Search Results (37)

Tailings generated by mining account for the largest world-wide waste from industrial activities. As an element, copper is relatively uncommon, with low concentrations in sediments and waters, yet is very elevated around mining operations. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901 and 1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. About 10 MMT formed a beach that has migrated 7 km from the original Gay pile to the Traverse River Seawall. Another 11 MMT are moving underwater along the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Here we use remote sensing techniques to document geospatial environmental impacts and initial phases of remediation. Aerial photos, multiple ALS (crewed aeroplane) LiDAR/MSS surveys, and recent UAS (uncrewed aircraft system) overflights aid comprehensive mapping efforts. Because natural beach quartz and basalt stamp sands are silicates of similar size and density, percentage stamp sand determinations utilise microscopic procedures. Studies show that stamp sand beaches contrast greatly with natural sand beaches in physical, chemical, and biological characteristics. Dispersed stamp sand particles retain copper, and release toxic levels of dissolved concentrations. Moreover, copper leaching is elevated by exposure to high DOC and low pH waters, characteristic of riparian environments. Lab and field toxicity experiments, plus benthic sampling, all confirm serious impacts of tailings on aquatic organisms, supporting stamp sand removal. Not only should mining companies end coastal discharges, we advocate that they should adopt the UNEP “Global Tailings Management Standard for the Mining Industry”. Full article

Thermal refuges in streams are essential for the survival of coldwater fish species such as Redband trout (Oncorhynchus mykiss) in landscapes with stressful or lethal stream temperatures. We utilized an uncrewed aerial system (UAS) mounted with thermal and natural color sensors to conduct hourly flights over a 24 h period in the desert stream Little Jacks Creek during late summer when temperatures were near seasonal maximums and streamflow was near seasonal minimums. We used fine-resolution imagery to map stream temperatures and characterize how our thermal sensor exhibits variability across a diel period in an environment where thermal sensor viability had not yet been assessed. Thermal imagery from 3 out of 24 flights showed no significant differences when compared to true water temperatures from in-stream temperature loggers, which appeared to be highly dependent on atmospheric conditions. The thermal imagery (range of $9.17$ to $21.04 °$C) consistently underestimated HOBO logger stream temperatures (range of $13.6$ to $17.1 °$C) during cooler, nighttime flights and overestimated temperatures during hotter, afternoon hours, resulting in a global RMSE of 2.12 $°$C. Between-flight RMSE values ranged from 0.53$°$C to 4.00$°$C, within the error range of the thermal sensor. The thermal data support existing findings of optimal hours for flying UAS thermal surveys and showed specific patterns in TIR sensor accuracy that were dependent on the time of flight. This study yields valuable lessons for future stream temperature data collection in environments with highly variable temperatures, aiding in the calibration of thermal sensors on UAS missions. Furthermore, our results provide insights into environmental stressors such as increased stream temperatures, which is vital for conservation efforts for organisms that rely on coldwater refuges within desert streams. Full article

Plant growth analysis provides insight regarding the variation behind yield differences in tested genotypes for plant breeders, but adopting this application solely for traditional plant phenotyping remains challenging. Here, we propose a procedure of using uncrewed aerial systems (UAS) to obtain successive phenotype data for growth analysis. The objectives of this study were to obtain high-temporal UAS-based phenotype data for growth analysis and investigate the correlation between the UAS-based phenotype and biomass yield. Seven different energy cane genotypes were grown in a random complete block design with four replications. Twenty-six UAS flight missions were flown throughout the growing season, and canopy cover (CC) and canopy height (CH) measurements were extracted. A five-parameter logistic (5PL) function was fitted through these temporal measurements of CC and CH. The first- and second-order derivatives of this function were calculated to obtain several growth parameters, which were then used to assess the growth of different genotypes with respect to weed competitiveness and biomass yield traits. The results show that CC and CH growth rates significantly differed among genotypes. TH16-16 was outstanding for its ground cover growth; therefore, it was identified as a weed-competitive genotype. Furthermore, TH16-22 had a higher CH maximum growth rate per day, yielding a higher biomass compared to other genotypes. The CH-based multi-temporal data as well as the growth parameters had a better relationship with biomass yield. This study highlights the application of UAS-based high-throughput phenotyping (HTP), along with growth analysis, for assisting plant breeders in decision-making. Full article

