a viable approach and affordable solution to estimate these critical forest parameters for predictive wildfire modeling. Full article

The development of unconventional and hybrid unoccupied aerial vehicles (UAVs) has gained significant momentum in recent years, with many designs utilizing small fans or rotary blades for vertical take-off and landing (VTOL). However, these systems often inherit the limitations of traditional helicopter rotors, including susceptibility to aerodynamic inefficiencies and mechanical issues. Additionally, achieving a seamless transition from VTOL to fixed-wing flight mode remains a significant challenge for hybrid UAVs. A novel approach is the reciprocating airfoil (RA) or reciprocating wing (RW) VTOL aircraft, which employs a fixed-wing configuration driven by a reciprocating mechanism to generate lift. The RA wing is uniquely designed to mimic a fixed-wing while leveraging its reciprocating motion for efficient lift production and a smooth transition between VTOL and forward flight. Despite its advantages, the RA wing endures substantial stress due to the high inertial forces involved in its operation. This study presents an optimized structural design of the RA wing through wing topology optimization and finite element analysis (FEA) to enhance its load-bearing capacity and stress performance. A comparative analysis with existing RA wing configurations at maximum operating velocities highlights significant improvements in the safety margin, failure criteria, and overall stress distribution. The key results of this study show an 80.4% reduction in deformation, a 43.8% reduction in stress, and a 78% improvement in safety margin. The results underscore the RA wing’s potential as an effective and structurally stable lift mechanism for RA-driven VTOL aircraft, demonstrating its capability to enhance the performance and reliability of next-generation UAVs. Full article

Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring. Full article

The endangered Gaoligong hoolock gibbon (Hoolock tianxing) faces significant threats from habitat degradation and loss, making accurate habitat assessment crucial for effective conservation. This study explored the effectiveness of high-resolution small unoccupied aerial system (sUAS) imagery for evaluating habitat quality, comparing its performance against Sentinel-2 satellite data. Focusing on the critically fragmented habitat of this primate in Yingjiang County, China, we aimed to (1) assess habitat quality at the patch level using a sUAS; (2) apply the InVEST Habitat Quality (IHQ) model; and (3) compare the effectiveness of sUAS and Sentinel-2 imagery, across different resolutions, for habitat quality evaluation. We utilized sUAS imagery (0.05 m resolution) obtained from a DJI Mavic 3 drone and Sentinel-2 data (10 m resolution) for a comparative analysis. The InVEST IHQ model was then used to analyze nine habitat patches, examining how data resolution impacts habitat quality assessments. Our results showed that habitat quality varied considerably across space, with lower quality observed near villages due to agricultural activity and infrastructure development. The sUAS imagery proved superior at capturing detailed landscape features and delineating small, fragmented patches compared to Sentinel-2. Furthermore, the sUAS achieved higher classification accuracy. Although both data sources indicated generally high habitat quality, Sentinel-2 tended to overestimate both habitat quality and degradation compared to the sUAS. High-resolution sUAS imagery therefore provides a clear advantage for detailed habitat quality assessment and targeted conservation planning, especially in fragmented landscapes. Integrating sUAS data with other remote sensing methods is essential to improve the protection of endangered primate habitats. This research emphasizes the value of sUAS for fine-scale habitat analysis, providing a strong scientific basis for developing targeted habitat restoration strategies and guiding conservation management. Full article

Unoccupied aerial systems (UASs) are increasingly being deployed in coastal environments to rapidly map and monitor changes to geomorphology, vegetation, and infrastructure, particularly in difficult to access areas. UAS data, relative to airplane or satellite data, typically have higher spatial resolution, sensor customization, and increased flexibility in temporal resolution, which benefits monitoring applications. UAS data have been used to map and monitor invasive species occurrence and expansion, such as Phragmites australis, a reed species in wetlands throughout the eastern United States. To date, the work on this species has been largely opportunistic or ad hoc. Here, we statistically and qualitatively compare results from several sensors and classification workflows to develop baseline understanding of the accuracy of different approaches used to map Phragmites. Two types of UAS imagery were collected in a Phragmites-invaded salt marsh setting—natural color red-green-blue (RGB) imagery and multispectral imagery spanning visible and near infrared wavelengths. We evaluated whether one imagery type provided significantly better classification results for mapping land cover than the other, also considering trade-offs like overall accuracy, financial costs, and effort. We tested the transferability of classification workflows that provided the highest thematic accuracy to another barrier island environment with known Phragmites stands. We showed that both UAS sensor types were effective in classifying Phragmites cover, with neither resulting in significantly better classification results than the other for Phragmites detection (overall accuracy up to 0.95, Phragmites recall up to 0.86 at the pilot study site). We also found the highest accuracy workflows were transferrable to sites in a barrier island setting, although the quality of results varied across these sites (overall accuracy up to 0.97, Phragmites recall up to 0.90 at the additional study sites). Full article

