Conventional Survey Methods Research Articles

Landslide Susceptibility Assessment Based on Multisource Remote Sensing Considering Inventory Quality and Modeling

The completeness of landslide inventories and the selection of evaluation models significantly impact the accuracy of landslide susceptibility assessments. Conventional field geological survey methods and single remote-sensing technology struggle to reliably identify landslides under complex environmental conditions. Moreover, prevalent landslide susceptibility evaluation models are often plagued by issues such as subjectivity and overfitting. Therefore, we investigated the uncertainty in susceptibility modeling from the aspects of landslide inventory quality and model selection. The study focused on Luquan County in Yunnan Province, China. Leveraging multisource remote-sensing technologies, particularly emphasizing optical remote sensing and InSAR time-series deformation detection, the existing historical landslide inventory was refined and updated. This updated inventory was subsequently used to serve as samples. Nine evaluation indicators, encompassing factors such as distance to faults and tributaries, lithology, distance to roads, elevation, slope, terrain undulation, distance to the main streams, and average annual precipitation, were selected on the basis of the collation and organization of regional geological data. The information value and two coupled machine-learning models were formulated to evaluate landslide susceptibility. The evaluation results indicate that the two coupled models are more appropriate for susceptibility modeling than the single information value (IV) model, with the random forest model optimized by genetic algorithm in Group I2 exhibiting higher predictive accuracy (AUC = 0.796). Furthermore, comparative evaluation results reveal that, under equivalent model conditions, the incorporation of a remote-sensing landslide inventory significantly enhances the accuracy of landslide susceptibility assessment results. This study not only investigates the impact of landslide inventories and models on susceptibility outcomes but also validates the feasibility and scientific validity of employing multisource remote-sensing technologies in landslide susceptibility assessment.

Environmental DNA metabarcoding in the Cape Fold aquatic ecoregion: Opportunities and challenges for eDNA uptake in an endemism hotspot

Abstract Environmental DNA (eDNA) metabarcoding has the potential to significantly improve surveys of biodiversity in freshwater systems. However, this methodology is still infrequently used in global hotspots of endemic species, in part because of two major barriers to the success of eDNA metabarcoding: (1) insufficient regional taxonomic representation in public reference sequence databases; and (2) inconsistent species incidence and abundance estimates when compared to conventional surveys. We sampled eDNA and conducted visual surveys in the headwaters of two rivers in the Cape Fold aquatic ecoregion, South Africa. A reference sequence database was generated for the regional diversity of fishes to improve taxonomic classification of endemic species. We also compared the consistency of incidence data and relative abundance estimates of fishes from eDNA metabarcoding sequencing results and visual surveys (snorkel and underwater cameras) at each site sampled. Only 1% of eDNA metabarcoding reads could be classified to fish species without the supplementation of reference sequence databases for local endemic species. Once regional reference sequences were added, a total of nine species were detected and >99% reads classified. A strong positive relationship (Φ = 0.75) was observed between the patterns of detections using eDNA and visual approaches. However, eDNA metabarcoding detected more species than visual methods. In addition, the relative read frequency and abundance observed in visual surveys was significantly correlated (R2 = 0.85–0.88, p < 0.001) in the majority of the small pools surveyed. Incomplete public reference sequence databases hinder the use of eDNA metabarcoding in regions of high endemicity. Local reference sequence libraries can overcome these challenges. When appropriately implemented, eDNA metabarcoding shows promise, producing results consistent or better than the more frequently used and labour‐intensive visual surveys. Efficient survey methods like eDNA metabarcoding are urgently needed to improve aquatic monitoring efforts globally. Understanding how eDNA metabarcoding sequencing results relate to conventional survey methods is a key step in its implementation in ecoregions with high endemicity.

