Manual Survey Research Articles

Remote sensing and computer vision for marine aquaculture.

Aquaculture, the cultivation of aquatic plants and animals, has grown rapidly since the 1990s, but sparse, self-reported, and aggregated production data limit the effective understanding and monitoring of the industry's trends and potential risks. Building on a manual survey of aquaculture production from remote sensing imagery, we train a computer vision model to identify marine aquaculture cages from aerial and satellite imagery and generate a spatially explicit dataset of finfish production locations in the French Mediterranean from 2000 to 2021 including 4010 cages (average cage area, 69 square meters). We demonstrate the value of our method as an easily adaptable, cost-effective approach that can improve the speed and reliability of aquaculture surveys and enables downstream analyses relevant to researchers and regulators. We illustrate its use to compute independent estimates of production and develop a flexible framework to quantify uncertainty in these estimates. Overall, our study presents an efficient, scalable, and adaptable method for monitoring aquaculture production from remote sensing imagery.

Incremental learning model for sustainable agricultural land assessment using multimodal satellite data

ABSTRACT The identification of agricultural lands is crucial for sustainable development in rural areas. In this paper, an augmented model that utilizes multimodal satellite-based data samples for identifying agricultural lands via incremental learning. The Whale Optimization Algorithm (WOA) is used for augmenting collected images and data samples to enhance the accuracy of the model under real-time conditions. To identify agricultural lands, Deep Convolutional Neural Networks (CNNs) are trained on the augmented data samples. Additionally, the incorporation of Q-Learning for continuous optimization of the model to enhance its efficiency and effectiveness in identifying agricultural lands. The proposed model offers many edges over existing methods. Firstly, the use of multimodal satellite-based data samples allows for a comprehensive and accurate analysis of agricultural lands. Secondly, the incorporation of the Whale Optimization Algorithm enables the augmentation of collected data samples, leading to improved accuracy and reliability of the model. Thirdly, Deep CNNs allows the extraction of complex features from the data, leading to more accurate identification of agricultural lands. Finally, the use of Q-Learning ensures that the model is continuously optimized to improve its efficiency and effectiveness. The need for this work arises from the limitations of existing methods in accurately identifying agricultural lands. Traditional methods based on manual surveys and visual interpretation are time-consuming, expensive, and prone to errors. Moreover, existing automated methods often lack the ability to analyse multimodal satellite-based data samples and fail to provide accurate results. Based on these observations, the proposed augmented model offers a promising solution for identifying agricultural lands for sustainable development. The use of multimodal satellite-based data samples, WOA, Deep CNNs, and Q-Learning allows for an accurate and efficient analysis of agricultural lands, which can aid in sustainable development planning and decision-making operations.

Towards automated detection of the endangered southern black-throated finch (Poephila cincta cincta)

Context Biodiversity is declining worldwide, with many species decreasing in both number and range. Acoustic monitoring is rapidly becoming a common survey method in the ecologist’s toolkit that may aid in the conservation of endangered species, but effective analysis of long-duration audio recordings is still challenging. Aims The aims of this study were to: (1) develop and test call recognisers for the endangered southern black-throated finch (Poephila cincta cincta) as well as the similar sounding, but non-endangered, double-barred finch (Taeniopygia bichenovii); and (2) compare the ability of these recognisers to detect these species with that of on-ground bird surveys at under-surveyed locations in the Desert Uplands bioregion which is at the edge of the known range of the black-throated finch. Methods A range of convolutional neural network call recognition models were built and tested for both target species, before being deployed over new audio recordings collected at 25 sites during 2020, 2021 and 2022, and compared with the results of on-ground bird surveys at those same sites. Key results Call recognisers for both species performed well on test datasets from locations in the same area as the training data with an average area under the precision-recall curve (PRAUC) of 0.82 for black-throated finch and 0.87 for double-barred finch. On-ground bird surveys in the Desert Uplands bioregion detected black-throated finches at two locations in different years, and our call recognisers confirmed this with minimal post-validation of detections. Similar agreement between methods were obtained for the double-barred finch, with site occupancy in the Desert Uplands bioregion confirmed with audio recognition in all nine surveys with on-ground detections, as well as during four additional surveys that had no on-ground detections. Conclusions Using call recognisers to survey new locations for black-throated finch presence was equally successful as on-ground surveys, and with further refinements, such as retraining models with examples of commonly misclassified vocalisations added to the training data, minimal validation should be required to detect site presence. Implications Acoustic monitoring should be considered as a valuable tool to be used alongside manual surveys to allow effective monitoring and conservation of this endangered species.