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements of Diameter at Breast Height with height (DBH and H), and optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity in AGB estimation and are labor-intensive. Recent advancements in remote sensing technologies, predominantly Light Detection and Ranging (LiDAR), offer potential improvements in accurate AGB estimation and ecological monitoring. Nonetheless, there is limited research on the combined use of UAS (Uncrewed Aerial System) and Backpack-LiDAR technologies for detailed forest biomass. Thus, our study aimed to estimate AGB at the plot level for Picea crassifolia forests in eastern Qinghai, China, by integrating UAS-LiDAR and Backpack-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed to segment the point clouds from the Backpack-LiDAR, detect seed points and calculate the DBH of individual trees. After that, using these initial seed point files, we segmented the individual trees from the UAS-LiDAR data by employing the Point Cloud Segmentation (PCS) method and measured individual tree heights, which enabled the calculation of the observed/measured AGB across three specific areas. Furthermore, advanced regression models, such as Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Regression (SVR), are used to estimate AGB using integrated data from both sources (UAS and Backpack-LiDAR). Our results show that: (1) Backpack-LiDAR extracted DBH compared to field extracted DBH shows about (R² = 0.88, RMSE = 0.04 m) whereas UAS-LiDAR extracted height achieved the accuracy (R² = 0.91, RMSE = 1.68 m), which verifies the reliability of the abstracted DBH and height obtained from the LiDAR data. (2) Individual Tree Segmentation (ITS) using a seed file of X and Y coordinates from Backpack to UAS-LiDAR, attaining a total accuracy F-score of 0.96. (3) Using the allometric equation, we obtained AGB ranges from 9.95–409 (Mg/ha). (4) The RF model demonstrated superior accuracy with a coefficient of determination (R²) of 89%, a relative Root Mean Square Error (rRMSE) of 29.34%, and a Root Mean Square Error (RMSE) of 33.92 Mg/ha compared to the MLR and SVR models in AGB prediction. (5) The combination of Backpack-LiDAR and UAS-LiDAR enhanced the ITS accuracy for the AGB estimation of forests. This work highlights the potential of integrating LiDAR technologies to advance ecological monitoring, which can be very important for climate change mitigation and sustainable environmental management in forest monitoring practices. Full article

Vegetation characteristics significantly influence the impact of wildfires on individual building structures, and these effects can be systematically analyzed using heat transfer modelling software. Close-range light detection and ranging (LiDAR) data obtained from uncrewed aerial systems (UASs) capture detailed vegetation morphology; however, the integration of dense vegetation and merged canopies into three-dimensional (3D) models for fire modelling software poses significant challenges. This study proposes a method for integrating the UAS–LiDAR-derived geometric features of vegetation components—such as bark, wooden core, and foliage—into heat transfer models. The data were collected from the natural woodland surrounding an elevated building in Samford, Queensland, Australia. Aboveground biomass (AGB) was estimated for 21 trees utilizing three 3D tree reconstruction tools, with validation against biomass allometric equations (BAEs) derived from field measurements. The most accurate reconstruction tool produced a tree mesh utilized for modelling vegetation geometry. A proof of concept was established with Eucalyptus siderophloia, incorporating vegetation data into heat transfer models. This non-destructive framework leverages available technologies to create reliable 3D tree reconstructions of complex vegetation in wildland–urban interfaces (WUIs). It facilitates realistic wildfire risk assessments by providing accurate heat flux estimations, which are critical for evaluating building safety during fire events, while addressing the limitations associated with direct measurements. Full article

Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates are sensitive to the accuracy of wind speed and direction measurements. In this study, we examined how filtering and correcting sUAS-based wind measurements affects data accuracy by comparing data from a miniature ultrasonic anemometer mounted on a sUAS in a joust configuration to highly accurate wind data taken from a nearby eddy covariance flux tower (aka the Tower). These corrections had a small effect on wind speed error, but reduced wind direction errors from 50° to >120° to 20–30°. A concurrent experiment examining the amount of error due to the sUAS and the Tower not being co-located showed that the impact of this separation was 0.16–0.21 ${\mathrm{ms}}^{-1}$, a small influence on wind speed errors. Lower wind speed errors were correlated with lower turbulence intensity and higher relative wind speeds. There were also some loose trends in diminished wind direction errors at higher relative wind speeds. Therefore, to improve the quality of sUAS-based wind measurements, our study suggested that flight planning consider optimizing conditions that can lower turbulence intensity and maximize relative wind speeds as well as include post-flight corrections. Full article