Assessing and interpreting perceived park accessibility, usability and attractiveness through texts and images from social media

Understanding public perceptions of urban parks is essential for their effective management. While conventional survey methods are resource-intensive, Social Media Data (SMD) offers a cost-effective alternative to gathering public insights. However, using SMD for park perception assessment remains challenges, particularly in integrating text and image analysis and filtering irrelevant content to identify influencing factors across various dimensions. Based on a manually curated dataset, this study introduces the Park Dual-modal Perception (PDP) model, a cutting-edge approach combining SMD text and image analysis to evaluate perceived park accessibility, usability, and attractiveness with an average accuracy of 86.81 %, outperforming the commonly used BERT model by 8.26 %. Utilizing SMD from 130 parks in Guangzhou, the model effectively quantifies the three dimensions, generating visual scoring maps to identify parks with lower perceived scores at the urban scale. Further incorporation of SHapley Additive exPlanations (SHAP) within the PDP model filters 82.79 % of irrelevant words and extracts 158 thematic words and 954 associated words, providing targeted suggestions for park improvements. Our findings indicate that (1) factors such as distance, travel time, ticket prices, and proximity to commercial amenities critically influence park accessibility. (2) Park usability hinges on park's ability to serve diverse groups and provide well-maintained, multifunctional facilities. (3) Park attractiveness is closely linked with the cultural and regulatory characteristics of ecosystem services. Our methodology combines assessment and interpretation of public perceptions at both city and park scales. It aids decision-makers in identifying low-rated parks and understanding the underlying reasons, thereby facilitating more informed urban planning decisions.

Validation and development of eDNA metabarcoding primers for comprehensive assessment of Chinese amphibians.

Environmental DNA (eDNA) metabarcoding has emerged as a powerful, non-invasive tool for biodiversity assessments. However, the accuracy and limitations of these assessment techniques are highly dependent on the choice of primer pairs being used. Although several primer sets have been used in eDNA metabarcoding studies of amphibians, there are few comparisons of their reliability and efficiency. Here, we employed lab- and field-tested sets of publicly available and de novo-designed primers in amplifying 83 species of amphibian from all three orders (Anura, Caudata, and Gymnophiona) and 13 families present in China to evaluate the versatility and specificity of these primers sets in amphibian eDNA metabarcoding studies. Three pairs of primers were highly effective, as they could successfully amplify all the major clades of Chinese amphibians in our study. A few non-amphibian taxa were also amplified by these primers, which implies that further optimization of amphibian-specific primers is still needed. The simultaneous use of three primer sets can completely cover all the species obtained by conventional survey methods and has even effectively distinguished quite a number of species (n = 20) in the Wenshan National Nature Reserve. No single primer set could individually detect all of the species from the studied region, indicating that multiple primers might be necessary for a comprehensive survey of Chinese amphibians. Besides, seasonal variations in amphibian species composition were also revealed by eDNA metabarcoding, which was consistent with traditional survey methods. These results indicate that eDNA metabarcoding has the potential to be a powerful tool for studying spatial and temporal community changes in amphibian species richness.

Developing an eDNA approach for wetland biomonitoring: Insights on technical and conventional approaches

AbstractWetlands are ecologically and culturally significant ecosystems that are experiencing biodiversity declines globally. Biomonitoring techniques that use environmental DNA (eDNA) to detect and monitor biodiversity are well established in lake, riverine, and marine ecosystems. However, their use in wetlands requires further development due to the presence of sediments that block eDNA filters to limit water filtration, alongside a lack of standardized methodology. In this study, we examined eDNA dynamics to understand spatiotemporal biodiversity patterns in an Aotearoa New Zealand wetland and to optimize their application to wetland‐specific challenges. We sampled four sites across Opuatia Wetland at three time points during an austral spring. We conducted conventional taxonomic surveys, tested three different filter sizes (1.2 μm, 5 μm, and semi‐quantitative dacron filters), and assessed our ability to detect foreign DNA (from kea; Nestor notabilis) at different time points and distances post‐release. We found significant differences in DNA sequence composition across time and space, and when using different sized filters. eDNA data generally complemented (versus replaced) conventional survey and identification methods, with certain species only detected by one method or the other. Taxonomic resolution of conventional sampling and identification methods often exceeded that of eDNA. Foreign DNA was detectable 10 m from its release point for up to 1 week post‐release. Our results provide new considerations for future eDNA research in wetland environments, where rapid biomonitoring techniques are needed to support conservation and preservation.