Multi-Source Data-Driven Extraction of Urban Residential Space: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration

The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through statistical analysis or a manual field survey. Currently, there are also superpixel segmentation and wavelet transform (WT) processes to extract urban spatial information, but these methods have shortcomings in extraction efficiency and accuracy. The superpixel wavelet fusion (SWF) method proposed in this paper is a convenient method to extract URS by integrating multi-source data such as Point of Interest (POI) data, Nighttime Light (NTL) data, LandScan (LDS) data, and High-resolution Image (HRI) data. This method fully considers the distribution law of image information in HRI and imparts the spatial information of URS into the WT so as to obtain the recognition results of URS based on multi-source data fusion under the perception of spatial structure. The steps of this study are as follows: Firstly, the SLIC algorithm is used to segment HRI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration. Then, the discrete cosine wavelet transform (DCWT) is applied to POI–NTL, POI–LDS, and POI–NTL–LDS data sets, and the SWF is carried out based on different superpixel scale perspectives. Finally, the OSTU adaptive threshold algorithm is used to extract URS. The results show that the extraction accuracy of the NLT–POI data set is 81.52%, that of the LDS–POI data set is 77.70%, and that of the NLT–LDS–POI data set is 90.40%. The method proposed in this paper not only improves the accuracy of the extraction of URS, but also has good practical value for the optimal layout of residential space and regional planning of urban agglomerations.

The Detection of Maize Seedling Quality from UAV Images Based on Deep Learning and Voronoi Diagram Algorithms

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.

Semantic Segmentation and Classification of Active and Abandoned Agricultural Fields through Deep Learning in the Southern Peruvian Andes

The monumental scale agricultural infrastructure systems built by Andean peoples during pre-Hispanic times have enabled intensive agriculture in the high-relief, arid/semi-arid landscape of the Southern Peruvian Andes. Large tracts of these labor-intensive systems have been abandoned, however, owing in large measure to a range of demographic, economic, and political crises precipitated by the Spanish invasion of the 16th century CE. This research seeks to better understand the dynamics of agricultural intensification and deintensification in the Andes by inventorying through the semantic segmentation of active and abandoned agricultural fields in satellite imagery across approximately 77,000 km2 of the Southern Peruvian Highlands. While manual digitization of agricultural fields in satellite imagery is time-consuming and labor-intensive, deep learning-based semantic segmentation makes it possible to map and classify en masse Andean agricultural infrastructure. Using high resolution satellite imagery, training and validation data were manually produced in distributed sample areas and were used to transfer-train a convolutional neural network for semantic segmentation. The resulting dataset was compared to manual surveys of the region and results suggest that deep learning can generate larger and more accurate datasets than those generated by hand.