Hyperspectral imaging technology holds great potential for various stages of the mining life cycle, both in active and abandoned mines, from exploration to reclamation. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite imagery yields high spectral resolution, a high signal-to-noise ratio (SNR), and global availability with breakthrough systems like EnMAP, EMIT, GaoFen-5, PRISMA, and Tanager-1, limited spatial and temporal resolution poses challenges for the mining sectors, which require decimetre-to-centimetre-scale spatial resolution for applications such as reconciliation and environmental monitoring and daily temporal revisit times, such as for ore/waste estimates and geotechnical assessments. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high-spatial-resolution data relevant to the pit/mine scale, with the capability for frequent, user-defined re-visit times for areas of limited extent. Areas of interest can be defined by the user and targeted explicitly. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers the detection of different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. This is related to but not exclusive to detecting deleterious minerals for different processes (e.g., clays, iron oxides, talc), secondary iron oxides indicating the leakage of acid mine drainage for rehabilitation efforts, swelling clays potentially affecting rock integrity and stability, and alteration minerals used to vector toward economic mineralisation (e.g., dickite, jarosite, alunite). In this paper, we review applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging datasets in or appropriate to the mining sector, with a particular focus on hyperspectral VNIR-SWIR UASs. Complementarily, we draw on previous insights from airborne, satellite, and ground-based imaging systems. We also discuss common practises for UAS survey planning and ground sampling considerations to aid in data interpretation. Full article

Uncrewed aerial system (UAS) structure from motion (SfM) monitoring strategies for individual trees has rapidly expanded in the early 21st century. It has become common for studies to report accuracies for individual tree heights and DBH, along with stand density metrics. This study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms. The study is designed to frame UAS-SfM individual tree monitoring accuracy in the context of data processing and storage demands as a function of SfM algorithm parameter levels. Results show that when SfM algorithms are properly tuned, differences between software types are negligible, with Metashape providing a median F-score improvement over OpenDroneMap of 0.02 and PIX4DMapper of 0.06. However, tree extraction performance varied greatly across algorithm parameters, with the greatest extraction rates typically coming from parameters causing increased density in dense point clouds and minimal point cloud filtering. Transferring UAS-SfM forest monitoring into management will require tradeoffs between accuracy and efficiency. Our analysis shows that a one-step reduction in dense point cloud quality saves 77–86% in point cloud processing time without decreasing tree extraction (F-score) or basal area precision using Metashape and PIX4DMapper but the same parameter change for OpenDroneMap caused a ~5% loss in precision. Providing reproducible processing strategies is a vital step in successfully transferring these technologies into usage as management tools. Full article

This study investigates how urban environments influence boundary layer processes during the passage of a Great Salt Lake breeze using a multi-scale modeling system, NCAR’s WRF-Coupled GPU-accelerated FastEddy^® (FE) model. Motivated by the need for sub-10 m scale decision support tools for uncrewed aerial systems (UAS), the FE model was used to simulate turbulent flows around urban structures at 5 m horizontal resolution with a 9 km × 9 km domain centered on the Salt Lake City International Airport. FE was one-way nested within a 1 km resolution Weather Research and Forecasting (WRF) domain spanning 400 × 400 km. Focused on the late morning lake breeze on 3 June 2022, an FE simulation was compared with WRF outputs and validated using surface and radar observations. The FE simulation revealed low sensible heat flux and cool near-surface temperatures, attributed to a relatively low specification of thermal roughness suitable for previously tested FE applications. Lake breeze characteristics were minimally affected, as FE effectively resolved interactions between the lake breeze and urban-induced turbulent eddies, providing insights into fine-scale boundary layer processes. FE’s GPU acceleration ensured efficient simulations, underscoring its potential for aiding decision support in UAS operations in complex urban environments. Full article