Consumer Feedback Analysis Using LDA Approach in Cross-border E-commerce

Purpose - The objective of this research is to gain insight into consumer feedback regarding cross-border e-commerce, which is a pivotal avenue for companies venturing into global markets. It seeks to investigate the variety of themes present in consumer feedback in order to gain a more profound understanding of their preferences and concerns, ultimately influencing future market strategies. Design/Methodology – The present study utilizes an extensive collection of textual online evaluations and a text-mining LDA framework to analyze and classify potential themes in consumer feedback pertaining to cross-border e-commerce platforms. By quantifying the sentiment tendencies in consumer-related comments, sentiment analysis models that rely on emotion dictionaries as a foundation identify the polarity and intensity of emotions. Findings – The present study examined 1,500 consumer evaluations pertaining to Korean cosmetic products, sourced from a cross-border platform in Southeast Asia. The analysis unveiled five key subjects frequently referenced in consumer feedback. A subsequent sentiment analysis revealed that a significant proportion of consumer sentiments were positive. Nevertheless, consumers identified certain concerns in the minority of negative reviews, including packaging and delivery, product quality, and the absence of freebies. Originality/value - In a practical sense, this provides significant ramifications for the optimization of consumer experiences and the development of successful market strategies in the ever-changing global marketplace. This is especially advantageous for organizations striving for success in the fiercely competitive cross-border e-commerce industry. This study exceeds conventional survey methods in its methodology by offering more comprehensive and datacentric understandings of consumer attitudes and behaviors.

Environmental DNA metabarcoding of pan trap water to monitor arthropod‐plant interactions

AbstractGlobally, the diversity of arthropods and the plants upon which they rely are under increasing pressure due to a combination of biotic and abiotic anthropogenic stressors. Unfortunately, conventional survey methods used to monitor ecosystems are often challenging to conduct on large scales. Pan traps are a commonly used pollinator survey method and environmental DNA (eDNA) metabarcoding of pan trap water may offer a high‐throughput alternative to aid in the detection of both arthropods and the plant resources they rely on. Here, we examined if eDNA metabarcoding can be used to identify arthropods and plant species from pan trap water, and investigated the effect of different DNA extraction methods. We then compared plant species identified by metabarcoding with observation‐based floral surveys and also assessed the contribution of airborne plant DNA (plant DNA not carried by arthropods) using marble traps to reduce putative false positives in the pan trap dataset. Arthropod eDNA was only detected in 17% of pan trap samples and there was minimal overlap between the eDNA results and morphological identifications. In contrast, for plants, we detected 64 taxa, of which 53 were unique to the eDNA dataset, and no differences were identified between the two extraction kits. We were able to significantly reduce the contribution of airborne plant DNA to the final dataset using marble traps. This study demonstrates that eDNA metabarcoding of pan trap water can detect plant resources used by arthropods and highlights the potential for eDNA metabarcoding to be applied to investigations of arthropod‐plant interactions.

A Digital Survey Approach for Large-Scale Landscape Heritage Resource Exploration: Auxiliary Beacons, the Uncharted Signal Structure of the Great Wall in China

Following the completion of the Great Wall Resource Survey in 2012, numerous landscape heritage resources along the Great Wall remained undiscovered, highlighting the limitations of conventional survey methods. This study aimed to conduct in-depth investigations of Great Wall signal sites through digital fieldwork methods, unveiling a crucial signaling structure—the auxiliary beacon—and presenting genuine historical scenes of the Great Wall signal network. Through the retrieval of the image database of the entire Great Wall and the utilization of UAVs (drones) for low-altitude remote sensing surveys, 252 auxiliary beacon sites were identified in diverse environments (e.g., deserts, mountains, plains) in Xinjiang, Gansu, Inner Mongolia, Qinghai, Ningxia, and other 10 regions. These case studies enable the categorization of layout types and the proposal of reconstruction hypotheses for the signal network of the Great Wall of China. The findings demonstrate that the beacon fire signals are not lit on the beacon tower tops, but through the ignition of various signals by auxiliary beacons, expressing pre-arranged information. Beacon towers and auxiliary beacons together form an efficient signal network along the Great Wall. This study explores how to use digital survey methods to unearth unknown landscape heritage resources of the Great Wall, enhancing the accuracy of observation for cross-regional and large-scale cultural heritage.