Unveiling mobility patterns beyond home/work activities: A topic modeling approach using transit smart card and land-use data

In this paper, a probabilistic topic modeling algorithm called Latent Dirichlet Allocation (LDA) is implemented to infer trip purposes from activity attributes revealed from smart card transit data in an unsupervised manner. While most literature focused on finding patterns for home and work activities, we further investigated non-home and non-work-related activities to detect patterns associated with them. Temporal attributes of activities are extracted from trip information recorded by Tehran subway’s automatic fare collection system. In addition, land-use data is also incorporated to further enhance spatial attributes for non-home/work activities. Various activity attributes such as start time, duration, and frequency in addition to land-use data are used to infer the activity purposes and patterns. We identified 14 different patterns related to non-commuting activities on the basis of both their temporal and spatial attributes including educational, recreational, commercial, and health and other service-related activity types. We investigated passengers’ activity pattern and behavior changes before and during COVID-19 pandemic by comparing the discovered patterns. For recreational patterns it is revealed that not only has the number of recreational patterns dropped, but also the duration of recreational activities decreased. Morning patterns of educational activities have also been eliminated and number of commercial activities was decreased during COVID-19. The proposed model demonstrates the ability to capture travel behavior changes for different disruptions using smart card transit data without performing costly and time consuming manual surveys which can be useful for authorties and decision makers.

A Systematic Review of Barley (Hordeum vulgare Linn.) in Unani Medicine with Recent Advances

Objective: The present review article focuses on exploring the wide dimensions of barley including botanical description, nutritional composition, phytochemistry, experimental pharmacology, and clinical studies along with its traditional uses in Unani medicine. Background: Barley (Hordeum vulgare L.) which is referred to as ‘Jao’ in the Unani system of medicine has a long history of utilization as a food and medicine. It belongs to family Poaceae and is considered as one of the founder crops of old-world agriculture. Methodology: A manual literature survey of classical Unani texts was conducted to collect the information available on ‘Jao’. Besides, a comprehensive search of electronic databases like PubMed, Google Scholar, and Science Direct, was carried out to collect all the available information regarding its phytochemical, pharmacological and clinical studies. Result: In Unani medicine, barley is an important drug commonly used in many disorders of the respiratory system, gastrointestinal system, musculoskeletal system, skin, etc. It is used to treat obesity, diabetes, headache, pain and inflammation of the throat, acute swelling of joints and gout, chronic swellings, diarrhoea, fever, skin eruptions, hyperpigmentation of the skin, etc. Recent studies revealed that barley constitutes beta-glucan and a myriad of bioactive compounds that play numerous biological activities such as hypoglycemic, hypocholesterolemic, anti-inflammatory, antioxidant, anti-obesity, anticancer, and wound healing activities. Conclusion: Barley is a potent drug that exhibits a wide range of health benefits, most of which have been confirmed via preclinical and clinical studies. This review may thus be helpful for the research community as well as common people to avail the benefits of this drug. Keywords: Barley, Beta-glucan, Hordeum vulgare, Jao, Unani medicine

Deep Learning for Pavement Condition Evaluation Using Satellite Imagery

Civil infrastructure systems cover large land areas and need frequent inspections to maintain their public service capabilities. Conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in the ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technological advancements, this research evaluated pavement conditions using deep learning models for analyzing satellite images. We gathered over 3000 satellite images of pavement sections, together with pavement evaluation ratings from the TxDOT’s PMIS database. The results of our study show an accuracy rate exceeding 90%. This research paves the way for a rapid and cost-effective approach for evaluating the pavement network in the future.

A Study on the Automation of Fish Species Recognition and Body Length Measurement System

The rapid depletion of fishery resources has led to the global implementation of Total Allowable Catch (TAC) systems. However, the current manual survey methods employed by land-based inspectors show limitations in accuracy and efficiency. This study proposes an automated system for fish species recognition and body length measurement, utilizing the RT-DETR (Real-Time Detection Transformer) model and ARCore technology to address these issues. The proposed system employs smartphone Time of Flight (ToF) functionality to measure object distance and automatically calculates the weight of 11 TAC-managed fish species by measuring their body length and height. Experimental results reveal that the RT-DETR-x model outperformed the YOLOv8x model by achieving an average mAP50 value 2.3% higher, with a mean recognition accuracy of 96.5% across the 11 species. Furthermore, the ARCore-based length measurement technique exhibited over 95% accuracy for all species. This system is expected to minimize data omissions and streamline labor-intensive processes, thereby contributing to the efficient operation of the TAC system and sustainable management of fishery resources. The study presents an innovative approach that significantly enhances the accuracy and efficiency of fishery resource management, providing a crucial technological foundation for the advancement of future fisheries management policies.