With the global population on the rise and arable land diminishing, the need for sustainable and precision agriculture has become increasingly important. This study explores the application of unoccupied aerial systems (UAS) in precision agriculture, specifically focusing on Montmorency cherry orchards in Payson, Utah. Despite the widespread use of UAS for various crops, there is a notable gap in research concerning cherry orchards, which present unique challenges due to their physical structure. UAS data were gathered using an RTK-enabled DJI Mavic 3M, equipped with both RGB and multispectral cameras, to capture high-resolution imagery. This research investigates two primary applications of UAS in cherry orchards: tree height mapping and crop health assessment. We also evaluate the accuracy of tree height measurements derived from three UAS data processing software packages: Pix4D, Drone2Map, and DroneDeploy. Our results indicated that DroneDeploy provided the closest relationship to ground truth data with an R² of 0.61 and an RMSE of 31.83 cm, while Pix4D showed the lowest accuracy. Furthermore, we examined the efficacy of RGB-based vegetation indices in predicting leaf area index (LAI), a key indicator of crop health, in the absence of more expensive multispectral sensors. Twelve RGB-based indices were tested for their correlation with LAI, with the IKAW index showing the strongest correlation (R = 0.36). However, the overall explanatory power of these indices was limited, with an R² of 0.135 in the best-fitting model. Despite the promising results for tree height estimation, the correlation between RGB-based indices and LAI was underwhelming, suggesting the need for further research. Full article

Since the publication of the seminal work People and Pixels: Linking Remote Sensing and the Social Sciences, the call to “socialize the pixel” and “pixelize the social” has gone largely unheeded from a truly participatory research context. Instead, participatory remote sensing has primarily involved ground truthing to verify remote sensing observations and/or participatory mapping methods to complement remotely sensed data products. However, the recent proliferation of relatively low-cost, ready-to-fly small unoccupied aerial systems (sUAS), colloquially known as drones, may be changing this trajectory. sUAS may provide a means for community participation in all aspects of the photogrammetric/remote sensing process, from mission planning and data acquisition to data processing and analysis. We present an overview of the present state of so-called participatory sUAS through a comprehensive literature review of recent English-language journal articles. This is followed by an overview of our own experiences with the use of sUAS in a multi-year participatory research project in an agroecological system encompassing a tri-county/tri-state region in the Southern Great Plains, USA. We conclude with a discussion of opportunities and challenges associated with our experience. Full article

On 21 May 2023, a hidden eruption occurred at the Southeast Crater (SEC) of Etna (Italy); indeed, bad weather prevented its direct and remote observation. Tephra fell toward the southwest, and two lava flows propagated along the SEC’s southern and eastern flanks. The monitoring system of the Istituto Nazionale di Geofisica e Vulcanologia testified to its occurrence. We analyzed the seismic and infrasound signals to constrain the temporal evolution of the fountain, which lasted about 5 h. We finally reached Etna’s summit two weeks later and found an unexpected pyroclastic density current (PDC) deposit covering the southern lava flow at its middle portion. We performed unoccupied aerial system and field surveys to reconstruct in 3D the SEC, lava flows, and PDC deposits and to collect some samples. The data allowed for detailed mapping, quantification, and characterization of the products. The resulting lava flows and PDC deposit volumes were (1.54 ± 0.47) × 10⁶ m³ and (1.30 ± 0.26) × 10⁵ m³, respectively. We also analyzed ground-radar and satellite data to evaluate that the plume height ranges between 10 and 15 km. This work is a comprehensive analysis of the fieldwork, UAS, volcanic tremor, infrasound, radar, and satellite data. Our results increase awareness of the volcanic activity and potential dangers for visitors to Etna’s summit area. Full article

Although many medium-to-large terrestrial vertebrates are still counted by ground or aerial surveys, remote-sensing technologies and image analysis have developed rapidly in recent decades, offering improved accuracy and repeatability, lower costs, speed, expanded spatial coverage and increased potential for public involvement. This review provides an introduction for wildlife biologists and managers relatively new to the field on how to implement remote-sensing techniques (satellite and unoccupied aircraft systems) for counting large vertebrates on land, including marine predators that return to land to breed, haul out or roost, to encourage wider application of these technological solutions. We outline the entire process, including the selection of the most appropriate technology, indicative costs, procedures for image acquisition and processing, observer training and annotation, automation, and citizen science campaigns. The review considers both the potential and the challenges associated with different approaches to remote surveys of vertebrates and outlines promising avenues for future research and method development. Full article

Unoccupied aerial systems (UASs) may provide cheaper, safer, and more accurate and precise alternatives to traditional waterfowl survey techniques while also reducing disturbance to waterfowl. We evaluated availability and perception bias based on machine-learning-based non-breeding waterfowl count estimates derived from aerial imagery collected using a DJI Mavic Pro 2 on Missouri Department of Conservation intensively managed wetland Conservation Areas. UASs imagery was collected using a proprietary software for automated flight path planning in a back-and-forth transect flight pattern at ground sampling distances (GSDs) of 0.38–2.29 cm/pixel (15–90 m in altitude). The waterfowl in the images were labeled by trained labelers and simultaneously analyzed using a modified YOLONAS image object detection algorithm developed to detect waterfowl in aerial images. We used three generalized linear mixed models with Bernoulli distributions to model availability and perception (correct detection and false-positive) detection probabilities. The variation in waterfowl availability was best explained by the interaction of vegetation cover type, sky condition, and GSD, with more complex and taller vegetation cover types reducing availability at lower GSDs. The probability of the algorithm correctly detecting available birds showed no pattern in terms of vegetation cover type, GSD, or sky condition; however, the probability of the algorithm generating incorrect false-positive detections was best explained by vegetation cover types with features similar in size and shape to the birds. We used a modified Horvitz–Thompson estimator to account for availability and perception biases (including false positives), resulting in a corrected count error of 5.59 percent. Our results indicate that vegetation cover type, sky condition, and GSD influence the availability and detection of waterfowl in UAS surveys; however, using well-trained algorithms may produce accurate counts per image under a variety of conditions. Full article