A New Forest Growing Stock Volume Estimation Model Based on AdaBoost and Random Forest Model

Forest growing stock volume is a crucial indicator for assessing forest resources. However, contemporary machine learning models used in estimating forest growing stock volume often exhibit fluctuating precision and are confined to specific tree species, lacking universality. This limitation impedes their capacity to provide comprehensive forest survey services. This study designed a novel model for predicting forest growing stock volume named RF-Adaboost. The model represented the inaugural application of the Adaboost algorithm in estimating forest growing stock volume. Additionally, the authors innovatively refined the Adaboost algorithm by integrating Random Forest as its weak learner. To substantiate the model’s effectiveness, the authors designed three data combination schemes at different scales and conducted regression estimation using the RF-Adaboost model, traditional Random Forest, and Adaboost models, respectively. The results indicated that the RF-Adaboost model consistently outperforms others across various data schemes. Furthermore, utilizing a combined data scheme of remote sensing and Continuous Forest Inventory, the RF-Adaboost model demonstrated optimal performance in estimating forest growing stock volume (R2 = 0.81, RMSE = 7.08 m3/site, MAE = 3.36 m3, MAPE = 8%). Finally, the RF-Adaboost model exhibits greater universality, eliminating the need for strict differentiation between tree species. This research presented an efficient and cost-effective approach to estimate forest growing stock, addressing the challenges associated with conventional survey methods.

CGAN-Based Forest Scene 3D Reconstruction from a Single Image

Forest scene 3D reconstruction serves as the fundamental basis for crucial applications such as forest resource inventory, forestry 3D visualization, and the perceptual capabilities of intelligent forestry robots in operational environments. However, traditional 3D reconstruction methods like LiDAR present challenges primarily because of their lack of portability. Additionally, they encounter complexities related to feature point extraction and matching within multi-view stereo vision sensors. In this research, we propose a new method that not only reconstructs the forest environment but also performs a more detailed tree reconstruction in the scene using conditional generative adversarial networks (CGANs) based on a single RGB image. Firstly, we introduced a depth estimation network based on a CGAN. This network aims to reconstruct forest scenes from images and has demonstrated remarkable performance in accurately reconstructing intricate outdoor environments. Subsequently, we designed a new tree silhouette depth map to represent the tree’s shape as derived from the tree prediction network. This network aims to accomplish a detailed 3D reconstruction of individual trees masked by instance segmentation. Our approach underwent validation using the Cityscapes and Make3D outdoor datasets and exhibited exceptional performance compared with state-of-the-art methods, such as GCNDepth. It achieved a relative error as low as 8% (with an absolute error of 1.76 cm) in estimating diameter at breast height (DBH). Remarkably, our method outperforms existing approaches for single-image reconstruction. It stands as a cost-effective and user-friendly alternative to conventional forest survey methods like LiDAR and SFM techniques. The significance of our method lies in its contribution to technical support, enabling the efficient and detailed utilization of 3D forest scene reconstruction for various applications.

Inferring trip purposes and mode substitution effect of rental e-scooters in London

The lack of information on trip purpose and alternative mode in micromobility service usage data remains a major analytical challenge. Conventional survey method is subject to significant sampling and stated preference biases. To overcome this challenge, this paper presents a new inference method through a case study of rental e-scooters in London. The inference method features a rule-based algorithm for matching observed rental e-scooter trips with filtered trip samples in the English National Travel Survey (NTS) series. Probability distribution of trip purposes and alternative modes are then retrieved from NTS. Inference results are validated using official data. Discrepancies, sources of biases and correction measures are investigated. Based on the inferred mode substitution pattern, we estimate greenhouse gas emissions reduction of selected rental e-scooter trips in London (36–103 g CO2e per mile). It is expected that the proposed method is applicable to a wide range of micromobility studies using service usage data.