Detection of Rice Leaf SPAD and Blast Disease Using Integrated Aerial and Ground Multiscale Canopy Reflectance Spectroscopy

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient (Rcv2) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields.

Analysis of Characteristics and Service Level of Pedestrian Facilities: Case Study of Ubud Tourist Destination, Gianyar Regency, Bali Province, Indonesia

The purpose of this study was to analyze the characteristics of sidewalks and pedestrians, the level of service and the type of pedestrian crossing facilities that are appropriate on Jalan Raya Ubud. Primary data collection using manual survey methods, including road geometric data, pedestrian volume, pedestrian speed, volume of road crossers and vehicles. Secondary data collection use literature study methods which include the location maps and population. Data analysis based on the guidelines of the Ministry of Public Works (2014). Based on the results of the analysis, the average width of the North and South sidewalks is 1.4 m with an average effective width of the North sidewalk is 0.9 m, while on the South sidewalk the average effective width of the sidewalk is 0.95 m. The largest pedestrian flow on the North and South Sidewalks on weekdays and holidays occurs in the afternoon with a total of 520 p/h with an average speed of 4.32 km/h, a density of 12 p/100 m2 and 8 m2/p pedestrian space for the North sidewalk, and the South sidewalk with a flow of 389 p/ h with an average speed of 3.72 km/h, a density of 10 p/100 m2, and 10 m2/p pedestrian space. On holidays, the flow is 744 p/ h with an average speed of 2.63 km/ h, a density of 28 p/100 m2 , and a pedestrian space of 4 m2 /org for the North sidewalk. While the South sidewalk has a flow of 351 org / hour with an average speed of 4.06 km / hour, a density of 9 org / 100 m2 , and a space of 12 m2 /org. The level of service of the North and South sidewalks on weekdays is included in the “B” category. The level of service of the North sidewalk on holidays is in the “C” category and the South sidewalk is in the “B” category.

A 10-m scale chemical industrial parks map along the Yangtze River in 2021 based on machine learning

Strengthening industrial pollution control in the Yangtze River is a fundamental national policy of China. There is a lack of detailed distribution of chemical industrial parks (CIPs). This Study utilized random forest (RF) and active learning to generate the distribution map of CIPs along the Yangtze River at 10-m resolution. Based on Sentinel-2 imagery, spectral and texture features are extracted. Combined with the Points of Interest (POI), a multidimensional feature space is constructed. By employing partitioned training, classification of CIPs map is achieved on Google Earth Engine (GEE). Technical validation along the entire Yangtze River demonstrates a model accuracy of 80%. Compared to traditional manual survey methods, this approach saves significant time and economic costs while also being timelier. As the first publicly available CIPs map within a 5-km range along the Yangtze River, this research will provide a scientific basis for the fine governance of chemical industries in the region. Additionally, it offers a model guide for the accurate identification of the chemical industry.

Effects of COVID-19 Pandemic on the Mental Health of Women and Treatment of Mental Disorders in the Unani System of Medicine: An Overview