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread. Full article

This study evaluated the performance of remote sensing (RS) algorithms for the estimation of actual maize evapotranspiration (ET_a) using different spaceborne, airborne, and proximal multispectral data in a semi-arid climate region to identify the optimal platform that provides the best ET_a estimates to improve irrigation water management and help make irrigated agriculture sustainable. The RS platforms used in the study included Landsat-8 (30 m pixel spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), multispectral radiometer or MSR (1 m), and a small uncrewed aerial system or sUAS (0.03 m). Two-source surface energy balance (TSEB) models, implementing the series and parallel surface resistance approaches, were used in this study to estimate hourly maize ET_a. The data used in this study were obtained from two maize research sites in Greeley and Fort Collins, CO, USA, in 2020 and 2021. Each research site had different irrigation systems. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Maize ET_a predictions were compared to observed maize ET_a data from an eddy covariance system installed at each research site. Results indicated that the MSR5 proximal platform (1 m) provided optimal RS data for the TSEB algorithms. The MSR5 “point-based” nadir-looking surface reflectance data and surface radiometric temperature combination resulted in the smallest error when predicting hourly (mm/h) maize ET_a. The mean bias and root mean square errors (MBE and RMSE, respectively), when predicting maize hourly ET_a using the MSR5 sensor data, were equal to −0.02 (−3%) ± 0.07 (11%) mm/h MBE ± RMSE and −0.02 (−3%) ± 0.09 (14%) mm/h for the TSEB parallel and series approaches, respectively. The poorest performance, when predicting hourly TSEB maize ET_a, was from Landsat-8 (30 m) multispectral data combined with its original thermal data, since the errors were −0.03 (−5%) ± 0.16 (29%) mm/h and −0.07 (−13%) ± 0.15 (29%) mm/h for the TSEB parallel and series approaches, respectively. These results indicate the need to develop methods to improve the quality of the RS data from sub-optimal platforms/sensors/scales/calibration to further advance sustainable irrigation water management. Full article

Highly diverse agroecosystems are increasingly of interest as the realization of farms’ invaluable ecosystem services grows. Simultaneously, there has been an increased use of uncrewed aerial systems (UASs) in remote sensing, as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UASs and the attention on agroecosystems, there is an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UASs to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and assembled accuracy assessments for each classification. The average overall accuracy (53.9%), though relatively low, was expected for such a diverse landscape with fine-resolution data. To bolster our understanding of the classifications, we interviewed farmers to understand their thoughts on how these maps may be best used to support their land management. After sharing imagery and land cover classifications with farmers, we found that while the prints were often a point of pride or curiosity for farmers, integrating the maps into farm management was perceived as impractical. These findings highlight that while researchers and government agencies can increasingly apply remote sensing to estimate land cover classes and ecosystem services in diverse agroecosystems, further work is needed to make these products relevant to diversified smallholder farmers. Full article

This study delves into the analysis of a vineyard in Carinthia, Austria, focusing on the automated derivation of ecosystem structures of individual vine parameters, including vine heights, leaf area index (LAI), leaf surface area (LSA), and the geographic positioning of single plants. For the derivation of these parameters, intricate segmentation processes and nuanced UAS-based data acquisition techniques are necessary. The detection of single vines was based on 3D point cloud data, generated at a phenological stage in which the plants were in the absence of foliage. The mean distance from derived vine locations to reference measurements taken with a GNSS device was 10.7 cm, with a root mean square error (RMSE) of 1.07. Vine height derivation from a normalized digital surface model (nDSM) using photogrammetric data showcased a strong correlation (R² = 0.83) with real-world measurements. Vines underwent automated classification through an object-based image analysis (OBIA) framework. This process enabled the computation of ecosystem structures at the individual plant level post-segmentation. Consequently, it delivered comprehensive canopy characteristics rapidly, surpassing the speed of manual measurements. With the use of uncrewed aerial systems (UAS) equipped with optical sensors, dense 3D point clouds were computed for the derivation of canopy-related ecosystem structures of vines. While LAI and LSA computations await validation, they underscore the technical feasibility of obtaining precise geometric and morphological datasets from UAS-collected data paired with 3D point cloud analysis and object-based image analysis. Full article

Despite the subjective and error-prone nature of manual visual inspection procedures, this type of inspection is still a common process in most construction projects. However, Automated Construction Inspection and Progress Monitoring (ACIPM) has the potential to improve inspection processes. The objective of this paper is to examine the applications, challenges, and future directions of ACIPM in a systematic review. It explores various application areas of ACIPM in two domains of (a) transportation construction inspection, and (b) building construction inspection. The review identifies key ACIPM tools and techniques including Laser Scanning (LS), Uncrewed Aerial Systems (UAS), Robots, Radio Frequency Identification (RFID), Augmented Reality (AR), Virtual Reality (VR), Computer Vision (CV), Deep Learning, and Building Information Modeling (BIM). It also explores the challenges in implementing ACIPM, including limited generalization, data quality and validity, data integration, and real-time considerations. Studying legal implications and ethical and social impacts are among the future directions in ACIPM that are pinpointed in this paper. As the main contribution, this paper provides a comprehensive understanding of ACIPM for academic researchers and industry professionals. Full article

Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral—rather than spatial—basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed. Full article