Assessment of Reservoir Sedimentation Using Geospatial Tools: A Case Study of Kadana Reservoir

Sediment deposition affects the storage capacity of the reservoirs. Periodic assessment of the storage capacity is needed for the efficient management of the reservoir. Conventional hydrographic survey methods are helpful in assessing the sediment deposition in the reservoir but costlier and time consuming. Integration of advanced remote sensing and geospatial technologies into reservoir sedimentation studies enhance data-driven decision-making, monitoring efficiency, and helps in sustainable reservoir operations under changing environmental conditions. This study was conducted to assess the sedimentation in the Kadana reservoir using the satellite data and water level records for the period 2000 to 2020 using QGIS & google earth engine (GEE). The results showed the loss of live storage due to sedimentation was 38.52% since impoundment and the loss in the live storage capacity was 22.79% after year 2000. GIS technique helps in reservoir management for the sustainable development of the reservoir by understanding the change in water storage capacity of the reservoirs and reservoir sedimentation.

Building Geometry Survey by Using Ultra-Wideband (UWB) Wireless Technology and Algorithm-Based BIM Modelling

The efficiency and degree of maturity of the architectural survey determine the construction process in many ways. From the economic and technical point of view, it is significant. It can be found in almost all activities and, depending on the technology used, it provides different qualities and quantities of initial data and information to prepare and support, among other things, the design, demolition, and construction work processes. The objective of the research and development project and this article is to investigate and demonstrate the application of a novel solution that differs from the conventional building survey tools, methods, and procedures of the information. It breaks with traditional methods by implementing wireless (using Ultra-Wideband (UWB) technology) spatial positioning and algorithm-based post-processing methods, as well as Building Information Modelling (BIM).

Assessment of App-Based Versus Conventional Survey Modalities for Reproductive Health Research in India, South Africa, and the United States: Comparative Cross-Sectional Study.

There is a widely acknowledged global need for more research on reproductive health (including contraception, menstrual health, sexuality, and maternal morbidities) and its impact on overall well-being. However, several factors-notably, high costs, considerable effort, and the sensitivity of these topics-impede the collection of the necessary data, especially in less accessible and lower-income populations. The burgeoning ownership of smartphones and growing use of menstrual tracking apps (MTAs) may present an opportunity to conduct reproductive health research with fewer impediments than those associated with conventional survey methods. The main objective was to ascertain the feasibility, potential usefulness, and limitations of conducting reproductive health research using a mainstream MTA. In each of the 3 countries, we evaluated questionnaire responses from (1) current users of an MTA (Clue) and (2) participants surveyed using conventional survey modalities (in-person interviews, SMS text messaging, and web-based questionnaires). We compared these responses with published data collected from large nationally representative benchmark samples (the United States Census and the Demographic and Health Surveys for South Africa and India). Given a sufficiently large user base, app-distributed surveys were able to quickly capture large samples on par with other methods and at low cost, with the additional advantage of being able to deploy remotely and simultaneously across countries. In each country, neither the app nor the conventional modality sample emerged as a consistently closer match to the distributions of the demographic attributes and the patterns of contraceptive use reported for the respective benchmark sample. Despite efforts to obtain representative samples, the conventional modality samples sometimes over- and other times underrepresented some subgroups (eg, underrepresentation of married persons in the United States and overrepresentation of rural residents in India). In all 3 countries, app users were younger, more educated, more likely to be urban residents, and more likely to use nonhormonal rather than hormonal contraceptive methods compared with the respective national benchmark. App users, compared with the conventional modality samples, consistently reported being more comfortable discussing their menstrual periods with other persons (eg, family, friends, and health care providers), suggesting that MTA users may be more likely to respond truthfully to questions on sensitive or taboo health topics. The app samples' consistency across countries regarding users' demographic profiles, contraceptive choices, and personal attitudes toward menstruation supports the validity of making cross-country comparisons of survey findings for a given app's users. MTAs such as Clue can provide a quick, scalable, and cost-effective method for collecting health data, including on sensitive topics, across a wide variety of settings and countries. With expanding global access to technology and the increasing use of these tools, consumer MTAs can be a viable survey modality to strengthen reproductive health research.