Background: The world miserably collapsed when the COVID-19 (Coronavirus disease) pandemic was going out of control. The ongoing crisis has damaging effects on the mental health of individuals, predominantly women, in both developed and developing countries. Women in developing countries, both as frontline workers and at home, are at increased risk of mental health problems (specifically mental disorders). A few observational studies related to the COVID- 19 pandemic have reported a higher prevalence of mental health problems like stress, anxiety, depression, insomnia, fear, delirium, hysteria etc. among women compared to men. Thus, understanding the effects of the COVID-19 pandemic on women's mental health are as important to discuss as their physical health effects. Objective: The Unani System of Medicine (USM) has a holistic approach to preventing, treating, and curing mental disorders. The present article aimed to provide an overview of the effects of the COVID-19 pandemic on women's mental health and its containment measures described in classical Unani texts besides their relevance in the current outbreak. Materials and Methods: A manual literature survey of classical Unani literature such as Firdaus- al-hikmat, Al Qanoon-fit-tib, Zakheera Khwarzam Shahi, and Kamilus Sana was conducted to collect the information available on the strategic approaches of health promotion and disease prevention in Unani Medicine. To obtain the appropriate English equivalent words for Unani terms and the scientific name of the drugs, “Standard Unani Medical Terminology” and “Classification of Unani Drugs” were consulted, respectively. Many pharmacological books and papers were considered from PubMed, Google Scholar etc. Results: According to the literature review results, Unani doctors have pushed the adoption of personalised and predictive methods for preserving health and preventing diseases. The main focus is to treat the fragile population, i.e., women affected by mental illnesses. Conclusion: The present article emphasizes the mental health problems among women provoked by the COVID-19 pandemic, their possible etiopathogenesis, prevention, and psychological, regimental and pharmacological management of the USM.

Wildlife Tracker using Deep Learning

Wildlife tracker using deep learning is a project aimed at developing a system that can automatically detect wild animals using deep learning techniques. Conservationists and researchers face challenges in accurately and efficiently detecting and monitoring wildlife populations in vast and often remote habitats. Traditional methods of wildlife monitoring, such as manual surveys or camera traps, are labour-intensive, time-consuming, and may not provide real-time insights into population dynamics or threats facing wildlife species. The proposed system aims to address these challenges by developing an automated animal detection system using the YOLOv8 object detection model. The system will be trained on a diverse dataset of wildlife images, encompassing various species and environmental conditions. Upon deployment, the system will analyse input images and accurately identify and localize animals within the scene in real-time. Through this approach, the system will provide conservationists and researchers with timely and actionable information for monitoring wildlife populations, assessing habitat health, and implementing targeted conservation interventions. Evaluation of the system's performance will involve metrics such as detection accuracy, precision, recall, and processing speed, ensuring reliable and efficient wildlife detection capabilities. Overall, the proposed system seeks to enhance wildlife conservation efforts by leveraging advanced technology to improve wildlife monitoring and management practices and provide real-time alert.

Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

Regional Truck Travel Characteristics Analysis and Freight Volume Estimation: Support for the Sustainable Development of Freight

In the field of freight transport, the goal of sustainable development requires us to improve the efficiency of freight transport while reducing its negative impact on the environment, such as reducing carbon emissions and noise pollution. There is no doubt that changes in freight characteristics and volumes are compatible with the objectives of sustainable development. Thus, mining the travel distribution and freight volume of trucks has an important supporting role in the freight transport industry. In terms of truck travel, most of the traditional approaches are based on the subjective definition of parameters from the trajectory data to obtain trips for certain vehicle types. As for freight volume, it is mostly estimated through manual surveys, which are heavy and inaccurate. In this study, a data-driven approach is adopted to obtain trips from the trajectory data of heavy trucks. Combined with the traffic percentage of different vehicle types collected by highway traffic survey stations, the trips of heavy trucks are extended to all trucks. The inter-city and intra-city freight volumes are estimated based on the average truck loads collected at the motorway entrance. The results show a higher proportion of intra-city trips by trucks in port cities and a higher proportion of inter-city trips by trucks in inland cities. Truck loading and unloading times are focused in the early morning or at night, and freight demand in Shandong Province is more concentrated in the south. These results would provide strong support for optimizing freight structures, improving transportation efficiency, and reducing transportation costs.

