Search Results (38)

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

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

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

The Charleston Estuarine System Stock (CESS) of Tamanend’s bottlenose dolphins (Tursiops erebennus) exhibit long-term site fidelity to the Charleston Harbor, and the Ashley, Cooper, and Wando Rivers in Charleston, South Carolina, USA. In the Cooper River, dolphins have been irregularly sighted in upper regions where salinity levels are below what is considered preferred dolphin habitat. We conducted unoccupied aerial system (UAS) surveys in high-salinity (>15 parts per thousand) and low-salinity (<15 parts per thousand) regions (n = 8 sites) of the Cooper River and surrounding waters to assess dolphin distribution in terms presence/absence, detection rate, abundance, and density. We also assessed the influence of ecological factors (salinity, water temperature, season, and prey availability) on dolphin distribution. Dolphins were detected at five sites, with higher salinity and water temperature being significant predictors of presence and abundance. Dolphins were detected year-round across high-salinity sites, and were infrequently detected in low-salinity sites during months with warmer water temperatures. The results from this study contribute to the overall understanding of dolphin distribution across various habitats within the Charleston Estuary System and the potential drivers for their movement into low-salinity waters. Full article

This study investigates the use of small unoccupied aerial systems (sUAS) as a new remote sensing tool to identify and track the spatial distribution of wrack on coastal tidal marsh systems. We used sUAS to map the wrack movement in a Spartina alterniflora-dominated salt marsh monthly for one year including before and after Hurricane Isaias that brought strong winds, rain, and storm surge to the area of interest in August 2020. Flight parameters for each data collection mission were held constant including collection only during low tide. Wrack was visually identified and digitized in a GIS using every mission orthomosaic created from the mission images. The digitized polygons were visualized using a raster data model and a combination of all of the digitized wrack polygons. Results indicate that wrack mats deposited before and as a result of a hurricane event remained for approximately three months. Furthermore, 55% of all wrack detritus was closer than 10 m to river or stream water bodies, 64% were within 15 m, and 71% were within 20 m, indicating the spatial dependence of wrack location in a marsh system on water and water movement. However, following the passing of Isaias, the percentage of wrack closer than 10 m to a river or creek decreased to a low of 44%, which was not seen again during the year-long study. This study highlights the on-demand image collection of a sUAS for providing new insights into how quickly wrack distribution and vegetation can change over a short time. Full article

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings. Full article

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks. Full article

Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel- and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.] Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment. Full article

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science. Full article