Accuracy assessment of UAS photogrammetry and structure from motion in surveying and mapping

Rapid and accurate surveying has always attracted great interest in all scientific and industrial activities that require high-resolution topographic data. The latest automation and advancement in geomatics engineering are remote sensing solutions using Unmanned Aerial Systems (UAS) and Structure from Motion (SfM) with Multi-View Stereo (MVS) photogrammetry. This research aimed to find the influence of flight height, Ground Control Point (GCP), and software on the geometric accuracy of UAS-SfM-derived Digital Surface Models (DSMs) and orthoimages, as well as to analyze and evaluate the accuracy of UAS-SfM as a rapid and low-cost alternative to conventional survey methods. To achieve the aim of the study, aerial surveys using a fixed-wing UAS and field surveys using RTK GNSS and total station were conducted. A total of 16 photogrammetric projects were processed using different GCP configurations, and detailed statistical analysis was performed on the results. Moreover, the contribution of cross flight on bundle adjustment was investigated empirically by conducting a combined photogrammetric image processing. The analysis revealed that flight height, GCP number and distribution, and the processing software significantly affect products' quality and accuracy. Evaluation of the achieved accuracies was made based on the American Society for Photogrammetry and Remote Sensing (ASPRS) positional accuracy standard for digital geospatial data. The findings of this study revealed that using the optimal flight height and GCP configuration, 3D models, orthomosaics and DSMs can be rapidly reconstructed from 2D images with the quality and accuracy sufficient for most terrain analysis applications, including civil engineering projects.

Hiding behaviour of bats in sandstone mines of North-Eastern Ukraine

Winter counting of bats in hibernacula is one of the main methods of estimating their population trends in Europe. However, it is not always possible for humans to identify and count all bats in the maze of big karstic caves or abandoned mines. Previously we notice a significant fluctuation in the numbers of bat species and individuals in sandstone mines located in the Kharkiv region (NE Ukraine), and hypothesized that a significant undercount happened due to the hiding of bats in deep crevices of sandstone. Here using a camera trap placed inside a mine (September 17-19, 2017) we tested this hypothesis. Firstly we identified significant levels of bat flight activity where no roosting bats had been observed through conventional visual survey methods. The proportion of identified bats (Myotis group and Plecotus auritus) on footage was similar to that obtained by usual winter counts. Finally, we filmed and documented cases of crawling bats inside the deep crevices in the wall depths. We ventured to make an extrapolation of bat numbers in a mine on the basis of autumn-spring mist-netting data, and our evidence that they do hide in crevices. Our observations clearly demonstrate that hiding bat behaviour in mines might result in an underestimation of the real number of hibernating bats in sand-stone mines.

Estimation of the Population Dynamics of Taxus cuspidata by Using a Static Life Table for Its Conservation

Taxus cuspidata is a rare and endangered plant species with an extremely small population which is endemic to China. This study focused on the natural T. cuspidata population in Jilin Province in China. Conventional population ecology survey methods were used to describe its population structure characteristics. Then, we chose diameter structure instead of temporal structure to establish a static population life table, draw a population survival curve, and quantify the future development trend by using population dynamic analysis and time sequence prediction. The results showed that: (1) the static life table suggested that the population of T. cuspidata was stable overall. The population survival curve tended to be Deevey Ⅱ, with a high early seedling mortality, and the later population growth tended to be stable; (2) the survival curve suggested that the population initially experienced higher mortality rates in the early stage. However, as time progressed and the population aged, the mortality rates decreased, resulting in a more stable population in the middle and late stages; (3) The diameter class structure of T. cuspidata was stable overall, and the dynamic indices showed that the population was fluctuating. The population was influenced by external disturbances and showed some resistance to human disturbance; (4) time sequence prediction analysis showed that the mortality rate of young individuals was high, natural renewal could be maintained, and the population size would remain at a certain amount in the future. The result shows that the Jilin region is a highly suitable area for the growth of Taxus cuspidata’s population in Northeast China. We recommend in situ conservation of remaining wild populations, relocation of germplasm resources, and reduction of human activities; these actions will be beneficial to Taxus cuspidata’s long-term survival.