Investigating hydrological recovery in regenerating coniferous stands in snow‐dominated watersheds using simultaneous localization and mapping‐enabled mobile terrestrial LiDAR

AbstractThe return of snow accumulation and ablation processes in regenerating forests to pre‐disturbance conditions, collectively referred to as hydrological recovery, has been investigated in past decades through manual snow surveys in adjacent open, juvenile, and mature stands. The outcomes of such studies provide a general understanding of hydrological recovery but lack transferability to areas where stand structure and terrain conditions differ from the reference sites. The application of mobile terrestrial LiDAR to investigate peak snow water equivalent (SWE) and ablation rates beneath regenerating trees in a space‐for‐time substitution study design provides new insights on the process of hydrological recovery in snowmelt forests of British Columbia, Canada. Outcomes of this study better quantify the influence of tree growth on peak SWE and ablation rate at both the tree and stand level for north aspect mixed conifer stands. Recovery of these two processes differ with recovery of Peak SWE beginning when the trees in a stand reach 3 m in height and recovery of ablation rates beginning once trees reach 5 m in height. Additionally, the process of negative ablation recovery in early juvenile stands reported in previous studies is herein clearly observed, providing an improved understanding of forest canopy effects on hydrological recovery in juvenile stands. The methods used in this study, which are internationally applicable, increase transferability of outcomes to stands where canopy characteristics (i.e., height, crown cover, and heterogeneity) are not represented in reference sites.

Underway measurement of cyanobacterial microcystins using a surface plasmon resonance sensor on an autonomous underwater vehicle

AbstractFreshwater cyanobacterial harmful algal blooms (CHABs) are a well‐known global public health threat. Monitoring and early detection of CHAB toxins are currently accomplished using labor‐intensive sampling techniques and subsequent shore‐based analyses, with results typically reported 24–48 h after sample collection. We have developed and implemented an uncrewed, autonomous mobile sampler‐analytical system capable of conducting targeted in situ toxin measurements in < 2 h. A surface plasmon resonance (SPR) instrument was combined with the environmental sample processor (ESP) to fully automate detection and quantification of particle‐associated cyanobacterial microcystins (pMC). This sensor‐sampler system was integrated with a long‐range autonomous underwater vehicle (LRAUV) and deployed in western Lake Erie for field trials in the summer of 2021. The LRAUV was remotely piloted to acquire samples at selected locations within and adjacent to a CHAB. Sixteen pMC measurements ranging from 0.09 to 0.55 μg/L lake water were obtained over a 14‐day period without recovery of the LRAUV. The SPR/ESP/LRAUV system complements existing satellite, aerial, and manual sampling CHAB survey techniques, and could be used to enhance predictive models that underpin bloom and toxicity forecasts. This system is also extensible to detection of other algal toxins in freshwater and marine environments, with its near real‐time assessment of bloom toxin levels potentially offering additional socioeconomic benefits and public health protection in a variety of settings.

Automatic identification and interpretation of discontinuities of rock slope from a 3D point cloud based on UAV nap-of-the-object photogrammetry

Research on rock slopes has attracted considerable attention in the fields of infrastructure construction and disaster prevention. The search for an efficient and reliable method for discontinuity collection is an important task for various studies of jointed rock slope. Capturing high-precision and complete rock exposure images based on unmanned aerial vehicle (UAV) multi-angle and nap-of-the-object photogrammetry technology is proposed to realize the construction of a millimeter-level 3D point cloud for the high and steep slope. This study develops a new automatic methodology for discontinuity identification and interpretation. A Delaunay triangulation network is created for connecting adjacent points in the point cloud to calculate normal vectors and search for neighbor points efficiently. Combined with the modified region-growing algorithm, an automatic procedure which identifies discontinuities and acquires geometrical parameters efficiently and accurately is established. The proposed approach is applied to the high-steep rock slope on the northern bank of the Sequ Grand Bridge in the Changdu area of Tibet. Compared with the results from the manual survey, the proposed method is more reliable and exhibits high accuracy for discontinuity identification and interpretation on rock exposure.