Drone-Based Monitoring and Mapping for LMO Confined Field Management under the Ministry of Environment

The objective of this study was to devise effective safety management systems for enclosed living modified organism (LMO) fields regulated by the Ministry of Environment (MOE), achieved through an assessment of the impact of LM crops on the surrounding flora. A combination of conventional survey methods and cutting-edge drone-based monitoring systems was employed, with a keen focus on their efficacy. Our investigation spans three distinct zones (forest, non-forest, and enclosed field), involving vegetation surveys, biodiversity index analyses, and drone-powered aerial observations to study topographical shifts. Over time, wild plants adjacent to the enclosed LMO field exhibited stability in terms of species composition. Nevertheless, disparities in growth patterns emerged across various areas. Predominantly, herbs thrived in enclosed and non-forest areas, while trees and shrubs flourished in forested regions. Annual plants predominantly populated the non-forest regions, whereas perennials dominated the forested areas. To this end, drones captured aerial photographs of a 31.65-hectare expanse with 40% coverage overlap, furnishing a real-time vegetation map that transcends the capacities of conventional methods. By combining vegetation surveys, drone-generated vegetation mapping, and dynamic monitoring of topographical changes, our research endeavors to facilitate the formulation of a robust safety management framework for LMO confined fields overseen by the MOE. This holistic approach aspires to prevent ecosystem contamination and establish a resilient, enduring system that averts LMO leakage, thereby safeguarding the environment.

Mapping Rice Area in the Cauvery Delta Zone of Tamil Nadu Using Sentinel 1A Synthetic Aperture Radar (SAR) Data

Since remote sensing based crop inventory provides accurate and timely information as compared to the conventional survey methods of estimating area, Multi-temporal Sentinel 1A Synthetic Aperture Radar data was used for the estimation of rice area during Samba season 2022 in the Cauvery delta zone comprising Thanjavur, Thiruvaru, Mayiladuthurai, and Nagapatnam districts of Tamil Nadu. SAR data was preferred over optical satellite data due to excess cloud cover during cropping the major season in Tamil Nadu. Temporal back-scatter (dB) signature of rice crop was generated from the multi-temporal processed SAR data utilizing the modules of a fully automated MAPscape software aiding the discriminating of the crop from others. The signatures revealed that the dB levels to be the lowest during agronomic floods, reached the highest during maximum tillering stage and started declining thereafter. Multi-temporal feature extraction module of Mapscape was used to estimate rice area and validated for accuracy using ground truth data collected during survey. A total of 3.05 lakh ha of rice area was estimated with an overall accuracy of 90.8 % and 0.82 kappa coefficient. Largest area of 1.12 lakh ha was recorded in Thanjavur followed by Thiruvarur and Mayiladuthurai with 0.95 and 0.51 lakh ha respectively.

Comparative efficacy of eDNA and conventional methods for monitoring wetland anuran communities

Documenting biodiversity, species occurrence, and species status require reliable monitoring techniques, but the complex life history and cryptic behavior of many anurans create challenges for conventional monitoring approaches. Environmental DNA (eDNA) surveys are a promising alternative (or complement) to conventional anuran monitoring, but their relative success has not been fully tested. We assessed the comparative efficacy of targeted eDNA detection via quantitative PCR (qPCR) and three conventional amphibian survey methods (visual encounter, breeding call, and larval dipnet surveys) for detecting nine anuran species in natural wetlands in southern Ontario, Canada. Our analyses revealed that all assessment methods yielded imperfect detection, with visual encounter and eDNA surveys detecting the greatest species richness and eDNA surveys requiring the fewest sampling events. Amphibian community composition results differed among survey methods and sampling events, and detection efficacy was markedly variable, with some species requiring two to three methods to maximize detection success. Notably, two relatively terrestrial species (Anaxyrus americanusandHyla versicolor) had relatively low and seasonally variable eDNA detection rates, suggesting that species-specific ecology likely affects eDNA presence or detection. These findings suggest that optimized monitoring for complex anuran communities may require application of multiple monitoring methods, which may need to be tailored to individual target species or communities.